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test_make_md.py
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import os,sys,json,glob,shutil,copy
import dpdata
import numpy as np
import unittest
from pathlib import Path
from dpgen.generator.run import parse_cur_job_sys_revmat
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
__package__ = 'generator'
from .context import make_model_devi
from .context import parse_cur_job
from .context import parse_cur_job_revmat
from .context import param_file, param_amber_file
from .context import machine_file
from .context import my_file_cmp
from .context import setUpModule
from .context import find_only_one_key
from .context import revise_lmp_input_model
from .context import revise_lmp_input_dump
from .context import revise_lmp_input_plm
from .context import revise_by_keys
from .comp_sys import test_atom_names
from .comp_sys import test_atom_types
from .comp_sys import test_coord
from .comp_sys import test_cell
def _make_fake_models(idx, numb_models) :
train_dir = os.path.join('iter.%06d' % idx,
'00.train')
os.makedirs(train_dir, exist_ok = True)
pwd = os.getcwd()
os.chdir(train_dir)
for ii in range(numb_models) :
os.makedirs('%03d' % ii, exist_ok = True)
with open(os.path.join('%03d' % ii, 'forzen_model.pb'), 'w') as fp:
fp.write(str(ii))
if not os.path.isfile('graph.%03d.pb' % ii) :
os.symlink(os.path.join('%03d' % ii, 'forzen_model.pb'),
'graph.%03d.pb' % ii)
os.chdir(pwd)
def _check_confs(testCase, idx, jdata) :
md_dir = os.path.join('iter.%06d' % idx,
'01.model_devi')
tasks = glob.glob(os.path.join(md_dir, 'task.*'))
tasks.sort()
cur_job = jdata['model_devi_jobs'][idx]
sys_idx = cur_job['sys_idx']
sys_configs = jdata['sys_configs']
poscars = []
for ii in sys_idx :
sys_poscars = []
for ss in sys_configs[ii]:
tmp_poscars = glob.glob(ss)
sys_poscars += tmp_poscars
sys_poscars.sort()
poscars.append(sys_poscars)
for ii in tasks :
conf_file = os.path.join(ii, 'conf.lmp')
l_conf_file = os.path.basename(os.readlink(conf_file))
poscar_file = poscars[int(l_conf_file.split('.')[0])][int(l_conf_file.split('.')[1])]
sys_0 = dpdata.System(conf_file, type_map = jdata['type_map'])
sys_1 = dpdata.System(poscar_file, type_map = jdata['type_map'])
test_atom_names(testCase, sys_0, sys_1)
test_atom_types(testCase, sys_0, sys_1)
test_cell(testCase, sys_0, sys_1)
test_coord(testCase, sys_0, sys_1)
def _check_pb(testCase, idx) :
md_dir = os.path.join('iter.%06d' % idx,
'01.model_devi')
tr_dir = os.path.join('iter.%06d' % idx,
'00.train')
md_pb = glob.glob(os.path.join(md_dir, 'grapb*pb'))
tr_pb = glob.glob(os.path.join(tr_dir, 'grapb*pb'))
md_pb.sort()
tr_pb.sort()
for ii,jj in zip(md_pb, tr_pb) :
my_file_cmp(testCase,ii,jj)
def _check_traj_dir(testCase, idx) :
md_dir = os.path.join('iter.%06d' % idx,
'01.model_devi')
tasks = glob.glob(os.path.join(md_dir, 'task.*'))
tasks.sort()
for ii in tasks:
testCase.assertTrue(os.path.isdir(os.path.join(ii, 'traj')))
def _get_lammps_pt(lmp_input) :
with open(lmp_input) as fp:
for ii in fp:
if 'variable' in ii and 'TEMP' in ii :
lt = float(ii.split()[3])
if 'variable' in ii and 'PRES' in ii :
lp = float(ii.split()[3])
return lt,lp
def _check_pt(testCase, idx, jdata) :
md_dir = os.path.join('iter.%06d' % idx,
'01.model_devi')
tasks = glob.glob(os.path.join(md_dir, 'task.*'))
tasks.sort()
cur_job = jdata['model_devi_jobs'][idx]
ensemble, nsteps, trj_freq, temps, press, pka_e, dt = parse_cur_job(cur_job)
testCase.assertTrue(ensemble, 'npt')
# get poscars
sys_idx = cur_job['sys_idx']
sys_configs = jdata['sys_configs']
poscars = []
for ii in sys_idx :
sys_poscars = []
for ss in sys_configs[ii]:
tmp_poscars = glob.glob(ss)
sys_poscars += tmp_poscars
sys_poscars.sort()
poscars.append(sys_poscars)
for sidx,ii in enumerate(poscars) :
count = 0
for ss in ii:
for tt in temps:
for pp in press:
task_dir = os.path.join(md_dir, 'task.%03d.%06d' % (sidx, count))
lt, lp = _get_lammps_pt(os.path.join(task_dir, 'input.lammps'))
testCase.assertAlmostEqual(lt, tt)
testCase.assertAlmostEqual(lp, pp)
count += 1
class TestMakeModelDevi(unittest.TestCase):
def tearDown(self):
if os.path.isdir('iter.000000') :
shutil.rmtree('iter.000000')
def test_make_model_devi (self) :
if os.path.isdir('iter.000000') :
shutil.rmtree('iter.000000')
with open (param_file, 'r') as fp :
jdata = json.load (fp)
with open (machine_file, 'r') as fp:
mdata = json.load (fp)
_make_fake_models(0, jdata['numb_models'])
make_model_devi(0, jdata, mdata)
_check_pb(self, 0)
_check_confs(self, 0, jdata)
_check_traj_dir(self, 0)
_check_pt(self, 0, jdata)
#shutil.rmtree('iter.000000')
def test_make_model_devi_nopbc_npt (self) :
if os.path.isdir('iter.000000') :
shutil.rmtree('iter.000000')
with open (param_file, 'r') as fp :
jdata = json.load (fp)
jdata['model_devi_nopbc'] = True
with open (machine_file, 'r') as fp:
mdata = json.load (fp)
_make_fake_models(0, jdata['numb_models'])
cwd = os.getcwd()
with self.assertRaises(RuntimeError) :
make_model_devi(0, jdata, mdata)
os.chdir(cwd)
def test_make_model_devi_nopbc_nvt (self) :
if os.path.isdir('iter.000000') :
shutil.rmtree('iter.000000')
with open (param_file, 'r') as fp :
jdata = json.load (fp)
jdata['model_devi_nopbc'] = True
jdata['model_devi_jobs'][0]['ensemble'] = 'nvt'
with open (machine_file, 'r') as fp:
mdata = json.load (fp)
_make_fake_models(0, jdata['numb_models'])
make_model_devi(0, jdata, mdata)
_check_pb(self, 0)
# _check_confs(self, 0, jdata)
_check_traj_dir(self, 0)
_check_pt(self, 0, jdata)
# shutil.rmtree('iter.000000')
class TestMakeModelDeviRevMat(unittest.TestCase):
def tearDown(self):
if os.path.isdir('iter.000000') :
shutil.rmtree('iter.000000')
def test_make_model_devi (self) :
if os.path.isdir('iter.000000') :
shutil.rmtree('iter.000000')
jdata = {
"type_map": ["Mg", "Al"],
"mass_map": [24, 27],
"init_data_prefix": "data",
"init_data_sys": ["deepmd"],
"init_batch_size": [16],
"sys_configs_prefix": os.getcwd(),
"sys_configs": [
["data/al.fcc.02x02x02/01.scale_pert/sys-0032/scale*/000001/POSCAR"],
["data/al.fcc.02x02x02/01.scale_pert/sys-0032/scale*/000000/POSCAR"]
],
"numb_models": 4,
"shuffle_poscar": False,
"model_devi_f_trust_lo": 0.050,
"model_devi_f_trust_hi": 0.150,
"model_devi_plumed": True,
"model_devi_jobs": [
{"sys_idx": [0, 1], 'traj_freq': 10, "template": {"lmp": "lmp/input.lammps", "plm": "lmp/input.plumed"},
"rev_mat": {
"lmp": {"V_NSTEPS": [1000], "V_TEMP": [50, 100]}, "plm": {"V_DIST0": [3, 4]}
},
"sys_rev_mat": {
"0": {
"lmp": {"V_PRES": [1, 10]}, "plm": {"V_DIST1": [5, 6]}
},
"1": {
"lmp": {"V_PRES": [1, 5, 10]}, "plm": {"V_DIST1": [5, 6, 7]}
}
}
}
]
}
mdata = {'deepmd_version': '1'}
_make_fake_models(0, jdata['numb_models'])
make_model_devi(0, jdata, mdata)
_check_pb(self, 0)
_check_confs(self, 0, jdata)
_check_traj_dir(self, 0)
# check the first task
md_dir = os.path.join('iter.%06d' % 0, '01.model_devi')
tasks = glob.glob(os.path.join(md_dir, 'task.*'))
tasks.sort()
# each system contains 2 frames
self.assertEqual(len(tasks), (len(jdata['model_devi_jobs'][0]['rev_mat']['lmp']['V_NSTEPS']) *
len(jdata['model_devi_jobs'][0]['rev_mat']['lmp']['V_TEMP']) *
len(jdata['model_devi_jobs'][0]['rev_mat']['plm']['V_DIST0']) *
(len(jdata['model_devi_jobs'][0]['sys_rev_mat']['0']['lmp']['V_PRES']) *
len(jdata['model_devi_jobs'][0]['sys_rev_mat']['0']['plm']['V_DIST1']) +
len(jdata['model_devi_jobs'][0]['sys_rev_mat']['1']['lmp']['V_PRES']) *
len(jdata['model_devi_jobs'][0]['sys_rev_mat']['1']['plm']['V_DIST1'])) *
2))
cur_job = jdata['model_devi_jobs'][0]
rev_keys = ['V_NSTEPS', 'V_TEMP', 'V_PRES', 'V_DIST0', 'V_DIST1']
rev_matrix = []
# 2 systems with each 2 frames
for i0 in cur_job['rev_mat']['lmp']['V_NSTEPS']:
for i1 in cur_job['rev_mat']['lmp']['V_TEMP']:
for i3 in cur_job['rev_mat']['plm']['V_DIST0']:
for i2 in cur_job['sys_rev_mat']['0']['lmp']['V_PRES']:
for i4 in cur_job['sys_rev_mat']['0']['plm']['V_DIST1']:
rev_matrix.append([i0, i1, i2, i3, i4])
for i0 in cur_job['rev_mat']['lmp']['V_NSTEPS']:
for i1 in cur_job['rev_mat']['lmp']['V_TEMP']:
for i3 in cur_job['rev_mat']['plm']['V_DIST0']:
for i2 in cur_job['sys_rev_mat']['0']['lmp']['V_PRES']:
for i4 in cur_job['sys_rev_mat']['0']['plm']['V_DIST1']:
rev_matrix.append([i0, i1, i2, i3, i4])
for i0 in cur_job['rev_mat']['lmp']['V_NSTEPS']:
for i1 in cur_job['rev_mat']['lmp']['V_TEMP']:
for i3 in cur_job['rev_mat']['plm']['V_DIST0']:
for i2 in cur_job['sys_rev_mat']['1']['lmp']['V_PRES']:
for i4 in cur_job['sys_rev_mat']['1']['plm']['V_DIST1']:
rev_matrix.append([i0, i1, i2, i3, i4])
for i0 in cur_job['rev_mat']['lmp']['V_NSTEPS']:
for i1 in cur_job['rev_mat']['lmp']['V_TEMP']:
for i3 in cur_job['rev_mat']['plm']['V_DIST0']:
for i2 in cur_job['sys_rev_mat']['1']['lmp']['V_PRES']:
for i4 in cur_job['sys_rev_mat']['1']['plm']['V_DIST1']:
rev_matrix.append([i0, i1, i2, i3, i4])
numb_rev = len(rev_matrix)
for ii in range(len(tasks)):
with open(os.path.join(tasks[ii], 'job.json')) as fp:
rev_values = rev_matrix[ii % numb_rev]
job_recd = json.load(fp)
for kk in job_recd.keys():
kidx = rev_keys.index(kk)
self.assertEqual(rev_values[kidx], job_recd[kk])
cwd_ = os.getcwd()
os.chdir(tasks[0])
with open('input.lammps') as fp:
lines = fp.readlines()
for ii in lines:
if 'variable' in ii and 'TEMP' in ii:
self.assertEqual('variable TEMP equal 50',
' '.join(ii.split()))
if 'variable' in ii and 'PRES' in ii:
self.assertEqual('variable PRES equal 1',
' '.join(ii.split()))
if 'variable' in ii and 'NSTEPS' in ii:
self.assertEqual('variable NSTEPS equal 1000',
' '.join(ii.split()))
with open('input.plumed') as fp:
lines = fp.readlines()
for ii in lines:
if 'RESTRAINT' in ii:
self.assertEqual('RESTRAINT ARG=d1,d2 AT=3,5 KAPPA=150.0,150.0 LABEL=restraint',
' '.join(ii.split()))
os.chdir(cwd_)
def test_make_model_devi_null (self) :
if os.path.isdir('iter.000000') :
shutil.rmtree('iter.000000')
jdata = {
"type_map": ["Mg", "Al"],
"mass_map": [24, 27],
"init_data_prefix": "data",
"init_data_sys": ["deepmd"],
"init_batch_size": [16],
"sys_configs_prefix": os.getcwd(),
"sys_configs": [
["data/al.fcc.02x02x02/01.scale_pert/sys-0032/scale*/000001/POSCAR"],
["data/al.fcc.02x02x02/01.scale_pert/sys-0032/scale*/000000/POSCAR"]
],
"numb_models": 4,
"shuffle_poscar": False,
"model_devi_f_trust_lo": 0.050,
"model_devi_f_trust_hi": 0.150,
"model_devi_plumed": True,
"model_devi_jobs": [
{"sys_idx": [0, 1], 'traj_freq': 10, "template":{"lmp": "lmp/input.lammps", "plm": "lmp/input.plumed"},
}
]
}
mdata = {'deepmd_version': '1'}
_make_fake_models(0, jdata['numb_models'])
make_model_devi(0, jdata, mdata)
_check_pb(self, 0)
_check_confs(self, 0, jdata)
_check_traj_dir(self, 0)
# check the first task
md_dir = os.path.join('iter.%06d' % 0, '01.model_devi')
tasks = glob.glob(os.path.join(md_dir, 'task.*'))
# 4 accounts for 2 systems each with 2 frames
self.assertEqual(len(tasks), (4))
tasks.sort()
cwd_ = os.getcwd()
os.chdir(tasks[0])
with open('input.lammps') as fp:
lines = fp.readlines()
for ii in lines:
if 'variable' in ii and 'TEMP' in ii:
self.assertEqual('variable TEMP equal V_TEMP',
' '.join(ii.split()))
if 'variable' in ii and 'PRES' in ii:
self.assertEqual('variable PRES equal V_PRES',
' '.join(ii.split()))
if 'variable' in ii and 'NSTEPS' in ii:
self.assertEqual('variable NSTEPS equal V_NSTEPS',
' '.join(ii.split()))
with open('input.plumed') as fp:
lines = fp.readlines()
for ii in lines:
if 'RESTRAINT' in ii:
self.assertEqual('RESTRAINT ARG=d1,d2 AT=V_DIST0,V_DIST1 KAPPA=150.0,150.0 LABEL=restraint',
' '.join(ii.split()))
os.chdir(cwd_)
class TestParseCurJobRevMat(unittest.TestCase):
def setUp(self):
self.cur_job = {
"sys_idx": [0, 1],
"template":{"lmp": "lmp/input.lammps", "plm": "lmp/input.plumed"},
"rev_mat":{
"lmp": {"V_NSTEPS": [1000], "V_TEMP": [50, 100], "V_PRES": [1, 10]}, "plm": {"V_DIST0": [3,4], "V_DIST1": [5, 6]}
}
}
self.ref_matrix = []
for i0 in self.cur_job['rev_mat']['lmp']['V_NSTEPS']:
for i1 in self.cur_job['rev_mat']['lmp']['V_TEMP']:
for i2 in self.cur_job['rev_mat']['lmp']['V_PRES']:
for i3 in self.cur_job['rev_mat']['plm']['V_DIST0']:
for i4 in self.cur_job['rev_mat']['plm']['V_DIST1']:
self.ref_matrix.append([i0, i1, i2, i3, i4])
self.ref_keys = ['V_NSTEPS', 'V_TEMP', 'V_PRES', 'V_DIST0', 'V_DIST1']
self.ref_nlmp = 3
def test_parse_cur_job(self):
rk, rm, nl = parse_cur_job_revmat(self.cur_job, use_plm = True)
self.assertEqual(rk, self.ref_keys)
self.assertEqual(nl, self.ref_nlmp)
self.assertEqual(rm, self.ref_matrix)
class TestParseCurJobSysRevMat(unittest.TestCase):
def setUp(self):
self.cur_job = {
"sys_idx": [0, 1],
"template":{"lmp": "lmp/input.lammps", "plm": "lmp/input.plumed"},
"rev_mat":{
"lmp": {"V_NSTEPS": [1000], "V_TEMP": [50, 100]}, "plm": {"V_DIST0": [3, 4]}
},
"sys_rev_mat": {
"0": {
"lmp": {"V_PRES": [1, 10]},
"plm": {"V_DIST1": [5, 6]}
},
"1": {
"lmp": {"V_PRES": [1, 10, 20]},
"plm": {"V_DIST1": [5, 6, 7]}
}
}
}
self.sys_ref_matrix = [[], []]
for i0 in self.cur_job['sys_rev_mat']['0']['lmp']['V_PRES']:
for i1 in self.cur_job['sys_rev_mat']['0']['plm']['V_DIST1']:
self.sys_ref_matrix[0].append([i0, i1])
for i0 in self.cur_job['sys_rev_mat']['1']['lmp']['V_PRES']:
for i1 in self.cur_job['sys_rev_mat']['1']['plm']['V_DIST1']:
self.sys_ref_matrix[1].append([i0, i1])
self.sys_ref_keys = ['V_PRES', 'V_DIST1']
self.sys_ref_nlmp_0 = 1
self.sys_ref_nlmp_1 = 1
def test_parse_cur_job(self):
rk0, rm0, nl0 = parse_cur_job_sys_revmat(self.cur_job, 0, use_plm=True)
rk1, rm1, nl1 = parse_cur_job_sys_revmat(self.cur_job, 1, use_plm=True)
self.assertEqual(rk0, self.sys_ref_keys)
self.assertEqual(nl0, self.sys_ref_nlmp_0)
self.assertEqual(rm0, self.sys_ref_matrix[0])
self.assertEqual(rk1, self.sys_ref_keys)
self.assertEqual(nl1, self.sys_ref_nlmp_1)
self.assertEqual(rm1, self.sys_ref_matrix[1])
class MakeModelDeviByReviseMatrix(unittest.TestCase):
def test_find_only_one_key_1(self):
lines = ['aaa bbb ccc\n', 'bbb ccc\n', 'ccc bbb ccc\n']
idx = find_only_one_key(lines, ['bbb', 'ccc'])
self.assertEqual(idx, 1)
def test_find_only_one_key_0(self):
lines = ['aaa bbb\n', 'bbb aaa\n', 'ccc ddd\n']
with self.assertRaises(RuntimeError):
idx = find_only_one_key(lines, ['ccc','eee'])
def test_find_only_one_key_2(self):
lines = ['aaa bbb\n', 'bbb ccc\n', 'bbb ccc\n', 'fff eee\n']
with self.assertRaises(RuntimeError):
idx = find_only_one_key(lines, ['bbb','ccc'])
def test_revise_lmp_input_model_0(self):
lines = ['foo\n', 'pair_style deepmd aaa ccc fff\n', 'bar\n', '\n']
ref_lines = copy.deepcopy(lines)
lines = revise_lmp_input_model(lines, ['model0', 'model1'], 10, '0.1')
for ii in [0, 2, 3] :
self.assertEqual(lines[ii], ref_lines[ii])
tmp = " ".join(lines[1].split())
self.assertEqual(tmp, "pair_style deepmd model0 model1 10 model_devi.out")
def test_revise_lmp_input_model_1(self):
lines = ['foo\n', 'pair_style deepmd aaa ccc fff\n', 'bar\n', '\n']
ref_lines = copy.deepcopy(lines)
lines = revise_lmp_input_model(lines, ['model0', 'model1'], 10, '1')
for ii in [0, 2, 3] :
self.assertEqual(lines[ii], ref_lines[ii])
tmp = " ".join(lines[1].split())
self.assertEqual(tmp, "pair_style deepmd model0 model1 out_freq 10 out_file model_devi.out")
def test_revise_lmp_input_dump(self):
lines = ['foo\n', 'dump dpgen_dump ccc fff\n', 'bar\n', '\n']
ref_lines = copy.deepcopy(lines)
lines = revise_lmp_input_dump(lines, 10)
for ii in [0, 2, 3] :
self.assertEqual(lines[ii], ref_lines[ii])
tmp = " ".join(lines[1].split())
self.assertEqual(tmp, "dump dpgen_dump all custom 10 traj/*.lammpstrj id type x y z")
def test_revise_lmp_input_plm(self):
lines = ['foo\n', 'fix dpgen_plm ccc fff\n', 'bar\n', '\n']
ref_lines = copy.deepcopy(lines)
lines = revise_lmp_input_plm(lines, 'input.plumed')
for ii in [0, 2, 3] :
self.assertEqual(lines[ii], ref_lines[ii])
tmp = " ".join(lines[1].split())
self.assertEqual(tmp, "fix dpgen_plm all plumed plumedfile input.plumed outfile output.plumed")
def test_revise_by_key(self):
lines = ['foo\n', 'aaa\n', 'bar\n', 'bbb\n', '\n']
ref_lines = copy.deepcopy(lines)
lines = revise_by_keys(lines, ['aaa', 'bbb'], ['ccc','ddd'])
for ii in [0, 2, 4] :
self.assertEqual(lines[ii], ref_lines[ii])
tmp = " ".join(lines[1].split())
self.assertEqual(tmp, "ccc")
tmp = " ".join(lines[3].split())
self.assertEqual(tmp, "ddd")
class TestMakeMDAMBER(unittest.TestCase):
def tearDown(self):
if os.path.isdir('iter.000000') :
shutil.rmtree('iter.000000')
def test_make_model_devi (self) :
if os.path.isdir('iter.000000') :
shutil.rmtree('iter.000000')
with open (param_amber_file, 'r') as fp :
jdata = json.load (fp)
with open (machine_file, 'r') as fp:
mdata = json.load (fp)
jdata['sys_prefix'] = os.path.abspath(jdata['sys_prefix'])
_make_fake_models(0, jdata['numb_models'])
make_model_devi(0, jdata, mdata)
_check_pb(self, 0)
_check_confs(self, 0, jdata)
_check_traj_dir(self, 0)
if __name__ == '__main__':
unittest.main()