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43 #include <gmock/gmock.h>
44 #include <gtest/gtest.h>
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"
52 #include "testutils/refdata.h"
53 #include "testutils/testasserts.h"
61 //! The number of lambda states to use in the tests.
62 const int numLambdaStates = 16;
65 * Struct that gathers all input for setting up and using a Bias
67 struct AwhFepLambdaStateTestParameters
69 AwhFepLambdaStateTestParameters() = default;
71 AwhFepLambdaStateTestParameters(AwhFepLambdaStateTestParameters&& o) noexcept :
73 awhDimParams(o.awhDimParams),
74 awhBiasParams(o.awhBiasParams),
75 awhParams(o.awhParams),
76 dimParams(std::move(o.dimParams))
78 awhBiasParams.dimParams = &awhDimParams;
79 awhParams.awhBiasParams = &awhBiasParams;
81 double beta; //!< 1/(kB*T)
83 AwhDimParams awhDimParams; //!< Dimension parameters pointed to by \p awhBiasParams
84 AwhBiasParams awhBiasParams; //!< Bias parameters pointed to by \[ awhParams
85 AwhParams awhParams; //!< AWH parameters, this is the struct to actually use
87 std::vector<DimParams> dimParams; //!< Dimension parameters for setting up Bias
90 //! Helper function to set up the C-style AWH parameters for the test
91 static AwhFepLambdaStateTestParameters getAwhFepLambdaTestParameters(AwhHistogramGrowthType eawhgrowth,
92 AwhPotentialType eawhpotential)
94 AwhFepLambdaStateTestParameters params;
98 AwhDimParams& awhDimParams = params.awhDimParams;
100 awhDimParams.period = 0;
101 // Correction for removal of GaussianGeometryFactor/2 in histogram size
102 awhDimParams.diffusion = 1e-4 / (0.12927243028700 * 2);
103 awhDimParams.origin = 0;
104 awhDimParams.end = numLambdaStates - 1;
105 awhDimParams.coordValueInit = awhDimParams.origin;
106 awhDimParams.coverDiameter = 0;
107 awhDimParams.eCoordProvider = AwhCoordinateProviderType::FreeEnergyLambda;
109 AwhBiasParams& awhBiasParams = params.awhBiasParams;
111 awhBiasParams.ndim = 1;
112 awhBiasParams.dimParams = &awhDimParams;
113 awhBiasParams.eTarget = AwhTargetType::Constant;
114 awhBiasParams.targetBetaScaling = 0;
115 awhBiasParams.targetCutoff = 0;
116 awhBiasParams.eGrowth = eawhgrowth;
117 awhBiasParams.bUserData = FALSE;
118 awhBiasParams.errorInitial = 1.0 / params.beta;
119 awhBiasParams.shareGroup = 0;
120 awhBiasParams.equilibrateHistogram = FALSE;
122 int64_t seed = 93471803;
124 params.dimParams.push_back(DimParams::fepLambdaDimParams(numLambdaStates, params.beta));
126 AwhParams& awhParams = params.awhParams;
128 awhParams.numBias = 1;
129 awhParams.awhBiasParams = &awhBiasParams;
130 awhParams.seed = seed;
131 awhParams.nstOut = 0;
132 awhParams.nstSampleCoord = 1;
133 awhParams.numSamplesUpdateFreeEnergy = 10;
134 awhParams.ePotential = eawhpotential;
135 awhParams.shareBiasMultisim = FALSE;
140 //! Convenience typedef: growth type enum, potential type enum, disable update skips
141 typedef std::tuple<AwhHistogramGrowthType, AwhPotentialType, BiasParams::DisableUpdateSkips> BiasTestParameters;
143 /*! \brief Test fixture for testing Bias updates
145 class BiasFepLambdaStateTest : public ::testing::TestWithParam<BiasTestParameters>
148 //! Random seed for AWH MC sampling
152 std::unique_ptr<Bias> bias_;
154 BiasFepLambdaStateTest()
156 /* We test all combinations of:
158 * eawhgrowthLINEAR: final, normal update phase
159 * ewahgrowthEXP_LINEAR: intial phase, updated size is constant
160 * eawhpotential (test both, but for the FEP lambda state dimension MC will in practice be used,
161 * except that eawhpotentialCONVOLVED also gives a potential output):
162 * eawhpotentialUMBRELLA: MC on lambda state
163 * eawhpotentialCONVOLVED: MD on a convolved potential landscape (falling back to MC on lambda state)
164 * disableUpdateSkips (should not affect the results):
165 * BiasParams::DisableUpdateSkips::yes: update the point state for every sample
166 * BiasParams::DisableUpdateSkips::no: update the point state at an interval > 1 sample
168 * Note: It would be nice to explicitly check that eawhpotential
169 * and disableUpdateSkips do not affect the point state.
170 * But the reference data will also ensure this.
172 AwhHistogramGrowthType eawhgrowth;
173 AwhPotentialType eawhpotential;
174 BiasParams::DisableUpdateSkips disableUpdateSkips;
175 std::tie(eawhgrowth, eawhpotential, disableUpdateSkips) = GetParam();
177 /* Set up a basic AWH setup with a single, 1D bias with parameters
178 * such that we can measure the effects of different parameters.
180 const AwhFepLambdaStateTestParameters params =
181 getAwhFepLambdaTestParameters(eawhgrowth, eawhpotential);
183 seed_ = params.awhParams.seed;
185 double mdTimeStep = 0.1;
187 bias_ = std::make_unique<Bias>(-1,
189 params.awhBiasParams,
195 Bias::ThisRankWillDoIO::No,
200 TEST_P(BiasFepLambdaStateTest, ForcesBiasPmf)
202 gmx::test::TestReferenceData data;
203 gmx::test::TestReferenceChecker checker(data.rootChecker());
207 /* Make strings with the properties we expect to be different in the tests.
208 * These also helps to interpret the reference data.
210 std::vector<std::string> props;
211 props.push_back(formatString("stage: %s", bias.state().inInitialStage() ? "initial" : "final"));
212 props.push_back(formatString("convolve forces: %s", bias.params().convolveForce ? "yes" : "no"));
213 props.push_back(formatString("skip updates: %s", bias.params().skipUpdates() ? "yes" : "no"));
215 SCOPED_TRACE(gmx::formatString("%s, %s, %s", props[0].c_str(), props[1].c_str(), props[2].c_str()));
217 std::vector<double> force, pot;
219 double potentialJump = 0;
220 double mdTimeStep = 0.1;
223 /* Some energies to use as base values (to which some noise is added later on). */
224 std::vector<double> neighborLambdaEnergies(numLambdaStates);
225 std::vector<double> neighborLambdaDhdl(numLambdaStates);
226 const double magnitude = 12.0;
227 for (int i = 0; i < numLambdaStates; i++)
229 neighborLambdaEnergies[i] = magnitude * std::sin(i * 0.1);
230 neighborLambdaDhdl[i] = magnitude * std::cos(i * 0.1);
233 for (int step = 0; step < nSteps; step++)
235 int umbrellaGridpointIndex = bias.state().coordState().umbrellaGridpoint();
236 awh_dvec coordValue = { bias.getGridCoordValue(umbrellaGridpointIndex)[0], 0, 0, 0 };
237 double potential = 0;
238 gmx::ArrayRef<const double> biasForce = bias.calcForceAndUpdateBias(coordValue,
239 neighborLambdaEnergies,
250 force.push_back(biasForce[0]);
251 pot.push_back(potential);
254 /* When skipping updates, ensure all skipped updates are performed here.
255 * This should result in the same bias state as at output in a normal run.
257 if (bias.params().skipUpdates())
259 bias.doSkippedUpdatesForAllPoints();
262 std::vector<double> pointBias, logPmfsum;
263 for (auto& point : bias.state().points())
265 pointBias.push_back(point.bias());
266 logPmfsum.push_back(point.logPmfSum());
269 constexpr int ulpTol = 10;
271 checker.checkSequence(props.begin(), props.end(), "Properties");
272 checker.setDefaultTolerance(absoluteTolerance(magnitude * GMX_DOUBLE_EPS * ulpTol));
273 checker.checkSequence(force.begin(), force.end(), "Force");
274 checker.checkSequence(pot.begin(), pot.end(), "Potential");
275 checker.setDefaultTolerance(relativeToleranceAsUlp(1.0, ulpTol));
276 checker.checkSequence(pointBias.begin(), pointBias.end(), "PointBias");
277 checker.checkSequence(logPmfsum.begin(), logPmfsum.end(), "PointLogPmfsum");
280 /* Scan initial/final phase, MC/convolved force and update skip (not) allowed
281 * Both the convolving and skipping should not affect the bias and PMF.
282 * It would be nice if the test would explicitly check for this.
283 * Currently this is tested through identical reference data.
285 INSTANTIATE_TEST_CASE_P(WithParameters,
286 BiasFepLambdaStateTest,
287 ::testing::Combine(::testing::Values(AwhHistogramGrowthType::Linear,
288 AwhHistogramGrowthType::ExponentialLinear),
289 ::testing::Values(AwhPotentialType::Umbrella,
290 AwhPotentialType::Convolved),
291 ::testing::Values(BiasParams::DisableUpdateSkips::yes,
292 BiasParams::DisableUpdateSkips::no)));
294 // Test that we detect coverings and exit the initial stage at the correct step
295 TEST(BiasFepLambdaStateTest, DetectsCovering)
297 const AwhFepLambdaStateTestParameters params = getAwhFepLambdaTestParameters(
298 AwhHistogramGrowthType::ExponentialLinear, AwhPotentialType::Convolved);
300 const double mdTimeStep = 0.1;
304 params.awhBiasParams,
310 Bias::ThisRankWillDoIO::No);
312 const int64_t exitStepRef = 320;
314 bool inInitialStage = bias.state().inInitialStage();
316 /* Some energies to use as base values (to which some noise is added later on). */
317 std::vector<double> neighborLambdaEnergies(numLambdaStates);
318 std::vector<double> neighborLambdaDhdl(numLambdaStates);
319 const double magnitude = 12.0;
320 for (int i = 0; i < numLambdaStates; i++)
322 neighborLambdaEnergies[i] = magnitude * std::sin(i * 0.1);
323 neighborLambdaDhdl[i] = magnitude * std::cos(i * 0.1);
327 /* Normally this loop exits at exitStepRef, but we extend with failure */
328 for (step = 0; step <= 2 * exitStepRef; step++)
330 int umbrellaGridpointIndex = bias.state().coordState().umbrellaGridpoint();
331 awh_dvec coordValue = { bias.getGridCoordValue(umbrellaGridpointIndex)[0], 0, 0, 0 };
333 double potential = 0;
334 double potentialJump = 0;
335 bias.calcForceAndUpdateBias(coordValue,
336 neighborLambdaEnergies,
344 params.awhParams.seed,
347 inInitialStage = bias.state().inInitialStage();
354 EXPECT_EQ(false, inInitialStage);
357 EXPECT_EQ(exitStepRef, step);