openmm-torch
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Published
20 hours ago •
openmm
openmm
Second step fails
I am following steps torchANI tutorial steps. The code fails in the second call to simulation function. Any source of error. I couldn't install mamba in my workstation, and I used conda with pytorch=1.12, pytorch=1.11 throws symbol error.
import openmmtools
ala2 = openmmtools.testsystems.AlanineDipeptideVacuum(constraints=None)
while ala2.system.getNumForces() > 0:
ala2.system.removeForce(0)
assert ala2.system.getNumConstraints() == 0
assert ala2.system.getNumForces() == 0
atomic_numbers = [atom.element.atomic_number for atom in ala2.topology.atoms()]
import torch as pt
from torchani.models import ANI2x
from NNPOps import OptimizedTorchANI
class NNP(pt.nn.Module):
def __init__(self, atomic_numbers):
super().__init__()
# Store the atomic numbers
self.atomic_numbers = pt.tensor(atomic_numbers).unsqueeze(0)
# Create an ANI-2x model
self.model = ANI2x(periodic_table_index=True)
# Accelerate the model
self.model = OptimizedTorchANI(self.model, self.atomic_numbers)
def forward(self, positions):
# Prepare the positions
positions = positions.unsqueeze(0).float() * 10 # nm --> Å
# Run ANI-2x
result = self.model((self.atomic_numbers, positions))
# Get the potential energy
energy = result.energies[0] * 2625.5 # Hartree --> kJ/mol
return energy
nnp = NNP(atomic_numbers)
pos = pt.tensor(ala2.positions.tolist())
energy_1 = float(nnp(pos))
from openmmtorch import TorchForce
pt.jit.script(nnp).save('model.pt')
force = TorchForce('model.pt')
ala2.system.addForce(force)
assert ala2.system.getNumForces() == 1
import sys
from openmm import LangevinMiddleIntegrator, BrownianIntegrator
from openmm.app import Simulation, StateDataReporter
from openmm.unit import kelvin, picosecond, femtosecond
temperature = 298.15 * kelvin
frictionCoeff = 1 / picosecond
timeStep = 1 * femtosecond
integrator = LangevinMiddleIntegrator(temperature, frictionCoeff, timeStep)
simulation = Simulation(ala2.topology, ala2.system, integrator)
simulation.context.setPositions(ala2.positions)
reporter = StateDataReporter(file=sys.stdout, reportInterval=1, step=True, time=True, potentialEnergy=True, temperature=True)
simulation.reporters.append(reporter)
from openmm.unit import kilojoule_per_mole
simulation.step(1000)
Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.
#"Step","Time (ps)","Potential Energy (kJ/mole)","Temperature (K)"
1,0.001,-1301525.4216868656,4.132715143549229
Traceback (most recent call last):
File "/home/amoradzadeh/Work/OpenMM-Torch/example-openmm-torch.py", line 83, in <module>
simulation.step(1000)
File "/home/amoradzadeh/anaconda3/envs/tmp-openmm-torch/lib/python3.9/site-packages/openmm/app/simulation.py", line 141, in step
self._simulate(endStep=self.currentStep+steps)
File "/home/amoradzadeh/anaconda3/envs/tmp-openmm-torch/lib/python3.9/site-packages/openmm/app/simulation.py", line 210, in _simulate
self.integrator.step(stepsToGo)
File "/home/amoradzadeh/anaconda3/envs/tmp-openmm-torch/lib/python3.9/site-packages/openmm/openmm.py", line 6145, in step
return _openmm.LangevinMiddleIntegrator_step(self, steps)
openmm.OpenMMException: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript (most recent call last):
File "<string>", line 57, in <backward op>
self_scalar_type = self.dtype
def backward(grad_output):
grad_self = AD_sum_backward(grad_output, self_size, dim, keepdim).to(self_scalar_type) / AD_safe_size(self_size, dim)
~~~~~~~~~~~~~~~ <--- HERE
return grad_self, None, None, None
File "<string>", line 24, in AD_sum_backward
if not keepdim and len(sizes) > 0:
if len(dims) == 1:
return grad.unsqueeze(dims[0]).expand(sizes)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
else:
res = AD_unsqueeze_multiple(grad, dims, len(sizes))
RuntimeError: expand(CUDADoubleType{[1, 1]}, size=[1]): the number of sizes provided (1) must be greater or equal to the number of dimensions in the tensor (2)
Here, it is.
# packages in environment at /home/amoradzadeh/anaconda3/envs/openmm-torch:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
astunparse 1.6.3 pyhd8ed1ab_0 conda-forge
blosc 1.21.1 h83bc5f7_3 conda-forge
brotlipy 0.7.0 py38h0a891b7_1004 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
c-ares 1.18.1 h7f98852_0 conda-forge
ca-certificates 2022.9.24 ha878542_0 conda-forge
certifi 2022.9.24 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py38h4a40e3a_0 conda-forge
cftime 1.6.2 py38h26c90d9_0 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
cryptography 37.0.1 py38h9ce1e76_0
cudatoolkit 11.7.0 hd8887f6_10 conda-forge
cudnn 8.4.1.50 hed8a83a_0 conda-forge
curl 7.85.0 h2283fc2_0 conda-forge
fftw 3.3.10 nompi_hf0379b8_105 conda-forge
hdf4 4.2.15 h9772cbc_4 conda-forge
hdf5 1.12.2 nompi_h4df4325_100 conda-forge
idna 3.4 pyhd8ed1ab_0 conda-forge
importlib-metadata 4.11.4 py38h578d9bd_0 conda-forge
intel-openmp 2022.1.0 h9e868ea_3769
jpeg 9e h166bdaf_2 conda-forge
keyutils 1.6.1 h166bdaf_0 conda-forge
krb5 1.19.3 h08a2579_0 conda-forge
lark-parser 0.12.0 pyhd8ed1ab_0 conda-forge
ld_impl_linux-64 2.36.1 hea4e1c9_2 conda-forge
libblas 3.9.0 16_linux64_mkl conda-forge
libcblas 3.9.0 16_linux64_mkl conda-forge
libcurl 7.85.0 h2283fc2_0 conda-forge
libedit 3.1.20191231 he28a2e2_2 conda-forge
libev 4.33 h516909a_1 conda-forge
libffi 3.4.2 h7f98852_5 conda-forge
libgcc-ng 12.1.0 h8d9b700_16 conda-forge
libgfortran-ng 12.1.0 h69a702a_16 conda-forge
libgfortran5 12.1.0 hdcd56e2_16 conda-forge
libgomp 12.1.0 h8d9b700_16 conda-forge
liblapack 3.9.0 16_linux64_mkl conda-forge
libllvm11 11.1.0 he0ac6c6_4 conda-forge
libnetcdf 4.8.1 nompi_h21705cb_104 conda-forge
libnghttp2 1.47.0 hff17c54_1 conda-forge
libnsl 2.0.0 h7f98852_0 conda-forge
libprotobuf 3.20.1 h6239696_4 conda-forge
libsqlite 3.39.4 h753d276_0 conda-forge
libssh2 1.10.0 hf14f497_3 conda-forge
libstdcxx-ng 12.1.0 ha89aaad_16 conda-forge
libuuid 2.32.1 h7f98852_1000 conda-forge
libzip 1.9.2 hc929e4a_1 conda-forge
libzlib 1.2.13 h166bdaf_4 conda-forge
llvmlite 0.39.1 py38h38d86a4_0 conda-forge
lz4-c 1.9.3 h9c3ff4c_1 conda-forge
lzo 2.10 h516909a_1000 conda-forge
magma 2.5.4 h6103c52_2 conda-forge
mdtraj 1.9.7 py38h4b5026a_2 conda-forge
mkl 2022.1.0 hc2b9512_224
mpiplus v0.0.1 pyhd8ed1ab_1003 conda-forge
nccl 2.14.3.1 h0800d71_0 conda-forge
ncurses 6.3 h27087fc_1 conda-forge
netcdf4 1.6.1 nompi_py38h2250339_100 conda-forge
ninja 1.11.0 h924138e_0 conda-forge
nnpops 0.2 cuda112py38hcdcc72c_5 conda-forge
nose 1.3.7 py_1006 conda-forge
numba 0.56.3 py38h9a4aae9_0 conda-forge
numexpr 2.8.3 mkl_py38h9924f95_0 conda-forge
numpy 1.23.3 py38h3a7f9d9_0 conda-forge
ocl-icd 2.3.1 h7f98852_0 conda-forge
ocl-icd-system 1.0.0 1 conda-forge
openmm 7.7.0 py38h4b4c9af_1 conda-forge
openmm-torch 0.8 cuda112py38hdf64721_2 conda-forge
openmmtools 0.21.5 pyhd8ed1ab_0 conda-forge
openssl 3.0.5 h166bdaf_2 conda-forge
packaging 21.3 pyhd8ed1ab_0 conda-forge
pandas 1.5.0 py38h8f669ce_0 conda-forge
pdbfixer 1.8.1 pyh6c4a22f_0 conda-forge
pip 22.3 pyhd8ed1ab_0 conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pymbar 3.1.0 py38h71d37f0_0 conda-forge
pyopenssl 22.0.0 pyhd8ed1ab_1 conda-forge
pyparsing 3.0.9 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyha2e5f31_6 conda-forge
pytables 3.7.0 py38hf632491_2 conda-forge
python 3.8.13 ha86cf86_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python_abi 3.8 2_cp38 conda-forge
pytorch 1.11.0 cuda112py38habe9d5a_202 conda-forge
pytz 2022.4 pyhd8ed1ab_0 conda-forge
pyyaml 6.0 py38h0a891b7_4 conda-forge
readline 8.1.2 h0f457ee_0 conda-forge
requests 2.28.1 pyhd8ed1ab_1 conda-forge
scipy 1.9.1 py38hea3f02b_0 conda-forge
setuptools 65.5.0 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sleef 3.5.1 h9b69904_2 conda-forge
snappy 1.1.9 hbd366e4_1 conda-forge
sqlite 3.39.4 h4ff8645_0 conda-forge
tk 8.6.12 h27826a3_0 conda-forge
torchani 2.2 pyh9f0ad1d_0 conda-forge
typing_extensions 4.4.0 pyha770c72_0 conda-forge
urllib3 1.26.11 pyhd8ed1ab_0 conda-forge
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
zipp 3.9.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 h166bdaf_4 conda-forge
zstd 1.5.2 h6239696_4 conda-forge
I have created an environment:
conda env create mmh/openmm-8-beta-linux
conda activate openmm-8-beta-linux
conda install -c conda-forge openmmtools
The script above works with problem:
python debug_84.py
/shared2/raimis/opt/miniconda/envs/openmm-8-beta-linux/lib/python3.10/site-packages/torchani/__init__.py:55: UserWarning: Dependency not satisfied, torchani.ase will not be available
warnings.warn("Dependency not satisfied, torchani.ase will not be available")
/shared2/raimis/opt/miniconda/envs/openmm-8-beta-linux/lib/python3.10/site-packages/torchani/resources/
Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.
#"Step","Time (ps)","Potential Energy (kJ/mole)","Temperature (K)"
1,0.001,-1301525.3878454717,4.122359749003946
2,0.002,-1301527.570361078,11.412210638851885
3,0.003,-1301529.5611348676,19.057213393688563
4,0.004,-1301531.0187404307,25.971037213133815
5,0.005,-1301531.831716344,30.366503161425385
...
@moradza try to create a new environment as indicated with the latest (22.9.0) conda.
@moradza this might be a conda issue (https://github.com/openmm/openmm-torch/issues/88#issuecomment-1310477870). Could you try to install with mamba?
@raimis I updated conda and tried installation with new environment, but the same error persists. I am trying mamba installation, but my conda fails to install mamba package. I need some time to figure out what is wrong with it.