src/gromacs/applied_forces/awh/tests/bias_fep_lambda_state.cpp

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  35 #include "gmxpre.h"
  36
  37 #include <cmath>
  38
  39 #include <memory>
  40 #include <tuple>
  41 #include <vector>
  42
  43 #include <gmock/gmock.h>
  44 #include <gtest/gtest.h>
  45
  46 #include "gromacs/applied_forces/awh/bias.h"
  47 #include "gromacs/applied_forces/awh/correlationgrid.h"
  48 #include "gromacs/applied_forces/awh/pointstate.h"
  49 #include "gromacs/mdtypes/awh_params.h"
  50 #include "gromacs/utility/stringutil.h"
  51
  52 #include "gromacs/applied_forces/awh/tests/awh_setup.h"
  53 #include "testutils/refdata.h"
  54 #include "testutils/testasserts.h"
  55
  56 namespace gmx
  57 {
  58
  59 namespace test
  60 {
  61
  62 //! The number of lambda states to use in the tests.
  63 static constexpr int c_numLambdaStates = 16;
  64
  65
  66 //! Convenience typedef: growth type enum, potential type enum, disable update skips
  67 typedef std::tuple<AwhHistogramGrowthType, AwhPotentialType, BiasParams::DisableUpdateSkips> BiasTestParameters;
  68
  69 /*! \brief Test fixture for testing Bias updates
  70  */
  71 class BiasFepLambdaStateTest : public ::testing::TestWithParam<BiasTestParameters>
  72 {
  73 private:
  74     //! Storage for test parameters.
  75     std::unique_ptr<AwhTestParameters> params_;
  76
  77 public:
  78     //! Random seed for AWH MC sampling
  79     int64_t seed_;
  80
  81     //! The awh Bias
  82     std::unique_ptr<Bias> bias_;
  83
  84     BiasFepLambdaStateTest()
  85     {
  86         /* We test all combinations of:
  87          *   eawhgrowth:
  88          *     eawhgrowthLINEAR:     final, normal update phase
  89          *     ewahgrowthEXP_LINEAR: intial phase, updated size is constant
  90          *   eawhpotential (test only eawhpotentialUMBRELLA (MC) for FEP lambda dimensions)
  91          *   disableUpdateSkips (should not affect the results):
  92          *     BiasParams::DisableUpdateSkips::yes: update the point state for every sample
  93          *     BiasParams::DisableUpdateSkips::no:  update the point state at an interval > 1 sample
  94          *
  95          * Note: It would be nice to explicitly check that eawhpotential
  96          *       and disableUpdateSkips do not affect the point state.
  97          *       But the reference data will also ensure this.
  98          */
  99         AwhHistogramGrowthType         eawhgrowth;
 100         AwhPotentialType               eawhpotential;
 101         BiasParams::DisableUpdateSkips disableUpdateSkips;
 102         std::tie(eawhgrowth, eawhpotential, disableUpdateSkips) = GetParam();
 103
 104         /* Set up a basic AWH setup with a single, 1D bias with parameters
 105          * such that we can measure the effects of different parameters.
 106          */
 107         constexpr AwhCoordinateProviderType coordinateProvider = AwhCoordinateProviderType::FreeEnergyLambda;
 108         constexpr int                       coordIndex = 0;
 109         constexpr double                    origin     = 0;
 110         constexpr double                    end        = c_numLambdaStates - 1;
 111         constexpr double                    period     = 0;
 112         // Correction for removal of GaussianGeometryFactor/2 in histogram size
 113         constexpr double diffusion = 1e-4 / (0.12927243028700 * 2);
 114         const auto       awhDimBuffer =
 115                 awhDimParamSerialized(coordinateProvider, coordIndex, origin, end, period, diffusion);
 116         const auto awhDimArrayRef = gmx::arrayRefFromArray(&awhDimBuffer, 1);
 117         params_                   = std::make_unique<AwhTestParameters>(getAwhTestParameters(
 118                 eawhgrowth, eawhpotential, awhDimArrayRef, false, 0.4, true, 1.0, c_numLambdaStates));
 119
 120         seed_ = params_->awhParams.seed();
 121
 122         double mdTimeStep = 0.1;
 123
 124         bias_ = std::make_unique<Bias>(-1,
 125                                        params_->awhParams,
 126                                        params_->awhParams.awhBiasParams()[0],
 127                                        params_->dimParams,
 128                                        params_->beta,
 129                                        mdTimeStep,
 130                                        1,
 131                                        "",
 132                                        Bias::ThisRankWillDoIO::No,
 133                                        disableUpdateSkips);
 134     }
 135 };
 136
 137 TEST_P(BiasFepLambdaStateTest, ForcesBiasPmf)
 138 {
 139     gmx::test::TestReferenceData    data;
 140     gmx::test::TestReferenceChecker checker(data.rootChecker());
 141
 142     Bias& bias = *bias_;
 143
 144     /* Make strings with the properties we expect to be different in the tests.
 145      * These also helps to interpret the reference data.
 146      */
 147     std::vector<std::string> props;
 148     props.push_back(formatString("stage:           %s", bias.state().inInitialStage() ? "initial" : "final"));
 149     props.push_back(formatString("convolve forces: %s", bias.params().convolveForce ? "yes" : "no"));
 150     props.push_back(formatString("skip updates:    %s", bias.params().skipUpdates() ? "yes" : "no"));
 151
 152     SCOPED_TRACE(gmx::formatString("%s, %s, %s", props[0].c_str(), props[1].c_str(), props[2].c_str()));
 153
 154     std::vector<double> force, pot;
 155
 156     double potentialJump = 0;
 157     double mdTimeStep    = 0.1;
 158     int    nSteps        = 501;
 159
 160     /* Some energies to use as base values (to which some noise is added later on). */
 161     std::vector<double> neighborLambdaEnergies(c_numLambdaStates);
 162     std::vector<double> neighborLambdaDhdl(c_numLambdaStates);
 163     const double        magnitude = 12.0;
 164     for (int i = 0; i < c_numLambdaStates; i++)
 165     {
 166         neighborLambdaEnergies[i] = magnitude * std::sin(i * 0.1);
 167         neighborLambdaDhdl[i]     = magnitude * std::cos(i * 0.1);
 168     }
 169
 170     for (int step = 0; step < nSteps; step++)
 171     {
 172         int      umbrellaGridpointIndex = bias.state().coordState().umbrellaGridpoint();
 173         awh_dvec coordValue = { bias.getGridCoordValue(umbrellaGridpointIndex)[0], 0, 0, 0 };
 174         double   potential  = 0;
 175         gmx::ArrayRef<const double> biasForce = bias.calcForceAndUpdateBias(coordValue,
 176                                                                             neighborLambdaEnergies,
 177                                                                             neighborLambdaDhdl,
 178                                                                             &potential,
 179                                                                             &potentialJump,
 180                                                                             nullptr,
 181                                                                             nullptr,
 182                                                                             step * mdTimeStep,
 183                                                                             step,
 184                                                                             seed_,
 185                                                                             nullptr);
 186
 187         force.push_back(biasForce[0]);
 188         pot.push_back(potential);
 189     }
 190
 191     /* When skipping updates, ensure all skipped updates are performed here.
 192      * This should result in the same bias state as at output in a normal run.
 193      */
 194     if (bias.params().skipUpdates())
 195     {
 196         bias.doSkippedUpdatesForAllPoints();
 197     }
 198
 199     std::vector<double> pointBias, logPmfsum;
 200     for (const auto& point : bias.state().points())
 201     {
 202         pointBias.push_back(point.bias());
 203         logPmfsum.push_back(point.logPmfSum());
 204     }
 205
 206     constexpr int ulpTol = 10;
 207
 208     checker.checkSequence(props.begin(), props.end(), "Properties");
 209     checker.setDefaultTolerance(absoluteTolerance(magnitude * GMX_DOUBLE_EPS * ulpTol));
 210     checker.checkSequence(force.begin(), force.end(), "Force");
 211     checker.checkSequence(pot.begin(), pot.end(), "Potential");
 212     checker.setDefaultTolerance(relativeToleranceAsUlp(1.0, ulpTol));
 213     checker.checkSequence(pointBias.begin(), pointBias.end(), "PointBias");
 214     checker.checkSequence(logPmfsum.begin(), logPmfsum.end(), "PointLogPmfsum");
 215 }
 216
 217 /* Scan initial/final phase, MC/convolved force and update skip (not) allowed
 218  * Both the convolving and skipping should not affect the bias and PMF.
 219  * It would be nice if the test would explicitly check for this.
 220  * Currently this is tested through identical reference data.
 221  */
 222 INSTANTIATE_TEST_CASE_P(WithParameters,
 223                         BiasFepLambdaStateTest,
 224                         ::testing::Combine(::testing::Values(AwhHistogramGrowthType::Linear,
 225                                                              AwhHistogramGrowthType::ExponentialLinear),
 226                                            ::testing::Values(AwhPotentialType::Umbrella),
 227                                            ::testing::Values(BiasParams::DisableUpdateSkips::yes,
 228                                                              BiasParams::DisableUpdateSkips::no)));
 229
 230 // Test that we detect coverings and exit the initial stage at the correct step
 231 TEST(BiasFepLambdaStateTest, DetectsCovering)
 232 {
 233     constexpr AwhCoordinateProviderType coordinateProvider = AwhCoordinateProviderType::FreeEnergyLambda;
 234     constexpr int                       coordIndex         = 0;
 235     constexpr double                    origin             = 0;
 236     constexpr double                    end                = c_numLambdaStates - 1;
 237     constexpr double                    period             = 0;
 238     constexpr double                    diffusion          = 1e-4 / (0.12927243028700 * 2);
 239     auto                                awhDimBuffer =
 240             awhDimParamSerialized(coordinateProvider, coordIndex, origin, end, period, diffusion);
 241     auto                    awhDimArrayRef = gmx::arrayRefFromArray(&awhDimBuffer, 1);
 242     const AwhTestParameters params(getAwhTestParameters(AwhHistogramGrowthType::ExponentialLinear,
 243                                                         AwhPotentialType::Umbrella,
 244                                                         awhDimArrayRef,
 245                                                         false,
 246                                                         0.4,
 247                                                         true,
 248                                                         1.0,
 249                                                         c_numLambdaStates));
 250
 251     const double mdTimeStep = 0.1;
 252
 253     Bias bias(-1,
 254               params.awhParams,
 255               params.awhParams.awhBiasParams()[0],
 256               params.dimParams,
 257               params.beta,
 258               mdTimeStep,
 259               1,
 260               "",
 261               Bias::ThisRankWillDoIO::No);
 262
 263     const int64_t exitStepRef = 320;
 264
 265     bool inInitialStage = bias.state().inInitialStage();
 266
 267     /* Some energies to use as base values (to which some noise is added later on). */
 268     std::vector<double> neighborLambdaEnergies(c_numLambdaStates);
 269     std::vector<double> neighborLambdaDhdl(c_numLambdaStates);
 270     const double        magnitude = 12.0;
 271     for (int i = 0; i < c_numLambdaStates; i++)
 272     {
 273         neighborLambdaEnergies[i] = magnitude * std::sin(i * 0.1);
 274         neighborLambdaDhdl[i]     = magnitude * std::cos(i * 0.1);
 275     }
 276
 277     int64_t step;
 278     /* Normally this loop exits at exitStepRef, but we extend with failure */
 279     for (step = 0; step <= 2 * exitStepRef; step++)
 280     {
 281         int      umbrellaGridpointIndex = bias.state().coordState().umbrellaGridpoint();
 282         awh_dvec coordValue = { bias.getGridCoordValue(umbrellaGridpointIndex)[0], 0, 0, 0 };
 283
 284         double potential     = 0;
 285         double potentialJump = 0;
 286         bias.calcForceAndUpdateBias(coordValue,
 287                                     neighborLambdaEnergies,
 288                                     neighborLambdaDhdl,
 289                                     &potential,
 290                                     &potentialJump,
 291                                     nullptr,
 292                                     nullptr,
 293                                     step,
 294                                     step,
 295                                     params.awhParams.seed(),
 296                                     nullptr);
 297
 298         inInitialStage = bias.state().inInitialStage();
 299         if (!inInitialStage)
 300         {
 301             break;
 302         }
 303     }
 304
 305     EXPECT_EQ(false, inInitialStage);
 306     if (!inInitialStage)
 307     {
 308         EXPECT_EQ(exitStepRef, step);
 309     }
 310 }
 311
 312 } // namespace test
 313 } // namespace gmx