From fb5eb676c1b4439eca8deb2bfba9841394192c2a Mon Sep 17 00:00:00 2001 From: Stephen Farr Date: Thu, 23 Feb 2023 16:13:05 +0000 Subject: [PATCH 01/17] implement MACE --- examples/mace/MACE_SPICE.model | Bin 0 -> 1578591 bytes examples/mace/MACE_SPICE_larger.model | Bin 0 -> 11466445 bytes examples/mace/run_mace.py | 37 +++++ examples/mace/run_mace_mixed.py | 61 ++++++++ openmmml/models/__init__.py | 2 +- openmmml/models/macepotential.py | 217 ++++++++++++++++++++++++++ 6 files changed, 316 insertions(+), 1 deletion(-) create mode 100644 examples/mace/MACE_SPICE.model create mode 100644 examples/mace/MACE_SPICE_larger.model create mode 100644 examples/mace/run_mace.py create mode 100644 examples/mace/run_mace_mixed.py create mode 100644 openmmml/models/macepotential.py diff --git a/examples/mace/MACE_SPICE.model b/examples/mace/MACE_SPICE.model new file mode 100644 index 0000000000000000000000000000000000000000..d938be495f4a09f6a649b8cdef4eec61b8ff66b8 GIT binary patch literal 1578591 zcmeFacU+a8`%B4uX50_#=X_wx+fb`x$dNcWa zl1ZIpGMUs#nPgHXZ8y7{&1N^do4lXrUJxwb-|sgwugxDj{(j$wbI)_?=X}~Z=Y7r- z?618?Lqk_r86l9rwpm4Vo#Ooc2>v6Ut&Q>KvX#YW+GQi>ueEhE`c{6L8OVE7?1 zcT99_a!#}m7nPWvo}4K+kV-xjenNbglo=JDm6EbMjr?$6xPkaKK3&+6@rabsFx+tG zs|-P=F88SP`dIi8@pYymS|N*yL+{jYk4tY)gd2%(Q_`d3b|(2+$Vn;LDOI<>OO~Fw zRorPQoso)W>LHS!ks{ApN{vs$cty!lV`Oo0;((mnJ(@8o>9NU~`VW%QWYIz(lHT1) z=`Fs|mm6bTP4eWXmWkSBiF>4i&nKz|t(}0XJo)3sKn)`f?{r%Wxeu zCliH5C&=W^sx10G7zD*5RiwV$C0r9}?BuTD+V~rrktKHv_ybLa-2I`y1_Da?HGe!j z$|ZVTSI9j>haF9Ph5vB@xfLMg7QXVK4 z5u_?2SRsEHA`i(@4dNrE{Bel6x^%@lm(M^y+^~pcv$@5iOD>gkfBPj)B zPlYm;7sN`5Vl(w+X<}-9d11KUc@2%=KcRgf7>KASN%W$kY*Zo`pKFsKL}w&MsWg)Q zJ@H3?WI#kqK?Ql=m)y(P+QeQ(Nv%f9#mwq=KPtlUYxAS*w=1@^ceXZlbg@3RT^*U>f1Jjb18FnHpBqT)FQ$Mk#8)K=rh zwqq))D%)-SVk-@gZG5inPW@t&@!H4R;~T$qmlOM|eo*tH0IH=9yTi1>(=^R9T<>e3 z<-(&hNA=vE9F%JN_BNLZoNtzR(1#WuaszMjD%IS`pdKDI=0(cq37(~OSDxl-ZgZl- zB&w#(D=rc5xh(s{Sza@D_d0)vN4fae8CvFEzeiL-o76{R*))v*t?;(!e_^<#jZ)oaco&q@BANMpIOOl}f4Dg)4l5 zX^eZR!o{15_+yYe&(RWhAEnYeTibFpaspJqZQ74a_p_+?G*h5v+6Twq7OB1OV4S3NlLyyMLlFDm( z=tJX47oOGKZ%><=i8|cObGPYJ%8TRA!!+p&8Zbni_uf%6Hz(8Jljl^)EzXHN$(1QQ z{>q#dUviN?RawRda3L=p;ssv0NMq)YspUw59amkT!e|pJ*n7b7s)39*pP|EpXlx~K zn)4TG;9(uErSh8C528BUe)U6FJv_!4|!Bc-bfbGVn2w`a{v9x%)KhKvNK(`kT~|di)Jdzu*p^ zd*GdRKZwKnTgX+Je%@Q^iSl^N-M8*RDpv*WqS2@5NC~yuQ5o0Yhn)4i%ca6Vl+?j} zRQDv67HIT3t!H!VMXqoL8&A=Y#XA{oP#bqUKZih$hobOuo=ruT0bVt}b_%p_rAb

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z*MFYgEZ)m7>r-Ku^wxi#yDVmiQv3hK{?BQTk{l!BZcL?LFCMx*|NaL5E&D&;{r>95 p`R^Z;; 0: + ala2.system.removeForce(0) + +# The system should not contain any additional force and constrains +assert ala2.system.getNumConstraints() == 0 +assert ala2.system.getNumForces() == 0 + +potential = MLPotential('mace', model_path='MACE_SPICE_larger.model') + + +system = potential.createSystem(ala2.topology) + +# run langevin dynamics at 300K for 1000 steps +integrator = openmm.LangevinIntegrator(300*unit.kelvin, 10.0/unit.picoseconds, 1.0*unit.femtosecond) +simulation=app.Simulation(ala2.topology, system, integrator, platform=openmm.Platform.getPlatformByName("CPU")) +simulation.context.setPositions(ala2.positions) +simulation.reporters.append(app.PDBReporter('output.pdb', 10)) +simulation.reporters.append(app.StateDataReporter(stdout, 10, step=True, + potentialEnergy=True, temperature=True, speed=True)) + +simulation.step(1000) + +# Minimize the energy +simulation.minimizeEnergy() +energy=simulation.context.getState(getEnergy=True).getPotentialEnergy() +print(energy, energy.in_units_of(unit.kilocalorie_per_mole)) diff --git a/examples/mace/run_mace_mixed.py b/examples/mace/run_mace_mixed.py new file mode 100644 index 0000000..20f3d5e --- /dev/null +++ b/examples/mace/run_mace_mixed.py @@ -0,0 +1,61 @@ +import openmm +import openmm.app as app +import openmm.unit as unit +from openmmml import MLPotential +from sys import stdout +from openmmtools.testsystems import AlanineDipeptideVacuum +import time +import torch.autograd.profiler as profiler + +# Get the system of alanine dipeptide +ala2 = AlanineDipeptideVacuum(constraints=None) + +# solvate +ff=openmm.app.ForceField('amber14-all.xml', 'amber14/tip3pfb.xml') + +modeller=openmm.app.Modeller(ala2.topology, ala2.positions) +modeller.addSolvent(ff, padding=1.0*unit.nanometers) + +print(modeller.topology) + +mm_system = ff.createSystem(modeller.topology, nonbondedMethod=openmm.app.PME, nonbondedCutoff=1.0*unit.nanometer, constraints=None) + +chains = list(modeller.topology.chains()) +ml_atoms = [atom.index for atom in chains[0].atoms()] + +potential = MLPotential('mace', model_path='MACE_SPICE_larger.model') + + +mm_ml_system = potential.createMixedSystem(modeller.topology, mm_system, ml_atoms, dtype="float64") + +# run langevin dynamics at 300K for 1000 steps +integrator = openmm.LangevinIntegrator(300*unit.kelvin, 1.0/unit.picoseconds, 1.0*unit.femtosecond) +simulation=app.Simulation(modeller.topology, mm_ml_system, integrator, platform=openmm.Platform.getPlatformByName("CPU")) +simulation.context.setPositions(modeller.positions) +#simulation.reporters.append(app.PDBReporter('output.pdb', 10, enforcePeriodicBox=False)) +simulation.reporters.append(app.StateDataReporter(stdout, 10, step=True, + potentialEnergy=True, temperature=True, speed=True)) + +# warm up +simulation.step(100) + +# time +Nstep = 100 +t1 = time.perf_counter() +simulation.step(Nstep) +t2 = time.perf_counter() + +print("time for 100 steps:", t2-t1, "s") +print("time for one steps:", ((t2-t1)/Nstep)*1000.0, "ms") + + +# profile +with profiler.profile() as prof: + simulation.step(1) + +print(prof.key_averages().table(sort_by='cpu_time_total', row_limit=20)) + +# Minimize the energy +# simulation.minimizeEnergy() +# energy=simulation.context.getState(getEnergy=True).getPotentialEnergy() +# print(energy, energy.in_units_of(unit.kilocalorie_per_mole)) diff --git a/openmmml/models/__init__.py b/openmmml/models/__init__.py index ee25f5a..688da71 100644 --- a/openmmml/models/__init__.py +++ b/openmmml/models/__init__.py @@ -1 +1 @@ -from . import anipotential \ No newline at end of file +from . import anipotential, macepotential \ No newline at end of file diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py new file mode 100644 index 0000000..d7f195f --- /dev/null +++ b/openmmml/models/macepotential.py @@ -0,0 +1,217 @@ +""" +macepotential.py: Implements the MACE potential function. + +This is part of the OpenMM molecular simulation toolkit originating from +Simbios, the NIH National Center for Physics-Based Simulation of +Biological Structures at Stanford, funded under the NIH Roadmap for +Medical Research, grant U54 GM072970. See https://simtk.org. + +Portions copyright (c) 2021 Stanford University and the Authors. +Authors: Peter Eastman +Contributors: Stephen Farr + +Permission is hereby granted, free of charge, to any person obtaining a +copy of this software and associated documentation files (the "Software"), +to deal in the Software without restriction, including without limitation +the rights to use, copy, modify, merge, publish, distribute, sublicense, +and/or sell copies of the Software, and to permit persons to whom the +Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +THE AUTHORS, CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +USE OR OTHER DEALINGS IN THE SOFTWARE. +""" + +from openmmml.mlpotential import MLPotential, MLPotentialImpl, MLPotentialImplFactory +import openmm +from typing import Iterable, Optional, Union + +class MACEPotentialImplFactory(MLPotentialImplFactory): + """This is the factory that creates MACEPotentialImpl objects.""" + + def createImpl(self, name: str, model_path: str, **args) -> MLPotentialImpl: + return MACEPotentialImpl(name, model_path) + +class MACEPotentialImpl(MLPotentialImpl): + """This is the MLPotentialImpl implementing the MACE potential. + + The potential is implemented using MACE to build a PyTorch model. A + TorchForce is used to add it to the OpenMM System. + + TorchForce requires the model to be saved to disk in a separate file. By default + it writes a file called 'macemodel.pt' in the current working directory. You can + use the filename argument to specify a different name. For example, + + >>> system = potential.createSystem(topology, filename='mymodel.pt') + """ + + def __init__(self, name, model_path): + self.name = name + self.model_path = model_path + + def addForces(self, + topology: openmm.app.Topology, + system: openmm.System, + atoms: Optional[Iterable[int]], + forceGroup: int, + filename: str = 'macemodel.pt', + implementation : str = None, + device: str = None, + dtype: str = "float64", + **args): + + + import torch + import openmmtorch + from torch_nl import compute_neighborlist + from e3nn.util import jit + from mace.tools import utils, to_one_hot, atomic_numbers_to_indices + + + + # Create the PyTorch model that will be invoked by OpenMM. + + includedAtoms = list(topology.atoms()) + if atoms is not None: + # check if atoms needs to be ordered + includedAtoms = [includedAtoms[i] for i in atoms] + + + class MACEForce(torch.nn.Module): + + def __init__(self, model_path, atomic_numbers, indices, periodic, device, dtype=torch.float64): + super(MACEForce, self).__init__() + + if device is None: # use cuda if available + self.device=torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + + else: # unless user has specified the device + self.device=torch.device(device) + + self.default_dtype = dtype + torch.set_default_dtype(self.default_dtype) + + print("Running MACEForce on device: ", self.device, " with dtype: ", self.default_dtype) + + + # conversion constants + self.nm_to_distance = 10.0 # nm->A + self.distance_to_nm = 0.1 # A->nm + self.energy_to_kJ = 96.49 # eV->kJ + + self.model = torch.load(model_path,map_location=device) + self.model.to(self.default_dtype) + self.model.eval() + + #print(self.model) + #for name, param in self.model.state_dict().items(): + # print(name, param.size()) + + self.r_max = self.model.r_max + self.z_table = utils.AtomicNumberTable([int(z) for z in self.model.atomic_numbers]) + + self.model = jit.compile(self.model) + + # setup input + N=len(atomic_numbers) + self.ptr = torch.tensor([0,N],dtype=torch.long, device=self.device) + self.batch = torch.zeros(N, dtype=torch.long, device=self.device) + + # one hot encoding of atomic number + self.node_attrs =to_one_hot( + torch.tensor(atomic_numbers_to_indices(atomic_numbers, z_table=self.z_table), dtype=torch.long, device=self.device).unsqueeze(-1), + num_classes=len(self.z_table), + ) + + if periodic: + self.pbc=torch.tensor([True, True, True], device=self.device) + else: + self.pbc=torch.tensor([False, False, False], device=self.device) + + + self.compute_nl = compute_neighborlist + + if indices is None: + self.indices = None + else: + self.indices = torch.tensor(indices, dtype=torch.int64) + + + + def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): + # setup positions + positions = positions.to(dtype=self.default_dtype) + if self.indices is not None: + positions = positions[self.indices] + + positions = positions*self.nm_to_distance + + if boxvectors is not None: + cell = boxvectors.to(dtype=self.default_dtype) * self.nm_to_distance + else: + cell = torch.eye(3, device=self.device) + + # compute edges + mapping, _ , shifts_idx = self.compute_nl(cutoff=self.r_max, + pos=positions, + cell=cell, + pbc=self.pbc, + batch=self.batch, + self_interaction=False) + + edge_index = torch.stack((mapping[0], mapping[1])) + + shifts = torch.mm(shifts_idx, cell) + + # create input dict + input_dict = { "ptr" : self.ptr, + "node_attrs": self.node_attrs, + "batch": self.batch, + "pbc": self.pbc, + "cell": cell, + "positions": positions, + "edge_index": edge_index, + "unit_shifts": shifts_idx, + "shifts": shifts} + + # predict + out = self.model(input_dict,compute_force=False) + + energy = out["energy"] + if energy is None: + energy = torch.tensor(0.0, device=self.device) + + # return energy + energy = energy*self.energy_to_kJ + + return energy + + + is_periodic = (topology.getPeriodicBoxVectors() is not None) or system.usesPeriodicBoundaryConditions() + + + atomic_numbers = [atom.element.atomic_number for atom in includedAtoms] + + torch_dtype = {"float32":torch.float32, "float64":torch.float64}[dtype] + + maceforce = MACEForce(self.model_path, atomic_numbers, atoms, is_periodic, device, dtype=torch_dtype) + + # Convert it to TorchScript and save it. + module = torch.jit.script(maceforce) + module.save(filename) + + # Create the TorchForce and add it to the System. + force = openmmtorch.TorchForce(filename) + force.setForceGroup(forceGroup) + force.setUsesPeriodicBoundaryConditions(is_periodic) + #force.setOutputsForces(True) + system.addForce(force) + +MLPotential.registerImplFactory('mace', MACEPotentialImplFactory()) From 217f3301fb6971385bc9a86e55d89254c8ea6658 Mon Sep 17 00:00:00 2001 From: Stephen Farr Date: Tue, 14 Mar 2023 09:43:54 +0000 Subject: [PATCH 02/17] change NL to fix PBCs --- examples/mace/MACE_SPICE.model | Bin 1578591 -> 0 bytes examples/mace/README.md | 37 ++++ examples/mace/run_mace.py | 15 +- examples/mace/run_mace_mixed.py | 35 +--- examples/mace/run_water_box.py | 23 +++ examples/mace/water.pdb | 312 +++++++++++++++++++++++++++++++ openmmml/models/macepotential.py | 94 ++++++++-- 7 files changed, 464 insertions(+), 52 deletions(-) delete mode 100644 examples/mace/MACE_SPICE.model create mode 100644 examples/mace/README.md create mode 100644 examples/mace/run_water_box.py create mode 100644 examples/mace/water.pdb diff --git a/examples/mace/MACE_SPICE.model b/examples/mace/MACE_SPICE.model deleted file mode 100644 index d938be495f4a09f6a649b8cdef4eec61b8ff66b8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1578591 zcmeFacU+a8`%B4uX50_#=X_wx+fb`x$dNcWa zl1ZIpGMUs#nPgHXZ8y7{&1N^do4lXrUJxwb-|sgwugxDj{(j$wbI)_?=X}~Z=Y7r- z?618?Lqk_r86l9rwpm4Vo#Ooc2>v6Ut&Q>KvX#YW+GQi>ueEhE`c{6L8OVE7?1 zcT99_a!#}m7nPWvo}4K+kV-xjenNbglo=JDm6EbMjr?$6xPkaKK3&+6@rabsFx+tG zs|-P=F88SP`dIi8@pYymS|N*yL+{jYk4tY)gd2%(Q_`d3b|(2+$Vn;LDOI<>OO~Fw zRorPQoso)W>LHS!ks{ApN{vs$cty!lV`Oo0;((mnJ(@8o>9NU~`VW%QWYIz(lHT1) z=`Fs|mm6bTP4eWXmWkSBiF>4i&nKz|t(}0XJo)3sKn)`f?{r%Wxeu zCliH5C&=W^sx10G7zD*5RiwV$C0r9}?BuTD+V~rrktKHv_ybLa-2I`y1_Da?HGe!j z$|ZVTSI9j>haF9Ph5vB@xfLMg7QXVK4 z5u_?2SRsEHA`i(@4dNrE{Bel6x^%@lm(M^y+^~pcv$@5iOD>gkfBPj)B zPlYm;7sN`5Vl(w+X<}-9d11KUc@2%=KcRgf7>KASN%W$kY*Zo`pKFsKL}w&MsWg)Q zJ@H3?WI#kqK?Ql=m)y(P+QeQ(Nv%f9#mwq=KPtlUYxAS*w=1@^ceXZlbg@3RT^*U>f1Jjb18FnHpBqT)FQ$Mk#8)K=rh zwqq))D%)-SVk-@gZG5inPW@t&@!H4R;~T$qmlOM|eo*tH0IH=9yTi1>(=^R9T<>e3 z<-(&hNA=vE9F%JN_BNLZoNtzR(1#WuaszMjD%IS`pdKDI=0(cq37(~OSDxl-ZgZl- zB&w#(D=rc5xh(s{Sza@D_d0)vN4fae8CvFEzeiL-o76{R*))v*t?;(!e_^<#jZ)oaco&q@BANMpIOOl}f4Dg)4l5 zX^eZR!o{15_+yYe&(RWhAEnYeTibFpaspJqZQ74a_p_+?G*h5v+6Twq7OB1OV4S3NlLyyMLlFDm( z=tJX47oOGKZ%><=i8|cObGPYJ%8TRA!!+p&8Zbni_uf%6Hz(8Jljl^)EzXHN$(1QQ z{>q#dUviN?RawRda3L=p;ssv0NMq)YspUw59amkT!e|pJ*n7b7s)39*pP|EpXlx~K zn)4TG;9(uErSh8C528BUe)U6FJv_!4|!Bc-bfbGVn2w`a{v9x%)KhKvNK(`kT~|di)Jdzu*p^ zd*GdRKZwKnTgX+Je%@Q^iSl^N-M8*RDpv*WqS2@5NC~yuQ5o0Yhn)4i%ca6Vl+?j} zRQDv67HIT3t!H!VMXqoL8&A=Y#XA{oP#bqUKZih$hobOuo=ruT0bVt}b_%p_rAb

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models in OpenMM + +This directory contains examples for running simulations using a [MACE](https://github.com/ACEsuit/mace) potential. + +## Install + +First you will need `openmm` and `openmm-torch` from conda-forge +``` +mamba install -c conda-forge openmm openmm-torch +``` + +Then you will need to install MACE and this fork of openmm-ml with pip: + +``` +pip install git+https://github.com/ACEsuit/mace +pip install git+https://github.com/sef43/openmm-ml@mace +``` + +## Usage +```python +from openmmml import MLPotential + +potential = MLPotential('mace', model_path='path/to/model') + +system = potential.createSystem(topology) +``` + + +## Examples +There are three examples: +- [run_mace.py](run_mace.py) runs a small molecule with pure MACE ML potential. +- [run_mace_mixed.py](run_mace_mixed.py) runs a small molecule in a water box where the small molecule's intra-molecular forces use MACE and the rest of the system in MM. +- [run_mace_waterbox.py](run_mace_waterbox.py) runs a water box with pure MACE ML potential. + +## References +The example pre-trained model is from here: https://github.com/jharrymoore/mace-openmm/tree/main/tests/example_data +The MACE repo is here: https://github.com/ACEsuit/mace diff --git a/examples/mace/run_mace.py b/examples/mace/run_mace.py index 8ab25ea..ad04203 100644 --- a/examples/mace/run_mace.py +++ b/examples/mace/run_mace.py @@ -21,17 +21,12 @@ system = potential.createSystem(ala2.topology) -# run langevin dynamics at 300K for 1000 steps + integrator = openmm.LangevinIntegrator(300*unit.kelvin, 10.0/unit.picoseconds, 1.0*unit.femtosecond) -simulation=app.Simulation(ala2.topology, system, integrator, platform=openmm.Platform.getPlatformByName("CPU")) +simulation=app.Simulation(ala2.topology, system, integrator) simulation.context.setPositions(ala2.positions) -simulation.reporters.append(app.PDBReporter('output.pdb', 10)) -simulation.reporters.append(app.StateDataReporter(stdout, 10, step=True, +simulation.reporters.append(app.PDBReporter('output.pdb', 100)) +simulation.reporters.append(app.StateDataReporter(stdout, 100, step=True, potentialEnergy=True, temperature=True, speed=True)) -simulation.step(1000) - -# Minimize the energy -simulation.minimizeEnergy() -energy=simulation.context.getState(getEnergy=True).getPotentialEnergy() -print(energy, energy.in_units_of(unit.kilocalorie_per_mole)) +simulation.step(10000) diff --git a/examples/mace/run_mace_mixed.py b/examples/mace/run_mace_mixed.py index 20f3d5e..d83bca4 100644 --- a/examples/mace/run_mace_mixed.py +++ b/examples/mace/run_mace_mixed.py @@ -4,8 +4,6 @@ from openmmml import MLPotential from sys import stdout from openmmtools.testsystems import AlanineDipeptideVacuum -import time -import torch.autograd.profiler as profiler # Get the system of alanine dipeptide ala2 = AlanineDipeptideVacuum(constraints=None) @@ -26,36 +24,15 @@ potential = MLPotential('mace', model_path='MACE_SPICE_larger.model') -mm_ml_system = potential.createMixedSystem(modeller.topology, mm_system, ml_atoms, dtype="float64") +mm_ml_system = potential.createMixedSystem(modeller.topology, mm_system, ml_atoms) -# run langevin dynamics at 300K for 1000 steps +# run langevin dynamics at 300K for 10000 steps integrator = openmm.LangevinIntegrator(300*unit.kelvin, 1.0/unit.picoseconds, 1.0*unit.femtosecond) -simulation=app.Simulation(modeller.topology, mm_ml_system, integrator, platform=openmm.Platform.getPlatformByName("CPU")) +simulation=app.Simulation(modeller.topology, mm_ml_system, integrator) simulation.context.setPositions(modeller.positions) -#simulation.reporters.append(app.PDBReporter('output.pdb', 10, enforcePeriodicBox=False)) -simulation.reporters.append(app.StateDataReporter(stdout, 10, step=True, +simulation.reporters.append(app.PDBReporter('output.pdb', 100, enforcePeriodicBox=False)) +simulation.reporters.append(app.StateDataReporter(stdout, 100, step=True, potentialEnergy=True, temperature=True, speed=True)) -# warm up -simulation.step(100) -# time -Nstep = 100 -t1 = time.perf_counter() -simulation.step(Nstep) -t2 = time.perf_counter() - -print("time for 100 steps:", t2-t1, "s") -print("time for one steps:", ((t2-t1)/Nstep)*1000.0, "ms") - - -# profile -with profiler.profile() as prof: - simulation.step(1) - -print(prof.key_averages().table(sort_by='cpu_time_total', row_limit=20)) - -# Minimize the energy -# simulation.minimizeEnergy() -# energy=simulation.context.getState(getEnergy=True).getPotentialEnergy() -# print(energy, energy.in_units_of(unit.kilocalorie_per_mole)) +simulation.step(10000) diff --git a/examples/mace/run_water_box.py b/examples/mace/run_water_box.py new file mode 100644 index 0000000..bf2efe9 --- /dev/null +++ b/examples/mace/run_water_box.py @@ -0,0 +1,23 @@ +import openmm +import openmm.app as app +import openmm.unit as unit +from openmmml import MLPotential +from sys import stdout + + +pdb=app.PDBFile("water.pdb") + +potential = MLPotential('mace', model_path='MACE_SPICE_larger.model') + + +system = potential.createSystem(pdb.topology) + +integrator = openmm.LangevinIntegrator(300*unit.kelvin, 1.0/unit.picoseconds, 1.0*unit.femtosecond) +simulation=app.Simulation(pdb.topology, system, integrator) +simulation.context.setPositions(pdb.positions) +simulation.reporters.append(app.PDBReporter('output.pdb', 100)) +simulation.reporters.append(app.StateDataReporter(stdout, 10, step=True, + potentialEnergy=True, temperature=True, speed=True)) + + +simulation.step(100) diff --git a/examples/mace/water.pdb b/examples/mace/water.pdb new file mode 100644 index 0000000..7f0f188 --- /dev/null +++ b/examples/mace/water.pdb @@ -0,0 +1,312 @@ +REMARK 1 CREATED WITH OPENMM 8.0, 2023-03-12 +CRYST1 15.000 15.000 15.000 90.00 90.00 90.00 P 1 1 +MODEL 1 +HETATM 1 O HOH A 1 24.016 55.722 39.544 1.00 0.00 O +HETATM 2 H1 HOH A 1 23.351 56.157 40.093 1.00 0.00 H +HETATM 3 H2 HOH A 1 24.559 56.458 39.352 1.00 0.00 H +HETATM 4 O HOH A 2 -17.337 9.397 43.983 1.00 0.00 O +HETATM 5 H1 HOH A 2 -17.338 9.807 43.088 1.00 0.00 H +HETATM 6 H2 HOH A 2 -17.814 8.616 43.788 1.00 0.00 H +HETATM 7 O HOH A 3 8.988 15.063 4.215 1.00 0.00 O +HETATM 8 H1 HOH A 3 9.282 15.178 3.279 1.00 0.00 H +HETATM 9 H2 HOH A 3 8.070 15.449 4.140 1.00 0.00 H +HETATM 10 O HOH A 4 8.718 18.448 32.221 1.00 0.00 O +HETATM 11 H1 HOH A 4 8.050 18.603 32.920 1.00 0.00 H +HETATM 12 H2 HOH A 4 8.350 18.961 31.515 1.00 0.00 H +HETATM 13 O HOH A 5 -14.573 39.144 31.914 1.00 0.00 O +HETATM 14 H1 HOH A 5 -15.049 39.929 31.772 1.00 0.00 H +HETATM 15 H2 HOH A 5 -14.648 38.668 31.039 1.00 0.00 H +HETATM 16 O HOH A 6 -15.937 -12.842 27.065 1.00 0.00 O +HETATM 17 H1 HOH A 6 -16.749 -13.211 27.365 1.00 0.00 H +HETATM 18 H2 HOH A 6 -15.256 -13.571 27.184 1.00 0.00 H +HETATM 19 O HOH A 7 -0.597 45.334 -15.247 1.00 0.00 O +HETATM 20 H1 HOH A 7 -0.504 46.143 -14.712 1.00 0.00 H +HETATM 21 H2 HOH A 7 0.117 45.347 -15.892 1.00 0.00 H +HETATM 22 O HOH A 8 -17.088 73.231 0.767 1.00 0.00 O +HETATM 23 H1 HOH A 8 -17.251 73.391 1.716 1.00 0.00 H +HETATM 24 H2 HOH A 8 -16.461 73.919 0.600 1.00 0.00 H +HETATM 25 O HOH A 9 11.920 28.903 63.270 1.00 0.00 O +HETATM 26 H1 HOH A 9 11.156 29.406 62.920 1.00 0.00 H +HETATM 27 H2 HOH A 9 12.329 29.573 63.867 1.00 0.00 H +HETATM 28 O HOH A 10 -7.427 -20.654 22.745 1.00 0.00 O +HETATM 29 H1 HOH A 10 -8.218 -20.385 23.264 1.00 0.00 H +HETATM 30 H2 HOH A 10 -6.686 -20.232 23.231 1.00 0.00 H +HETATM 31 O HOH A 11 -8.469 29.392 11.464 1.00 0.00 O +HETATM 32 H1 HOH A 11 -8.937 28.585 11.481 1.00 0.00 H +HETATM 33 H2 HOH A 11 -7.611 29.091 11.573 1.00 0.00 H +HETATM 34 O HOH A 12 12.523 25.022 11.157 1.00 0.00 O +HETATM 35 H1 HOH A 12 13.044 25.183 10.361 1.00 0.00 H +HETATM 36 H2 HOH A 12 11.936 24.310 10.867 1.00 0.00 H +HETATM 37 O HOH A 13 17.684 62.114 15.757 1.00 0.00 O +HETATM 38 H1 HOH A 13 16.833 62.646 15.635 1.00 0.00 H +HETATM 39 H2 HOH A 13 18.307 62.823 16.007 1.00 0.00 H +HETATM 40 O HOH A 14 21.696 -18.439 29.968 1.00 0.00 O +HETATM 41 H1 HOH A 14 22.182 -18.597 30.764 1.00 0.00 H +HETATM 42 H2 HOH A 14 22.141 -18.960 29.295 1.00 0.00 H +HETATM 43 O HOH A 15 -16.327 -19.355 33.883 1.00 0.00 O +HETATM 44 H1 HOH A 15 -16.457 -19.204 32.914 1.00 0.00 H +HETATM 45 H2 HOH A 15 -16.591 -20.254 33.980 1.00 0.00 H +HETATM 46 O HOH A 16 11.481 -8.071 29.432 1.00 0.00 O +HETATM 47 H1 HOH A 16 12.411 -8.444 29.376 1.00 0.00 H +HETATM 48 H2 HOH A 16 11.036 -8.650 28.751 1.00 0.00 H +HETATM 49 O HOH A 17 -11.306 35.159 -11.018 1.00 0.00 O +HETATM 50 H1 HOH A 17 -11.220 34.469 -11.749 1.00 0.00 H +HETATM 51 H2 HOH A 17 -12.134 35.612 -11.377 1.00 0.00 H +HETATM 52 O HOH A 18 -1.539 18.707 32.174 1.00 0.00 O +HETATM 53 H1 HOH A 18 -2.182 18.613 32.916 1.00 0.00 H +HETATM 54 H2 HOH A 18 -1.606 19.668 32.085 1.00 0.00 H +HETATM 55 O HOH A 19 1.495 -8.517 62.660 1.00 0.00 O +HETATM 56 H1 HOH A 19 1.687 -8.242 61.802 1.00 0.00 H +HETATM 57 H2 HOH A 19 0.570 -8.353 62.695 1.00 0.00 H +HETATM 58 O HOH A 20 -20.907 47.450 21.608 1.00 0.00 O +HETATM 59 H1 HOH A 20 -21.170 48.364 21.756 1.00 0.00 H +HETATM 60 H2 HOH A 20 -20.559 47.436 20.771 1.00 0.00 H +HETATM 61 O HOH A 21 -9.590 -0.539 29.387 1.00 0.00 O +HETATM 62 H1 HOH A 21 -9.268 -0.106 28.615 1.00 0.00 H +HETATM 63 H2 HOH A 21 -8.765 -0.681 29.808 1.00 0.00 H +HETATM 64 O HOH A 22 11.011 33.565 3.621 1.00 0.00 O +HETATM 65 H1 HOH A 22 10.180 33.276 3.120 1.00 0.00 H +HETATM 66 H2 HOH A 22 10.930 34.462 3.791 1.00 0.00 H +HETATM 67 O HOH A 23 -1.573 21.870 2.797 1.00 0.00 O +HETATM 68 H1 HOH A 23 -1.541 22.535 2.096 1.00 0.00 H +HETATM 69 H2 HOH A 23 -1.961 22.364 3.545 1.00 0.00 H +HETATM 70 O HOH A 24 -16.867 -24.972 6.651 1.00 0.00 O +HETATM 71 H1 HOH A 24 -17.809 -24.685 6.654 1.00 0.00 H +HETATM 72 H2 HOH A 24 -16.579 -24.423 7.384 1.00 0.00 H +HETATM 73 O HOH A 25 1.917 10.566 11.039 1.00 0.00 O +HETATM 74 H1 HOH A 25 1.653 9.643 10.941 1.00 0.00 H +HETATM 75 H2 HOH A 25 2.878 10.625 11.066 1.00 0.00 H +HETATM 76 O HOH A 26 -0.451 46.730 39.318 1.00 0.00 O +HETATM 77 H1 HOH A 26 -0.055 47.609 39.102 1.00 0.00 H +HETATM 78 H2 HOH A 26 -1.091 46.925 40.050 1.00 0.00 H +HETATM 79 O HOH A 27 2.438 60.033 32.373 1.00 0.00 O +HETATM 80 H1 HOH A 27 2.708 60.898 31.853 1.00 0.00 H +HETATM 81 H2 HOH A 27 1.769 60.401 33.001 1.00 0.00 H +HETATM 82 O HOH A 28 -7.052 24.551 58.299 1.00 0.00 O +HETATM 83 H1 HOH A 28 -6.219 24.316 58.749 1.00 0.00 H +HETATM 84 H2 HOH A 28 -7.249 23.656 57.816 1.00 0.00 H +HETATM 85 O HOH A 29 4.162 27.972 18.398 1.00 0.00 O +HETATM 86 H1 HOH A 29 3.600 28.733 18.233 1.00 0.00 H +HETATM 87 H2 HOH A 29 4.170 27.577 17.508 1.00 0.00 H +HETATM 88 O HOH A 30 7.578 -18.151 26.767 1.00 0.00 O +HETATM 89 H1 HOH A 30 7.820 -18.989 27.305 1.00 0.00 H +HETATM 90 H2 HOH A 30 8.201 -17.445 27.058 1.00 0.00 H +HETATM 91 O HOH A 31 10.014 1.559 13.234 1.00 0.00 O +HETATM 92 H1 HOH A 31 10.667 1.461 12.563 1.00 0.00 H +HETATM 93 H2 HOH A 31 10.590 1.757 13.927 1.00 0.00 H +HETATM 94 O HOH A 32 -37.741 69.723 20.067 1.00 0.00 O +HETATM 95 H1 HOH A 32 -37.449 69.708 20.981 1.00 0.00 H +HETATM 96 H2 HOH A 32 -38.559 70.289 20.033 1.00 0.00 H +HETATM 97 O HOH A 33 33.605 18.358 13.031 1.00 0.00 O +HETATM 98 H1 HOH A 33 33.155 17.838 12.309 1.00 0.00 H +HETATM 99 H2 HOH A 33 33.308 17.779 13.808 1.00 0.00 H +HETATM 100 O HOH A 34 2.884 13.131 -0.114 1.00 0.00 O +HETATM 101 H1 HOH A 34 3.621 13.710 -0.239 1.00 0.00 H +HETATM 102 H2 HOH A 34 2.173 13.678 0.295 1.00 0.00 H +HETATM 103 O HOH A 35 -4.619 6.718 6.121 1.00 0.00 O +HETATM 104 H1 HOH A 35 -5.523 6.755 6.469 1.00 0.00 H +HETATM 105 H2 HOH A 35 -4.876 6.476 5.232 1.00 0.00 H +HETATM 106 O HOH A 36 22.043 4.544 34.058 1.00 0.00 O +HETATM 107 H1 HOH A 36 21.344 5.126 34.201 1.00 0.00 H +HETATM 108 H2 HOH A 36 22.079 4.062 34.874 1.00 0.00 H +HETATM 109 O HOH A 37 -12.135 31.319 -22.436 1.00 0.00 O +HETATM 110 H1 HOH A 37 -12.154 31.017 -23.391 1.00 0.00 H +HETATM 111 H2 HOH A 37 -11.191 31.079 -22.215 1.00 0.00 H +HETATM 112 O HOH A 38 53.089 37.164 41.904 1.00 0.00 O +HETATM 113 H1 HOH A 38 52.534 36.613 41.262 1.00 0.00 H +HETATM 114 H2 HOH A 38 53.490 36.513 42.524 1.00 0.00 H +HETATM 115 O HOH A 39 6.450 35.443 10.707 1.00 0.00 O +HETATM 116 H1 HOH A 39 6.582 34.791 11.423 1.00 0.00 H +HETATM 117 H2 HOH A 39 5.521 35.616 10.749 1.00 0.00 H +HETATM 118 O HOH A 40 27.244 38.222 19.801 1.00 0.00 O +HETATM 119 H1 HOH A 40 26.378 38.666 19.572 1.00 0.00 H +HETATM 120 H2 HOH A 40 26.934 37.413 20.217 1.00 0.00 H +HETATM 121 O HOH A 41 23.007 5.330 60.614 1.00 0.00 O +HETATM 122 H1 HOH A 41 22.263 5.863 60.960 1.00 0.00 H +HETATM 123 H2 HOH A 41 23.688 6.012 60.652 1.00 0.00 H +HETATM 124 O HOH A 42 -18.886 34.027 25.445 1.00 0.00 O +HETATM 125 H1 HOH A 42 -18.927 34.644 24.694 1.00 0.00 H +HETATM 126 H2 HOH A 42 -19.286 33.231 25.032 1.00 0.00 H +HETATM 127 O HOH A 43 -25.233 19.632 36.497 1.00 0.00 O +HETATM 128 H1 HOH A 43 -24.951 18.715 36.637 1.00 0.00 H +HETATM 129 H2 HOH A 43 -25.495 19.717 35.583 1.00 0.00 H +HETATM 130 O HOH A 44 2.305 -1.672 7.959 1.00 0.00 O +HETATM 131 H1 HOH A 44 2.148 -2.114 7.126 1.00 0.00 H +HETATM 132 H2 HOH A 44 2.410 -0.782 7.638 1.00 0.00 H +HETATM 133 O HOH A 45 -16.081 50.908 -0.799 1.00 0.00 O +HETATM 134 H1 HOH A 45 -15.339 51.548 -0.597 1.00 0.00 H +HETATM 135 H2 HOH A 45 -15.962 50.635 -1.726 1.00 0.00 H +HETATM 136 O HOH A 46 3.919 24.075 46.303 1.00 0.00 O +HETATM 137 H1 HOH A 46 4.067 24.857 45.733 1.00 0.00 H +HETATM 138 H2 HOH A 46 2.976 23.927 46.270 1.00 0.00 H +HETATM 139 O HOH A 47 -10.467 33.850 16.831 1.00 0.00 O +HETATM 140 H1 HOH A 47 -9.986 33.007 16.865 1.00 0.00 H +HETATM 141 H2 HOH A 47 -9.754 34.437 16.671 1.00 0.00 H +HETATM 142 O HOH A 48 -7.858 4.382 13.128 1.00 0.00 O +HETATM 143 H1 HOH A 48 -7.538 4.605 14.064 1.00 0.00 H +HETATM 144 H2 HOH A 48 -7.819 3.398 13.197 1.00 0.00 H +HETATM 145 O HOH A 49 1.553 -20.092 49.189 1.00 0.00 O +HETATM 146 H1 HOH A 49 1.502 -20.159 48.227 1.00 0.00 H +HETATM 147 H2 HOH A 49 0.648 -20.001 49.411 1.00 0.00 H +HETATM 148 O HOH A 50 -9.735 -20.321 9.024 1.00 0.00 O +HETATM 149 H1 HOH A 50 -9.838 -20.610 9.986 1.00 0.00 H +HETATM 150 H2 HOH A 50 -10.385 -20.964 8.521 1.00 0.00 H +HETATM 151 O HOH A 51 -4.715 53.350 55.571 1.00 0.00 O +HETATM 152 H1 HOH A 51 -5.489 52.890 55.995 1.00 0.00 H +HETATM 153 H2 HOH A 51 -5.170 54.127 55.220 1.00 0.00 H +HETATM 154 O HOH A 52 31.165 33.683 -6.647 1.00 0.00 O +HETATM 155 H1 HOH A 52 31.972 33.150 -6.602 1.00 0.00 H +HETATM 156 H2 HOH A 52 31.400 34.217 -7.375 1.00 0.00 H +HETATM 157 O HOH A 53 11.313 6.193 23.883 1.00 0.00 O +HETATM 158 H1 HOH A 53 11.261 6.916 24.531 1.00 0.00 H +HETATM 159 H2 HOH A 53 10.899 6.495 23.076 1.00 0.00 H +HETATM 160 O HOH A 54 -14.572 -11.678 30.309 1.00 0.00 O +HETATM 161 H1 HOH A 54 -15.363 -11.425 30.856 1.00 0.00 H +HETATM 162 H2 HOH A 54 -14.431 -10.932 29.667 1.00 0.00 H +HETATM 163 O HOH A 55 -0.066 20.886 9.107 1.00 0.00 O +HETATM 164 H1 HOH A 55 0.074 19.960 8.865 1.00 0.00 H +HETATM 165 H2 HOH A 55 -0.605 20.757 9.956 1.00 0.00 H +HETATM 166 O HOH A 56 39.914 37.338 1.313 1.00 0.00 O +HETATM 167 H1 HOH A 56 40.568 37.199 0.606 1.00 0.00 H +HETATM 168 H2 HOH A 56 39.801 38.328 1.171 1.00 0.00 H +HETATM 169 O HOH A 57 14.203 13.054 5.040 1.00 0.00 O +HETATM 170 H1 HOH A 57 13.696 12.996 5.814 1.00 0.00 H +HETATM 171 H2 HOH A 57 13.851 12.328 4.514 1.00 0.00 H +HETATM 172 O HOH A 58 -3.966 57.787 9.070 1.00 0.00 O +HETATM 173 H1 HOH A 58 -3.194 57.212 9.277 1.00 0.00 H +HETATM 174 H2 HOH A 58 -3.921 57.894 8.096 1.00 0.00 H +HETATM 175 O HOH A 59 16.873 -17.723 35.456 1.00 0.00 O +HETATM 176 H1 HOH A 59 16.932 -18.375 34.768 1.00 0.00 H +HETATM 177 H2 HOH A 59 15.994 -17.298 35.292 1.00 0.00 H +HETATM 178 O HOH A 60 -53.901 36.802 31.801 1.00 0.00 O +HETATM 179 H1 HOH A 60 -54.397 37.538 31.316 1.00 0.00 H +HETATM 180 H2 HOH A 60 -54.004 37.052 32.695 1.00 0.00 H +HETATM 181 O HOH A 61 5.397 54.236 26.764 1.00 0.00 O +HETATM 182 H1 HOH A 61 5.983 53.590 27.260 1.00 0.00 H +HETATM 183 H2 HOH A 61 5.078 54.796 27.554 1.00 0.00 H +HETATM 184 O HOH A 62 42.724 17.248 -22.686 1.00 0.00 O +HETATM 185 H1 HOH A 62 43.598 16.999 -22.298 1.00 0.00 H +HETATM 186 H2 HOH A 62 42.864 18.181 -23.034 1.00 0.00 H +HETATM 187 O HOH A 63 -18.655 43.347 51.166 1.00 0.00 O +HETATM 188 H1 HOH A 63 -18.340 44.219 51.354 1.00 0.00 H +HETATM 189 H2 HOH A 63 -18.552 43.264 50.235 1.00 0.00 H +HETATM 190 O HOH A 64 -26.654 38.135 4.999 1.00 0.00 O +HETATM 191 H1 HOH A 64 -27.390 38.819 4.870 1.00 0.00 H +HETATM 192 H2 HOH A 64 -26.997 37.506 4.404 1.00 0.00 H +HETATM 193 O HOH A 65 54.110 29.030 27.090 1.00 0.00 O +HETATM 194 H1 HOH A 65 54.902 28.392 27.116 1.00 0.00 H +HETATM 195 H2 HOH A 65 54.463 29.850 27.391 1.00 0.00 H +HETATM 196 O HOH A 66 -72.613 37.697 27.611 1.00 0.00 O +HETATM 197 H1 HOH A 66 -71.645 37.584 27.786 1.00 0.00 H +HETATM 198 H2 HOH A 66 -72.655 37.687 26.613 1.00 0.00 H +HETATM 199 O HOH A 67 4.739 -24.257 27.733 1.00 0.00 O +HETATM 200 H1 HOH A 67 5.587 -24.422 28.204 1.00 0.00 H +HETATM 201 H2 HOH A 67 4.277 -25.127 27.822 1.00 0.00 H +HETATM 202 O HOH A 68 21.035 32.254 21.736 1.00 0.00 O +HETATM 203 H1 HOH A 68 21.243 31.473 22.259 1.00 0.00 H +HETATM 204 H2 HOH A 68 21.401 32.905 22.364 1.00 0.00 H +HETATM 205 O HOH A 69 26.129 47.527 15.489 1.00 0.00 O +HETATM 206 H1 HOH A 69 26.840 47.953 15.905 1.00 0.00 H +HETATM 207 H2 HOH A 69 25.364 47.754 15.992 1.00 0.00 H +HETATM 208 O HOH A 70 10.295 16.887 8.936 1.00 0.00 O +HETATM 209 H1 HOH A 70 9.710 16.890 8.215 1.00 0.00 H +HETATM 210 H2 HOH A 70 11.163 16.987 8.575 1.00 0.00 H +HETATM 211 O HOH A 71 7.601 21.629 21.918 1.00 0.00 O +HETATM 212 H1 HOH A 71 7.477 22.535 22.313 1.00 0.00 H +HETATM 213 H2 HOH A 71 7.036 21.639 21.173 1.00 0.00 H +HETATM 214 O HOH A 72 -7.041 -3.810 2.320 1.00 0.00 O +HETATM 215 H1 HOH A 72 -7.477 -4.467 2.952 1.00 0.00 H +HETATM 216 H2 HOH A 72 -6.665 -3.192 2.962 1.00 0.00 H +HETATM 217 O HOH A 73 -50.628 36.048 18.772 1.00 0.00 O +HETATM 218 H1 HOH A 73 -50.367 36.426 17.855 1.00 0.00 H +HETATM 219 H2 HOH A 73 -51.416 35.479 18.726 1.00 0.00 H +HETATM 220 O HOH A 74 6.071 30.650 4.404 1.00 0.00 O +HETATM 221 H1 HOH A 74 6.359 31.221 5.187 1.00 0.00 H +HETATM 222 H2 HOH A 74 5.228 30.317 4.756 1.00 0.00 H +HETATM 223 O HOH A 75 -17.627 -14.152 19.893 1.00 0.00 O +HETATM 224 H1 HOH A 75 -17.454 -13.645 20.721 1.00 0.00 H +HETATM 225 H2 HOH A 75 -18.238 -13.589 19.450 1.00 0.00 H +HETATM 226 O HOH A 76 -48.396 0.579 25.996 1.00 0.00 O +HETATM 227 H1 HOH A 76 -48.946 0.882 25.305 1.00 0.00 H +HETATM 228 H2 HOH A 76 -47.995 -0.181 25.563 1.00 0.00 H +HETATM 229 O HOH A 77 -5.028 0.042 16.638 1.00 0.00 O +HETATM 230 H1 HOH A 77 -5.357 -0.714 16.138 1.00 0.00 H +HETATM 231 H2 HOH A 77 -4.742 0.713 15.990 1.00 0.00 H +HETATM 232 O HOH A 78 -22.120 34.323 -21.604 1.00 0.00 O +HETATM 233 H1 HOH A 78 -22.391 34.733 -20.733 1.00 0.00 H +HETATM 234 H2 HOH A 78 -22.170 35.047 -22.224 1.00 0.00 H +HETATM 235 O HOH A 79 4.252 26.041 13.951 1.00 0.00 O +HETATM 236 H1 HOH A 79 3.564 26.755 14.283 1.00 0.00 H +HETATM 237 H2 HOH A 79 5.082 26.415 14.044 1.00 0.00 H +HETATM 238 O HOH A 80 15.435 23.070 29.413 1.00 0.00 O +HETATM 239 H1 HOH A 80 14.839 23.718 29.022 1.00 0.00 H +HETATM 240 H2 HOH A 80 16.102 22.866 28.739 1.00 0.00 H +HETATM 241 O HOH A 81 3.806 22.452 37.747 1.00 0.00 O +HETATM 242 H1 HOH A 81 4.291 21.623 37.838 1.00 0.00 H +HETATM 243 H2 HOH A 81 3.685 22.573 36.792 1.00 0.00 H +HETATM 244 O HOH A 82 0.697 30.435 34.339 1.00 0.00 O +HETATM 245 H1 HOH A 82 0.181 31.254 34.372 1.00 0.00 H +HETATM 246 H2 HOH A 82 0.090 29.692 34.683 1.00 0.00 H +HETATM 247 O HOH A 83 2.290 22.632 -5.160 1.00 0.00 O +HETATM 248 H1 HOH A 83 1.441 22.155 -5.348 1.00 0.00 H +HETATM 249 H2 HOH A 83 2.830 22.406 -5.980 1.00 0.00 H +HETATM 250 O HOH A 84 -24.256 -28.496 46.912 1.00 0.00 O +HETATM 251 H1 HOH A 84 -24.796 -29.125 46.403 1.00 0.00 H +HETATM 252 H2 HOH A 84 -24.090 -28.884 47.799 1.00 0.00 H +HETATM 253 O HOH A 85 13.821 26.152 39.037 1.00 0.00 O +HETATM 254 H1 HOH A 85 14.558 25.688 38.634 1.00 0.00 H +HETATM 255 H2 HOH A 85 14.143 27.041 39.063 1.00 0.00 H +HETATM 256 O HOH A 86 16.011 30.427 -17.476 1.00 0.00 O +HETATM 257 H1 HOH A 86 17.030 30.493 -17.548 1.00 0.00 H +HETATM 258 H2 HOH A 86 15.805 29.676 -18.032 1.00 0.00 H +HETATM 259 O HOH A 87 -41.588 15.173 50.160 1.00 0.00 O +HETATM 260 H1 HOH A 87 -42.388 15.500 49.821 1.00 0.00 H +HETATM 261 H2 HOH A 87 -41.621 14.270 49.838 1.00 0.00 H +HETATM 262 O HOH A 88 -3.739 27.685 28.424 1.00 0.00 O +HETATM 263 H1 HOH A 88 -3.379 28.024 29.247 1.00 0.00 H +HETATM 264 H2 HOH A 88 -3.107 27.008 28.229 1.00 0.00 H +HETATM 265 O HOH A 89 -20.451 9.807 0.728 1.00 0.00 O +HETATM 266 H1 HOH A 89 -21.221 10.123 1.218 1.00 0.00 H +HETATM 267 H2 HOH A 89 -19.710 10.271 1.150 1.00 0.00 H +HETATM 268 O HOH A 90 35.005 12.412 -6.593 1.00 0.00 O +HETATM 269 H1 HOH A 90 34.060 12.554 -6.748 1.00 0.00 H +HETATM 270 H2 HOH A 90 34.956 11.477 -6.328 1.00 0.00 H +HETATM 271 O HOH A 91 0.969 -1.815 25.507 1.00 0.00 O +HETATM 272 H1 HOH A 91 1.238 -2.716 25.757 1.00 0.00 H +HETATM 273 H2 HOH A 91 1.541 -1.658 24.728 1.00 0.00 H +HETATM 274 O HOH A 92 28.839 49.949 11.242 1.00 0.00 O +HETATM 275 H1 HOH A 92 29.038 49.182 11.736 1.00 0.00 H +HETATM 276 H2 HOH A 92 27.968 49.683 10.983 1.00 0.00 H +HETATM 277 O HOH A 93 28.404 40.668 31.188 1.00 0.00 O +HETATM 278 H1 HOH A 93 28.119 40.182 30.341 1.00 0.00 H +HETATM 279 H2 HOH A 93 28.226 41.537 30.936 1.00 0.00 H +HETATM 280 O HOH A 94 -21.049 42.435 34.539 1.00 0.00 O +HETATM 281 H1 HOH A 94 -21.031 43.398 34.613 1.00 0.00 H +HETATM 282 H2 HOH A 94 -21.012 42.135 35.478 1.00 0.00 H +HETATM 283 O HOH A 95 -9.029 37.420 34.570 1.00 0.00 O +HETATM 284 H1 HOH A 95 -9.966 37.751 34.695 1.00 0.00 H +HETATM 285 H2 HOH A 95 -8.439 38.145 34.870 1.00 0.00 H +HETATM 286 O HOH A 96 -40.110 10.942 20.073 1.00 0.00 O +HETATM 287 H1 HOH A 96 -41.064 10.799 20.231 1.00 0.00 H +HETATM 288 H2 HOH A 96 -40.128 11.645 19.367 1.00 0.00 H +HETATM 289 O HOH A 97 -34.774 35.066 12.974 1.00 0.00 O +HETATM 290 H1 HOH A 97 -34.605 34.880 12.062 1.00 0.00 H +HETATM 291 H2 HOH A 97 -35.002 34.244 13.377 1.00 0.00 H +HETATM 292 O HOH A 98 5.779 30.107 8.294 1.00 0.00 O +HETATM 293 H1 HOH A 98 6.167 30.054 9.162 1.00 0.00 H +HETATM 294 H2 HOH A 98 5.542 29.153 8.188 1.00 0.00 H +HETATM 295 O HOH A 99 1.751 35.453 14.013 1.00 0.00 O +HETATM 296 H1 HOH A 99 1.988 36.183 13.427 1.00 0.00 H +HETATM 297 H2 HOH A 99 2.487 34.849 13.880 1.00 0.00 H +HETATM 298 O HOH A 100 -4.923 9.697 4.099 1.00 0.00 O +HETATM 299 H1 HOH A 100 -4.920 10.644 4.042 1.00 0.00 H +HETATM 300 H2 HOH A 100 -5.870 9.405 4.137 1.00 0.00 H +HETATM 301 O HOH A 101 -7.717 31.667 -2.150 1.00 0.00 O +HETATM 302 H1 HOH A 101 -6.695 31.672 -2.152 1.00 0.00 H +HETATM 303 H2 HOH A 101 -8.000 30.977 -2.819 1.00 0.00 H +HETATM 304 O HOH A 102 -26.537 15.678 -3.659 1.00 0.00 O +HETATM 305 H1 HOH A 102 -27.176 15.600 -4.364 1.00 0.00 H +HETATM 306 H2 HOH A 102 -25.727 15.279 -4.006 1.00 0.00 H +TER 307 HOH A 102 +ENDMDL +END diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index d7f195f..7dd9650 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -31,7 +31,73 @@ from openmmml.mlpotential import MLPotential, MLPotentialImpl, MLPotentialImplFactory import openmm -from typing import Iterable, Optional, Union +from typing import Iterable, Optional, Union, Tuple +import torch + + +@torch.jit.script +def _simple_nl(positions: torch.Tensor, cell: torch.Tensor, pbc: torch.Tensor, cutoff: float, self_interaction: bool=False) -> Tuple[torch.Tensor, torch.Tensor]: + """simple torchscriptable neighborlist. + + It aims are to be correct, clear, and torchscript compatible, no effort has been put into making it fast. + It outputs nieghbors and shifts in the same format as ASE: + + neighbors, shifts = simple_nl(..) + + is equivalent to + + [i, j], S = primitive_neighbor_list( quantities="ijS", ...) + """ + + num_atoms = positions.shape[0] + device=positions.device + + i = torch.repeat_interleave(torch.range(0,num_atoms-1,dtype=torch.long, device=device), num_atoms) + j = torch.range(0,num_atoms-1,dtype=torch.long, device=device).repeat(num_atoms) + neighbors=torch.vstack((i,j)) + + if not self_interaction: + mask = i==j + neighbors = neighbors[:,~mask] + + full_deltas = positions[neighbors[0]] - positions[neighbors[1]] + deltas=full_deltas.clone() + + if pbc[0]: + + shifts_x = torch.round(full_deltas[:,0]/cell[0,0]) + shifts_y = torch.round(full_deltas[:,1]/cell[1,1]) + shifts_z = torch.round(full_deltas[:,2]/cell[2,2]) + + deltas[:,0] = full_deltas[:,0] - shifts_x*cell[0,0] + deltas[:,1] = full_deltas[:,1] - shifts_y*cell[1,1] + deltas[:,2] = full_deltas[:,2] - shifts_z*cell[2,2] + + else: + shifts_x = torch.zeros(full_deltas.shape[0]) + shifts_y = torch.zeros(full_deltas.shape[0]) + shifts_z = torch.zeros(full_deltas.shape[0]) + + + distances = torch.linalg.norm(deltas, dim=1) + + # filter + mask = distances > cutoff + neighbors = neighbors[:,~mask] + deltas = deltas[~mask,:] + shifts_x = shifts_x[~mask] + shifts_y = shifts_y[~mask] + shifts_z = shifts_z[~mask] + + shifts = torch.vstack((shifts_x, shifts_y, shifts_z,)).T + + return neighbors, shifts + + + + + + class MACEPotentialImplFactory(MLPotentialImplFactory): """This is the factory that creates MACEPotentialImpl objects.""" @@ -62,7 +128,7 @@ def addForces(self, atoms: Optional[Iterable[int]], forceGroup: int, filename: str = 'macemodel.pt', - implementation : str = None, + #implementation : str = None, device: str = None, dtype: str = "float64", **args): @@ -70,7 +136,7 @@ def addForces(self, import torch import openmmtorch - from torch_nl import compute_neighborlist + #from torch_nl import compute_neighborlist from e3nn.util import jit from mace.tools import utils, to_one_hot, atomic_numbers_to_indices @@ -136,7 +202,7 @@ def __init__(self, model_path, atomic_numbers, indices, periodic, device, dtype= self.pbc=torch.tensor([False, False, False], device=self.device) - self.compute_nl = compute_neighborlist + #self.compute_nl = compute_neighborlist if indices is None: self.indices = None @@ -147,24 +213,26 @@ def __init__(self, model_path, atomic_numbers, indices, periodic, device, dtype= def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): # setup positions - positions = positions.to(dtype=self.default_dtype) + + positions = positions.to(device=self.device,dtype=self.default_dtype) if self.indices is not None: positions = positions[self.indices] positions = positions*self.nm_to_distance if boxvectors is not None: - cell = boxvectors.to(dtype=self.default_dtype) * self.nm_to_distance + cell = boxvectors.to(device=self.device,dtype=self.default_dtype) * self.nm_to_distance else: cell = torch.eye(3, device=self.device) # compute edges - mapping, _ , shifts_idx = self.compute_nl(cutoff=self.r_max, - pos=positions, - cell=cell, - pbc=self.pbc, - batch=self.batch, - self_interaction=False) + # mapping, _ , shifts_idx = self.compute_nl(cutoff=self.r_max, + # pos=positions, + # cell=cell, + # pbc=self.pbc, + # batch=self.batch, + # self_interaction=False) + mapping, shifts_idx = _simple_nl(positions, cell, self.pbc, self.r_max) edge_index = torch.stack((mapping[0], mapping[1])) @@ -184,7 +252,7 @@ def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): # predict out = self.model(input_dict,compute_force=False) - energy = out["energy"] + energy = out["interaction_energy"] if energy is None: energy = torch.tensor(0.0, device=self.device) From f2f11439b6320eeef70cf2f32f0b28ef3b72fb96 Mon Sep 17 00:00:00 2001 From: Stephen Farr Date: Fri, 17 Mar 2023 13:10:55 +0000 Subject: [PATCH 03/17] improve simple_nl code --- examples/mace/README.md | 2 +- openmmml/models/macepotential.py | 74 +++++++++++++++++--------------- 2 files changed, 41 insertions(+), 35 deletions(-) diff --git a/examples/mace/README.md b/examples/mace/README.md index c0a4410..2023d82 100644 --- a/examples/mace/README.md +++ b/examples/mace/README.md @@ -30,7 +30,7 @@ system = potential.createSystem(topology) There are three examples: - [run_mace.py](run_mace.py) runs a small molecule with pure MACE ML potential. - [run_mace_mixed.py](run_mace_mixed.py) runs a small molecule in a water box where the small molecule's intra-molecular forces use MACE and the rest of the system in MM. -- [run_mace_waterbox.py](run_mace_waterbox.py) runs a water box with pure MACE ML potential. +- [run_water_box.py](run_water_box.py) runs a water box with pure MACE ML potential. ## References The example pre-trained model is from here: https://github.com/jharrymoore/mace-openmm/tree/main/tests/example_data diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index 7dd9650..ee8bde5 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -36,63 +36,67 @@ @torch.jit.script -def _simple_nl(positions: torch.Tensor, cell: torch.Tensor, pbc: torch.Tensor, cutoff: float, self_interaction: bool=False) -> Tuple[torch.Tensor, torch.Tensor]: +def _simple_nl(positions: torch.Tensor, cell: torch.Tensor, pbc: bool, cutoff: float, sorti: bool=False) -> Tuple[torch.Tensor, torch.Tensor]: """simple torchscriptable neighborlist. - It aims are to be correct, clear, and torchscript compatible, no effort has been put into making it fast. + It aims are to be correct, clear, and torchscript compatible. + It is O(n^2) but with pytorch vectorisation the prefactor is small. It outputs nieghbors and shifts in the same format as ASE: - neighbors, shifts = simple_nl(..) - is equivalent to [i, j], S = primitive_neighbor_list( quantities="ijS", ...) + + Limitations: + - cell must be orthogonal + - either no PBCs or PBCs in all x,y,z + - cutoff must be less than half the smallest box length + """ num_atoms = positions.shape[0] device=positions.device - i = torch.repeat_interleave(torch.range(0,num_atoms-1,dtype=torch.long, device=device), num_atoms) - j = torch.range(0,num_atoms-1,dtype=torch.long, device=device).repeat(num_atoms) - neighbors=torch.vstack((i,j)) - - if not self_interaction: - mask = i==j - neighbors = neighbors[:,~mask] + # get i,j indices where j>i + uij = torch.triu_indices(num_atoms, num_atoms, 1, device=device) + triu_deltas = positions[uij[0]] - positions[uij[1]] - full_deltas = positions[neighbors[0]] - positions[neighbors[1]] - deltas=full_deltas.clone() + cell_size = torch.diag(cell) - if pbc[0]: + if pbc: + assert(cutoff < torch.min(cell_size)*0.5) - shifts_x = torch.round(full_deltas[:,0]/cell[0,0]) - shifts_y = torch.round(full_deltas[:,1]/cell[1,1]) - shifts_z = torch.round(full_deltas[:,2]/cell[2,2]) + wrapped_triu_deltas=triu_deltas.clone() - deltas[:,0] = full_deltas[:,0] - shifts_x*cell[0,0] - deltas[:,1] = full_deltas[:,1] - shifts_y*cell[1,1] - deltas[:,2] = full_deltas[:,2] - shifts_z*cell[2,2] + if pbc: + shifts = torch.round(triu_deltas/cell_size) + wrapped_triu_deltas = triu_deltas - shifts*cell_size else: - shifts_x = torch.zeros(full_deltas.shape[0]) - shifts_y = torch.zeros(full_deltas.shape[0]) - shifts_z = torch.zeros(full_deltas.shape[0]) + shifts = torch.zeros(triu_deltas.shape[0],3,device=device) - - distances = torch.linalg.norm(deltas, dim=1) + + triu_distances = torch.linalg.norm(wrapped_triu_deltas, dim=1) # filter - mask = distances > cutoff - neighbors = neighbors[:,~mask] - deltas = deltas[~mask,:] - shifts_x = shifts_x[~mask] - shifts_y = shifts_y[~mask] - shifts_z = shifts_z[~mask] + mask = triu_distances > cutoff + uij = uij[:,~mask] + wrapped_triu_deltas = wrapped_triu_deltas[~mask,:] + + shifts = shifts[~mask, :] + + # get the ij pairs where j Date: Wed, 22 Mar 2023 15:48:09 +0000 Subject: [PATCH 04/17] triclinic simple_nl --- examples/mace/README.md | 2 +- openmmml/models/__init__.py | 2 +- openmmml/models/macepotential.py | 71 ++------------------------ openmmml/models/utils.py | 88 ++++++++++++++++++++++++++++++++ 4 files changed, 94 insertions(+), 69 deletions(-) create mode 100644 openmmml/models/utils.py diff --git a/examples/mace/README.md b/examples/mace/README.md index c0a4410..2023d82 100644 --- a/examples/mace/README.md +++ b/examples/mace/README.md @@ -30,7 +30,7 @@ system = potential.createSystem(topology) There are three examples: - [run_mace.py](run_mace.py) runs a small molecule with pure MACE ML potential. - [run_mace_mixed.py](run_mace_mixed.py) runs a small molecule in a water box where the small molecule's intra-molecular forces use MACE and the rest of the system in MM. -- [run_mace_waterbox.py](run_mace_waterbox.py) runs a water box with pure MACE ML potential. +- [run_water_box.py](run_water_box.py) runs a water box with pure MACE ML potential. ## References The example pre-trained model is from here: https://github.com/jharrymoore/mace-openmm/tree/main/tests/example_data diff --git a/openmmml/models/__init__.py b/openmmml/models/__init__.py index 688da71..73eb4f4 100644 --- a/openmmml/models/__init__.py +++ b/openmmml/models/__init__.py @@ -1 +1 @@ -from . import anipotential, macepotential \ No newline at end of file +from . import anipotential, macepotential, utils \ No newline at end of file diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index 7dd9650..ff0aee2 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -32,71 +32,7 @@ from openmmml.mlpotential import MLPotential, MLPotentialImpl, MLPotentialImplFactory import openmm from typing import Iterable, Optional, Union, Tuple -import torch - - -@torch.jit.script -def _simple_nl(positions: torch.Tensor, cell: torch.Tensor, pbc: torch.Tensor, cutoff: float, self_interaction: bool=False) -> Tuple[torch.Tensor, torch.Tensor]: - """simple torchscriptable neighborlist. - - It aims are to be correct, clear, and torchscript compatible, no effort has been put into making it fast. - It outputs nieghbors and shifts in the same format as ASE: - - neighbors, shifts = simple_nl(..) - - is equivalent to - - [i, j], S = primitive_neighbor_list( quantities="ijS", ...) - """ - - num_atoms = positions.shape[0] - device=positions.device - - i = torch.repeat_interleave(torch.range(0,num_atoms-1,dtype=torch.long, device=device), num_atoms) - j = torch.range(0,num_atoms-1,dtype=torch.long, device=device).repeat(num_atoms) - neighbors=torch.vstack((i,j)) - - if not self_interaction: - mask = i==j - neighbors = neighbors[:,~mask] - - full_deltas = positions[neighbors[0]] - positions[neighbors[1]] - deltas=full_deltas.clone() - - if pbc[0]: - - shifts_x = torch.round(full_deltas[:,0]/cell[0,0]) - shifts_y = torch.round(full_deltas[:,1]/cell[1,1]) - shifts_z = torch.round(full_deltas[:,2]/cell[2,2]) - - deltas[:,0] = full_deltas[:,0] - shifts_x*cell[0,0] - deltas[:,1] = full_deltas[:,1] - shifts_y*cell[1,1] - deltas[:,2] = full_deltas[:,2] - shifts_z*cell[2,2] - - else: - shifts_x = torch.zeros(full_deltas.shape[0]) - shifts_y = torch.zeros(full_deltas.shape[0]) - shifts_z = torch.zeros(full_deltas.shape[0]) - - - distances = torch.linalg.norm(deltas, dim=1) - - # filter - mask = distances > cutoff - neighbors = neighbors[:,~mask] - deltas = deltas[~mask,:] - shifts_x = shifts_x[~mask] - shifts_y = shifts_y[~mask] - shifts_z = shifts_z[~mask] - - shifts = torch.vstack((shifts_x, shifts_y, shifts_z,)).T - - return neighbors, shifts - - - - - +from openmmml.models.utils import simple_nl class MACEPotentialImplFactory(MLPotentialImplFactory): @@ -222,9 +158,10 @@ def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): if boxvectors is not None: cell = boxvectors.to(device=self.device,dtype=self.default_dtype) * self.nm_to_distance + pbc = True else: cell = torch.eye(3, device=self.device) - + pbc = False # compute edges # mapping, _ , shifts_idx = self.compute_nl(cutoff=self.r_max, # pos=positions, @@ -232,7 +169,7 @@ def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): # pbc=self.pbc, # batch=self.batch, # self_interaction=False) - mapping, shifts_idx = _simple_nl(positions, cell, self.pbc, self.r_max) + mapping, shifts_idx = simple_nl(positions, cell, pbc, self.r_max) edge_index = torch.stack((mapping[0], mapping[1])) diff --git a/openmmml/models/utils.py b/openmmml/models/utils.py new file mode 100644 index 0000000..ebd719d --- /dev/null +++ b/openmmml/models/utils.py @@ -0,0 +1,88 @@ +import torch +from typing import Tuple + +@torch.jit.script +def simple_nl(positions: torch.Tensor, cell: torch.Tensor, pbc: bool, cutoff: float, sorti: bool=False) -> Tuple[torch.Tensor, torch.Tensor]: + """simple torchscriptable neighborlist. + + It aims are to be correct, clear, and torchscript compatible. + It is O(n^2) but with pytorch vectorisation the prefactor is small. + It outputs neighbors and shifts in the same format as ASE + https://wiki.fysik.dtu.dk/ase/ase/neighborlist.html#ase.neighborlist.primitive_neighbor_list + + neighbors, shifts = simple_nl(..) + is equivalent to + + [i, j], S = primitive_neighbor_list( quantities="ijS", ...) + + Limitations: + - either no PBCs or PBCs in all x,y,z + - cutoff must be less than half the smallest box length + - cell must be rectangular or triclinic in OpenMM format: + http://docs.openmm.org/development/userguide/theory/05_other_features.html#periodic-boundary-conditions + + Parameters + ---------- + positions: torch.Tensor + Coordinates, shape [N,3] + cell: torch.Tensor + Triclinic unit cell, shape [3,3], must be in OpenMM format: http://docs.openmm.org/development/userguide/theory/05_other_features.html#periodic-boundary-conditions + pbc: bool + should PBCs be applied + cutoff: float + Distances beyond cutoff are not included in the nieghborlist + soti: bool=False + if true the returned nieghborlist will be sorted in the i index. The default is False (no sorting). + + Returns + ------- + neighbors: torch.Tensor + neighbor list, shape [2, number of neighbors] + shifts: torch.Tensor + shift vector, shape [number of neighbors, 3], From ASE docs: + shift vector (number of cell boundaries crossed by the bond between atom i and j). + With the shift vector S, the distances D between atoms can be computed from: + D = positions[j]-positions[i]+S.dot(cell) + """ + + num_atoms = positions.shape[0] + device=positions.device + + # get i,j indices where j>i + uij = torch.triu_indices(num_atoms, num_atoms, 1, device=device) + triu_deltas = positions[uij[0]] - positions[uij[1]] + + wrapped_triu_deltas=triu_deltas.clone() + + if pbc: + # using method from: https://github.com/openmm/NNPOps/blob/master/src/pytorch/neighbors/getNeighborPairsCPU.cpp + wrapped_triu_deltas -= torch.outer(torch.round(wrapped_triu_deltas[:,2]/cell[2,2]), cell[2]) + wrapped_triu_deltas -= torch.outer(torch.round(wrapped_triu_deltas[:,1]/cell[1,1]), cell[1]) + wrapped_triu_deltas -= torch.outer(torch.round(wrapped_triu_deltas[:,0]/cell[0,0]), cell[0]) + + # From ASE docs: + # wrapped_delta = pos[i] - pos[j] - shift.cell + # => shift = ((pos[i]-pos[j]) - wrapped_delta).cell^-1 + shifts = torch.mm(triu_deltas - wrapped_triu_deltas, torch.linalg.inv(cell)) + + else: + shifts = torch.zeros(triu_deltas.shape, device=device) + + triu_distances = torch.linalg.norm(wrapped_triu_deltas, dim=1) + + # filter + mask = triu_distances > cutoff + uij = uij[:,~mask] + shifts = shifts[~mask, :] + + # get the ij pairs where j Date: Thu, 10 Aug 2023 11:22:06 +0100 Subject: [PATCH 05/17] update to openmm-torch 1.1 syntax --- openmmml/models/macepotential.py | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index ff0aee2..c93f169 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -46,12 +46,7 @@ class MACEPotentialImpl(MLPotentialImpl): The potential is implemented using MACE to build a PyTorch model. A TorchForce is used to add it to the OpenMM System. - - TorchForce requires the model to be saved to disk in a separate file. By default - it writes a file called 'macemodel.pt' in the current working directory. You can - use the filename argument to specify a different name. For example, - - >>> system = potential.createSystem(topology, filename='mymodel.pt') + """ def __init__(self, name, model_path): @@ -63,10 +58,11 @@ def addForces(self, system: openmm.System, atoms: Optional[Iterable[int]], forceGroup: int, - filename: str = 'macemodel.pt', + #filename: str = 'macemodel.pt', #implementation : str = None, device: str = None, dtype: str = "float64", + interaction_energy: bool=True, **args): @@ -88,7 +84,7 @@ def addForces(self, class MACEForce(torch.nn.Module): - def __init__(self, model_path, atomic_numbers, indices, periodic, device, dtype=torch.float64): + def __init__(self, model_path, atomic_numbers, indices, periodic, device, dtype=torch.float64, interaction_energy=True): super(MACEForce, self).__init__() if device is None: # use cuda if available @@ -145,7 +141,11 @@ def __init__(self, model_path, atomic_numbers, indices, periodic, device, dtype= else: self.indices = torch.tensor(indices, dtype=torch.int64) - + + if interaction_energy is True: + self.return_energy_type = "interaction_energy" + else: + self.return_energy_type = "energy" def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): # setup positions @@ -189,7 +189,7 @@ def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): # predict out = self.model(input_dict,compute_force=False) - energy = out["interaction_energy"] + energy = out[self.return_energy_type] if energy is None: energy = torch.tensor(0.0, device=self.device) @@ -206,14 +206,13 @@ def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): torch_dtype = {"float32":torch.float32, "float64":torch.float64}[dtype] - maceforce = MACEForce(self.model_path, atomic_numbers, atoms, is_periodic, device, dtype=torch_dtype) + maceforce = MACEForce(self.model_path, atomic_numbers, atoms, is_periodic, device, dtype=torch_dtype, interaction_energy=interaction_energy) - # Convert it to TorchScript and save it. + # Convert it to TorchScript module = torch.jit.script(maceforce) - module.save(filename) # Create the TorchForce and add it to the System. - force = openmmtorch.TorchForce(filename) + force = openmmtorch.TorchForce(module) force.setForceGroup(forceGroup) force.setUsesPeriodicBoundaryConditions(is_periodic) #force.setOutputsForces(True) From b3f048c207fd77facd1a46f3c42c1b2eeee44534 Mon Sep 17 00:00:00 2001 From: Stephen Farr Date: Wed, 4 Oct 2023 16:55:55 +0100 Subject: [PATCH 06/17] update --- examples/mace/README.md | 11 +- examples/mace/ani500k_small_CC.model | Bin 0 -> 3348160 bytes examples/mace/run_mace.ipynb | 155 +++++++++++++ examples/mace/run_mace.py | 6 +- examples/mace/run_mace_mixed.py | 38 ---- examples/mace/run_water_box.py | 23 -- examples/mace/water.pdb | 312 --------------------------- openmmml/models/macepotential.py | 119 +++++----- openmmml/models/utils.py | 2 +- 9 files changed, 224 insertions(+), 442 deletions(-) create mode 100644 examples/mace/ani500k_small_CC.model create mode 100644 examples/mace/run_mace.ipynb delete mode 100644 examples/mace/run_mace_mixed.py delete mode 100644 examples/mace/run_water_box.py delete mode 100644 examples/mace/water.pdb diff --git a/examples/mace/README.md b/examples/mace/README.md index 2023d82..a9a2f4e 100644 --- a/examples/mace/README.md +++ b/examples/mace/README.md @@ -26,12 +26,11 @@ system = potential.createSystem(topology) ``` -## Examples -There are three examples: -- [run_mace.py](run_mace.py) runs a small molecule with pure MACE ML potential. -- [run_mace_mixed.py](run_mace_mixed.py) runs a small molecule in a water box where the small molecule's intra-molecular forces use MACE and the rest of the system in MM. -- [run_water_box.py](run_water_box.py) runs a water box with pure MACE ML potential. +## Example +There is an example which runs a simulation of an alanine dipeptide molecule using a MACE model trained on the ANI-1x dataset from https://doi.org/10.1063/5.0155322. It is avaible as a python script [`run_mace.py`](run_mace.py) and a Jupyter notebook [`run_mace.ipynb`](run_mace.ipynb) which can be run on Colab. + ## References -The example pre-trained model is from here: https://github.com/jharrymoore/mace-openmm/tree/main/tests/example_data +The example pre-trained model is from https://doi.org/10.1063/5.0155322 avaiable to download at https://github.com/ACEsuit/mace/blob/docs/docs/examples/ANI_trained_MACE.zip. + The MACE repo is here: https://github.com/ACEsuit/mace diff --git a/examples/mace/ani500k_small_CC.model b/examples/mace/ani500k_small_CC.model new file mode 100644 index 0000000000000000000000000000000000000000..41e735ecb1b8efe0bdd9ef2b0a3593e9e0b6e28a GIT binary patch literal 3348160 zcmeFabzD{7yC+VV7=(d{C zJKcBYPTcA4`n~r)2Rs;`@ArG}ef{Q-89pEP*?aAk&w6UD^$|_> zS_ie_GE!aa?VroC)8o?8>=NUYakg2{(|o=6X+^33igXgv;}Yby>6wW+Y4U9GSDu!w z7nTs0CJ#$VO;QHky6vN<(6TC4Xcs7SEF;B|ltNr;MruZ~LbodN0G?%8aY9_WTqzf_ 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browser: [![Open On Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sef43/openmm-ml/blob/mace/examples/nequip/run_mace.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install Conda\n", + "\n", + "[Conda](https://docs.conda.io/) is a package and environment manager. On Google Colab, Conda is installed with [conda-colab](https://github.com/jaimergp/condacolab). On your computer, you should follow these [installation instructions](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html).\n", + "\n", + "⚠️ Do not use conda-colab on your computer!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install_mambaforge() # use mamba on colab because it is faster than conda" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install software\n", + "\n", + "First install everything we can from [conda-forge](https://conda-forge.org/).\n", + "Then use pip.\n", + "\n", + "⚠️ The installation might take up to 10 min!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#https://github.com/openmm/openmm-torch/issues/88\n", + "%env CONDA_OVERRIDE_CUDA=12.0\n", + "!mamba install -c conda-forge openmm-torch pytorch=*=cuda* openmmtools\n", + "\n", + "!pip install git+https://github.com/ACEsuit/mace\n", + "!pip install git+https://github.com/sef43/openmm-ml@mace" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get the files we need to run the example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!wget https://raw.githubusercontent.com/sef43/openmm-ml/mace/examples/mace/mace/openmm-ml/examples/mace/ani500k_small_CC.model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run simulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import openmm\n", + "import openmm.app as app\n", + "import openmm.unit as unit\n", + "from openmmml import MLPotential\n", + "from sys import stdout\n", + "from openmmtools.testsystems import AlanineDipeptideVacuum\n", + "\n", + "# Get the system of alanine dipeptide\n", + "ala2 = AlanineDipeptideVacuum(constraints=None)\n", + "\n", + "# Remove MM forces\n", + "while ala2.system.getNumForces() > 0:\n", + " ala2.system.removeForce(0)\n", + "\n", + "# The system should not contain any additional force and constrains\n", + "assert ala2.system.getNumConstraints() == 0\n", + "assert ala2.system.getNumForces() == 0\n", + "\n", + "potential = MLPotential('mace', model_path='ani500k_small_CC.model')\n", + "\n", + "\n", + "system = potential.createSystem(ala2.topology)\n", + "\n", + "\n", + "integrator = openmm.LangevinIntegrator(300*unit.kelvin, 10.0/unit.picoseconds, 1.0*unit.femtosecond)\n", + "simulation=app.Simulation(ala2.topology, system, integrator)\n", + "simulation.context.setPositions(ala2.positions)\n", + "simulation.reporters.append(app.PDBReporter('output.pdb', 100))\n", + "simulation.reporters.append(app.StateDataReporter(stdout, 100, step=True,\n", + " potentialEnergy=True, temperature=True))\n", + "\n", + "simulation.step(1000)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "neqmm", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "413bac2e6b7bbc56e28392734551129ace8c921fbbb9e9273c3258497d1da304" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/mace/run_mace.py b/examples/mace/run_mace.py index ad04203..4c60bce 100644 --- a/examples/mace/run_mace.py +++ b/examples/mace/run_mace.py @@ -16,7 +16,7 @@ assert ala2.system.getNumConstraints() == 0 assert ala2.system.getNumForces() == 0 -potential = MLPotential('mace', model_path='MACE_SPICE_larger.model') +potential = MLPotential('mace', model_path='ani500k_small_CC.model') system = potential.createSystem(ala2.topology) @@ -27,6 +27,6 @@ simulation.context.setPositions(ala2.positions) simulation.reporters.append(app.PDBReporter('output.pdb', 100)) simulation.reporters.append(app.StateDataReporter(stdout, 100, step=True, - potentialEnergy=True, temperature=True, speed=True)) + potentialEnergy=True, temperature=True)) -simulation.step(10000) +simulation.step(1000) diff --git a/examples/mace/run_mace_mixed.py b/examples/mace/run_mace_mixed.py deleted file mode 100644 index d83bca4..0000000 --- a/examples/mace/run_mace_mixed.py +++ /dev/null @@ -1,38 +0,0 @@ -import openmm -import openmm.app as app -import openmm.unit as unit -from openmmml import MLPotential -from sys import stdout -from openmmtools.testsystems import AlanineDipeptideVacuum - -# Get the system of alanine dipeptide -ala2 = AlanineDipeptideVacuum(constraints=None) - -# solvate -ff=openmm.app.ForceField('amber14-all.xml', 'amber14/tip3pfb.xml') - -modeller=openmm.app.Modeller(ala2.topology, ala2.positions) -modeller.addSolvent(ff, padding=1.0*unit.nanometers) - -print(modeller.topology) - -mm_system = ff.createSystem(modeller.topology, nonbondedMethod=openmm.app.PME, nonbondedCutoff=1.0*unit.nanometer, constraints=None) - -chains = list(modeller.topology.chains()) -ml_atoms = [atom.index for atom in chains[0].atoms()] - -potential = MLPotential('mace', model_path='MACE_SPICE_larger.model') - - -mm_ml_system = potential.createMixedSystem(modeller.topology, mm_system, ml_atoms) - -# run langevin dynamics at 300K for 10000 steps -integrator = openmm.LangevinIntegrator(300*unit.kelvin, 1.0/unit.picoseconds, 1.0*unit.femtosecond) -simulation=app.Simulation(modeller.topology, mm_ml_system, integrator) -simulation.context.setPositions(modeller.positions) -simulation.reporters.append(app.PDBReporter('output.pdb', 100, enforcePeriodicBox=False)) -simulation.reporters.append(app.StateDataReporter(stdout, 100, step=True, - potentialEnergy=True, temperature=True, speed=True)) - - -simulation.step(10000) diff --git a/examples/mace/run_water_box.py b/examples/mace/run_water_box.py deleted file mode 100644 index bf2efe9..0000000 --- a/examples/mace/run_water_box.py +++ /dev/null @@ -1,23 +0,0 @@ -import openmm -import openmm.app as app -import openmm.unit as unit -from openmmml import MLPotential -from sys import stdout - - -pdb=app.PDBFile("water.pdb") - -potential = MLPotential('mace', model_path='MACE_SPICE_larger.model') - - -system = potential.createSystem(pdb.topology) - -integrator = openmm.LangevinIntegrator(300*unit.kelvin, 1.0/unit.picoseconds, 1.0*unit.femtosecond) -simulation=app.Simulation(pdb.topology, system, integrator) -simulation.context.setPositions(pdb.positions) -simulation.reporters.append(app.PDBReporter('output.pdb', 100)) -simulation.reporters.append(app.StateDataReporter(stdout, 10, step=True, - potentialEnergy=True, temperature=True, speed=True)) - - -simulation.step(100) diff --git a/examples/mace/water.pdb b/examples/mace/water.pdb deleted file mode 100644 index 7f0f188..0000000 --- a/examples/mace/water.pdb +++ /dev/null @@ -1,312 +0,0 @@ -REMARK 1 CREATED WITH OPENMM 8.0, 2023-03-12 -CRYST1 15.000 15.000 15.000 90.00 90.00 90.00 P 1 1 -MODEL 1 -HETATM 1 O HOH A 1 24.016 55.722 39.544 1.00 0.00 O -HETATM 2 H1 HOH A 1 23.351 56.157 40.093 1.00 0.00 H -HETATM 3 H2 HOH A 1 24.559 56.458 39.352 1.00 0.00 H -HETATM 4 O HOH A 2 -17.337 9.397 43.983 1.00 0.00 O -HETATM 5 H1 HOH A 2 -17.338 9.807 43.088 1.00 0.00 H -HETATM 6 H2 HOH A 2 -17.814 8.616 43.788 1.00 0.00 H -HETATM 7 O HOH A 3 8.988 15.063 4.215 1.00 0.00 O -HETATM 8 H1 HOH A 3 9.282 15.178 3.279 1.00 0.00 H -HETATM 9 H2 HOH A 3 8.070 15.449 4.140 1.00 0.00 H -HETATM 10 O HOH A 4 8.718 18.448 32.221 1.00 0.00 O -HETATM 11 H1 HOH A 4 8.050 18.603 32.920 1.00 0.00 H -HETATM 12 H2 HOH A 4 8.350 18.961 31.515 1.00 0.00 H -HETATM 13 O HOH A 5 -14.573 39.144 31.914 1.00 0.00 O -HETATM 14 H1 HOH A 5 -15.049 39.929 31.772 1.00 0.00 H -HETATM 15 H2 HOH A 5 -14.648 38.668 31.039 1.00 0.00 H -HETATM 16 O HOH A 6 -15.937 -12.842 27.065 1.00 0.00 O -HETATM 17 H1 HOH A 6 -16.749 -13.211 27.365 1.00 0.00 H -HETATM 18 H2 HOH A 6 -15.256 -13.571 27.184 1.00 0.00 H -HETATM 19 O HOH A 7 -0.597 45.334 -15.247 1.00 0.00 O -HETATM 20 H1 HOH A 7 -0.504 46.143 -14.712 1.00 0.00 H -HETATM 21 H2 HOH A 7 0.117 45.347 -15.892 1.00 0.00 H -HETATM 22 O HOH A 8 -17.088 73.231 0.767 1.00 0.00 O -HETATM 23 H1 HOH A 8 -17.251 73.391 1.716 1.00 0.00 H -HETATM 24 H2 HOH A 8 -16.461 73.919 0.600 1.00 0.00 H -HETATM 25 O HOH A 9 11.920 28.903 63.270 1.00 0.00 O -HETATM 26 H1 HOH A 9 11.156 29.406 62.920 1.00 0.00 H -HETATM 27 H2 HOH A 9 12.329 29.573 63.867 1.00 0.00 H -HETATM 28 O HOH A 10 -7.427 -20.654 22.745 1.00 0.00 O -HETATM 29 H1 HOH A 10 -8.218 -20.385 23.264 1.00 0.00 H -HETATM 30 H2 HOH A 10 -6.686 -20.232 23.231 1.00 0.00 H -HETATM 31 O HOH A 11 -8.469 29.392 11.464 1.00 0.00 O -HETATM 32 H1 HOH A 11 -8.937 28.585 11.481 1.00 0.00 H -HETATM 33 H2 HOH A 11 -7.611 29.091 11.573 1.00 0.00 H -HETATM 34 O HOH A 12 12.523 25.022 11.157 1.00 0.00 O -HETATM 35 H1 HOH A 12 13.044 25.183 10.361 1.00 0.00 H -HETATM 36 H2 HOH A 12 11.936 24.310 10.867 1.00 0.00 H -HETATM 37 O HOH A 13 17.684 62.114 15.757 1.00 0.00 O -HETATM 38 H1 HOH A 13 16.833 62.646 15.635 1.00 0.00 H -HETATM 39 H2 HOH A 13 18.307 62.823 16.007 1.00 0.00 H -HETATM 40 O HOH A 14 21.696 -18.439 29.968 1.00 0.00 O -HETATM 41 H1 HOH A 14 22.182 -18.597 30.764 1.00 0.00 H -HETATM 42 H2 HOH A 14 22.141 -18.960 29.295 1.00 0.00 H -HETATM 43 O HOH A 15 -16.327 -19.355 33.883 1.00 0.00 O -HETATM 44 H1 HOH A 15 -16.457 -19.204 32.914 1.00 0.00 H -HETATM 45 H2 HOH A 15 -16.591 -20.254 33.980 1.00 0.00 H -HETATM 46 O HOH A 16 11.481 -8.071 29.432 1.00 0.00 O -HETATM 47 H1 HOH A 16 12.411 -8.444 29.376 1.00 0.00 H -HETATM 48 H2 HOH A 16 11.036 -8.650 28.751 1.00 0.00 H -HETATM 49 O HOH A 17 -11.306 35.159 -11.018 1.00 0.00 O -HETATM 50 H1 HOH A 17 -11.220 34.469 -11.749 1.00 0.00 H -HETATM 51 H2 HOH A 17 -12.134 35.612 -11.377 1.00 0.00 H -HETATM 52 O HOH A 18 -1.539 18.707 32.174 1.00 0.00 O -HETATM 53 H1 HOH A 18 -2.182 18.613 32.916 1.00 0.00 H -HETATM 54 H2 HOH A 18 -1.606 19.668 32.085 1.00 0.00 H -HETATM 55 O HOH A 19 1.495 -8.517 62.660 1.00 0.00 O -HETATM 56 H1 HOH A 19 1.687 -8.242 61.802 1.00 0.00 H -HETATM 57 H2 HOH A 19 0.570 -8.353 62.695 1.00 0.00 H -HETATM 58 O HOH A 20 -20.907 47.450 21.608 1.00 0.00 O -HETATM 59 H1 HOH A 20 -21.170 48.364 21.756 1.00 0.00 H -HETATM 60 H2 HOH A 20 -20.559 47.436 20.771 1.00 0.00 H -HETATM 61 O HOH A 21 -9.590 -0.539 29.387 1.00 0.00 O -HETATM 62 H1 HOH A 21 -9.268 -0.106 28.615 1.00 0.00 H -HETATM 63 H2 HOH A 21 -8.765 -0.681 29.808 1.00 0.00 H -HETATM 64 O HOH A 22 11.011 33.565 3.621 1.00 0.00 O -HETATM 65 H1 HOH A 22 10.180 33.276 3.120 1.00 0.00 H -HETATM 66 H2 HOH A 22 10.930 34.462 3.791 1.00 0.00 H -HETATM 67 O HOH A 23 -1.573 21.870 2.797 1.00 0.00 O -HETATM 68 H1 HOH A 23 -1.541 22.535 2.096 1.00 0.00 H -HETATM 69 H2 HOH A 23 -1.961 22.364 3.545 1.00 0.00 H -HETATM 70 O HOH A 24 -16.867 -24.972 6.651 1.00 0.00 O -HETATM 71 H1 HOH A 24 -17.809 -24.685 6.654 1.00 0.00 H -HETATM 72 H2 HOH A 24 -16.579 -24.423 7.384 1.00 0.00 H -HETATM 73 O HOH A 25 1.917 10.566 11.039 1.00 0.00 O -HETATM 74 H1 HOH A 25 1.653 9.643 10.941 1.00 0.00 H -HETATM 75 H2 HOH A 25 2.878 10.625 11.066 1.00 0.00 H -HETATM 76 O HOH A 26 -0.451 46.730 39.318 1.00 0.00 O -HETATM 77 H1 HOH A 26 -0.055 47.609 39.102 1.00 0.00 H -HETATM 78 H2 HOH A 26 -1.091 46.925 40.050 1.00 0.00 H -HETATM 79 O HOH A 27 2.438 60.033 32.373 1.00 0.00 O -HETATM 80 H1 HOH A 27 2.708 60.898 31.853 1.00 0.00 H -HETATM 81 H2 HOH A 27 1.769 60.401 33.001 1.00 0.00 H -HETATM 82 O HOH A 28 -7.052 24.551 58.299 1.00 0.00 O -HETATM 83 H1 HOH A 28 -6.219 24.316 58.749 1.00 0.00 H -HETATM 84 H2 HOH A 28 -7.249 23.656 57.816 1.00 0.00 H -HETATM 85 O HOH A 29 4.162 27.972 18.398 1.00 0.00 O -HETATM 86 H1 HOH A 29 3.600 28.733 18.233 1.00 0.00 H -HETATM 87 H2 HOH A 29 4.170 27.577 17.508 1.00 0.00 H -HETATM 88 O HOH A 30 7.578 -18.151 26.767 1.00 0.00 O -HETATM 89 H1 HOH A 30 7.820 -18.989 27.305 1.00 0.00 H -HETATM 90 H2 HOH A 30 8.201 -17.445 27.058 1.00 0.00 H -HETATM 91 O HOH A 31 10.014 1.559 13.234 1.00 0.00 O -HETATM 92 H1 HOH A 31 10.667 1.461 12.563 1.00 0.00 H -HETATM 93 H2 HOH A 31 10.590 1.757 13.927 1.00 0.00 H -HETATM 94 O HOH A 32 -37.741 69.723 20.067 1.00 0.00 O -HETATM 95 H1 HOH A 32 -37.449 69.708 20.981 1.00 0.00 H -HETATM 96 H2 HOH A 32 -38.559 70.289 20.033 1.00 0.00 H -HETATM 97 O HOH A 33 33.605 18.358 13.031 1.00 0.00 O -HETATM 98 H1 HOH A 33 33.155 17.838 12.309 1.00 0.00 H -HETATM 99 H2 HOH A 33 33.308 17.779 13.808 1.00 0.00 H -HETATM 100 O HOH A 34 2.884 13.131 -0.114 1.00 0.00 O -HETATM 101 H1 HOH A 34 3.621 13.710 -0.239 1.00 0.00 H -HETATM 102 H2 HOH A 34 2.173 13.678 0.295 1.00 0.00 H -HETATM 103 O HOH A 35 -4.619 6.718 6.121 1.00 0.00 O -HETATM 104 H1 HOH A 35 -5.523 6.755 6.469 1.00 0.00 H -HETATM 105 H2 HOH A 35 -4.876 6.476 5.232 1.00 0.00 H -HETATM 106 O HOH A 36 22.043 4.544 34.058 1.00 0.00 O -HETATM 107 H1 HOH A 36 21.344 5.126 34.201 1.00 0.00 H -HETATM 108 H2 HOH A 36 22.079 4.062 34.874 1.00 0.00 H -HETATM 109 O HOH A 37 -12.135 31.319 -22.436 1.00 0.00 O -HETATM 110 H1 HOH A 37 -12.154 31.017 -23.391 1.00 0.00 H -HETATM 111 H2 HOH A 37 -11.191 31.079 -22.215 1.00 0.00 H -HETATM 112 O HOH A 38 53.089 37.164 41.904 1.00 0.00 O -HETATM 113 H1 HOH A 38 52.534 36.613 41.262 1.00 0.00 H -HETATM 114 H2 HOH A 38 53.490 36.513 42.524 1.00 0.00 H -HETATM 115 O HOH A 39 6.450 35.443 10.707 1.00 0.00 O -HETATM 116 H1 HOH A 39 6.582 34.791 11.423 1.00 0.00 H -HETATM 117 H2 HOH A 39 5.521 35.616 10.749 1.00 0.00 H -HETATM 118 O HOH A 40 27.244 38.222 19.801 1.00 0.00 O -HETATM 119 H1 HOH A 40 26.378 38.666 19.572 1.00 0.00 H -HETATM 120 H2 HOH A 40 26.934 37.413 20.217 1.00 0.00 H -HETATM 121 O HOH A 41 23.007 5.330 60.614 1.00 0.00 O -HETATM 122 H1 HOH A 41 22.263 5.863 60.960 1.00 0.00 H -HETATM 123 H2 HOH A 41 23.688 6.012 60.652 1.00 0.00 H -HETATM 124 O HOH A 42 -18.886 34.027 25.445 1.00 0.00 O -HETATM 125 H1 HOH A 42 -18.927 34.644 24.694 1.00 0.00 H -HETATM 126 H2 HOH A 42 -19.286 33.231 25.032 1.00 0.00 H -HETATM 127 O HOH A 43 -25.233 19.632 36.497 1.00 0.00 O -HETATM 128 H1 HOH A 43 -24.951 18.715 36.637 1.00 0.00 H -HETATM 129 H2 HOH A 43 -25.495 19.717 35.583 1.00 0.00 H -HETATM 130 O HOH A 44 2.305 -1.672 7.959 1.00 0.00 O -HETATM 131 H1 HOH A 44 2.148 -2.114 7.126 1.00 0.00 H -HETATM 132 H2 HOH A 44 2.410 -0.782 7.638 1.00 0.00 H -HETATM 133 O HOH A 45 -16.081 50.908 -0.799 1.00 0.00 O -HETATM 134 H1 HOH A 45 -15.339 51.548 -0.597 1.00 0.00 H -HETATM 135 H2 HOH A 45 -15.962 50.635 -1.726 1.00 0.00 H -HETATM 136 O HOH A 46 3.919 24.075 46.303 1.00 0.00 O -HETATM 137 H1 HOH A 46 4.067 24.857 45.733 1.00 0.00 H -HETATM 138 H2 HOH A 46 2.976 23.927 46.270 1.00 0.00 H -HETATM 139 O HOH A 47 -10.467 33.850 16.831 1.00 0.00 O -HETATM 140 H1 HOH A 47 -9.986 33.007 16.865 1.00 0.00 H -HETATM 141 H2 HOH A 47 -9.754 34.437 16.671 1.00 0.00 H -HETATM 142 O HOH A 48 -7.858 4.382 13.128 1.00 0.00 O -HETATM 143 H1 HOH A 48 -7.538 4.605 14.064 1.00 0.00 H -HETATM 144 H2 HOH A 48 -7.819 3.398 13.197 1.00 0.00 H -HETATM 145 O HOH A 49 1.553 -20.092 49.189 1.00 0.00 O -HETATM 146 H1 HOH A 49 1.502 -20.159 48.227 1.00 0.00 H -HETATM 147 H2 HOH A 49 0.648 -20.001 49.411 1.00 0.00 H -HETATM 148 O HOH A 50 -9.735 -20.321 9.024 1.00 0.00 O -HETATM 149 H1 HOH A 50 -9.838 -20.610 9.986 1.00 0.00 H -HETATM 150 H2 HOH A 50 -10.385 -20.964 8.521 1.00 0.00 H -HETATM 151 O HOH A 51 -4.715 53.350 55.571 1.00 0.00 O -HETATM 152 H1 HOH A 51 -5.489 52.890 55.995 1.00 0.00 H -HETATM 153 H2 HOH A 51 -5.170 54.127 55.220 1.00 0.00 H -HETATM 154 O HOH A 52 31.165 33.683 -6.647 1.00 0.00 O -HETATM 155 H1 HOH A 52 31.972 33.150 -6.602 1.00 0.00 H -HETATM 156 H2 HOH A 52 31.400 34.217 -7.375 1.00 0.00 H -HETATM 157 O HOH A 53 11.313 6.193 23.883 1.00 0.00 O -HETATM 158 H1 HOH A 53 11.261 6.916 24.531 1.00 0.00 H -HETATM 159 H2 HOH A 53 10.899 6.495 23.076 1.00 0.00 H -HETATM 160 O HOH A 54 -14.572 -11.678 30.309 1.00 0.00 O -HETATM 161 H1 HOH A 54 -15.363 -11.425 30.856 1.00 0.00 H -HETATM 162 H2 HOH A 54 -14.431 -10.932 29.667 1.00 0.00 H -HETATM 163 O HOH A 55 -0.066 20.886 9.107 1.00 0.00 O -HETATM 164 H1 HOH A 55 0.074 19.960 8.865 1.00 0.00 H -HETATM 165 H2 HOH A 55 -0.605 20.757 9.956 1.00 0.00 H -HETATM 166 O HOH A 56 39.914 37.338 1.313 1.00 0.00 O -HETATM 167 H1 HOH A 56 40.568 37.199 0.606 1.00 0.00 H -HETATM 168 H2 HOH A 56 39.801 38.328 1.171 1.00 0.00 H -HETATM 169 O HOH A 57 14.203 13.054 5.040 1.00 0.00 O -HETATM 170 H1 HOH A 57 13.696 12.996 5.814 1.00 0.00 H -HETATM 171 H2 HOH A 57 13.851 12.328 4.514 1.00 0.00 H -HETATM 172 O HOH A 58 -3.966 57.787 9.070 1.00 0.00 O -HETATM 173 H1 HOH A 58 -3.194 57.212 9.277 1.00 0.00 H -HETATM 174 H2 HOH A 58 -3.921 57.894 8.096 1.00 0.00 H -HETATM 175 O HOH A 59 16.873 -17.723 35.456 1.00 0.00 O -HETATM 176 H1 HOH A 59 16.932 -18.375 34.768 1.00 0.00 H -HETATM 177 H2 HOH A 59 15.994 -17.298 35.292 1.00 0.00 H -HETATM 178 O HOH A 60 -53.901 36.802 31.801 1.00 0.00 O -HETATM 179 H1 HOH A 60 -54.397 37.538 31.316 1.00 0.00 H -HETATM 180 H2 HOH A 60 -54.004 37.052 32.695 1.00 0.00 H -HETATM 181 O HOH A 61 5.397 54.236 26.764 1.00 0.00 O -HETATM 182 H1 HOH A 61 5.983 53.590 27.260 1.00 0.00 H -HETATM 183 H2 HOH A 61 5.078 54.796 27.554 1.00 0.00 H -HETATM 184 O HOH A 62 42.724 17.248 -22.686 1.00 0.00 O -HETATM 185 H1 HOH A 62 43.598 16.999 -22.298 1.00 0.00 H -HETATM 186 H2 HOH A 62 42.864 18.181 -23.034 1.00 0.00 H -HETATM 187 O HOH A 63 -18.655 43.347 51.166 1.00 0.00 O -HETATM 188 H1 HOH A 63 -18.340 44.219 51.354 1.00 0.00 H -HETATM 189 H2 HOH A 63 -18.552 43.264 50.235 1.00 0.00 H -HETATM 190 O HOH A 64 -26.654 38.135 4.999 1.00 0.00 O -HETATM 191 H1 HOH A 64 -27.390 38.819 4.870 1.00 0.00 H -HETATM 192 H2 HOH A 64 -26.997 37.506 4.404 1.00 0.00 H -HETATM 193 O HOH A 65 54.110 29.030 27.090 1.00 0.00 O -HETATM 194 H1 HOH A 65 54.902 28.392 27.116 1.00 0.00 H -HETATM 195 H2 HOH A 65 54.463 29.850 27.391 1.00 0.00 H -HETATM 196 O HOH A 66 -72.613 37.697 27.611 1.00 0.00 O -HETATM 197 H1 HOH A 66 -71.645 37.584 27.786 1.00 0.00 H -HETATM 198 H2 HOH A 66 -72.655 37.687 26.613 1.00 0.00 H -HETATM 199 O HOH A 67 4.739 -24.257 27.733 1.00 0.00 O -HETATM 200 H1 HOH A 67 5.587 -24.422 28.204 1.00 0.00 H -HETATM 201 H2 HOH A 67 4.277 -25.127 27.822 1.00 0.00 H -HETATM 202 O HOH A 68 21.035 32.254 21.736 1.00 0.00 O -HETATM 203 H1 HOH A 68 21.243 31.473 22.259 1.00 0.00 H -HETATM 204 H2 HOH A 68 21.401 32.905 22.364 1.00 0.00 H -HETATM 205 O HOH A 69 26.129 47.527 15.489 1.00 0.00 O -HETATM 206 H1 HOH A 69 26.840 47.953 15.905 1.00 0.00 H -HETATM 207 H2 HOH A 69 25.364 47.754 15.992 1.00 0.00 H -HETATM 208 O HOH A 70 10.295 16.887 8.936 1.00 0.00 O -HETATM 209 H1 HOH A 70 9.710 16.890 8.215 1.00 0.00 H -HETATM 210 H2 HOH A 70 11.163 16.987 8.575 1.00 0.00 H -HETATM 211 O HOH A 71 7.601 21.629 21.918 1.00 0.00 O -HETATM 212 H1 HOH A 71 7.477 22.535 22.313 1.00 0.00 H -HETATM 213 H2 HOH A 71 7.036 21.639 21.173 1.00 0.00 H -HETATM 214 O HOH A 72 -7.041 -3.810 2.320 1.00 0.00 O -HETATM 215 H1 HOH A 72 -7.477 -4.467 2.952 1.00 0.00 H -HETATM 216 H2 HOH A 72 -6.665 -3.192 2.962 1.00 0.00 H -HETATM 217 O HOH A 73 -50.628 36.048 18.772 1.00 0.00 O -HETATM 218 H1 HOH A 73 -50.367 36.426 17.855 1.00 0.00 H -HETATM 219 H2 HOH A 73 -51.416 35.479 18.726 1.00 0.00 H -HETATM 220 O HOH A 74 6.071 30.650 4.404 1.00 0.00 O -HETATM 221 H1 HOH A 74 6.359 31.221 5.187 1.00 0.00 H -HETATM 222 H2 HOH A 74 5.228 30.317 4.756 1.00 0.00 H -HETATM 223 O HOH A 75 -17.627 -14.152 19.893 1.00 0.00 O -HETATM 224 H1 HOH A 75 -17.454 -13.645 20.721 1.00 0.00 H -HETATM 225 H2 HOH A 75 -18.238 -13.589 19.450 1.00 0.00 H -HETATM 226 O HOH A 76 -48.396 0.579 25.996 1.00 0.00 O -HETATM 227 H1 HOH A 76 -48.946 0.882 25.305 1.00 0.00 H -HETATM 228 H2 HOH A 76 -47.995 -0.181 25.563 1.00 0.00 H -HETATM 229 O HOH A 77 -5.028 0.042 16.638 1.00 0.00 O -HETATM 230 H1 HOH A 77 -5.357 -0.714 16.138 1.00 0.00 H -HETATM 231 H2 HOH A 77 -4.742 0.713 15.990 1.00 0.00 H -HETATM 232 O HOH A 78 -22.120 34.323 -21.604 1.00 0.00 O -HETATM 233 H1 HOH A 78 -22.391 34.733 -20.733 1.00 0.00 H -HETATM 234 H2 HOH A 78 -22.170 35.047 -22.224 1.00 0.00 H -HETATM 235 O HOH A 79 4.252 26.041 13.951 1.00 0.00 O -HETATM 236 H1 HOH A 79 3.564 26.755 14.283 1.00 0.00 H -HETATM 237 H2 HOH A 79 5.082 26.415 14.044 1.00 0.00 H -HETATM 238 O HOH A 80 15.435 23.070 29.413 1.00 0.00 O -HETATM 239 H1 HOH A 80 14.839 23.718 29.022 1.00 0.00 H -HETATM 240 H2 HOH A 80 16.102 22.866 28.739 1.00 0.00 H -HETATM 241 O HOH A 81 3.806 22.452 37.747 1.00 0.00 O -HETATM 242 H1 HOH A 81 4.291 21.623 37.838 1.00 0.00 H -HETATM 243 H2 HOH A 81 3.685 22.573 36.792 1.00 0.00 H -HETATM 244 O HOH A 82 0.697 30.435 34.339 1.00 0.00 O -HETATM 245 H1 HOH A 82 0.181 31.254 34.372 1.00 0.00 H -HETATM 246 H2 HOH A 82 0.090 29.692 34.683 1.00 0.00 H -HETATM 247 O HOH A 83 2.290 22.632 -5.160 1.00 0.00 O -HETATM 248 H1 HOH A 83 1.441 22.155 -5.348 1.00 0.00 H -HETATM 249 H2 HOH A 83 2.830 22.406 -5.980 1.00 0.00 H -HETATM 250 O HOH A 84 -24.256 -28.496 46.912 1.00 0.00 O -HETATM 251 H1 HOH A 84 -24.796 -29.125 46.403 1.00 0.00 H -HETATM 252 H2 HOH A 84 -24.090 -28.884 47.799 1.00 0.00 H -HETATM 253 O HOH A 85 13.821 26.152 39.037 1.00 0.00 O -HETATM 254 H1 HOH A 85 14.558 25.688 38.634 1.00 0.00 H -HETATM 255 H2 HOH A 85 14.143 27.041 39.063 1.00 0.00 H -HETATM 256 O HOH A 86 16.011 30.427 -17.476 1.00 0.00 O -HETATM 257 H1 HOH A 86 17.030 30.493 -17.548 1.00 0.00 H -HETATM 258 H2 HOH A 86 15.805 29.676 -18.032 1.00 0.00 H -HETATM 259 O HOH A 87 -41.588 15.173 50.160 1.00 0.00 O -HETATM 260 H1 HOH A 87 -42.388 15.500 49.821 1.00 0.00 H -HETATM 261 H2 HOH A 87 -41.621 14.270 49.838 1.00 0.00 H -HETATM 262 O HOH A 88 -3.739 27.685 28.424 1.00 0.00 O -HETATM 263 H1 HOH A 88 -3.379 28.024 29.247 1.00 0.00 H -HETATM 264 H2 HOH A 88 -3.107 27.008 28.229 1.00 0.00 H -HETATM 265 O HOH A 89 -20.451 9.807 0.728 1.00 0.00 O -HETATM 266 H1 HOH A 89 -21.221 10.123 1.218 1.00 0.00 H -HETATM 267 H2 HOH A 89 -19.710 10.271 1.150 1.00 0.00 H -HETATM 268 O HOH A 90 35.005 12.412 -6.593 1.00 0.00 O -HETATM 269 H1 HOH A 90 34.060 12.554 -6.748 1.00 0.00 H -HETATM 270 H2 HOH A 90 34.956 11.477 -6.328 1.00 0.00 H -HETATM 271 O HOH A 91 0.969 -1.815 25.507 1.00 0.00 O -HETATM 272 H1 HOH A 91 1.238 -2.716 25.757 1.00 0.00 H -HETATM 273 H2 HOH A 91 1.541 -1.658 24.728 1.00 0.00 H -HETATM 274 O HOH A 92 28.839 49.949 11.242 1.00 0.00 O -HETATM 275 H1 HOH A 92 29.038 49.182 11.736 1.00 0.00 H -HETATM 276 H2 HOH A 92 27.968 49.683 10.983 1.00 0.00 H -HETATM 277 O HOH A 93 28.404 40.668 31.188 1.00 0.00 O -HETATM 278 H1 HOH A 93 28.119 40.182 30.341 1.00 0.00 H -HETATM 279 H2 HOH A 93 28.226 41.537 30.936 1.00 0.00 H -HETATM 280 O HOH A 94 -21.049 42.435 34.539 1.00 0.00 O -HETATM 281 H1 HOH A 94 -21.031 43.398 34.613 1.00 0.00 H -HETATM 282 H2 HOH A 94 -21.012 42.135 35.478 1.00 0.00 H -HETATM 283 O HOH A 95 -9.029 37.420 34.570 1.00 0.00 O -HETATM 284 H1 HOH A 95 -9.966 37.751 34.695 1.00 0.00 H -HETATM 285 H2 HOH A 95 -8.439 38.145 34.870 1.00 0.00 H -HETATM 286 O HOH A 96 -40.110 10.942 20.073 1.00 0.00 O -HETATM 287 H1 HOH A 96 -41.064 10.799 20.231 1.00 0.00 H -HETATM 288 H2 HOH A 96 -40.128 11.645 19.367 1.00 0.00 H -HETATM 289 O HOH A 97 -34.774 35.066 12.974 1.00 0.00 O -HETATM 290 H1 HOH A 97 -34.605 34.880 12.062 1.00 0.00 H -HETATM 291 H2 HOH A 97 -35.002 34.244 13.377 1.00 0.00 H -HETATM 292 O HOH A 98 5.779 30.107 8.294 1.00 0.00 O -HETATM 293 H1 HOH A 98 6.167 30.054 9.162 1.00 0.00 H -HETATM 294 H2 HOH A 98 5.542 29.153 8.188 1.00 0.00 H -HETATM 295 O HOH A 99 1.751 35.453 14.013 1.00 0.00 O -HETATM 296 H1 HOH A 99 1.988 36.183 13.427 1.00 0.00 H -HETATM 297 H2 HOH A 99 2.487 34.849 13.880 1.00 0.00 H -HETATM 298 O HOH A 100 -4.923 9.697 4.099 1.00 0.00 O -HETATM 299 H1 HOH A 100 -4.920 10.644 4.042 1.00 0.00 H -HETATM 300 H2 HOH A 100 -5.870 9.405 4.137 1.00 0.00 H -HETATM 301 O HOH A 101 -7.717 31.667 -2.150 1.00 0.00 O -HETATM 302 H1 HOH A 101 -6.695 31.672 -2.152 1.00 0.00 H -HETATM 303 H2 HOH A 101 -8.000 30.977 -2.819 1.00 0.00 H -HETATM 304 O HOH A 102 -26.537 15.678 -3.659 1.00 0.00 O -HETATM 305 H1 HOH A 102 -27.176 15.600 -4.364 1.00 0.00 H -HETATM 306 H2 HOH A 102 -25.727 15.279 -4.006 1.00 0.00 H -TER 307 HOH A 102 -ENDMDL -END diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index c93f169..1fb9fe0 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -33,6 +33,7 @@ import openmm from typing import Iterable, Optional, Union, Tuple from openmmml.models.utils import simple_nl +import logging class MACEPotentialImplFactory(MLPotentialImplFactory): @@ -45,7 +46,21 @@ class MACEPotentialImpl(MLPotentialImpl): """This is the MLPotentialImpl implementing the MACE potential. The potential is implemented using MACE to build a PyTorch model. A - TorchForce is used to add it to the OpenMM System. + TorchForce is used to add it to the OpenMM System. + + You must specify the path to a trained MACE model e.g. + >>> potential = MLPotential('mace', model_path='MACE.model') + + When you create the system can optionally change the dtype of the model + with the keyword argument dtype='float32' or dtype='float64'. The default behavoir is + to use the dtype of the loaded MACE model. + e.g. + >>> system = potential.createSystem(topology, dtype='float32') + + You can also specify that the full atomic energy (including the atom self energy) is returned + rather than the default of the interaction energy. + e.g. + >>> system = potential.createSystem(topology, interaction_energy=False) """ @@ -58,118 +73,100 @@ def addForces(self, system: openmm.System, atoms: Optional[Iterable[int]], forceGroup: int, - #filename: str = 'macemodel.pt', - #implementation : str = None, - device: str = None, - dtype: str = "float64", + implementation : str=None, + dtype: str=None, interaction_energy: bool=True, **args): import torch import openmmtorch - #from torch_nl import compute_neighborlist from e3nn.util import jit from mace.tools import utils, to_one_hot, atomic_numbers_to_indices - - - # Create the PyTorch model that will be invoked by OpenMM. - includedAtoms = list(topology.atoms()) if atoms is not None: - # check if atoms needs to be ordered includedAtoms = [includedAtoms[i] for i in atoms] - class MACEForce(torch.nn.Module): - def __init__(self, model_path, atomic_numbers, indices, periodic, device, dtype=torch.float64, interaction_energy=True): + def __init__(self, model_path, atomic_numbers, indices, periodic, dtype=None, interaction_energy=True): super(MACEForce, self).__init__() - if device is None: # use cuda if available - self.device=torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - else: # unless user has specified the device - self.device=torch.device(device) + if not torch.cuda.is_available(): + self.model = torch.load(model_path, map_location=torch.device('cpu')) + else: + self.model = torch.load(model_path) + + # get the dtype of the saved model + model_dtype = [p.dtype for p in self.model.parameters()][0] + + # check is user has request a different dtype + if dtype is not None and dtype != model_dtype: + logging.getLogger().warning(f'Warning: Loaded MACE model has dtype of {model_dtype} which is being changed to {dtype} as requested!') + self.default_dtype = dtype + self.model.to(self.default_dtype) + else: + self.default_dtype = model_dtype - self.default_dtype = dtype torch.set_default_dtype(self.default_dtype) - print("Running MACEForce on device: ", self.device, " with dtype: ", self.default_dtype) - - # conversion constants - self.nm_to_distance = 10.0 # nm->A - self.distance_to_nm = 0.1 # A->nm - self.energy_to_kJ = 96.49 # eV->kJ + self.register_buffer('nm_to_distance', torch.tensor(10.0)) # nm->A + self.register_buffer('distance_to_nm', torch.tensor(0.1)) # A->nm + self.register_buffer('energy_to_kJ', torch.tensor(96.49)) # eV->kJ - self.model = torch.load(model_path,map_location=device) - self.model.to(self.default_dtype) - self.model.eval() - #print(self.model) - #for name, param in self.model.state_dict().items(): - # print(name, param.size()) - - self.r_max = self.model.r_max + #self.register_buffer('r_max', self.model.r_max) self.z_table = utils.AtomicNumberTable([int(z) for z in self.model.atomic_numbers]) self.model = jit.compile(self.model) # setup input - N=len(atomic_numbers) - self.ptr = torch.tensor([0,N],dtype=torch.long, device=self.device) - self.batch = torch.zeros(N, dtype=torch.long, device=self.device) + N = len(atomic_numbers) + self.ptr = torch.nn.Parameter(torch.tensor([0,N], dtype=torch.long), requires_grad=False) + self.batch = torch.nn.Parameter(torch.zeros(N, dtype=torch.long), requires_grad=False) # one hot encoding of atomic number - self.node_attrs =to_one_hot( - torch.tensor(atomic_numbers_to_indices(atomic_numbers, z_table=self.z_table), dtype=torch.long, device=self.device).unsqueeze(-1), + self.node_attrs = torch.nn.Parameter(to_one_hot( + torch.tensor(atomic_numbers_to_indices(atomic_numbers, z_table=self.z_table), dtype=torch.long).unsqueeze(-1), num_classes=len(self.z_table), - ) + ), requires_grad=False) if periodic: - self.pbc=torch.tensor([True, True, True], device=self.device) + self.pbc = torch.nn.Parameter(torch.tensor([True, True, True]), requires_grad=False) else: - self.pbc=torch.tensor([False, False, False], device=self.device) + self.pbc = torch.nn.Parameter(torch.tensor([False, False, False]), requires_grad=False) - #self.compute_nl = compute_neighborlist - if indices is None: self.indices = None else: self.indices = torch.tensor(indices, dtype=torch.int64) - if interaction_energy is True: self.return_energy_type = "interaction_energy" else: self.return_energy_type = "energy" def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): + # setup positions - - positions = positions.to(device=self.device,dtype=self.default_dtype) + positions = positions.to(self.default_dtype) if self.indices is not None: positions = positions[self.indices] positions = positions*self.nm_to_distance if boxvectors is not None: - cell = boxvectors.to(device=self.device,dtype=self.default_dtype) * self.nm_to_distance + cell = boxvectors.to(self.default_dtype) * self.nm_to_distance pbc = True else: - cell = torch.eye(3, device=self.device) + cell = torch.eye(3, device=positions.device) pbc = False - # compute edges - # mapping, _ , shifts_idx = self.compute_nl(cutoff=self.r_max, - # pos=positions, - # cell=cell, - # pbc=self.pbc, - # batch=self.batch, - # self_interaction=False) - mapping, shifts_idx = simple_nl(positions, cell, pbc, self.r_max) + + mapping, shifts_idx = simple_nl(positions, cell, pbc, self.model.r_max) edge_index = torch.stack((mapping[0], mapping[1])) @@ -191,7 +188,7 @@ def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): energy = out[self.return_energy_type] if energy is None: - energy = torch.tensor(0.0, device=self.device) + energy = torch.tensor(0.0, dtype=self.default_dtype, device=positions.device) # return energy energy = energy*self.energy_to_kJ @@ -204,9 +201,14 @@ def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): atomic_numbers = [atom.element.atomic_number for atom in includedAtoms] - torch_dtype = {"float32":torch.float32, "float64":torch.float64}[dtype] + if dtype is None: + torch_dtype = None + elif dtype in ['float32', 'float64']: + torch_dtype = {'float32':torch.float32, 'float64':torch.float64}[dtype] + else: + raise ValueError(f'Specified dtype of {dtype} is not valid. Allowed values are None, "float32" or "float64"') - maceforce = MACEForce(self.model_path, atomic_numbers, atoms, is_periodic, device, dtype=torch_dtype, interaction_energy=interaction_energy) + maceforce = MACEForce(self.model_path, atomic_numbers, atoms, is_periodic, dtype=torch_dtype, interaction_energy=interaction_energy) # Convert it to TorchScript module = torch.jit.script(maceforce) @@ -215,7 +217,6 @@ def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): force = openmmtorch.TorchForce(module) force.setForceGroup(forceGroup) force.setUsesPeriodicBoundaryConditions(is_periodic) - #force.setOutputsForces(True) system.addForce(force) MLPotential.registerImplFactory('mace', MACEPotentialImplFactory()) diff --git a/openmmml/models/utils.py b/openmmml/models/utils.py index ebd719d..fc43a93 100644 --- a/openmmml/models/utils.py +++ b/openmmml/models/utils.py @@ -46,7 +46,7 @@ def simple_nl(positions: torch.Tensor, cell: torch.Tensor, pbc: bool, cutoff: fl """ num_atoms = positions.shape[0] - device=positions.device + device = positions.device # get i,j indices where j>i uij = torch.triu_indices(num_atoms, num_atoms, 1, device=device) From 0e22700ecb1455ecfade0dcf8547328cd59e266a Mon Sep 17 00:00:00 2001 From: Stephen Farr Date: Wed, 4 Oct 2023 16:57:21 +0100 Subject: [PATCH 07/17] cleanup file --- examples/mace/MACE_SPICE_larger.model | Bin 11466445 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/mace/MACE_SPICE_larger.model diff --git a/examples/mace/MACE_SPICE_larger.model b/examples/mace/MACE_SPICE_larger.model deleted file mode 100644 index 8bf577232c84c269b6c66497817093d986d486cc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 11466445 zcmeFacU+ZSyB-KwC@LUgK~S*+(mSY#4G;trX@aubaXVn`!m!q(uk8Mq@?Cb?Gob@akg16(|qpeYeen&E7D3xk4uo+re`MRq)D@d zPib1V-jjs5H0hI+)FegV?YsB&WEz&mGR;DnmPMpclR_Ssnvt53EZbKZc?jPnS#k2X zbg4or&z9{^NXSe}lO`xqGc&UFg5`-)xis-!YJx&fcEB=3c2F!p5}%WlB&60(5h8UW zO@&f2GBYGuh)vB_NHY}MX(ZWFg-ka=g72wmiqs5=L=Q1~GQAktA*Jkai0nwDfsi{s zF5zWfoIFvIl9~B3Tc$6TY!G=uc$bo%xDy8wEI<- zT&gbjnE2W_@~H4STM?&_N)pjK_1oj(+Y^zeg|}&$af!Q=d@bapnCz6Q+utS2%-$}} zL`-*DMKkpf$xOvaGYhft85l2#G(BFLm?#X$nZ2VKpO%^MGF$IqYKAmU9)zU#w^QB` z-ss8BVqDJ^%FHcN_Eo0r7Yjb0q8gM7DY_}3@kOQVQi$wwii)0BQVfGqR1~_Jq9TPw zimKC=DXLysrKnncEk)H(>&Sy3Mw-|JnN5P!DI>!+)5%t%icz;sPiBjj*cHm`ceTVp z%$X-mO-@nB91|3o;AUG%jv^HdDv?VCen|YvdU`UapCkb{WoODIxsG}=XA6r+Z8Rqv zg~cUHWiF~LdiOGO;=%B$SUs6*q$U#C%iJRO;WHsCN9G>*JNpzekH-NT2q*7ydK90kM=?r0iqYy(d=_blexznV z#xvs5B&tElmc>+xT$r5_2Pxl)eO?)#(aL^&W`t^klK$y^#dw zq&<-9$>IVGzI!L;(v!ubyPD;)1f?voSSBr(B`Iae#j+HoELACcQ7n5IsrR*ZkWZGD z5}Ohi;GLq*m#&m$6w5NpWm!s@tXL*j%CeO*MX@X=^03&)Y=t~WrN3mkF|xd`tx&V< zgN&UNda`^AVfX?J0=&y*1-`d_uRL_{5B}pHDrB#UWv}B@S|T$`k(!?R3UZp7k)4yC zEqhZb)}$h$BqJwXnkFj*Uy1D&ctsueHb6tAUnL2U-DD|rpX^NU?;t#6(qYv7Bq>7Ou|Lma4?+l6R8IrDDZm z6Vzb~p#ldNAwxdmcamo2&b>jmpJTHY~kQyZSLx9xhu2Sw4L^*=cMh*=xAfqq=wPwkl&Z_i(Vx|H`s2MmXLlp41G~1n*7O2B|yXg`q zYjcZehZt*f5Yf`w99l%(8=;ANTO)L7r=jp)(sLA2p(c?-QLC*w&LZ00+T0N&iV@1# zdA&RL_OLi3Qk`puM!N_NN}+_WRqed`QCTjC+$ndbG%)FIVha_j_;#l!a;YLmp0ShF zMdWSSew9Lk2bdt0OA^wuWe0+C;$;VI9|dUZP-&>{t4lC=!vBKeF@|UQy=!Wxacjpj z6;zeoj()KdhsQ1{-)^^lvCS&*xpVy+$90bv`>VcC%ag!N8nQQ}4PJ`i<&;a>w_P7l zlOL^d+fAP0&otsjW!BmcspN${&C+{n*rpf;vpIsLbUgzdyaNXd=dOjD`Z0bECm zrl^0#iyNQ5)Q#c|nsuZ_7b-J$qaprC4KziKv`VYDX&B9@=2hN!Tfl<|_0k74QprnE zR_C6hy923u)N_)ycp*3CAeX**WXfx3ZQV5*c|xBPc;jgnb>QPI3DrJ! zrV2;&Xp+0!=tCw~QFHncQ2Mh6H-ww{>fbiGO`o_s;c1Mo8BfZ%-zbzPU8P1zG-j8^ z{YOv4zuCw2di0S;xXbY=El1hK9N={n-IaZoXYPli&n-#mfl=H>{W;t&4Tx}}8C}!x z*n18yjcC(=o6kue+t2__Q>{CdW*8qm%xgN&xYQoBUdW}sa~SV6-r#`(o}+FwZT1a+ z^57XzU@|88R0Hu2~@Sdmi=T&xSk{iAFT)MBU;=nk(S( zt69`WL)0FFUawhto#SHAqMeqg;RbE&6J@7SgVyhH85~dNUz8pXjpM(m^w=iCSx%OS z6TqY2%TZ2fOs465V_tw52+TUiE4-P;jjmj!WfS#;D`?>iS3ICjSE>*71`*zaVh^SF zsns--2h+ho4Ya^*mw1FmX#I$t4%O>~9lc@BGj37mFFm3O?*p1f{N={^^B`ea0Iz_i z9o$2o`3tQEQgaF~L3lc{QT1lE#~m$QtfIHS6Gw4&fWM zJ?Glwh;WQ@A1&SS>Pc(%uNq?)A}@bMbSb^IL}i_ZaCMR<`y5CI?F9d z)apWWFQ_lfhLrj|>q4F3ZakSp8}#uw55`MFQCA6Vx^kDTLP9INsB`AX3)KA{{r}7h zZ^C)}`Wx^_C)&9h#=3)J%_-LprG3U>AXgZn~f6X>ICF z;35yB)4atU4xoEUI=AuM1E{-oT0EC``Vmdtrd1>#3`F7MS==b0_LtPm?``hVRw#Ws z?T~)O1d>t~t;n|I@ylNHky{O+MNN)V^?ejpMl;u;8KH(0*0f4xhiI9q<0OyWsM|Tn zmcPWD1gA7+-=QV`a?Jz3U2nrfj!N>k0a z}ZTtFi9=(4>BdJuA4guNVA?|fUE~-qmq|HL=;u5{&?7$$d z=LK3k^Dvt~9J`fq;>}5F%ew#O*)y(rz^l)YY{U$@w~rSC(b+1&RvG4S&pRz{OG5nG zL6ls>MR{PnR33YEGnv+(TJRVzJ)|{irmduB68eyA&F^UuhN}KN%|pL_=Cv?x0$IoM z@M7wv^e!xDqcaLp-k>ch*cnXon5M4sLLel1+Rc3b`J|gXa)X!cc*WmJ_uwU}zfHyV zW>l;XK`7HvP_Z7w1m<+Z(1<>ILB88_sPUm+VpKc|>*00q_XMxTX&j8xq4K!>K5>Up ze~J4;E^=_!zZz>qqtAH3naZdmM=yhmt{+bHxgh5WX77 z7?;`rKf5GeJwVGxcm`?@|UC7I#X*+wc&&VT}%aGu@Fz$InwbagQo;;o(!A1M1>J5L0Gr0H; 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z*MFYgEZ)m7>r-Ku^wxi#yDVmiQv3hK{?BQTk{l!BZcL?LFCMx*|NaL5E&D&;{r>95 p`R^Z;; Date: Wed, 4 Oct 2023 16:59:00 +0100 Subject: [PATCH 08/17] update notebook --- examples/mace/run_mace.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/mace/run_mace.ipynb b/examples/mace/run_mace.ipynb index 4c56b4b..dcc3d45 100644 --- a/examples/mace/run_mace.ipynb +++ b/examples/mace/run_mace.ipynb @@ -5,9 +5,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Example: using OpenMM to run a simulation with a NequIP ML potential\n", + "# Example: using OpenMM to run a simulation with a MACE ML potential\n", "\n", - "You can run this example directly in your browser: [![Open On Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sef43/openmm-ml/blob/mace/examples/nequip/run_mace.ipynb)" + "You can run this example directly in your browser: [![Open On Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sef43/openmm-ml/blob/mace/examples/mace/run_mace.ipynb)" ] }, { From d5ff44ec9ac16c00422f72b9c7d8300ab1f1b300 Mon Sep 17 00:00:00 2001 From: Stephen Farr Date: Thu, 5 Oct 2023 16:27:01 +0100 Subject: [PATCH 09/17] fix colab install --- examples/mace/run_mace.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/mace/run_mace.ipynb b/examples/mace/run_mace.ipynb index dcc3d45..8772d4b 100644 --- a/examples/mace/run_mace.ipynb +++ b/examples/mace/run_mace.ipynb @@ -53,7 +53,7 @@ "source": [ "#https://github.com/openmm/openmm-torch/issues/88\n", "%env CONDA_OVERRIDE_CUDA=12.0\n", - "!mamba install -c conda-forge openmm-torch pytorch=*=cuda* openmmtools\n", + "!mamba install -c conda-forge openmm-torch pytorch=*=cuda* openmmtools pymbar=3 #https://github.com/choderalab/openmmtools/issues/718\n", "\n", "!pip install git+https://github.com/ACEsuit/mace\n", "!pip install git+https://github.com/sef43/openmm-ml@mace" @@ -73,7 +73,7 @@ "metadata": {}, "outputs": [], "source": [ - "!wget https://raw.githubusercontent.com/sef43/openmm-ml/mace/examples/mace/mace/openmm-ml/examples/mace/ani500k_small_CC.model" + "!wget https://raw.githubusercontent.com/sef43/openmm-ml/mace/examples/mace/ani500k_small_CC.model" ] }, { From 1b844d5bdf048037514b3bdde0e722621cf25231 Mon Sep 17 00:00:00 2001 From: Joao Morado Date: Wed, 28 Feb 2024 12:27:19 +0000 Subject: [PATCH 10/17] Overall refactor of macepotential.py --- openmmml/models/macepotential.py | 469 +++++++++++++++++++++---------- 1 file changed, 323 insertions(+), 146 deletions(-) diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index 1fb9fe0..ccf35df 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -8,7 +8,7 @@ Portions copyright (c) 2021 Stanford University and the Authors. Authors: Peter Eastman -Contributors: Stephen Farr +Contributors: Stephen Farr, Joao Morado Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), @@ -28,195 +28,372 @@ OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ - -from openmmml.mlpotential import MLPotential, MLPotentialImpl, MLPotentialImplFactory import openmm -from typing import Iterable, Optional, Union, Tuple -from openmmml.models.utils import simple_nl -import logging +from openmmml.mlpotential import MLPotential, MLPotentialImpl, MLPotentialImplFactory +from typing import Iterable, Optional, Tuple class MACEPotentialImplFactory(MLPotentialImplFactory): """This is the factory that creates MACEPotentialImpl objects.""" - def createImpl(self, name: str, model_path: str, **args) -> MLPotentialImpl: - return MACEPotentialImpl(name, model_path) + def createImpl(self, name: str, modelPath: Optional[str] = None, **args) -> MLPotentialImpl: + return MACEPotentialImpl(name, modelPath) + class MACEPotentialImpl(MLPotentialImpl): """This is the MLPotentialImpl implementing the MACE potential. - The potential is implemented using MACE to build a PyTorch model. A - TorchForce is used to add it to the OpenMM System. + The MACE potential is constructed using MACE to build a PyTorch model, + and then integrated into the OpenMM System using a TorchForce. + This implementation supports both MACE-OFF23 and locally trained MACE models. + + To use one of the pre-trained MACE-OFF23 models, specify the model name. For example: + >>> potential = MLPotential('mace-off23-small') + + Other available MACE-OFF23 models include `mace-off23-medium` and `mace-off23-large`. - You must specify the path to a trained MACE model e.g. - >>> potential = MLPotential('mace', model_path='MACE.model') + To use a locally trained MACE model, provide the path to the model file. For example: + >>> potential = MLPotential('mace', modelPath='MACE.model') - When you create the system can optionally change the dtype of the model - with the keyword argument dtype='float32' or dtype='float64'. The default behavoir is - to use the dtype of the loaded MACE model. - e.g. - >>> system = potential.createSystem(topology, dtype='float32') + During system creation, you can optionally specify the precision of the model using the + `precision` keyword argument. Supported options are 'single' and 'double'. For example: + >>> system = potential.createSystem(topology, precision='single') - You can also specify that the full atomic energy (including the atom self energy) is returned - rather than the default of the interaction energy. - e.g. - >>> system = potential.createSystem(topology, interaction_energy=False) - + By default, the implementation uses the precision of the loaded MACE model. + According to the MACE documentation, 'single' precision is recommended for MD (faster but + less accurate), while 'double' precision is recommended for geometry optimization. + + Additionally, you can request computation of the full atomic energy, including the atom + self-energy, instead of the default interaction energy, by setting `returnEnergyType` to + `energy`. For example: + >>> system = potential.createSystem(topology, returnEnergyType=`energy`) + + The default is to compute the interaction energy, which can be made explicit by setting + `returnEnergyType=interaction_energy`. + + Attributes + ---------- + name : str + The name of the MACE model. + modelPath : str + The path to the locally trained MACE model if `name` is `mace`. """ - def __init__(self, name, model_path): + def __init__(self, name: str, modelPath) -> None: + """ + Initialize the MACEPotentialImpl. + + Parameters + ---------- + name : str + The name of the MACE model. + Options include `mace-off23-small`, `mace-off23-medium`, `mace-off23-large`, and `mace`. + modelPath : str, optional + The path to the locally trained MACE model if `name` is `mace`. + """ self.name = name - self.model_path = model_path - - def addForces(self, - topology: openmm.app.Topology, - system: openmm.System, - atoms: Optional[Iterable[int]], - forceGroup: int, - implementation : str=None, - dtype: str=None, - interaction_energy: bool=True, - **args): - - + self.modelPath = modelPath + + def addForces( + self, + topology: openmm.app.Topology, + system: openmm.System, + atoms: Optional[Iterable[int]], + forceGroup: int, + precision: Optional[str] = None, + returnEnergyType: str = "energy", + filename: str = "macemodel.pt", + **args, + ) -> None: + """ + Add the MACEForce to the OpenMM System. + + Parameters + ---------- + topology : openmm.app.Topology + The topology of the system. + system : openmm.System + The system to which the force will be added. + atoms : iterable of int + The indices of the atoms to include in the model. If None, all atoms are included. + forceGroup : int + The force group to which the force should be assigned. + precision : str, optional + The precision of the model. Supported options are 'single' and 'double'. + If None, the default precision of the model is used. + returnEnergyType : str, optional + Whether to return the interaction energy or the energy including the self-energy. + Default is `interaction_energy`. Supported options are `interaction_energy` and `energy`. + filename : str, optional + The filename to which the TorchForce will be saved. Default is 'macemodel.pt'. + """ import torch import openmmtorch - from e3nn.util import jit - from mace.tools import utils, to_one_hot, atomic_numbers_to_indices + + try: + from mace.tools import utils, to_one_hot, atomic_numbers_to_indices + from mace.calculators.foundations_models import mace_off + except ImportError as e: + raise ImportError( + f"Failed to import `mace` with error: {e}. Install `mace` with `pip install torch-mace`." + ) + try: + from e3nn.util import jit + except ImportError as e: + raise ImportError( + f"Failed to import `e3nn` with error: {e}. Install `e3nn` with `pip install e3nn`." + ) + try: + from NNPOps.neighbors import getNeighborPairs + except ImportError as e: + raise ImportError( + f"Failed to import `NNPOps` with error: {e}." + "Install `nnpops` with `conda install -c conda-forge nnpops`." + ) + assert returnEnergyType in [ + "interaction_energy", + "energy", + ], "Supported options for `returnEnergyType` are `interaction_energy` and `energy`." + + # Load the model and compile it to TorchScript. + if self.name.startswith("mace-off23"): + size = self.name.split("-")[-1] + assert ( + size in ["small", "medium", "large"] + ), "Available MACE-OFF23 models are: `mace-off23-small`, `mace-off23-medium`, `mace-off23-large`" + model = mace_off(model=size, return_raw_model=True) + elif self.name == "mace": + if self.modelPath is not None: + model = torch.load(self.modelPath) + else: + raise ValueError("No `modelPath` provided for local MACE model.") + else: + raise ValueError(f"Unsupported MACE model: {self.name}") + model = jit.compile(model) + + # Get the atomic numbers of the ML region. includedAtoms = list(topology.atoms()) if atoms is not None: includedAtoms = [includedAtoms[i] for i in atoms] - - class MACEForce(torch.nn.Module): + atomicNumbers = [atom.element.atomic_number for atom in includedAtoms] + + # Set the precision that the model will be used with. + modelDefaultDtype = next(model.parameters()).dtype + if precision is None: + dtype = modelDefaultDtype + elif precision == "single": + dtype = torch.float32 + elif precision == "double": + dtype = torch.float64 + else: + raise ValueError( + f"Unsupported precision `{precision}` for the model. " + "Supported values are `single` and `double`." + ) + if dtype != modelDefaultDtype: + print( + f"Model dtype is {modelDefaultDtype} " + f"and requested dtype is {dtype}. " + "The model will be converted to the requested dtype." + ) + + # One hot encoding of atomic numbers + zTable = utils.AtomicNumberTable([int(z) for z in model.atomic_numbers]) + nodeAttrs = to_one_hot( + torch.tensor( + atomic_numbers_to_indices(atomicNumbers, z_table=zTable), + dtype=torch.long, + ).unsqueeze(-1), + num_classes=len(zTable), + ) - def __init__(self, model_path, atomic_numbers, indices, periodic, dtype=None, interaction_energy=True): + class MACEForce(torch.nn.Module): + """ + MACEForce class to be used with TorchForce. + + Parameters + ---------- + model : torch.jit._script.RecursiveScriptModule + The compiled MACE model. + dtype : torch.dtype + The precision with which the model will be used. + energyScale : float + Conversion factor for the energy, viz. eV to kJ/mol. + lengthScale : float + Conversion factor for the length, viz. nm to Angstrom. + indices : torch.Tensor + The indices of the atoms to calculate the energy for. + returnEnergyType : str + Whether to return the interaction energy or the energy including the self-energy. + inputDict : dict + The input dictionary passed to the model. + """ + + def __init__( + self, + model: torch.jit._script.RecursiveScriptModule, + nodeAttrs: torch.Tensor, + atoms: Optional[Iterable[int]], + periodic: bool, + dtype: torch.dtype, + returnEnergyType: str, + ) -> None: + """ + Initialize the MACEForce. + + Parameters + ---------- + model : torch.jit._script.RecursiveScriptModule + The MACE model. + nodeAttrs : torch.Tensor + The one-hot encoded atomic numbers. + atoms : iterable of int + The indices of the atoms. If None, all atoms are included. + periodic : bool + Whether the system is periodic. + dtype : torch.dtype + The precision of the model. + returnEnergyType : str + Whether to return the interaction energy or the energy including the self-energy. + """ super(MACEForce, self).__init__() + self.dtype = dtype + self.model = model.to(self.dtype) + self.energyScale = 96.4853 + self.lengthScale = 10.0 + self.returnEnergyType = returnEnergyType - if not torch.cuda.is_available(): - self.model = torch.load(model_path, map_location=torch.device('cpu')) - else: - self.model = torch.load(model_path) - - # get the dtype of the saved model - model_dtype = [p.dtype for p in self.model.parameters()][0] - - # check is user has request a different dtype - if dtype is not None and dtype != model_dtype: - logging.getLogger().warning(f'Warning: Loaded MACE model has dtype of {model_dtype} which is being changed to {dtype} as requested!') - self.default_dtype = dtype - self.model.to(self.default_dtype) - else: - self.default_dtype = model_dtype - - torch.set_default_dtype(self.default_dtype) - - # conversion constants - self.register_buffer('nm_to_distance', torch.tensor(10.0)) # nm->A - self.register_buffer('distance_to_nm', torch.tensor(0.1)) # A->nm - self.register_buffer('energy_to_kJ', torch.tensor(96.49)) # eV->kJ - - - #self.register_buffer('r_max', self.model.r_max) - self.z_table = utils.AtomicNumberTable([int(z) for z in self.model.atomic_numbers]) - - self.model = jit.compile(self.model) - - # setup input - N = len(atomic_numbers) - self.ptr = torch.nn.Parameter(torch.tensor([0,N], dtype=torch.long), requires_grad=False) - self.batch = torch.nn.Parameter(torch.zeros(N, dtype=torch.long), requires_grad=False) - - # one hot encoding of atomic number - self.node_attrs = torch.nn.Parameter(to_one_hot( - torch.tensor(atomic_numbers_to_indices(atomic_numbers, z_table=self.z_table), dtype=torch.long).unsqueeze(-1), - num_classes=len(self.z_table), - ), requires_grad=False) - - if periodic: - self.pbc = torch.nn.Parameter(torch.tensor([True, True, True]), requires_grad=False) - else: - self.pbc = torch.nn.Parameter(torch.tensor([False, False, False]), requires_grad=False) - - - if indices is None: + if atoms is None: self.indices = None else: - self.indices = torch.tensor(indices, dtype=torch.int64) - - if interaction_energy is True: - self.return_energy_type = "interaction_energy" + self.indices = torch.tensor(sorted(atoms), dtype=torch.int64) + + # Create the default input dict. + self.inputDict = { + "ptr": torch.tensor([0, nodeAttrs.shape[0]], dtype=torch.long, requires_grad=False), + "node_attrs": nodeAttrs.to(self.dtype), + "batch": torch.zeros(nodeAttrs.shape[0], dtype=torch.long, requires_grad=False), + "pbc": torch.tensor([periodic, periodic, periodic], dtype=torch.bool, requires_grad=False), + } + + def _getNeighborPairs( + self, positions: torch.Tensor, cell: Optional[torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Get the shifts and edge indices. + + This method calculates the shifts and edge indices by determining neighbor pairs (`neighbors`) + and respective wrapped distances (`wrappedDeltas`) using `NNPOps.neighbors.getNeighborPairs`. + After obtaining the `neighbors` and `wrappedDeltas`, the pairs with negative indices (r>cutoff) + are filtered out, and the edge indices and shifts are finally calculated. + + Parameters + ---------- + positions : torch.Tensor + The positions of the atoms. + cell : torch.Tensor + The cell vectors. + + Returns + ------- + edgeIndex : torch.Tensor + The edge indices. + shifts : torch.Tensor + The shifts. + """ + # Get the neighbor pairs, shifts and edge indeces. + neighbors, wrappedDeltas, _, _ = getNeighborPairs( + positions, self.model.r_max, -1, cell + ) + mask = neighbors >= 0 + neighbors = neighbors[mask].view(2, -1) + wrappedDeltas = wrappedDeltas[mask[0], :] + + edgeIndex = torch.hstack((neighbors, neighbors.flip(0))).to(torch.int64) + if cell is not None: + deltas = positions[edgeIndex[0]] - positions[edgeIndex[1]] + wrappedDeltas = torch.vstack((wrappedDeltas, -wrappedDeltas)) + shiftsIdx = torch.mm(deltas - wrappedDeltas, torch.linalg.inv(cell)) + shifts = torch.mm(shiftsIdx, cell) else: - self.return_energy_type = "energy" - - def forward(self, positions, boxvectors: Optional[torch.Tensor] = None): - - # setup positions - positions = positions.to(self.default_dtype) + shifts = torch.zeros((edgeIndex.shape[1], 3), dtype=self.dtype) + + return edgeIndex, shifts + + def forward( + self, positions: torch.Tensor, boxvectors: Optional[torch.Tensor] = None + ) -> torch.Tensor: + """ + Forward pass of the model. + + Parameters + ---------- + positions : torch.Tensor + The positions of the atoms. + box_vectors : torch.Tensor + The box vectors. + + Returns + ------- + energy : torch.Tensor + The predicted energy in kJ/mol. + """ + # Setup positions and cell. if self.indices is not None: positions = positions[self.indices] - positions = positions*self.nm_to_distance + positions = positions.to(self.dtype) * self.lengthScale if boxvectors is not None: - cell = boxvectors.to(self.default_dtype) * self.nm_to_distance - pbc = True + cell = boxvectors.to(self.dtype) * self.lengthScale else: - cell = torch.eye(3, device=positions.device) - pbc = False - - mapping, shifts_idx = simple_nl(positions, cell, pbc, self.model.r_max) - - edge_index = torch.stack((mapping[0], mapping[1])) - - shifts = torch.mm(shifts_idx, cell) - - # create input dict - input_dict = { "ptr" : self.ptr, - "node_attrs": self.node_attrs, - "batch": self.batch, - "pbc": self.pbc, - "cell": cell, - "positions": positions, - "edge_index": edge_index, - "unit_shifts": shifts_idx, - "shifts": shifts} - - # predict - out = self.model(input_dict,compute_force=False) + cell = None - energy = out[self.return_energy_type] - if energy is None: - energy = torch.tensor(0.0, dtype=self.default_dtype, device=positions.device) - - # return energy - energy = energy*self.energy_to_kJ + # Get the shifts and edge indices. + edgeIndex, shifts = self._getNeighborPairs(positions, cell) - return energy - + # Update input dictionary. + self.inputDict["positions"] = positions + self.inputDict["edge_index"] = edgeIndex + self.inputDict["shifts"] = shifts - is_periodic = (topology.getPeriodicBoxVectors() is not None) or system.usesPeriodicBoundaryConditions() + # Predict the energy. + energy = self.model(self.inputDict, compute_force=False)[ + self.returnEnergyType + ] + assert ( + energy is not None + ), "The model did not return any energy. Please check the input." + + return energy * self.energyScale - atomic_numbers = [atom.element.atomic_number for atom in includedAtoms] + isPeriodic = (topology.getPeriodicBoxVectors() is not None + ) or system.usesPeriodicBoundaryConditions() - if dtype is None: - torch_dtype = None - elif dtype in ['float32', 'float64']: - torch_dtype = {'float32':torch.float32, 'float64':torch.float64}[dtype] - else: - raise ValueError(f'Specified dtype of {dtype} is not valid. Allowed values are None, "float32" or "float64"') + maceForce = MACEForce( + model, + nodeAttrs, + atoms, + isPeriodic, + dtype, + returnEnergyType, + ) - maceforce = MACEForce(self.model_path, atomic_numbers, atoms, is_periodic, dtype=torch_dtype, interaction_energy=interaction_energy) - - # Convert it to TorchScript - module = torch.jit.script(maceforce) + # Convert it to TorchScript and save it. + module = torch.jit.script(maceForce) + module.save(filename) # Create the TorchForce and add it to the System. - force = openmmtorch.TorchForce(module) + force = openmmtorch.TorchForce(filename) force.setForceGroup(forceGroup) - force.setUsesPeriodicBoundaryConditions(is_periodic) + force.setUsesPeriodicBoundaryConditions(isPeriodic) system.addForce(force) -MLPotential.registerImplFactory('mace', MACEPotentialImplFactory()) + +MLPotential.registerImplFactory("mace", MACEPotentialImplFactory()) +MLPotential.registerImplFactory("mace-off23-small", MACEPotentialImplFactory()) +MLPotential.registerImplFactory("mace-off23-medium", MACEPotentialImplFactory()) +MLPotential.registerImplFactory("mace-off23-large", MACEPotentialImplFactory()) From ecabaf51fcf979c59ef8b42c771c1cd8b8c32130 Mon Sep 17 00:00:00 2001 From: Joao Morado Date: Wed, 28 Feb 2024 12:28:15 +0000 Subject: [PATCH 11/17] Removed deprecated utils.py module --- openmmml/models/utils.py | 88 ---------------------------------------- 1 file changed, 88 deletions(-) delete mode 100644 openmmml/models/utils.py diff --git a/openmmml/models/utils.py b/openmmml/models/utils.py deleted file mode 100644 index fc43a93..0000000 --- a/openmmml/models/utils.py +++ /dev/null @@ -1,88 +0,0 @@ -import torch -from typing import Tuple - -@torch.jit.script -def simple_nl(positions: torch.Tensor, cell: torch.Tensor, pbc: bool, cutoff: float, sorti: bool=False) -> Tuple[torch.Tensor, torch.Tensor]: - """simple torchscriptable neighborlist. - - It aims are to be correct, clear, and torchscript compatible. - It is O(n^2) but with pytorch vectorisation the prefactor is small. - It outputs neighbors and shifts in the same format as ASE - https://wiki.fysik.dtu.dk/ase/ase/neighborlist.html#ase.neighborlist.primitive_neighbor_list - - neighbors, shifts = simple_nl(..) - is equivalent to - - [i, j], S = primitive_neighbor_list( quantities="ijS", ...) - - Limitations: - - either no PBCs or PBCs in all x,y,z - - cutoff must be less than half the smallest box length - - cell must be rectangular or triclinic in OpenMM format: - http://docs.openmm.org/development/userguide/theory/05_other_features.html#periodic-boundary-conditions - - Parameters - ---------- - positions: torch.Tensor - Coordinates, shape [N,3] - cell: torch.Tensor - Triclinic unit cell, shape [3,3], must be in OpenMM format: http://docs.openmm.org/development/userguide/theory/05_other_features.html#periodic-boundary-conditions - pbc: bool - should PBCs be applied - cutoff: float - Distances beyond cutoff are not included in the nieghborlist - soti: bool=False - if true the returned nieghborlist will be sorted in the i index. The default is False (no sorting). - - Returns - ------- - neighbors: torch.Tensor - neighbor list, shape [2, number of neighbors] - shifts: torch.Tensor - shift vector, shape [number of neighbors, 3], From ASE docs: - shift vector (number of cell boundaries crossed by the bond between atom i and j). - With the shift vector S, the distances D between atoms can be computed from: - D = positions[j]-positions[i]+S.dot(cell) - """ - - num_atoms = positions.shape[0] - device = positions.device - - # get i,j indices where j>i - uij = torch.triu_indices(num_atoms, num_atoms, 1, device=device) - triu_deltas = positions[uij[0]] - positions[uij[1]] - - wrapped_triu_deltas=triu_deltas.clone() - - if pbc: - # using method from: https://github.com/openmm/NNPOps/blob/master/src/pytorch/neighbors/getNeighborPairsCPU.cpp - wrapped_triu_deltas -= torch.outer(torch.round(wrapped_triu_deltas[:,2]/cell[2,2]), cell[2]) - wrapped_triu_deltas -= torch.outer(torch.round(wrapped_triu_deltas[:,1]/cell[1,1]), cell[1]) - wrapped_triu_deltas -= torch.outer(torch.round(wrapped_triu_deltas[:,0]/cell[0,0]), cell[0]) - - # From ASE docs: - # wrapped_delta = pos[i] - pos[j] - shift.cell - # => shift = ((pos[i]-pos[j]) - wrapped_delta).cell^-1 - shifts = torch.mm(triu_deltas - wrapped_triu_deltas, torch.linalg.inv(cell)) - - else: - shifts = torch.zeros(triu_deltas.shape, device=device) - - triu_distances = torch.linalg.norm(wrapped_triu_deltas, dim=1) - - # filter - mask = triu_distances > cutoff - uij = uij[:,~mask] - shifts = shifts[~mask, :] - - # get the ij pairs where j Date: Wed, 28 Feb 2024 12:30:06 +0000 Subject: [PATCH 12/17] Fixed formatting --- openmmml/models/macepotential.py | 50 ++++++++++++++++++++------------ 1 file changed, 32 insertions(+), 18 deletions(-) diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index ccf35df..41c6cfc 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -36,7 +36,9 @@ class MACEPotentialImplFactory(MLPotentialImplFactory): """This is the factory that creates MACEPotentialImpl objects.""" - def createImpl(self, name: str, modelPath: Optional[str] = None, **args) -> MLPotentialImpl: + def createImpl( + self, name: str, modelPath: Optional[str] = None, **args + ) -> MLPotentialImpl: return MACEPotentialImpl(name, modelPath) @@ -150,11 +152,14 @@ def addForces( f"Failed to import `NNPOps` with error: {e}." "Install `nnpops` with `conda install -c conda-forge nnpops`." ) - - assert returnEnergyType in [ - "interaction_energy", - "energy", - ], "Supported options for `returnEnergyType` are `interaction_energy` and `energy`." + + assert ( + returnEnergyType + in [ + "interaction_energy", + "energy", + ] + ), "Supported options for `returnEnergyType` are `interaction_energy` and `energy`." # Load the model and compile it to TorchScript. if self.name.startswith("mace-off23"): @@ -201,12 +206,12 @@ def addForces( # One hot encoding of atomic numbers zTable = utils.AtomicNumberTable([int(z) for z in model.atomic_numbers]) nodeAttrs = to_one_hot( - torch.tensor( - atomic_numbers_to_indices(atomicNumbers, z_table=zTable), - dtype=torch.long, - ).unsqueeze(-1), - num_classes=len(zTable), - ) + torch.tensor( + atomic_numbers_to_indices(atomicNumbers, z_table=zTable), + dtype=torch.long, + ).unsqueeze(-1), + num_classes=len(zTable), + ) class MACEForce(torch.nn.Module): """ @@ -261,7 +266,7 @@ def __init__( self.dtype = dtype self.model = model.to(self.dtype) - self.energyScale = 96.4853 + self.energyScale = 96.4853 self.lengthScale = 10.0 self.returnEnergyType = returnEnergyType @@ -272,10 +277,18 @@ def __init__( # Create the default input dict. self.inputDict = { - "ptr": torch.tensor([0, nodeAttrs.shape[0]], dtype=torch.long, requires_grad=False), + "ptr": torch.tensor( + [0, nodeAttrs.shape[0]], dtype=torch.long, requires_grad=False + ), "node_attrs": nodeAttrs.to(self.dtype), - "batch": torch.zeros(nodeAttrs.shape[0], dtype=torch.long, requires_grad=False), - "pbc": torch.tensor([periodic, periodic, periodic], dtype=torch.bool, requires_grad=False), + "batch": torch.zeros( + nodeAttrs.shape[0], dtype=torch.long, requires_grad=False + ), + "pbc": torch.tensor( + [periodic, periodic, periodic], + dtype=torch.bool, + requires_grad=False, + ), } def _getNeighborPairs( @@ -367,10 +380,11 @@ def forward( assert ( energy is not None ), "The model did not return any energy. Please check the input." - + return energy * self.energyScale - isPeriodic = (topology.getPeriodicBoxVectors() is not None + isPeriodic = ( + topology.getPeriodicBoxVectors() is not None ) or system.usesPeriodicBoundaryConditions() maceForce = MACEForce( From 8870825eba08d44afcb2057a4c5fe356b2d919e4 Mon Sep 17 00:00:00 2001 From: Joao Morado Date: Wed, 28 Feb 2024 12:37:45 +0000 Subject: [PATCH 13/17] Removed examples as new ones will be created for the new implementation --- examples/mace/README.md | 36 ------- examples/mace/ani500k_small_CC.model | Bin 3348160 -> 0 bytes examples/mace/run_mace.ipynb | 155 --------------------------- examples/mace/run_mace.py | 32 ------ 4 files changed, 223 deletions(-) delete mode 100644 examples/mace/README.md delete mode 100644 examples/mace/ani500k_small_CC.model delete mode 100644 examples/mace/run_mace.ipynb delete mode 100644 examples/mace/run_mace.py diff --git a/examples/mace/README.md b/examples/mace/README.md deleted file mode 100644 index a9a2f4e..0000000 --- a/examples/mace/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# MACE models in OpenMM - -This directory contains examples for running simulations using a [MACE](https://github.com/ACEsuit/mace) potential. - -## Install - -First you will need `openmm` and `openmm-torch` from conda-forge -``` -mamba install -c conda-forge openmm openmm-torch -``` - -Then you will need to install MACE and this fork of openmm-ml with pip: - -``` -pip install git+https://github.com/ACEsuit/mace -pip install git+https://github.com/sef43/openmm-ml@mace -``` - -## Usage -```python -from openmmml import MLPotential - -potential = MLPotential('mace', model_path='path/to/model') - -system = potential.createSystem(topology) -``` - - -## Example -There is an example which runs a simulation of an alanine dipeptide molecule using a MACE model trained on the ANI-1x dataset from https://doi.org/10.1063/5.0155322. It is avaible as a python script [`run_mace.py`](run_mace.py) and a Jupyter notebook [`run_mace.ipynb`](run_mace.ipynb) which can be run on Colab. - - -## References -The example pre-trained model is from https://doi.org/10.1063/5.0155322 avaiable to download at https://github.com/ACEsuit/mace/blob/docs/docs/examples/ANI_trained_MACE.zip. - -The MACE repo is here: https://github.com/ACEsuit/mace diff --git a/examples/mace/ani500k_small_CC.model b/examples/mace/ani500k_small_CC.model deleted file mode 100644 index 41e735ecb1b8efe0bdd9ef2b0a3593e9e0b6e28a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3348160 zcmeFabzD{7yC+VV7=(d{C zJKcBYPTcA4`n~r)2Rs;`@ArG}ef{Q-89pEP*?aAk&w6UD^$|_> zS_ie_GE!aa?VroC)8o?8>=NUYakg2{(|o=6X+^33igXgv;}Yby>6wW+Y4U9GSDu!w z7nTs0CJ#$VO;QHky6vN<(6TC4Xcs7SEF;B|ltNr;MruZ~LbodN0G?%8aY9_WTqzf_ z6}u7=GSkxJ3Ch&WjBLI8LZV!dC*Da-Q0ggmTZJn2NEyiDbCQz8*n3mNzjksIm%R2NhZkSb5he1WlDKQcBUZ9b<|VnSz1Q!Mapc% 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Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sef43/openmm-ml/blob/mace/examples/mace/run_mace.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Install Conda\n", - "\n", - "[Conda](https://docs.conda.io/) is a package and environment manager. On Google Colab, Conda is installed with [conda-colab](https://github.com/jaimergp/condacolab). On your computer, you should follow these [installation instructions](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html).\n", - "\n", - "⚠️ Do not use conda-colab on your computer!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install -q condacolab\n", - "import condacolab\n", - "condacolab.install_mambaforge() # use mamba on colab because it is faster than conda" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Install software\n", - "\n", - "First install everything we can from [conda-forge](https://conda-forge.org/).\n", - "Then use pip.\n", - "\n", - "⚠️ The installation might take up to 10 min!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#https://github.com/openmm/openmm-torch/issues/88\n", - "%env CONDA_OVERRIDE_CUDA=12.0\n", - "!mamba install -c conda-forge openmm-torch pytorch=*=cuda* openmmtools pymbar=3 #https://github.com/choderalab/openmmtools/issues/718\n", - "\n", - "!pip install git+https://github.com/ACEsuit/mace\n", - "!pip install git+https://github.com/sef43/openmm-ml@mace" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get the files we need to run the example" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!wget https://raw.githubusercontent.com/sef43/openmm-ml/mace/examples/mace/ani500k_small_CC.model" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import openmm\n", - "import openmm.app as app\n", - "import openmm.unit as unit\n", - "from openmmml import MLPotential\n", - "from sys import stdout\n", - "from openmmtools.testsystems import AlanineDipeptideVacuum\n", - "\n", - "# Get the system of alanine dipeptide\n", - "ala2 = AlanineDipeptideVacuum(constraints=None)\n", - "\n", - "# Remove MM forces\n", - "while ala2.system.getNumForces() > 0:\n", - " ala2.system.removeForce(0)\n", - "\n", - "# The system should not contain any additional force and constrains\n", - "assert ala2.system.getNumConstraints() == 0\n", - "assert ala2.system.getNumForces() == 0\n", - "\n", - "potential = MLPotential('mace', model_path='ani500k_small_CC.model')\n", - "\n", - "\n", - "system = potential.createSystem(ala2.topology)\n", - "\n", - "\n", - "integrator = openmm.LangevinIntegrator(300*unit.kelvin, 10.0/unit.picoseconds, 1.0*unit.femtosecond)\n", - "simulation=app.Simulation(ala2.topology, system, integrator)\n", - "simulation.context.setPositions(ala2.positions)\n", - "simulation.reporters.append(app.PDBReporter('output.pdb', 100))\n", - "simulation.reporters.append(app.StateDataReporter(stdout, 100, step=True,\n", - " potentialEnergy=True, temperature=True))\n", - "\n", - "simulation.step(1000)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "neqmm", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "413bac2e6b7bbc56e28392734551129ace8c921fbbb9e9273c3258497d1da304" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/mace/run_mace.py b/examples/mace/run_mace.py deleted file mode 100644 index 4c60bce..0000000 --- a/examples/mace/run_mace.py +++ /dev/null @@ -1,32 +0,0 @@ -import openmm -import openmm.app as app -import openmm.unit as unit -from openmmml import MLPotential -from sys import stdout -from openmmtools.testsystems import AlanineDipeptideVacuum - -# Get the system of alanine dipeptide -ala2 = AlanineDipeptideVacuum(constraints=None) - -# Remove MM forces -while ala2.system.getNumForces() > 0: - ala2.system.removeForce(0) - -# The system should not contain any additional force and constrains -assert ala2.system.getNumConstraints() == 0 -assert ala2.system.getNumForces() == 0 - -potential = MLPotential('mace', model_path='ani500k_small_CC.model') - - -system = potential.createSystem(ala2.topology) - - -integrator = openmm.LangevinIntegrator(300*unit.kelvin, 10.0/unit.picoseconds, 1.0*unit.femtosecond) -simulation=app.Simulation(ala2.topology, system, integrator) -simulation.context.setPositions(ala2.positions) -simulation.reporters.append(app.PDBReporter('output.pdb', 100)) -simulation.reporters.append(app.StateDataReporter(stdout, 100, step=True, - potentialEnergy=True, temperature=True)) - -simulation.step(1000) From 5041b175540a057ee7c076108d0bbb42cdc9d7d5 Mon Sep 17 00:00:00 2001 From: Joao Morado Date: Wed, 28 Feb 2024 12:38:52 +0000 Subject: [PATCH 14/17] Removed import of utils --- openmmml/models/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmmml/models/__init__.py b/openmmml/models/__init__.py index 73eb4f4..688da71 100644 --- a/openmmml/models/__init__.py +++ b/openmmml/models/__init__.py @@ -1 +1 @@ -from . import anipotential, macepotential, utils \ No newline at end of file +from . import anipotential, macepotential \ No newline at end of file From 1482cba9c9d6ecc11899c13a6cd1af5aa00ef9f8 Mon Sep 17 00:00:00 2001 From: Joao Morado Date: Thu, 29 Feb 2024 10:25:53 +0000 Subject: [PATCH 15/17] Fixed improper use of backquotes and single quotes, removed unnecessary model writing, and corrected default value of returnEnergyType parameter --- openmmml/models/macepotential.py | 68 ++++++++++++++++---------------- 1 file changed, 33 insertions(+), 35 deletions(-) diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index 41c6cfc..75d1c1c 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -52,13 +52,13 @@ class MACEPotentialImpl(MLPotentialImpl): To use one of the pre-trained MACE-OFF23 models, specify the model name. For example: >>> potential = MLPotential('mace-off23-small') - Other available MACE-OFF23 models include `mace-off23-medium` and `mace-off23-large`. + Other available MACE-OFF23 models include 'mace-off23-medium' and 'mace-off23-large'. To use a locally trained MACE model, provide the path to the model file. For example: >>> potential = MLPotential('mace', modelPath='MACE.model') During system creation, you can optionally specify the precision of the model using the - `precision` keyword argument. Supported options are 'single' and 'double'. For example: + precision` keyword argument. Supported options are 'single' and 'double'. For example: >>> system = potential.createSystem(topology, precision='single') By default, the implementation uses the precision of the loaded MACE model. @@ -67,18 +67,18 @@ class MACEPotentialImpl(MLPotentialImpl): Additionally, you can request computation of the full atomic energy, including the atom self-energy, instead of the default interaction energy, by setting `returnEnergyType` to - `energy`. For example: - >>> system = potential.createSystem(topology, returnEnergyType=`energy`) + 'energy'. For example: + >>> system = potential.createSystem(topology, returnEnergyType='energy') The default is to compute the interaction energy, which can be made explicit by setting - `returnEnergyType=interaction_energy`. + `returnEnergyType='interaction_energy'`. Attributes ---------- name : str The name of the MACE model. modelPath : str - The path to the locally trained MACE model if `name` is `mace`. + The path to the locally trained MACE model if `name` is 'mace'. """ def __init__(self, name: str, modelPath) -> None: @@ -89,9 +89,9 @@ def __init__(self, name: str, modelPath) -> None: ---------- name : str The name of the MACE model. - Options include `mace-off23-small`, `mace-off23-medium`, `mace-off23-large`, and `mace`. + Options include 'mace-off23-small', 'mace-off23-medium', 'mace-off23-large', and 'mace'. modelPath : str, optional - The path to the locally trained MACE model if `name` is `mace`. + The path to the locally trained MACE model if `name` is 'mace'. """ self.name = name self.modelPath = modelPath @@ -103,8 +103,7 @@ def addForces( atoms: Optional[Iterable[int]], forceGroup: int, precision: Optional[str] = None, - returnEnergyType: str = "energy", - filename: str = "macemodel.pt", + returnEnergyType: str = "interaction_energy", **args, ) -> None: """ @@ -117,17 +116,15 @@ def addForces( system : openmm.System The system to which the force will be added. atoms : iterable of int - The indices of the atoms to include in the model. If None, all atoms are included. + The indices of the atoms to include in the model. If `None`, all atoms are included. forceGroup : int The force group to which the force should be assigned. precision : str, optional The precision of the model. Supported options are 'single' and 'double'. - If None, the default precision of the model is used. + If `None`, the default precision of the model is used. returnEnergyType : str, optional Whether to return the interaction energy or the energy including the self-energy. - Default is `interaction_energy`. Supported options are `interaction_energy` and `energy`. - filename : str, optional - The filename to which the TorchForce will be saved. Default is 'macemodel.pt'. + Default is 'interaction_energy'. Supported options are 'interaction_energy' and 'energy'. """ import torch import openmmtorch @@ -137,44 +134,44 @@ def addForces( from mace.calculators.foundations_models import mace_off except ImportError as e: raise ImportError( - f"Failed to import `mace` with error: {e}. Install `mace` with `pip install torch-mace`." + f"Failed to import mace with error: {e}. " + "Install mace with 'pip install mace-torch'." ) try: from e3nn.util import jit except ImportError as e: raise ImportError( - f"Failed to import `e3nn` with error: {e}. Install `e3nn` with `pip install e3nn`." + f"Failed to import e3nn with error: {e}. " + "Install e3nn with 'pip install e3nn'." ) try: from NNPOps.neighbors import getNeighborPairs except ImportError as e: raise ImportError( - f"Failed to import `NNPOps` with error: {e}." - "Install `nnpops` with `conda install -c conda-forge nnpops`." + f"Failed to import NNPOps with error: {e}. " + "Install NNPOps with 'conda install -c conda-forge nnpops'." ) - assert ( - returnEnergyType - in [ - "interaction_energy", - "energy", - ] - ), "Supported options for `returnEnergyType` are `interaction_energy` and `energy`." + assert returnEnergyType in [ + "interaction_energy", + "energy", + ], f"Unsupported returnEnergyType: '{returnEnergyType}'. Supported options are 'interaction_energy' or 'energy'." # Load the model and compile it to TorchScript. if self.name.startswith("mace-off23"): size = self.name.split("-")[-1] assert ( size in ["small", "medium", "large"] - ), "Available MACE-OFF23 models are: `mace-off23-small`, `mace-off23-medium`, `mace-off23-large`" + ), f"Unsupported MACE model: '{self.name}'. Available MACE-OFF23 models are 'mace-off23-small', 'mace-off23-medium', 'mace-off23-large'" model = mace_off(model=size, return_raw_model=True) elif self.name == "mace": if self.modelPath is not None: model = torch.load(self.modelPath) else: - raise ValueError("No `modelPath` provided for local MACE model.") + raise ValueError("No modelPath provided for local MACE model.") else: raise ValueError(f"Unsupported MACE model: {self.name}") + model = jit.compile(model) # Get the atomic numbers of the ML region. @@ -193,8 +190,8 @@ def addForces( dtype = torch.float64 else: raise ValueError( - f"Unsupported precision `{precision}` for the model. " - "Supported values are `single` and `double`." + f"Unsupported precision {precision} for the model. " + "Supported values are 'single' and 'double'." ) if dtype != modelDefaultDtype: print( @@ -254,7 +251,7 @@ def __init__( nodeAttrs : torch.Tensor The one-hot encoded atomic numbers. atoms : iterable of int - The indices of the atoms. If None, all atoms are included. + The indices of the atoms. If `None`, all atoms are included. periodic : bool Whether the system is periodic. dtype : torch.dtype @@ -297,6 +294,8 @@ def _getNeighborPairs( """ Get the shifts and edge indices. + Notes + ----- This method calculates the shifts and edge indices by determining neighbor pairs (`neighbors`) and respective wrapped distances (`wrappedDeltas`) using `NNPOps.neighbors.getNeighborPairs`. After obtaining the `neighbors` and `wrappedDeltas`, the pairs with negative indices (r>cutoff) @@ -316,7 +315,7 @@ def _getNeighborPairs( shifts : torch.Tensor The shifts. """ - # Get the neighbor pairs, shifts and edge indeces. + # Get the neighbor pairs, shifts and edge indices. neighbors, wrappedDeltas, _, _ = getNeighborPairs( positions, self.model.r_max, -1, cell ) @@ -396,12 +395,11 @@ def forward( returnEnergyType, ) - # Convert it to TorchScript and save it. + # Convert it to TorchScript. module = torch.jit.script(maceForce) - module.save(filename) # Create the TorchForce and add it to the System. - force = openmmtorch.TorchForce(filename) + force = openmmtorch.TorchForce(module) force.setForceGroup(forceGroup) force.setUsesPeriodicBoundaryConditions(isPeriodic) system.addForce(force) From 8dfc4fcd62cd694949d48aa0533570c669d00e3f Mon Sep 17 00:00:00 2001 From: Joao Morado Date: Thu, 29 Feb 2024 12:31:19 +0000 Subject: [PATCH 16/17] Fix to ensure correct device allocation --- openmmml/models/macepotential.py | 36 +++++++++++++++----------------- 1 file changed, 17 insertions(+), 19 deletions(-) diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index 75d1c1c..c74a09a 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -157,22 +157,23 @@ def addForces( "energy", ], f"Unsupported returnEnergyType: '{returnEnergyType}'. Supported options are 'interaction_energy' or 'energy'." - # Load the model and compile it to TorchScript. + # Load the model to the CPU (OpenMM-Torch takes care of loading to the right devices) if self.name.startswith("mace-off23"): size = self.name.split("-")[-1] assert ( size in ["small", "medium", "large"] ), f"Unsupported MACE model: '{self.name}'. Available MACE-OFF23 models are 'mace-off23-small', 'mace-off23-medium', 'mace-off23-large'" - model = mace_off(model=size, return_raw_model=True) + model = mace_off(model=size, device="cpu", return_raw_model=True) elif self.name == "mace": if self.modelPath is not None: - model = torch.load(self.modelPath) + model = torch.load(self.modelPath, map_location="cpu") else: raise ValueError("No modelPath provided for local MACE model.") else: raise ValueError(f"Unsupported MACE model: {self.name}") - model = jit.compile(model) + # Compile the model. + model = jit.compile(model) # Get the atomic numbers of the ML region. includedAtoms = list(topology.atoms()) @@ -271,22 +272,19 @@ def __init__( self.indices = None else: self.indices = torch.tensor(sorted(atoms), dtype=torch.int64) - + # Create the default input dict. + self.register_buffer("ptr", torch.tensor([0, nodeAttrs.shape[0]], dtype=torch.long, requires_grad=False)) + self.register_buffer("node_attrs", nodeAttrs.to(self.dtype)) + self.register_buffer("batch", torch.zeros(nodeAttrs.shape[0], dtype=torch.long, requires_grad=False)) + self.register_buffer("pbc", torch.tensor([periodic, periodic, periodic], dtype=torch.bool, requires_grad=False)) + self.inputDict = { - "ptr": torch.tensor( - [0, nodeAttrs.shape[0]], dtype=torch.long, requires_grad=False - ), - "node_attrs": nodeAttrs.to(self.dtype), - "batch": torch.zeros( - nodeAttrs.shape[0], dtype=torch.long, requires_grad=False - ), - "pbc": torch.tensor( - [periodic, periodic, periodic], - dtype=torch.bool, - requires_grad=False, - ), - } + "ptr": self.ptr, + "node_attrs": self.node_attrs, + "batch": self.batch, + "pbc": self.pbc, + } def _getNeighborPairs( self, positions: torch.Tensor, cell: Optional[torch.Tensor] @@ -330,7 +328,7 @@ def _getNeighborPairs( shiftsIdx = torch.mm(deltas - wrappedDeltas, torch.linalg.inv(cell)) shifts = torch.mm(shiftsIdx, cell) else: - shifts = torch.zeros((edgeIndex.shape[1], 3), dtype=self.dtype) + shifts = torch.zeros((edgeIndex.shape[1], 3), dtype=self.dtype, device=positions.device) return edgeIndex, shifts From 1bd4dd78c486eaea1b23ba8373d38c115d95854e Mon Sep 17 00:00:00 2001 From: Joao Morado Date: Thu, 29 Feb 2024 16:29:34 +0000 Subject: [PATCH 17/17] Fixed typo --- openmmml/models/macepotential.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmmml/models/macepotential.py b/openmmml/models/macepotential.py index c74a09a..2f3e970 100644 --- a/openmmml/models/macepotential.py +++ b/openmmml/models/macepotential.py @@ -58,7 +58,7 @@ class MACEPotentialImpl(MLPotentialImpl): >>> potential = MLPotential('mace', modelPath='MACE.model') During system creation, you can optionally specify the precision of the model using the - precision` keyword argument. Supported options are 'single' and 'double'. For example: + `precision` keyword argument. Supported options are 'single' and 'double'. For example: >>> system = potential.createSystem(topology, precision='single') By default, the implementation uses the precision of the loaded MACE model.

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