openmm-torch
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20 hours ago •
openmm
openmm
OpenMMException when adding TorchForce to the system
I am trying to use TorchForce to bias a simulation(box-full of waters). The torch model that calculates the CV looks for nearest neighbors of reference water molecule (within cutoff) then calculates pairwise distance between them, this is my feature. Now when adding this jitted model to openmm system it throws OpenMMException. Probably the issue is regarding grad of the tensor I'm returning from my TorchForce model.
The model used in TorchForce:
class Regres_CV2(nn.Module):
def __init__(self):
"""
input_dim : flattened input vector length
hidden1 : node number of hidden layer 1
hidden2 : node number of hidden layer 2
code : dimension of latent space
learning_rate : name suggests
thresh : thresh to compare while earlystopping
train_data : trainning dataset
val_data : validation dataset
"""
super(Regres_CV2, self).__init__()
self.input_dim = 100
self.hidden1 = 1024
self.hidden2 = 512
self.hidden3 = 100
self.hidden4 = 2
torch.manual_seed(1)
np.random.seed(1)
self.encoder = nn.Sequential(
nn.Linear(self.input_dim, self.hidden1),
nn.GELU(),
nn.Linear(self.hidden1, self.hidden2),
nn.GELU(),
nn.Linear(self.hidden2, self.hidden3),
nn.GELU(),
nn.Linear(self.hidden3, self.hidden4),
)
def transform(self, x):
mean = torch.tensor([2.71049179, 2.7858348 , 2.8624867 , 2.98495639, 3.7791522 ,
3.97462569, 4.10705615, 4.21363918, 4.30592099, 4.38936177,
4.46746004, 4.54203554, 4.61539552, 4.68864327, 4.76333205,
4.84088127, 4.92357368, 5.01360315, 5.10504332, 5.19608193,
5.28742064, 5.37936281, 5.4723322 , 5.56674506, 5.66513154,
5.77511278, 5.92270633, 6.02856036, 6.11510288, 6.19097639,
6.26000642, 6.32406077, 6.38447937, 6.44241717, 6.4985381 ,
6.55356604, 6.60778704, 6.6614738 , 6.71468639, 6.76745529,
6.81982973, 6.87181133, 6.92385802, 6.97626398, 7.02942503,
7.08359104, 7.139082 , 7.19567081, 7.25226343, 7.30630277,
7.35744459, 7.40608602, 7.45265641, 7.49777495, 7.54154908,
7.58434985, 7.62622659, 7.66738141, 7.7078045 , 7.74760026,
7.78690909, 7.82570807, 7.86406368, 7.90206851, 7.93969587,
7.97700164, 8.01400973, 8.05068654, 8.08717092, 8.12329801,
8.15917206, 8.1947259 , 8.22994791, 8.26485376, 8.29941423,
8.33366086, 8.36766181, 8.40139063, 8.43490873, 8.46820756,
8.50136546, 8.53428456, 8.56699235, 8.59918515, 8.6309053 ,
8.66224525, 8.69325112, 8.72396482, 8.75443437, 8.78466108,
8.81469268, 8.84469551, 8.87456353, 8.9043148 , 8.93397793,
8.96364157, 8.99321065, 9.02271019, 9.05217446, 9.08154678])/10.0
var = torch.tensor([0.06644448, 0.0720813 , 0.09388647, 0.15218329, 0.37277633,
0.31781363, 0.27569099, 0.24100772, 0.21193125, 0.18754059,
0.16740371, 0.15137762, 0.13928688, 0.13143184, 0.12765353,
0.12734585, 0.1298747 , 0.13416642, 0.13855183, 0.14191056,
0.1447239 , 0.1472054 , 0.15001784, 0.1522338 , 0.15287467,
0.15215965, 0.16101845, 0.1660292 , 0.16650034, 0.16420291,
0.16062643, 0.15659865, 0.15245382, 0.14878753, 0.14553498,
0.1429001 , 0.14109884, 0.14015729, 0.14015134, 0.141089 ,
0.1427195 , 0.1450639 , 0.14823157, 0.15220138, 0.15710731,
0.16332683, 0.17076869, 0.17942144, 0.18831005, 0.19544865,
0.20027134, 0.2031551 , 0.20431226, 0.20431089, 0.20333588,
0.20176965, 0.19970677, 0.19729833, 0.194629 , 0.1918973 ,
0.18898873, 0.1861282 , 0.18330681, 0.18048504, 0.17777579,
0.1751567 , 0.17269368, 0.17036853, 0.1680993 , 0.16591258,
0.16380179, 0.16170763, 0.15970761, 0.15767716, 0.15568707,
0.15367787, 0.15177837, 0.14989855, 0.14813889, 0.1464694 ,
0.14496545, 0.14371142, 0.14268205, 0.14166094, 0.14057099,
0.13943382, 0.13808576, 0.13678063, 0.13545642, 0.13413571,
0.13285683, 0.1315521 , 0.13035399, 0.12925464, 0.12816313,
0.12720239, 0.12632232, 0.12558945, 0.12489357, 0.12431238])/10.0
return (x - mean)/var
def periodic_neighbours(self, pos, maxdist, L):
"""
Finds periodic neighbors within a given maximum distance in a PyTorch tensor context.
Args:
pos (torch.Tensor): Positions of particles (N x D tensor).
maxdist (float): Maximum distance for neighbors.
L (torch.Tensor): Box dimensions (D-dimensional tensor).
Returns:
torch.Tensor: Indices of neighbor pairs (M x 2 tensor).
torch.Tensor: Distances to neighbors (M-dimensional tensor).
"""
maxdistsq = maxdist**2
rL = 1. / L # Inverse box dimensions (D-dimensional tensor)
# Calculate pairwise squared distances using broadcasting
diff = pos.unsqueeze(1) - pos.unsqueeze(0) # N x N x D
diff_wrapped = diff - L.unsqueeze(0) * torch.floor(diff * rL.unsqueeze(0) + 0.5)
distsq = torch.sum(diff_wrapped * diff_wrapped, dim=2) # N x N
# Mask out distances above the threshold and calculate square root efficiently
dists = torch.sqrt(distsq[distsq < maxdistsq])
# Efficiently collect neighbor indices using gather_nd
idx_1, idx_2 = torch.where(distsq < maxdistsq)
bonds = torch.stack([idx_1, idx_2], dim=1) # M x 2
return bonds, dists
def nnDistance(self, i, bonds, dist):
# Convert i to a 1D tensor for broadcasting
i_tensor = i
# Efficiently filter and sort distances using indexing and slicing
neighbor_idx = torch.where((bonds[:, 0] == i_tensor) | (bonds[:, 1] == i_tensor))[0]
distances = dist[neighbor_idx]
sorted_distances, _ = torch.topk(distances, 201, dim=0, largest=False, sorted=True)
return sorted_distances[1::2]
def getForOneFrame(self, bonds, dist):
num_frames = 2000
indices = torch.arange(num_frames)
features = torch.stack([self.nnDistance(i, bonds, dist) for i in indices]) # Stack tensors for each frame
return features
def forward(self, positions, boxvectors):
box = torch.tensor([float(boxvectors[i][i]) for i in range(3)])
pos = positions[::4].to("cpu")
bonds, dist = self.periodic_neighbours(pos, torch.tensor([1.0]), box)
features = self.getForOneFrame(bonds, dist).detach()# Update to handle combined tensor
x = self.transform(features).to(torch.float)
y = self.encoder(x)[:,1].sum()
return y```
The openmm simulation(with MetaD):
import os, sys
import openmm as mm
import openmm.app as app
from openmm.app import GromacsGroFile, GromacsTopFile
from openmm.app import StateDataReporter
from openmm.app import XTCReporter
from openmm.app import PME, HBonds
from openmm.unit import nanometer, kelvin, picosecond, picoseconds, bar, kilojoules_per_mole
from openmmtorch import TorchForce
from openmm.app import BiasVariable
from openmm.app.metadynamics import Metadynamics
gro = GromacsGroFile('/home/dm/Dibyendu/Projects/ICE_AE/Phase_Space_Scaling/Liquid_test/md.gro')
top = GromacsTopFile('/home/dm/Dibyendu/Projects/ICE_AE/Phase_Space_Scaling/Liquid_test/topol.top',
periodicBoxVectors=gro.getPeriodicBoxVectors(),
includeDir='/home/dm/Soft/GMX22/share/gromacs/top/')
##### Create the OpenMM System based on the topology
system = top.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, constraints=HBonds)
##### Remove MM forces
while len(system.getForces()) > 0:
system.removeForce(0)
force = TorchForce('forcemodel2.pt')
force.setUsesPeriodicBoundaryConditions(periodic=True)
psi = BiasVariable(force, -5, 5, 0.5, True)
meta = Metadynamics(system, [ psi],
250*kelvin, 1.2, 1.2*kilojoules_per_mole, 100)
platform = mm.Platform.getPlatformByName('CPU')
# Specify properties for CUDA platform (e.g., mixed precision)
#prop = dict(CudaPrecision='mixed') # Use mixed single/double precision
# Add thermostat and barostat forces to the system
system.addForce(mm.AndersenThermostat(250*kelvin, 1/picosecond))
system.addForce(mm.MonteCarloBarostat(1*bar, 250*kelvin))
# Create Langevin integrator for molecular dynamics simulation
integrator = mm.LangevinMiddleIntegrator(250*kelvin, 1/picosecond, 0.0001*picoseconds)
# Create the OpenMM Simulation object
sim = app.Simulation(top.topology, system, integrator, platform)
config = gro
sim.context.setPositions(config.positions)
sim.minimizeEnergy()
meta.step(sim, 50000)
#sim.step(50000)
reporter = StateDataReporter(file=sys.stdout, reportInterval=100, step=True, time=True, potentialEnergy=True, temperature=True)
sim.reporters.append(reporter)
The error it throws:
---------------------------------------------------------------------------
OpenMMException Traceback (most recent call last)
/tmp/ipykernel_357599/4025831078.py in ?()
32 # Create the OpenMM Simulation object
33 sim = app.Simulation(top.topology, system, integrator, platform)
34 config = gro
35 sim.context.setPositions(config.positions)
---> 36 sim.minimizeEnergy()
37 meta.step(sim, 50000)
38 #sim.step(50000)
39 reporter = StateDataReporter(file=sys.stdout, reportInterval=100, step=True, time=True, potentialEnergy=True, temperature=True)
~/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/app/simulation.py in ?(self, tolerance, maxIterations, reporter)
139 reporter : MinimizationReporter = None
140 an optional reporter to invoke after each iteration. This can be used to monitor the progress
141 of minimization or to stop minimization early.
142 """
--> 143 mm.LocalEnergyMinimizer.minimize(self.context, tolerance, maxIterations, reporter)
~/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/openmm.py in ?(context, tolerance, maxIterations, reporter)
4421 if unit.is_quantity(tolerance):
4422 tolerance = tolerance.value_in_unit(unit.kilojoules_per_mole/unit.nanometer)
4423
4424
-> 4425 return _openmm.LocalEnergyMinimizer_minimize(context, tolerance, maxIterations, reporter)
OpenMMException: Expected a proper Tensor but got None (or an undefined Tensor in C++) for argument #0 'self'
Exception raised from checked_cast_variable at /home/conda/feedstock_root/build_artifacts/libtorch_1706712676143/work/torch/csrc/autograd/VariableTypeManual.cpp:60 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0xaa (0x77db8353587a in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/../libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xf3 (0x77db834eac7e in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/../libc10.so)
frame #2: <unknown function> + 0x4a04cda (0x77db2fa04cda in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/../libtorch_cpu.so)
frame #3: <unknown function> + 0x36a1f5a (0x77db2e6a1f5a in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/../libtorch_cpu.so)
frame #4: <unknown function> + 0x36a25d0 (0x77db2e6a25d0 in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/../libtorch_cpu.so)
frame #5: at::_ops::_to_copy::call(at::Tensor const&, c10::optional<c10::ScalarType>, c10::optional<c10::Layout>, c10::optional<c10::Device>, c10::optional<bool>, bool, c10::optional<c10::MemoryFormat>) + 0x1f7 (0x77db2cacd127 in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/../libtorch_cpu.so)
frame #6: at::native::to(at::Tensor const&, c10::ScalarType, bool, bool, c10::optional<c10::MemoryFormat>) + 0xbc (0x77db2c5e311c in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/../libtorch_cpu.so)
frame #7: <unknown function> + 0x2446ea5 (0x77db2d446ea5 in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/../libtorch_cpu.so)
frame #8: at::_ops::to_dtype::call(at::Tensor const&, c10::ScalarType, bool, bool, c10::optional<c10::MemoryFormat>) + 0x188 (0x77db2cc44b98 in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/../libtorch_cpu.so)
frame #9: TorchPlugin::ReferenceCalcTorchForceKernel::execute(OpenMM::ContextImpl&, bool, bool) + 0xac0 (0x77db82114790 in /home/dm/Soft/miniconda3/envs/torch/lib/plugins/libOpenMMTorchReference.so)
frame #10: OpenMM::ContextImpl::calcForcesAndEnergy(bool, bool, int) + 0xc9 (0x77db7bb20109 in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/../../../libOpenMM.so.8.1)
frame #11: OpenMM::ReferenceCustomCVForce::calculateIxn(OpenMM::ContextImpl&, std::vector<OpenMM::Vec3, std::allocator<OpenMM::Vec3> >&, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, double, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, double> > > const&, std::vector<OpenMM::Vec3, std::allocator<OpenMM::Vec3> >&, double*, std::map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, double, std::less<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, double> > >&) + 0xfc (0x77db7bc3363c in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/../../../libOpenMM.so.8.1)
frame #12: OpenMM::ReferenceCalcCustomCVForceKernel::execute(OpenMM::ContextImpl&, OpenMM::ContextImpl&, bool, bool) + 0x2ff (0x77db7bc0b06f in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/../../../libOpenMM.so.8.1)
frame #13: OpenMM::ContextImpl::calcForcesAndEnergy(bool, bool, int) + 0xc9 (0x77db7bb20109 in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/../../../libOpenMM.so.8.1)
frame #14: OpenMM::Context::getState(int, bool, int) const + 0x122 (0x77db7bb1d772 in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/../../../libOpenMM.so.8.1)
frame #15: <unknown function> + 0x18847b (0x77db7bb8847b in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/../../../libOpenMM.so.8.1)
frame #16: <unknown function> + 0x188c26 (0x77db7bb88c26 in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/../../../libOpenMM.so.8.1)
frame #17: lbfgs + 0x584 (0x77db7bbe8444 in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/../../../libOpenMM.so.8.1)
frame #18: OpenMM::LocalEnergyMinimizer::minimize(OpenMM::Context&, double, int, OpenMM::MinimizationReporter*) + 0x7d9 (0x77db7bb89769 in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/../../../libOpenMM.so.8.1)
frame #19: <unknown function> + 0x1293ae (0x77db887293ae in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/site-packages/openmm/_openmm.cpython-310-x86_64-linux-gnu.so)
frame #20: <unknown function> + 0x144468 (0x620ee873e468 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #21: _PyObject_MakeTpCall + 0x26b (0x620ee873797b in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #22: _PyEval_EvalFrameDefault + 0x54b6 (0x620ee87338c6 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #23: _PyFunction_Vectorcall + 0x6c (0x620ee873e8cc in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #24: _PyEval_EvalFrameDefault + 0x4c12 (0x620ee8733022 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #25: _PyFunction_Vectorcall + 0x6c (0x620ee873e8cc in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #26: _PyEval_EvalFrameDefault + 0x72c (0x620ee872eb3c in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #27: <unknown function> + 0x1d7870 (0x620ee87d1870 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #28: PyEval_EvalCode + 0x87 (0x620ee87d17b7 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #29: <unknown function> + 0x1de9ba (0x620ee87d89ba in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #30: <unknown function> + 0x144a93 (0x620ee873ea93 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #31: _PyEval_EvalFrameDefault + 0x320 (0x620ee872e730 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #32: <unknown function> + 0x1e0fe4 (0x620ee87dafe4 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #33: _PyEval_EvalFrameDefault + 0x1bc0 (0x620ee872ffd0 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #34: <unknown function> + 0x1e0fe4 (0x620ee87dafe4 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #35: _PyEval_EvalFrameDefault + 0x1bc0 (0x620ee872ffd0 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #36: <unknown function> + 0x1e0fe4 (0x620ee87dafe4 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #37: <unknown function> + 0x1f55f7 (0x620ee87ef5f7 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #38: <unknown function> + 0x14f3bd (0x620ee87493bd in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #39: _PyEval_EvalFrameDefault + 0x72c (0x620ee872eb3c in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #40: _PyFunction_Vectorcall + 0x6c (0x620ee873e8cc in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #41: _PyEval_EvalFrameDefault + 0x320 (0x620ee872e730 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #42: _PyFunction_Vectorcall + 0x6c (0x620ee873e8cc in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #43: _PyEval_EvalFrameDefault + 0x72c (0x620ee872eb3c in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #44: <unknown function> + 0x150402 (0x620ee874a402 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #45: PyObject_Call + 0xbc (0x620ee874ad9c in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #46: _PyEval_EvalFrameDefault + 0x2d84 (0x620ee8731194 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #47: <unknown function> + 0x150402 (0x620ee874a402 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #48: _PyEval_EvalFrameDefault + 0x13cc (0x620ee872f7dc in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #49: <unknown function> + 0x1e0fe4 (0x620ee87dafe4 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #50: _PyEval_EvalFrameDefault + 0x1bc0 (0x620ee872ffd0 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #51: <unknown function> + 0x1e0fe4 (0x620ee87dafe4 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #52: _PyEval_EvalFrameDefault + 0x1bc0 (0x620ee872ffd0 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #53: <unknown function> + 0x1e0fe4 (0x620ee87dafe4 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #54: _PyEval_EvalFrameDefault + 0x1bc0 (0x620ee872ffd0 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #55: <unknown function> + 0x1e0fe4 (0x620ee87dafe4 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #56: _PyEval_EvalFrameDefault + 0x1bc0 (0x620ee872ffd0 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #57: <unknown function> + 0x1e0fe4 (0x620ee87dafe4 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #58: _PyEval_EvalFrameDefault + 0x1bc0 (0x620ee872ffd0 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #59: <unknown function> + 0x1e0fe4 (0x620ee87dafe4 in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #60: <unknown function> + 0x7bf6 (0x77db8ddf3bf6 in /home/dm/Soft/miniconda3/envs/torch/lib/python3.10/lib-dynload/_asyncio.cpython-310-x86_64-linux-gnu.so)
frame #61: <unknown function> + 0x143d2a (0x620ee873dd2a in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #62: <unknown function> + 0x25f22c (0x620ee885922c in /home/dm/Soft/miniconda3/envs/torch/bin/python)
frame #63: <unknown function> + 0xfda7b (0x620ee86f7a7b in /home/dm/Soft/miniconda3/envs/torch/bin/python)
My conda environment:
conda list:
# packages in environment at /home/dm/Soft/miniconda3/envs/torch:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_kmp_llvm conda-forge
anyio 4.2.0 pyhd8ed1ab_0 conda-forge
argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge
argon2-cffi-bindings 21.2.0 py310h2372a71_4 conda-forge
arrow 1.3.0 pyhd8ed1ab_0 conda-forge
asttokens 2.4.1 pyhd8ed1ab_0 conda-forge
async-lru 2.0.4 pyhd8ed1ab_0 conda-forge
attrs 23.2.0 pyh71513ae_0 conda-forge
babel 2.14.0 pyhd8ed1ab_0 conda-forge
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Used mamba to install openmm-torch. I suppose this is a question and not likely a bug. It will be really helpful you can find where am I making mistake!
I believe this is an error with your model. In particular because lines like this:
pos = positions[::4].to("cpu")
...
y = self.encoder(x)[:,1].sum()
Pytorch does not like when you run backwards only on a subset of the output. To test this try to run backwards on the model alone (no openmm or openmm-torch involved). My guess is you will see a similar error. Something like this:
import torch
pos = torch.rand(10, 3)
box = torch.eye(3) * 10
model = torch.jit.load("model.pt")
pos.requires_grad_()
y = model(pos, box)
y.backward() # compute gradients
print(pos.grad)
As a side note, the box is already passed to your model as a 3x3 pytorch tensor, you should not need to convert it. You can extract its diagonal with "box.diag()"
Thanks! I got your point. It does returns a NoneType. It makes sense. I'll try to modify the model to operable on whole system. But are there any trick to do certain operation on subset of the positions? Say I want only the oxygen atom of my system!
Also I did noticed the box.diag() in the documentation, but was lazy to change :).
You want your TorchForce to act only on a subset of the system? As an easy workaround you can just multiply by zero the ones you do not want, right?