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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].beta
251 * (neighborLambdaEnergies[pointLambdaIndex]
252 - neighborLambdaEnergies[gridpointLambdaIndex]);
257 double dev = getDeviationFromPointAlongGridAxis(grid, d, pointIndex, value[d]);
258 logWeight -= 0.5 * dimParams[d].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 /* The negative PMF is a positive bias. */
318 double biasNeighbor = -pmf[neighbor];
320 /* Add the convolved PMF weights for the neighbors of this point.
321 Note that this function only adds point within the target > 0 region.
322 Sum weights, take the logarithm last to get the free energy. */
323 double logWeight = biasedLogWeightFromPoint(dimParams, points_, grid, neighbor,
324 biasNeighbor, point.coordValue, {}, m);
325 freeEnergyWeights += std::exp(logWeight);
328 GMX_RELEASE_ASSERT(freeEnergyWeights > 0,
329 "Attempting to do log(<= 0) in AWH convolved PMF calculation.");
330 (*convolvedPmf)[m] = -std::log(static_cast<float>(freeEnergyWeights));
338 * Updates the target distribution for all points.
340 * The target distribution is always updated for all points
343 * \param[in,out] pointState The state of all points.
344 * \param[in] params The bias parameters.
346 void updateTargetDistribution(gmx::ArrayRef<PointState> pointState, const BiasParams& params)
348 double freeEnergyCutoff = 0;
349 if (params.eTarget == eawhtargetCUTOFF)
351 freeEnergyCutoff = freeEnergyMinimumValue(pointState) + params.freeEnergyCutoffInKT;
354 double sumTarget = 0;
355 for (PointState& ps : pointState)
357 sumTarget += ps.updateTargetWeight(params, freeEnergyCutoff);
359 GMX_RELEASE_ASSERT(sumTarget > 0, "We should have a non-zero distribution");
362 double invSum = 1.0 / sumTarget;
363 for (PointState& ps : pointState)
365 ps.scaleTarget(invSum);
370 * Puts together a string describing a grid point.
372 * \param[in] grid The grid.
373 * \param[in] point BiasGrid point index.
374 * \returns a string for the point.
376 std::string gridPointValueString(const BiasGrid& grid, int point)
378 std::string pointString;
382 for (int d = 0; d < grid.numDimensions(); d++)
384 pointString += gmx::formatString("%g", grid.point(point).coordValue[d]);
385 if (d < grid.numDimensions() - 1)
400 int BiasState::warnForHistogramAnomalies(const BiasGrid& grid, int biasIndex, double t, FILE* fplog, int maxNumWarnings) const
402 GMX_ASSERT(fplog != nullptr, "Warnings can only be issued if there is log file.");
403 const double maxHistogramRatio = 0.5; /* Tolerance for printing a warning about the histogram ratios */
405 /* Sum up the histograms and get their normalization */
406 double sumVisits = 0;
407 double sumWeights = 0;
408 for (auto& pointState : points_)
410 if (pointState.inTargetRegion())
412 sumVisits += pointState.numVisitsTot();
413 sumWeights += pointState.weightSumTot();
416 GMX_RELEASE_ASSERT(sumVisits > 0, "We should have visits");
417 GMX_RELEASE_ASSERT(sumWeights > 0, "We should have weight");
418 double invNormVisits = 1.0 / sumVisits;
419 double invNormWeight = 1.0 / sumWeights;
421 /* Check all points for warnings */
423 size_t numPoints = grid.numPoints();
424 for (size_t m = 0; m < numPoints; m++)
426 /* Skip points close to boundary or non-target region */
427 const GridPoint& gridPoint = grid.point(m);
428 bool skipPoint = false;
429 for (size_t n = 0; (n < gridPoint.neighbor.size()) && !skipPoint; n++)
431 int neighbor = gridPoint.neighbor[n];
432 skipPoint = !points_[neighbor].inTargetRegion();
433 for (int d = 0; (d < grid.numDimensions()) && !skipPoint; d++)
435 const GridPoint& neighborPoint = grid.point(neighbor);
436 skipPoint = neighborPoint.index[d] == 0
437 || neighborPoint.index[d] == grid.axis(d).numPoints() - 1;
441 /* Warn if the coordinate distribution is less than the target distribution with a certain fraction somewhere */
442 const double relativeWeight = points_[m].weightSumTot() * invNormWeight;
443 const double relativeVisits = points_[m].numVisitsTot() * invNormVisits;
444 if (!skipPoint && relativeVisits < relativeWeight * maxHistogramRatio)
446 std::string pointValueString = gridPointValueString(grid, m);
447 std::string warningMessage = gmx::formatString(
449 "at t = %g ps the obtained coordinate distribution at coordinate value %s "
450 "is less than a fraction %g of the reference distribution at that point. "
451 "If you are not certain about your settings you might want to increase your "
452 "pull force constant or "
453 "modify your sampling region.\n",
454 biasIndex + 1, t, pointValueString.c_str(), maxHistogramRatio);
455 gmx::TextLineWrapper wrapper;
456 wrapper.settings().setLineLength(c_linewidth);
457 fprintf(fplog, "%s", wrapper.wrapToString(warningMessage).c_str());
461 if (numWarnings >= maxNumWarnings)
470 double BiasState::calcUmbrellaForceAndPotential(const std::vector<DimParams>& dimParams,
471 const BiasGrid& grid,
473 ArrayRef<const double> neighborLambdaDhdl,
474 gmx::ArrayRef<double> force) const
476 double potential = 0;
477 for (size_t d = 0; d < dimParams.size(); d++)
479 if (dimParams[d].isFepLambdaDimension())
481 if (!neighborLambdaDhdl.empty())
483 const int coordpointLambdaIndex = grid.point(point).coordValue[d];
484 force[d] = neighborLambdaDhdl[coordpointLambdaIndex];
485 /* The potential should not be affected by the lambda dimension. */
491 getDeviationFromPointAlongGridAxis(grid, d, point, coordState_.coordValue()[d]);
492 double k = dimParams[d].k;
494 /* Force from harmonic potential 0.5*k*dev^2 */
495 force[d] = -k * deviation;
496 potential += 0.5 * k * deviation * deviation;
503 void BiasState::calcConvolvedForce(const std::vector<DimParams>& dimParams,
504 const BiasGrid& grid,
505 gmx::ArrayRef<const double> probWeightNeighbor,
506 ArrayRef<const double> neighborLambdaDhdl,
507 gmx::ArrayRef<double> forceWorkBuffer,
508 gmx::ArrayRef<double> force) const
510 for (size_t d = 0; d < dimParams.size(); d++)
515 /* Only neighboring points have non-negligible contribution. */
516 const std::vector<int>& neighbor = grid.point(coordState_.gridpointIndex()).neighbor;
517 gmx::ArrayRef<double> forceFromNeighbor = forceWorkBuffer;
518 for (size_t n = 0; n < neighbor.size(); n++)
520 double weightNeighbor = probWeightNeighbor[n];
521 int indexNeighbor = neighbor[n];
523 /* Get the umbrella force from this point. The returned potential is ignored here. */
524 calcUmbrellaForceAndPotential(dimParams, grid, indexNeighbor, neighborLambdaDhdl, forceFromNeighbor);
526 /* Add the weighted umbrella force to the convolved force. */
527 for (size_t d = 0; d < dimParams.size(); d++)
529 force[d] += forceFromNeighbor[d] * weightNeighbor;
534 double BiasState::moveUmbrella(const std::vector<DimParams>& dimParams,
535 const BiasGrid& grid,
536 gmx::ArrayRef<const double> probWeightNeighbor,
537 ArrayRef<const double> neighborLambdaDhdl,
538 gmx::ArrayRef<double> biasForce,
542 bool onlySampleUmbrellaGridpoint)
544 /* Generate and set a new coordinate reference value */
545 coordState_.sampleUmbrellaGridpoint(grid, coordState_.gridpointIndex(), probWeightNeighbor,
546 step, seed, indexSeed);
548 if (onlySampleUmbrellaGridpoint)
553 std::vector<double> newForce(dimParams.size());
554 double newPotential = calcUmbrellaForceAndPotential(
555 dimParams, grid, coordState_.umbrellaGridpoint(), neighborLambdaDhdl, newForce);
557 /* A modification of the reference value at time t will lead to a different
558 force over t-dt/2 to t and over t to t+dt/2. For high switching rates
559 this means the force and velocity will change signs roughly as often.
560 To avoid any issues we take the average of the previous and new force
561 at steps when the reference value has been moved. E.g. if the ref. value
562 is set every step to (coord dvalue +/- delta) would give zero force.
564 for (gmx::index d = 0; d < biasForce.ssize(); d++)
566 /* Average of the current and new force */
567 biasForce[d] = 0.5 * (biasForce[d] + newForce[d]);
577 * Sets the histogram rescaling factors needed to control the histogram size.
579 * For sake of robustness, the reference weight histogram can grow at a rate
580 * different from the actual sampling rate. Typically this happens for a limited
581 * initial time, alternatively growth is scaled down by a constant factor for all
582 * times. Since the size of the reference histogram sets the size of the free
583 * energy update this should be reflected also in the PMF. Thus the PMF histogram
584 * needs to be rescaled too.
586 * This function should only be called by the bias update function or wrapped by a function that
587 * knows what scale factors should be applied when, e.g,
588 * getSkippedUpdateHistogramScaleFactors().
590 * \param[in] params The bias parameters.
591 * \param[in] newHistogramSize New reference weight histogram size.
592 * \param[in] oldHistogramSize Previous reference weight histogram size (before adding new samples).
593 * \param[out] weightHistScaling Scaling factor for the reference weight histogram.
594 * \param[out] logPmfSumScaling Log of the scaling factor for the PMF histogram.
596 void setHistogramUpdateScaleFactors(const BiasParams& params,
597 double newHistogramSize,
598 double oldHistogramSize,
599 double* weightHistScaling,
600 double* logPmfSumScaling)
603 /* The two scaling factors below are slightly different (ignoring the log factor) because the
604 reference and the PMF histogram apply weight scaling differently. The weight histogram
605 applies is locally, i.e. each sample is scaled down meaning all samples get equal weight.
606 It is done this way because that is what target type local Boltzmann (for which
607 target = weight histogram) needs. In contrast, the PMF histogram is rescaled globally
608 by repeatedly scaling down the whole histogram. The reasons for doing it this way are:
609 1) empirically this is necessary for converging the PMF; 2) since the extraction of
610 the PMF is theoretically only valid for a constant bias, new samples should get more
611 weight than old ones for which the bias is fluctuating more. */
613 newHistogramSize / (oldHistogramSize + params.updateWeight * params.localWeightScaling);
614 *logPmfSumScaling = std::log(newHistogramSize / (oldHistogramSize + params.updateWeight));
619 void BiasState::getSkippedUpdateHistogramScaleFactors(const BiasParams& params,
620 double* weightHistScaling,
621 double* logPmfSumScaling) const
623 GMX_ASSERT(params.skipUpdates(),
624 "Calling function for skipped updates when skipping updates is not allowed");
626 if (inInitialStage())
628 /* In between global updates the reference histogram size is kept constant so we trivially
629 know what the histogram size was at the time of the skipped update. */
630 double histogramSize = histogramSize_.histogramSize();
631 setHistogramUpdateScaleFactors(params, histogramSize, histogramSize, weightHistScaling,
636 /* In the final stage, the reference histogram grows at the sampling rate which gives trivial scale factors. */
637 *weightHistScaling = 1;
638 *logPmfSumScaling = 0;
642 void BiasState::doSkippedUpdatesForAllPoints(const BiasParams& params)
644 double weightHistScaling;
645 double logPmfsumScaling;
647 getSkippedUpdateHistogramScaleFactors(params, &weightHistScaling, &logPmfsumScaling);
649 for (auto& pointState : points_)
651 bool didUpdate = pointState.performPreviouslySkippedUpdates(
652 params, histogramSize_.numUpdates(), weightHistScaling, logPmfsumScaling);
654 /* Update the bias for this point only if there were skipped updates in the past to avoid calculating the log unneccessarily */
657 pointState.updateBias();
662 void BiasState::doSkippedUpdatesInNeighborhood(const BiasParams& params, const BiasGrid& grid)
664 double weightHistScaling;
665 double logPmfsumScaling;
667 getSkippedUpdateHistogramScaleFactors(params, &weightHistScaling, &logPmfsumScaling);
669 /* For each neighbor point of the center point, refresh its state by adding the results of all past, skipped updates. */
670 const std::vector<int>& neighbors = grid.point(coordState_.gridpointIndex()).neighbor;
671 for (auto& neighbor : neighbors)
673 bool didUpdate = points_[neighbor].performPreviouslySkippedUpdates(
674 params, histogramSize_.numUpdates(), weightHistScaling, logPmfsumScaling);
678 points_[neighbor].updateBias();
687 * Merge update lists from multiple sharing simulations.
689 * \param[in,out] updateList Update list for this simulation (assumed >= npoints long).
690 * \param[in] numPoints Total number of points.
691 * \param[in] commRecord Struct for intra-simulation communication.
692 * \param[in] multiSimComm Struct for multi-simulation communication.
694 void mergeSharedUpdateLists(std::vector<int>* updateList,
696 const t_commrec* commRecord,
697 const gmx_multisim_t* multiSimComm)
699 std::vector<int> numUpdatesOfPoint;
701 /* Flag the update points of this sim.
702 TODO: we can probably avoid allocating this array and just use the input array. */
703 numUpdatesOfPoint.resize(numPoints, 0);
704 for (auto& pointIndex : *updateList)
706 numUpdatesOfPoint[pointIndex] = 1;
709 /* Sum over the sims to get all the flagged points */
710 sumOverSimulations(arrayRefFromArray(numUpdatesOfPoint.data(), numPoints), commRecord, multiSimComm);
712 /* Collect the indices of the flagged points in place. The resulting array will be the merged update list.*/
714 for (int m = 0; m < numPoints; m++)
716 if (numUpdatesOfPoint[m] > 0)
718 updateList->push_back(m);
724 * Generate an update list of points sampled since the last update.
726 * \param[in] grid The AWH bias.
727 * \param[in] points The point state.
728 * \param[in] originUpdatelist The origin of the rectangular region that has been sampled since
729 * last update. \param[in] endUpdatelist The end of the rectangular that has been sampled since
730 * last update. \param[in,out] updateList Local update list to set (assumed >= npoints long).
732 void makeLocalUpdateList(const BiasGrid& grid,
733 const std::vector<PointState>& points,
734 const awh_ivec originUpdatelist,
735 const awh_ivec endUpdatelist,
736 std::vector<int>* updateList)
741 /* Define the update search grid */
742 for (int d = 0; d < grid.numDimensions(); d++)
744 origin[d] = originUpdatelist[d];
745 numPoints[d] = endUpdatelist[d] - originUpdatelist[d] + 1;
747 /* Because the end_updatelist is unwrapped it can be > (npoints - 1) so that numPoints can be > npoints in grid.
748 This helps for calculating the distance/number of points but should be removed and fixed when the way of
749 updating origin/end updatelist is changed (see sampleProbabilityWeights). */
750 numPoints[d] = std::min(grid.axis(d).numPoints(), numPoints[d]);
753 /* Make the update list */
756 bool pointExists = true;
759 pointExists = advancePointInSubgrid(grid, origin, numPoints, &pointIndex);
761 if (pointExists && points[pointIndex].inTargetRegion())
763 updateList->push_back(pointIndex);
770 void BiasState::resetLocalUpdateRange(const BiasGrid& grid)
772 const int gridpointIndex = coordState_.gridpointIndex();
773 for (int d = 0; d < grid.numDimensions(); d++)
775 /* This gives the minimum range consisting only of the current closest point. */
776 originUpdatelist_[d] = grid.point(gridpointIndex).index[d];
777 endUpdatelist_[d] = grid.point(gridpointIndex).index[d];
785 * Add partial histograms (accumulating between updates) to accumulating histograms.
787 * \param[in,out] pointState The state of the points in the bias.
788 * \param[in,out] weightSumCovering The weights for checking covering.
789 * \param[in] numSharedUpdate The number of biases sharing the histrogram.
790 * \param[in] commRecord Struct for intra-simulation communication.
791 * \param[in] multiSimComm Struct for multi-simulation communication.
792 * \param[in] localUpdateList List of points with data.
794 void sumHistograms(gmx::ArrayRef<PointState> pointState,
795 gmx::ArrayRef<double> weightSumCovering,
797 const t_commrec* commRecord,
798 const gmx_multisim_t* multiSimComm,
799 const std::vector<int>& localUpdateList)
801 /* The covering checking histograms are added before summing over simulations, so that the
802 weights from different simulations are kept distinguishable. */
803 for (int globalIndex : localUpdateList)
805 weightSumCovering[globalIndex] += pointState[globalIndex].weightSumIteration();
808 /* Sum histograms over multiple simulations if needed. */
809 if (numSharedUpdate > 1)
811 GMX_ASSERT(numSharedUpdate == multiSimComm->numSimulations_,
812 "Sharing within a simulation is not implemented (yet)");
814 /* Collect the weights and counts in linear arrays to be able to use gmx_sumd_sim. */
815 std::vector<double> weightSum;
816 std::vector<double> coordVisits;
818 weightSum.resize(localUpdateList.size());
819 coordVisits.resize(localUpdateList.size());
821 for (size_t localIndex = 0; localIndex < localUpdateList.size(); localIndex++)
823 const PointState& ps = pointState[localUpdateList[localIndex]];
825 weightSum[localIndex] = ps.weightSumIteration();
826 coordVisits[localIndex] = ps.numVisitsIteration();
829 sumOverSimulations(gmx::ArrayRef<double>(weightSum), commRecord, multiSimComm);
830 sumOverSimulations(gmx::ArrayRef<double>(coordVisits), commRecord, multiSimComm);
832 /* Transfer back the result */
833 for (size_t localIndex = 0; localIndex < localUpdateList.size(); localIndex++)
835 PointState& ps = pointState[localUpdateList[localIndex]];
837 ps.setPartialWeightAndCount(weightSum[localIndex], coordVisits[localIndex]);
841 /* Now add the partial counts and weights to the accumulating histograms.
842 Note: we still need to use the weights for the update so we wait
843 with resetting them until the end of the update. */
844 for (int globalIndex : localUpdateList)
846 pointState[globalIndex].addPartialWeightAndCount();
851 * Label points along an axis as covered or not.
853 * A point is covered if it is surrounded by visited points up to a radius = coverRadius.
855 * \param[in] visited Visited? For each point.
856 * \param[in] checkCovering Check for covering? For each point.
857 * \param[in] numPoints The number of grid points along this dimension.
858 * \param[in] period Period in number of points.
859 * \param[in] coverRadius Cover radius, in points, needed for defining a point as covered.
860 * \param[in,out] covered In this array elements are 1 for covered points and 0 for
861 * non-covered points, this routine assumes that \p covered has at least size \p numPoints.
863 void labelCoveredPoints(const std::vector<bool>& visited,
864 const std::vector<bool>& checkCovering,
868 gmx::ArrayRef<int> covered)
870 GMX_ASSERT(covered.ssize() >= numPoints, "covered should be at least as large as the grid");
872 bool haveFirstNotVisited = false;
873 int firstNotVisited = -1;
874 int notVisitedLow = -1;
875 int notVisitedHigh = -1;
877 for (int n = 0; n < numPoints; n++)
879 if (checkCovering[n] && !visited[n])
881 if (!haveFirstNotVisited)
885 haveFirstNotVisited = true;
891 /* Have now an interval I = [notVisitedLow,notVisitedHigh] of visited points bounded
892 by unvisited points. The unvisted end points affect the coveredness of the
893 visited with a reach equal to the cover radius. */
894 int notCoveredLow = notVisitedLow + coverRadius;
895 int notCoveredHigh = notVisitedHigh - coverRadius;
896 for (int i = notVisitedLow; i <= notVisitedHigh; i++)
898 covered[i] = static_cast<int>((i > notCoveredLow) && (i < notCoveredHigh));
901 /* Find a new interval to set covering for. Make the notVisitedHigh of this interval
902 the notVisitedLow of the next. */
903 notVisitedLow = notVisitedHigh;
908 /* Have labelled all the internal points. Now take care of the boundary regions. */
909 if (!haveFirstNotVisited)
911 /* No non-visited points <=> all points visited => all points covered. */
913 for (int n = 0; n < numPoints; n++)
920 int lastNotVisited = notVisitedLow;
922 /* For periodic boundaries, non-visited points can influence points
923 on the other side of the boundary so we need to wrap around. */
925 /* Lower end. For periodic boundaries the last upper end not visited point becomes the low-end not visited point.
926 For non-periodic boundaries there is no lower end point so a dummy value is used. */
927 int notVisitedHigh = firstNotVisited;
928 int notVisitedLow = period > 0 ? (lastNotVisited - period) : -(coverRadius + 1);
930 int notCoveredLow = notVisitedLow + coverRadius;
931 int notCoveredHigh = notVisitedHigh - coverRadius;
933 for (int i = 0; i <= notVisitedHigh; i++)
935 /* For non-periodic boundaries notCoveredLow = -1 will impose no restriction. */
936 covered[i] = static_cast<int>((i > notCoveredLow) && (i < notCoveredHigh));
939 /* Upper end. Same as for lower end but in the other direction. */
940 notVisitedHigh = period > 0 ? (firstNotVisited + period) : (numPoints + coverRadius);
941 notVisitedLow = lastNotVisited;
943 notCoveredLow = notVisitedLow + coverRadius;
944 notCoveredHigh = notVisitedHigh - coverRadius;
946 for (int i = notVisitedLow; i <= numPoints - 1; i++)
948 /* For non-periodic boundaries notCoveredHigh = numPoints will impose no restriction. */
949 covered[i] = static_cast<int>((i > notCoveredLow) && (i < notCoveredHigh));
956 bool BiasState::isSamplingRegionCovered(const BiasParams& params,
957 const std::vector<DimParams>& dimParams,
958 const BiasGrid& grid,
959 const t_commrec* commRecord,
960 const gmx_multisim_t* multiSimComm) const
962 /* Allocate and initialize arrays: one for checking visits along each dimension,
963 one for keeping track of which points to check and one for the covered points.
964 Possibly these could be kept as AWH variables to avoid these allocations. */
967 std::vector<bool> visited;
968 std::vector<bool> checkCovering;
969 // We use int for the covering array since we might use gmx_sumi_sim.
970 std::vector<int> covered;
973 std::vector<CheckDim> checkDim;
974 checkDim.resize(grid.numDimensions());
976 for (int d = 0; d < grid.numDimensions(); d++)
978 const size_t numPoints = grid.axis(d).numPoints();
979 checkDim[d].visited.resize(numPoints, false);
980 checkDim[d].checkCovering.resize(numPoints, false);
981 checkDim[d].covered.resize(numPoints, 0);
984 /* Set visited points along each dimension and which points should be checked for covering.
985 Specifically, points above the free energy cutoff (if there is one) or points outside
986 of the target region are ignored. */
988 /* Set the free energy cutoff */
989 double maxFreeEnergy = GMX_FLOAT_MAX;
991 if (params.eTarget == eawhtargetCUTOFF)
993 maxFreeEnergy = freeEnergyMinimumValue(points_) + params.freeEnergyCutoffInKT;
996 /* Set the threshold weight for a point to be considered visited. */
997 double weightThreshold = 1;
998 for (int d = 0; d < grid.numDimensions(); d++)
1000 if (grid.axis(d).isFepLambdaAxis())
1002 /* TODO: Verify that a threshold of 1.0 is OK. With a very high sample weight 1.0 can be
1003 * reached quickly even in regions with low probability. Should the sample weight be
1004 * taken into account here? */
1005 weightThreshold *= 1.0;
1009 weightThreshold *= grid.axis(d).spacing() * std::sqrt(dimParams[d].betak * 0.5 * M_1_PI);
1013 /* Project the sampling weights onto each dimension */
1014 for (size_t m = 0; m < grid.numPoints(); m++)
1016 const PointState& pointState = points_[m];
1018 for (int d = 0; d < grid.numDimensions(); d++)
1020 int n = grid.point(m).index[d];
1022 /* Is visited if it was already visited or if there is enough weight at the current point */
1023 checkDim[d].visited[n] = checkDim[d].visited[n] || (weightSumCovering_[m] > weightThreshold);
1025 /* Check for covering if there is at least point in this slice that is in the target region and within the cutoff */
1026 checkDim[d].checkCovering[n] =
1027 checkDim[d].checkCovering[n]
1028 || (pointState.inTargetRegion() && pointState.freeEnergy() < maxFreeEnergy);
1032 /* Label each point along each dimension as covered or not. */
1033 for (int d = 0; d < grid.numDimensions(); d++)
1035 labelCoveredPoints(checkDim[d].visited, checkDim[d].checkCovering, grid.axis(d).numPoints(),
1036 grid.axis(d).numPointsInPeriod(), params.coverRadius()[d], checkDim[d].covered);
1039 /* Now check for global covering. Each dimension needs to be covered separately.
1040 A dimension is covered if each point is covered. Multiple simulations collectively
1041 cover the points, i.e. a point is covered if any of the simulations covered it.
1042 However, visited points are not shared, i.e. if a point is covered or not is
1043 determined by the visits of a single simulation. In general the covering criterion is
1044 all points covered => all points are surrounded by visited points up to a radius = coverRadius.
1045 For 1 simulation, all points covered <=> all points visited. For multiple simulations
1046 however, all points visited collectively !=> all points covered, except for coverRadius = 0.
1047 In the limit of large coverRadius, all points covered => all points visited by at least one
1048 simulation (since no point will be covered until all points have been visited by a
1049 single simulation). Basically coverRadius sets how much "connectedness" (or mixing) a point
1050 needs with surrounding points before sharing covering information with other simulations. */
1052 /* Communicate the covered points between sharing simulations if needed. */
1053 if (params.numSharedUpdate > 1)
1055 /* For multiple dimensions this may not be the best way to do it. */
1056 for (int d = 0; d < grid.numDimensions(); d++)
1059 gmx::arrayRefFromArray(checkDim[d].covered.data(), grid.axis(d).numPoints()),
1060 commRecord, multiSimComm);
1064 /* Now check if for each dimension all points are covered. Break if not true. */
1065 bool allPointsCovered = true;
1066 for (int d = 0; d < grid.numDimensions() && allPointsCovered; d++)
1068 for (int n = 0; n < grid.axis(d).numPoints() && allPointsCovered; n++)
1070 allPointsCovered = (checkDim[d].covered[n] != 0);
1074 return allPointsCovered;
1078 * Normalizes the free energy and PMF sum.
1080 * \param[in] pointState The state of the points.
1082 static void normalizeFreeEnergyAndPmfSum(std::vector<PointState>* pointState)
1084 double minF = freeEnergyMinimumValue(*pointState);
1086 for (PointState& ps : *pointState)
1088 ps.normalizeFreeEnergyAndPmfSum(minF);
1092 void BiasState::updateFreeEnergyAndAddSamplesToHistogram(const std::vector<DimParams>& dimParams,
1093 const BiasGrid& grid,
1094 const BiasParams& params,
1095 const t_commrec* commRecord,
1096 const gmx_multisim_t* multiSimComm,
1100 std::vector<int>* updateList)
1102 /* Note hat updateList is only used in this scope and is always
1103 * re-initialized. We do not use a local vector, because that would
1104 * cause reallocation every time this funtion is called and the vector
1105 * can be the size of the whole grid.
1108 /* Make a list of all local points, i.e. those that could have been touched since
1109 the last update. These are the points needed for summing histograms below
1110 (non-local points only add zeros). For local updates, this will also be the
1111 final update list. */
1112 makeLocalUpdateList(grid, points_, originUpdatelist_, endUpdatelist_, updateList);
1113 if (params.numSharedUpdate > 1)
1115 mergeSharedUpdateLists(updateList, points_.size(), commRecord, multiSimComm);
1118 /* Reset the range for the next update */
1119 resetLocalUpdateRange(grid);
1121 /* Add samples to histograms for all local points and sync simulations if needed */
1122 sumHistograms(points_, weightSumCovering_, params.numSharedUpdate, commRecord, multiSimComm, *updateList);
1124 sumPmf(points_, params.numSharedUpdate, commRecord, multiSimComm);
1126 /* Renormalize the free energy if values are too large. */
1127 bool needToNormalizeFreeEnergy = false;
1128 for (int& globalIndex : *updateList)
1130 /* We want to keep the absolute value of the free energies to be less
1131 c_largePositiveExponent to be able to safely pass these values to exp(). The check below
1132 ensures this as long as the free energy values grow less than 0.5*c_largePositiveExponent
1133 in a return time to this neighborhood. For reasonable update sizes it's unlikely that
1134 this requirement would be broken. */
1135 if (std::abs(points_[globalIndex].freeEnergy()) > 0.5 * detail::c_largePositiveExponent)
1137 needToNormalizeFreeEnergy = true;
1142 /* Update target distribution? */
1143 bool needToUpdateTargetDistribution =
1144 (params.eTarget != eawhtargetCONSTANT && params.isUpdateTargetStep(step));
1146 /* In the initial stage, the histogram grows dynamically as a function of the number of coverings. */
1147 bool detectedCovering = false;
1148 if (inInitialStage())
1151 (params.isCheckCoveringStep(step)
1152 && isSamplingRegionCovered(params, dimParams, grid, commRecord, multiSimComm));
1155 /* The weighthistogram size after this update. */
1156 double newHistogramSize = histogramSize_.newHistogramSize(params, t, detectedCovering, points_,
1157 weightSumCovering_, fplog);
1159 /* Make the update list. Usually we try to only update local points,
1160 * but if the update has non-trivial or non-deterministic effects
1161 * on non-local points a global update is needed. This is the case when:
1162 * 1) a covering occurred in the initial stage, leading to non-trivial
1163 * histogram rescaling factors; or
1164 * 2) the target distribution will be updated, since we don't make any
1165 * assumption on its form; or
1166 * 3) the AWH parameters are such that we never attempt to skip non-local
1168 * 4) the free energy values have grown so large that a renormalization
1171 if (needToUpdateTargetDistribution || detectedCovering || !params.skipUpdates() || needToNormalizeFreeEnergy)
1173 /* Global update, just add all points. */
1174 updateList->clear();
1175 for (size_t m = 0; m < points_.size(); m++)
1177 if (points_[m].inTargetRegion())
1179 updateList->push_back(m);
1184 /* Set histogram scale factors. */
1185 double weightHistScalingSkipped = 0;
1186 double logPmfsumScalingSkipped = 0;
1187 if (params.skipUpdates())
1189 getSkippedUpdateHistogramScaleFactors(params, &weightHistScalingSkipped, &logPmfsumScalingSkipped);
1191 double weightHistScalingNew;
1192 double logPmfsumScalingNew;
1193 setHistogramUpdateScaleFactors(params, newHistogramSize, histogramSize_.histogramSize(),
1194 &weightHistScalingNew, &logPmfsumScalingNew);
1196 /* Update free energy and reference weight histogram for points in the update list. */
1197 for (int pointIndex : *updateList)
1199 PointState* pointStateToUpdate = &points_[pointIndex];
1201 /* Do updates from previous update steps that were skipped because this point was at that time non-local. */
1202 if (params.skipUpdates())
1204 pointStateToUpdate->performPreviouslySkippedUpdates(params, histogramSize_.numUpdates(),
1205 weightHistScalingSkipped,
1206 logPmfsumScalingSkipped);
1209 /* Now do an update with new sampling data. */
1210 pointStateToUpdate->updateWithNewSampling(params, histogramSize_.numUpdates(),
1211 weightHistScalingNew, logPmfsumScalingNew);
1214 /* Only update the histogram size after we are done with the local point updates */
1215 histogramSize_.setHistogramSize(newHistogramSize, weightHistScalingNew);
1217 if (needToNormalizeFreeEnergy)
1219 normalizeFreeEnergyAndPmfSum(&points_);
1222 if (needToUpdateTargetDistribution)
1224 /* The target distribution is always updated for all points at once. */
1225 updateTargetDistribution(points_, params);
1228 /* Update the bias. The bias is updated separately and last since it simply a function of
1229 the free energy and the target distribution and we want to avoid doing extra work. */
1230 for (int pointIndex : *updateList)
1232 points_[pointIndex].updateBias();
1235 /* Increase the update counter. */
1236 histogramSize_.incrementNumUpdates();
1239 double BiasState::updateProbabilityWeightsAndConvolvedBias(const std::vector<DimParams>& dimParams,
1240 const BiasGrid& grid,
1241 gmx::ArrayRef<const double> neighborLambdaEnergies,
1242 std::vector<double, AlignedAllocator<double>>* weight) const
1244 /* Only neighbors of the current coordinate value will have a non-negligible chance of getting sampled */
1245 const std::vector<int>& neighbors = grid.point(coordState_.gridpointIndex()).neighbor;
1247 #if GMX_SIMD_HAVE_DOUBLE
1248 typedef SimdDouble PackType;
1249 constexpr int packSize = GMX_SIMD_DOUBLE_WIDTH;
1251 typedef double PackType;
1252 constexpr int packSize = 1;
1254 /* Round the size of the weight array up to packSize */
1255 const int weightSize = ((neighbors.size() + packSize - 1) / packSize) * packSize;
1256 weight->resize(weightSize);
1258 double* gmx_restrict weightData = weight->data();
1259 PackType weightSumPack(0.0);
1260 for (size_t i = 0; i < neighbors.size(); i += packSize)
1262 for (size_t n = i; n < i + packSize; n++)
1264 if (n < neighbors.size())
1266 const int neighbor = neighbors[n];
1267 (*weight)[n] = biasedLogWeightFromPoint(
1268 dimParams, points_, grid, neighbor, points_[neighbor].bias(),
1269 coordState_.coordValue(), neighborLambdaEnergies, coordState_.gridpointIndex());
1273 /* Pad with values that don't affect the result */
1274 (*weight)[n] = detail::c_largeNegativeExponent;
1277 PackType weightPack = load<PackType>(weightData + i);
1278 weightPack = gmx::exp(weightPack);
1279 weightSumPack = weightSumPack + weightPack;
1280 store(weightData + i, weightPack);
1282 /* Sum of probability weights */
1283 double weightSum = reduce(weightSumPack);
1284 GMX_RELEASE_ASSERT(weightSum > 0,
1285 "zero probability weight when updating AWH probability weights.");
1287 /* Normalize probabilities to sum to 1 */
1288 double invWeightSum = 1 / weightSum;
1290 /* When there is a free energy lambda state axis remove the convolved contributions along that
1291 * axis from the total bias. This must be done after calculating invWeightSum (since weightSum
1292 * will be modified), but before normalizing the weights (below). */
1293 if (grid.hasLambdaAxis())
1295 /* If there is only one axis the bias will not be convolved in any dimension. */
1296 if (grid.axis().size() == 1)
1298 weightSum = gmx::exp(points_[coordState_.gridpointIndex()].bias());
1302 for (size_t i = 0; i < neighbors.size(); i++)
1304 const int neighbor = neighbors[i];
1305 if (pointsHaveDifferentLambda(grid, coordState_.gridpointIndex(), neighbor))
1307 weightSum -= weightData[i];
1313 for (double& w : *weight)
1318 /* Return the convolved bias */
1319 return std::log(weightSum);
1322 double BiasState::calcConvolvedBias(const std::vector<DimParams>& dimParams,
1323 const BiasGrid& grid,
1324 const awh_dvec& coordValue) const
1326 int point = grid.nearestIndex(coordValue);
1327 const GridPoint& gridPoint = grid.point(point);
1329 /* Sum the probability weights from the neighborhood of the given point */
1330 double weightSum = 0;
1331 for (int neighbor : gridPoint.neighbor)
1333 /* No convolution is required along the lambda dimension. */
1334 if (pointsHaveDifferentLambda(grid, point, neighbor))
1338 double logWeight = biasedLogWeightFromPoint(dimParams, points_, grid, neighbor,
1339 points_[neighbor].bias(), coordValue, {}, point);
1340 weightSum += std::exp(logWeight);
1343 /* Returns -GMX_FLOAT_MAX if no neighboring points were in the target region. */
1344 return (weightSum > 0) ? std::log(weightSum) : -GMX_FLOAT_MAX;
1347 void BiasState::sampleProbabilityWeights(const BiasGrid& grid, gmx::ArrayRef<const double> probWeightNeighbor)
1349 const std::vector<int>& neighbor = grid.point(coordState_.gridpointIndex()).neighbor;
1351 /* Save weights for next update */
1352 for (size_t n = 0; n < neighbor.size(); n++)
1354 points_[neighbor[n]].increaseWeightSumIteration(probWeightNeighbor[n]);
1357 /* Update the local update range. Two corner points define this rectangular
1358 * domain. We need to choose two new corner points such that the new domain
1359 * contains both the old update range and the current neighborhood.
1360 * In the simplest case when an update is performed every sample,
1361 * the update range would simply equal the current neighborhood.
1363 int neighborStart = neighbor[0];
1364 int neighborLast = neighbor[neighbor.size() - 1];
1365 for (int d = 0; d < grid.numDimensions(); d++)
1367 int origin = grid.point(neighborStart).index[d];
1368 int last = grid.point(neighborLast).index[d];
1372 /* Unwrap if wrapped around the boundary (only happens for periodic
1373 * boundaries). This has been already for the stored index interval.
1375 /* TODO: what we want to do is to find the smallest the update
1376 * interval that contains all points that need to be updated.
1377 * This amounts to combining two intervals, the current
1378 * [origin, end] update interval and the new touched neighborhood
1379 * into a new interval that contains all points from both the old
1382 * For periodic boundaries it becomes slightly more complicated
1383 * than for closed boundaries because then it needs not be
1384 * true that origin < end (so one can't simply relate the origin/end
1385 * in the min()/max() below). The strategy here is to choose the
1386 * origin closest to a reference point (index 0) and then unwrap
1387 * the end index if needed and choose the largest end index.
1388 * This ensures that both intervals are in the new interval
1389 * but it's not necessarily the smallest.
1390 * Currently we solve this by going through each possibility
1391 * and checking them.
1393 last += grid.axis(d).numPointsInPeriod();
1396 originUpdatelist_[d] = std::min(originUpdatelist_[d], origin);
1397 endUpdatelist_[d] = std::max(endUpdatelist_[d], last);
1401 void BiasState::sampleCoordAndPmf(const std::vector<DimParams>& dimParams,
1402 const BiasGrid& grid,
1403 gmx::ArrayRef<const double> probWeightNeighbor,
1404 double convolvedBias)
1406 /* Sampling-based deconvolution extracting the PMF.
1407 * Update the PMF histogram with the current coordinate value.
1409 * Because of the finite width of the harmonic potential, the free energy
1410 * defined for each coordinate point does not exactly equal that of the
1411 * actual coordinate, the PMF. However, the PMF can be estimated by applying
1412 * the relation exp(-PMF) = exp(-bias_convolved)*P_biased/Z, i.e. by keeping a
1413 * reweighted histogram of the coordinate value. Strictly, this relies on
1414 * the unknown normalization constant Z being either constant or known. Here,
1415 * neither is true except in the long simulation time limit. Empirically however,
1416 * it works (mainly because how the PMF histogram is rescaled).
1419 const int gridPointIndex = coordState_.gridpointIndex();
1420 const std::optional<int> lambdaAxisIndex = grid.lambdaAxisIndex();
1422 /* Update the PMF of points along a lambda axis with their bias. */
1423 if (lambdaAxisIndex)
1425 const std::vector<int>& neighbors = grid.point(gridPointIndex).neighbor;
1427 std::vector<double> lambdaMarginalDistribution =
1428 calculateFELambdaMarginalDistribution(grid, neighbors, probWeightNeighbor);
1430 awh_dvec coordValueAlongLambda = { coordState_.coordValue()[0], coordState_.coordValue()[1],
1431 coordState_.coordValue()[2], coordState_.coordValue()[3] };
1432 for (size_t i = 0; i < neighbors.size(); i++)
1434 const int neighbor = neighbors[i];
1436 if (pointsAlongLambdaAxis(grid, gridPointIndex, neighbor))
1438 const double neighborLambda = grid.point(neighbor).coordValue[lambdaAxisIndex.value()];
1439 if (neighbor == gridPointIndex)
1441 bias = convolvedBias;
1445 coordValueAlongLambda[lambdaAxisIndex.value()] = neighborLambda;
1446 bias = calcConvolvedBias(dimParams, grid, coordValueAlongLambda);
1449 const double probWeight = lambdaMarginalDistribution[neighborLambda];
1450 const double weightedBias = bias - std::log(std::max(probWeight, GMX_DOUBLE_MIN)); // avoid log(0)
1452 if (neighbor == gridPointIndex && grid.covers(coordState_.coordValue()))
1454 points_[neighbor].samplePmf(weightedBias);
1458 points_[neighbor].updatePmfUnvisited(weightedBias);
1465 /* Only save coordinate data that is in range (the given index is always
1466 * in range even if the coordinate value is not).
1468 if (grid.covers(coordState_.coordValue()))
1470 /* Save PMF sum and keep a histogram of the sampled coordinate values */
1471 points_[gridPointIndex].samplePmf(convolvedBias);
1475 /* Save probability weights for the update */
1476 sampleProbabilityWeights(grid, probWeightNeighbor);
1479 void BiasState::initHistoryFromState(AwhBiasHistory* biasHistory) const
1481 biasHistory->pointState.resize(points_.size());
1484 void BiasState::updateHistory(AwhBiasHistory* biasHistory, const BiasGrid& grid) const
1486 GMX_RELEASE_ASSERT(biasHistory->pointState.size() == points_.size(),
1487 "The AWH history setup does not match the AWH state.");
1489 AwhBiasStateHistory* stateHistory = &biasHistory->state;
1490 stateHistory->umbrellaGridpoint = coordState_.umbrellaGridpoint();
1492 for (size_t m = 0; m < biasHistory->pointState.size(); m++)
1494 AwhPointStateHistory* psh = &biasHistory->pointState[m];
1496 points_[m].storeState(psh);
1498 psh->weightsum_covering = weightSumCovering_[m];
1501 histogramSize_.storeState(stateHistory);
1503 stateHistory->origin_index_updatelist = multiDimGridIndexToLinear(grid, originUpdatelist_);
1504 stateHistory->end_index_updatelist = multiDimGridIndexToLinear(grid, endUpdatelist_);
1507 void BiasState::restoreFromHistory(const AwhBiasHistory& biasHistory, const BiasGrid& grid)
1509 const AwhBiasStateHistory& stateHistory = biasHistory.state;
1511 coordState_.restoreFromHistory(stateHistory);
1513 if (biasHistory.pointState.size() != points_.size())
1516 InvalidInputError("Bias grid size in checkpoint and simulation do not match. "
1517 "Likely you provided a checkpoint from a different simulation."));
1519 for (size_t m = 0; m < points_.size(); m++)
1521 points_[m].setFromHistory(biasHistory.pointState[m]);
1524 for (size_t m = 0; m < weightSumCovering_.size(); m++)
1526 weightSumCovering_[m] = biasHistory.pointState[m].weightsum_covering;
1529 histogramSize_.restoreFromHistory(stateHistory);
1531 linearGridindexToMultiDim(grid, stateHistory.origin_index_updatelist, originUpdatelist_);
1532 linearGridindexToMultiDim(grid, stateHistory.end_index_updatelist, endUpdatelist_);
1535 void BiasState::broadcast(const t_commrec* commRecord)
1537 gmx_bcast(sizeof(coordState_), &coordState_, commRecord->mpi_comm_mygroup);
1539 gmx_bcast(points_.size() * sizeof(PointState), points_.data(), commRecord->mpi_comm_mygroup);
1541 gmx_bcast(weightSumCovering_.size() * sizeof(double), weightSumCovering_.data(),
1542 commRecord->mpi_comm_mygroup);
1544 gmx_bcast(sizeof(histogramSize_), &histogramSize_, commRecord->mpi_comm_mygroup);
1547 void BiasState::setFreeEnergyToConvolvedPmf(const std::vector<DimParams>& dimParams, const BiasGrid& grid)
1549 std::vector<float> convolvedPmf;
1551 calcConvolvedPmf(dimParams, grid, &convolvedPmf);
1553 for (size_t m = 0; m < points_.size(); m++)
1555 points_[m].setFreeEnergy(convolvedPmf[m]);
1560 * Count trailing data rows containing only zeros.
1562 * \param[in] data 2D data array.
1563 * \param[in] numRows Number of rows in array.
1564 * \param[in] numColumns Number of cols in array.
1565 * \returns the number of trailing zero rows.
1567 static int countTrailingZeroRows(const double* const* data, int numRows, int numColumns)
1569 int numZeroRows = 0;
1570 for (int m = numRows - 1; m >= 0; m--)
1572 bool rowIsZero = true;
1573 for (int d = 0; d < numColumns; d++)
1575 if (data[d][m] != 0)
1584 /* At a row with non-zero data */
1589 /* Still at a zero data row, keep checking rows higher up. */
1598 * Initializes the PMF and target with data read from an input table.
1600 * \param[in] dimParams The dimension parameters.
1601 * \param[in] grid The grid.
1602 * \param[in] filename The filename to read PMF and target from.
1603 * \param[in] numBias Number of biases.
1604 * \param[in] biasIndex The index of the bias.
1605 * \param[in,out] pointState The state of the points in this bias.
1607 static void readUserPmfAndTargetDistribution(const std::vector<DimParams>& dimParams,
1608 const BiasGrid& grid,
1609 const std::string& filename,
1612 std::vector<PointState>* pointState)
1614 /* Read the PMF and target distribution.
1615 From the PMF, the convolved PMF, or the reference value free energy, can be calculated
1616 base on the force constant. The free energy and target together determine the bias.
1618 std::string filenameModified(filename);
1621 size_t n = filenameModified.rfind('.');
1622 GMX_RELEASE_ASSERT(n != std::string::npos,
1623 "The filename should contain an extension starting with .");
1624 filenameModified.insert(n, formatString("%d", biasIndex));
1627 std::string correctFormatMessage = formatString(
1628 "%s is expected in the following format. "
1629 "The first ndim column(s) should contain the coordinate values for each point, "
1630 "each column containing values of one dimension (in ascending order). "
1631 "For a multidimensional coordinate, points should be listed "
1632 "in the order obtained by traversing lower dimensions first. "
1633 "E.g. for two-dimensional grid of size nxn: "
1634 "(1, 1), (1, 2),..., (1, n), (2, 1), (2, 2), ..., , (n, n - 1), (n, n). "
1635 "Column ndim + 1 should contain the PMF value for each coordinate value. "
1636 "The target distribution values should be in column ndim + 2 or column ndim + 5. "
1637 "Make sure the input file ends with a new line but has no trailing new lines.",
1639 gmx::TextLineWrapper wrapper;
1640 wrapper.settings().setLineLength(c_linewidth);
1641 correctFormatMessage = wrapper.wrapToString(correctFormatMessage);
1645 int numRows = read_xvg(filenameModified.c_str(), &data, &numColumns);
1647 /* Check basic data properties here. BiasGrid takes care of more complicated things. */
1651 std::string mesg = gmx::formatString("%s is empty!.\n\n%s", filename.c_str(),
1652 correctFormatMessage.c_str());
1653 GMX_THROW(InvalidInputError(mesg));
1656 /* Less than 2 points is not useful for PMF or target. */
1659 std::string mesg = gmx::formatString(
1660 "%s contains too few data points (%d)."
1661 "The minimum number of points is 2.",
1662 filename.c_str(), numRows);
1663 GMX_THROW(InvalidInputError(mesg));
1666 /* Make sure there are enough columns of data.
1668 Two formats are allowed. Either with columns {coords, PMF, target} or
1669 {coords, PMF, x, y, z, target, ...}. The latter format is allowed since that
1670 is how AWH output is written (x, y, z being other AWH variables). For this format,
1671 trailing columns are ignored.
1673 int columnIndexTarget;
1674 int numColumnsMin = dimParams.size() + 2;
1675 int columnIndexPmf = dimParams.size();
1676 if (numColumns == numColumnsMin)
1678 columnIndexTarget = columnIndexPmf + 1;
1682 columnIndexTarget = columnIndexPmf + 4;
1685 if (numColumns < numColumnsMin)
1687 std::string mesg = gmx::formatString(
1688 "The number of columns in %s should be at least %d."
1690 filename.c_str(), numColumnsMin, correctFormatMessage.c_str());
1691 GMX_THROW(InvalidInputError(mesg));
1694 /* read_xvg can give trailing zero data rows for trailing new lines in the input. We allow 1 zero row,
1695 since this could be real data. But multiple trailing zero rows cannot correspond to valid data. */
1696 int numZeroRows = countTrailingZeroRows(data, numRows, numColumns);
1697 if (numZeroRows > 1)
1699 std::string mesg = gmx::formatString(
1700 "Found %d trailing zero data rows in %s. Please remove trailing empty lines and "
1702 numZeroRows, filename.c_str());
1703 GMX_THROW(InvalidInputError(mesg));
1706 /* Convert from user units to internal units before sending the data of to grid. */
1707 for (size_t d = 0; d < dimParams.size(); d++)
1709 double scalingFactor = dimParams[d].scaleUserInputToInternal(1);
1710 if (scalingFactor == 1)
1714 for (size_t m = 0; m < pointState->size(); m++)
1716 data[d][m] *= scalingFactor;
1720 /* Get a data point for each AWH grid point so that they all get data. */
1721 std::vector<int> gridIndexToDataIndex(grid.numPoints());
1722 mapGridToDataGrid(&gridIndexToDataIndex, data, numRows, filename, grid, correctFormatMessage);
1724 /* Extract the data for each grid point.
1725 * We check if the target distribution is zero for all points.
1727 bool targetDistributionIsZero = true;
1728 for (size_t m = 0; m < pointState->size(); m++)
1730 (*pointState)[m].setLogPmfSum(-data[columnIndexPmf][gridIndexToDataIndex[m]]);
1731 double target = data[columnIndexTarget][gridIndexToDataIndex[m]];
1733 /* Check if the values are allowed. */
1736 std::string mesg = gmx::formatString(
1737 "Target distribution weight at point %zu (%g) in %s is negative.", m, target,
1739 GMX_THROW(InvalidInputError(mesg));
1743 targetDistributionIsZero = false;
1745 (*pointState)[m].setTargetConstantWeight(target);
1748 if (targetDistributionIsZero)
1751 gmx::formatString("The target weights given in column %d in %s are all 0",
1752 columnIndexTarget, filename.c_str());
1753 GMX_THROW(InvalidInputError(mesg));
1756 /* Free the arrays. */
1757 for (int m = 0; m < numColumns; m++)
1764 void BiasState::normalizePmf(int numSharingSims)
1766 /* The normalization of the PMF estimate matters because it determines how big effect the next sample has.
1767 Approximately (for large enough force constant) we should have:
1768 sum_x(exp(-pmf(x)) = nsamples*sum_xref(exp(-f(xref)).
1771 /* Calculate the normalization factor, i.e. divide by the pmf sum, multiply by the number of samples and the f sum */
1772 double expSumPmf = 0;
1774 for (const PointState& pointState : points_)
1776 if (pointState.inTargetRegion())
1778 expSumPmf += std::exp(pointState.logPmfSum());
1779 expSumF += std::exp(-pointState.freeEnergy());
1782 double numSamples = histogramSize_.histogramSize() / numSharingSims;
1785 double logRenorm = std::log(numSamples * expSumF / expSumPmf);
1786 for (PointState& pointState : points_)
1788 if (pointState.inTargetRegion())
1790 pointState.setLogPmfSum(pointState.logPmfSum() + logRenorm);
1795 void BiasState::initGridPointState(const AwhBiasParams& awhBiasParams,
1796 const std::vector<DimParams>& dimParams,
1797 const BiasGrid& grid,
1798 const BiasParams& params,
1799 const std::string& filename,
1802 /* Modify PMF, free energy and the constant target distribution factor
1803 * to user input values if there is data given.
1805 if (awhBiasParams.bUserData)
1807 readUserPmfAndTargetDistribution(dimParams, grid, filename, numBias, params.biasIndex, &points_);
1808 setFreeEnergyToConvolvedPmf(dimParams, grid);
1811 /* The local Boltzmann distribution is special because the target distribution is updated as a function of the reference weighthistogram. */
1812 GMX_RELEASE_ASSERT(params.eTarget != eawhtargetLOCALBOLTZMANN || points_[0].weightSumRef() != 0,
1813 "AWH reference weight histogram not initialized properly with local "
1814 "Boltzmann target distribution.");
1816 updateTargetDistribution(points_, params);
1818 for (PointState& pointState : points_)
1820 if (pointState.inTargetRegion())
1822 pointState.updateBias();
1826 /* Note that for zero target this is a value that represents -infinity but should not be used for biasing. */
1827 pointState.setTargetToZero();
1831 /* Set the initial reference weighthistogram. */
1832 const double histogramSize = histogramSize_.histogramSize();
1833 for (auto& pointState : points_)
1835 pointState.setInitialReferenceWeightHistogram(histogramSize);
1838 /* Make sure the pmf is normalized consistently with the histogram size.
1839 Note: the target distribution and free energy need to be set here. */
1840 normalizePmf(params.numSharedUpdate);
1843 BiasState::BiasState(const AwhBiasParams& awhBiasParams,
1844 double histogramSizeInitial,
1845 const std::vector<DimParams>& dimParams,
1846 const BiasGrid& grid) :
1847 coordState_(awhBiasParams, dimParams, grid),
1848 points_(grid.numPoints()),
1849 weightSumCovering_(grid.numPoints()),
1850 histogramSize_(awhBiasParams, histogramSizeInitial)
1852 /* The minimum and maximum multidimensional point indices that are affected by the next update */
1853 for (size_t d = 0; d < dimParams.size(); d++)
1855 int index = grid.point(coordState_.gridpointIndex()).index[d];
1856 originUpdatelist_[d] = index;
1857 endUpdatelist_[d] = index;