def _runem_all(self):
"""
This function checks the status of all tasks,
tries to fix tasks that went unconverged, abicritical, or queuecritical
and tries to run all the tasks that can be submitted.+
"""
excs = []
flow = self.flow
# Allow to change the manager at run-time
if self.use_dynamic_manager:
from pymatgen.io.abinit.tasks import TaskManager
new_manager = TaskManager.from_user_config()
for work in flow:
work.set_manager(new_manager)
nqjobs = 0
if self.contact_resource_manager: # and flow.TaskManager.qadapter.QTYPE == "shell":
# This call is expensive and therefore it's optional (must be activate in manager.yml)
nqjobs = flow.get_njobs_in_queue()
if nqjobs is None:
nqjobs = 0
if flow.manager.has_queue:
logger.warning('Cannot get njobs_inqueue')
else:
# Here we just count the number of tasks in the flow who are running.
# This logic breaks down if there are multiple schedulers runnig
# but it's easy to implement without having to contact the resource manager.
nqjobs = (len(list(flow.iflat_tasks(status=flow.S_RUN))) +
len(list(flow.iflat_tasks(status=flow.S_SUB))))
if nqjobs >= self.max_njobs_inqueue:
print("Too many jobs in the queue: %s. No job will be submitted." % nqjobs)
flow.check_status(show=False)
return
if self.max_nlaunches == -1:
max_nlaunch = self.max_njobs_inqueue - nqjobs
else:
max_nlaunch = min(self.max_njobs_inqueue - nqjobs, self.max_nlaunches)
# check status.
flow.check_status(show=False)
# This check is not perfect, we should make a list of tasks to sumbit
# and select only the subset so that we don't exceeed mac_ncores_used
# Many sections of this code should be rewritten.
#if self.max_ncores_used is not None and flow.ncores_used > self.max_ncores_used:
if self.max_ncores_used is not None and flow.ncores_allocated > self.max_ncores_used:
print("Cannot exceed max_ncores_used %s" % self.max_ncores_used)
return
# Try to restart the unconverged tasks
# TODO: do not fire here but prepare for fireing in rapidfire
for task in self.flow.unconverged_tasks:
try:
logger.info("Flow will try restart task %s" % task)
fired = task.restart()
if fired:
self.nlaunch += 1
max_nlaunch -= 1
if max_nlaunch == 0:
logger.info("Restart: too many jobs in the queue, returning")
flow.pickle_dump()
return
except task.RestartError:
excs.append(straceback())
# Temporarily disable by MG because I don't know if fix_critical works after the
# introduction of the new qadapters
# reenabled by MsS disable things that do not work at low level
# fix only prepares for restarting, and sets to ready
if self.fix_qcritical:
nfixed = flow.fix_queue_critical()
if nfixed: print("Fixed %d QCritical error(s)" % nfixed)
nfixed = flow.fix_abicritical()
if nfixed: print("Fixed %d AbiCritical error(s)" % nfixed)
# update database
flow.pickle_dump()
# Submit the tasks that are ready.
try:
nlaunch = PyLauncher(flow).rapidfire(max_nlaunch=max_nlaunch, sleep_time=10)
self.nlaunch += nlaunch
if nlaunch:
cprint("[%s] Number of launches: %d" % (time.asctime(), nlaunch), "yellow")
except Exception:
excs.append(straceback())
# check status.
flow.show_status()
if excs:
logger.critical("*** Scheduler exceptions:\n *** %s" % "\n".join(excs))
self.exceptions.extend(excs)
def create(self):
"""
create single abinit G0W0 flow
"""
# manager = 'slurm' if 'ceci' in self.spec['mode'] else 'shell'
# an AbiStructure object has an overwritten version of get_sorted_structure that sorts according to Z
# this could also be pulled into the constructor of Abistructure
# abi_structure = self.structure.get_sorted_structure()
from abipy import abilab
item = copy.copy(self.structure.item)
self.structure.__class__ = abilab.Structure
self.structure = self.structure.get_sorted_structure_z()
self.structure.item = item
abi_structure = self.structure
manager = TaskManager.from_user_config()
# Initialize the flow.
flow = Flow(self.work_dir, manager, pickle_protocol=0)
# flow = Flow(self.work_dir, manager)
# kpoint grid defined over density 40 > ~ 3 3 3
if self.spec['converge'] and not self.all_converged:
# (2x2x2) gamma centered mesh for the convergence test on nbands and ecuteps
# if kp_in is present in the specs a kp_in X kp_in x kp_in mesh is used for the convergence study
if 'kp_in' in self.spec.data.keys():
if self.spec['kp_in'] > 9:
print('WARNING:\nkp_in should be < 13 to generate an n x n x n mesh\nfor larger values a grid with '
'density kp_in will be generated')
kppa = self.spec['kp_in']
else:
kppa = 2
else:
# use the specified density for the final calculation with the converged nbands and ecuteps of other
# stand alone calculations
kppa = self.spec['kp_grid_dens']
gamma = True
# 'standard' parameters for stand alone calculation
scf_nband = self.get_bands(self.structure) + 20
# additional bands to accommodate for nbdbuf and a bit extra
nscf_nband = [10 * self.get_bands(self.structure)]
nksmall = None
ecuteps = [8]
extra_abivars = dict()
# read user defined extra abivars from file 'extra_abivars' should be dictionary
extra_abivars.update(read_extra_abivars())
# self.bands_fac = 0.5 if 'gwcomp' in extra_abivars.keys() else 1
# self.convs['nscf_nbands']['test_range'] =
# tuple([self.bands_fac*x for x in self.convs['nscf_nbands']['test_range']])
ecut = extra_abivars.pop('ecut', 44)
ecutsigx = extra_abivars.pop('ecutsigx', 44)
if ecutsigx > ecut:
raise RuntimeError('ecutsigx can not be largen than ecut')
if ecutsigx < max(ecuteps):
raise RuntimeError('ecutsigx < ecuteps this is not realistic')
response_models = ['godby']
if 'ppmodel' in extra_abivars.keys():
response_models = [extra_abivars.pop('ppmodel')]
if self.option is not None:
for k in self.option.keys():
if k == 'ecut':
ecut = self.option[k]
if k in ['ecuteps', 'nscf_nbands']:
pass
else:
extra_abivars.update({k: self.option[k]})
try:
grid = read_grid_from_file(s_name(self.structure)+".full_res")['grid']
all_done = read_grid_from_file(s_name(self.structure)+".full_res")['all_done']
workdir = os.path.join(s_name(self.structure), 'w'+str(grid))
except (IOError, OSError):
grid = 0
all_done = False
workdir = None
if not all_done:
if (self.spec['test'] or self.spec['converge']) and not self.all_converged:
if self.spec['test']:
print('| setting test calculation')
tests = SingleAbinitGWWork(self.structure, self.spec).tests
response_models = []
else:
if grid == 0:
print('| setting convergence calculations for grid 0')
# tests = SingleAbinitGWWorkFlow(self.structure, self.spec).convs
tests = self.convs
else:
print('| extending grid')
# tests = expand(SingleAbinitGWWorkFlow(self.structure, self.spec).convs, grid)
tests = expand(self.convs, grid)
ecuteps = []
nscf_nband = []
for test in tests:
if tests[test]['level'] == 'scf':
if self.option is None:
extra_abivars.update({test + '_s': tests[test]['test_range']})
elif test in self.option:
extra_abivars.update({test: self.option[test]})
else:
extra_abivars.update({test + '_s': tests[test]['test_range']})
else:
for value in tests[test]['test_range']:
if test == 'nscf_nbands':
nscf_nband.append(value * self.get_bands(self.structure))
# scr_nband takes nscf_nbands if not specified
# sigma_nband takes scr_nbands if not specified
if test == 'ecuteps':
ecuteps.append(value)
if test == 'response_model':
response_models.append(value)
elif self.all_converged:
print('| setting up for testing the converged values at the high kp grid ')
# add a bandstructure and dos calculation
if os.path.isfile('bands'):
nksmall = -30
# negative value > only bandstructure
else:
nksmall = 30
# in this case a convergence study has already been performed.
# The resulting parameters are passed as option
ecuteps = [self.option['ecuteps'], self.option['ecuteps'] + self.convs['ecuteps']['test_range'][1] -
self.convs['ecuteps']['test_range'][0]]
nscf_nband = [self.option['nscf_nbands'], self.option['nscf_nbands'] + self.convs['nscf_nbands'][
'test_range'][1] - self.convs['nscf_nbands']['test_range'][0]]
# for option in self.option:
# if option not in ['ecuteps', 'nscf_nband']:
# extra_abivars.update({option + '_s': self.option[option]})
else:
print('| all is done for this material')
return
logger.info('ecuteps : %s ' % str(ecuteps))
logger.info('extra : %s ' % str(extra_abivars))
logger.info('nscf_nb : %s ' % str(nscf_nband))
inputs = g0w0_convergence_inputs(abi_structure, self.pseudo_table, kppa, nscf_nband, ecuteps, ecutsigx,
scf_nband, ecut, accuracy="normal", spin_mode="unpolarized", smearing=None,
response_models=response_models, charge=0.0, sigma_nband=None, scr_nband=None,
gamma=gamma, nksmall=nksmall, extra_abivars=extra_abivars)
work = G0W0Work(scf_inputs=inputs[0], nscf_inputs=inputs[1], scr_inputs=inputs[2], sigma_inputs=inputs[3])
# work = g0w0_extended_work(abi_structure, self.pseudo_table, kppa, nscf_nband, ecuteps, ecutsigx, scf_nband,
# accuracy="normal", spin_mode="unpolarized", smearing=None, response_models=response_models,
# charge=0.0, sigma_nband=None, scr_nband=None, gamma=gamma, nksmall=nksmall, **extra_abivars)
print(workdir)
flow.register_work(work, workdir=workdir)
return flow.allocate()
def _runem_all(self):
"""
This function checks the status of all tasks,
tries to fix tasks that went unconverged, abicritical, or queuecritical
and tries to run all the tasks that can be submitted.+
"""
excs = []
flow = self.flow
# Allow to change the manager at run-time
if self.use_dynamic_manager:
from pymatgen.io.abinit.tasks import TaskManager
new_manager = TaskManager.from_user_config()
for work in flow:
work.set_manager(new_manager)
nqjobs = 0
if self.contact_resource_manager:
# This call is expensive and therefore it's optional
nqjobs = flow.get_njobs_in_queue()
if nqjobs is None:
nqjobs = 0
if flow.manager.has_queue:
logger.warning('Cannot get njobs_inqueue')
if nqjobs >= self.max_njobs_inqueue:
print("Too many jobs in the queue: %s, returning" % nqjobs)
return
if self.max_nlaunches == -1:
max_nlaunch = self.max_njobs_inqueue - nqjobs
else:
max_nlaunch = min(self.max_njobs_inqueue - nqjobs,
self.max_nlaunches)
# check status.
flow.check_status(show=False)
# This check is not perfect, we should make a list of tasks to sumbit
# and select only the subset so that we don't exceeed mac_ncores_used
# Many sections of this code should be rewritten.
#if self.max_ncores_used is not None and flow.ncores_used > self.max_ncores_used:
if self.max_ncores_used is not None and flow.ncores_allocated > self.max_ncores_used:
print("Cannot exceed max_ncores_use:d %s" % self.max_ncores_used)
return
# Try to restart the unconverged tasks
# TODO: do not fire here but prepare for fireing in rapidfire
for task in self.flow.unconverged_tasks:
try:
logger.info("Flow will try restart task %s" % task)
fired = task.restart()
if fired:
self.nlaunch += 1
max_nlaunch -= 1
if max_nlaunch == 0:
logger.info(
"Restart: too many jobs in the queue, returning")
flow.pickle_dump()
return
except task.RestartError:
excs.append(straceback())
# Temporarily disable by MG because I don't know if fix_critical works after the
# introduction of the new qadapters
# reenabled by MsS disable things that do not work at low level
# fix only prepares for restarting, and sets to ready
if self.fix_qcritical:
nfixed = flow.fix_queue_critical()
if nfixed: print("Fixed %d QCritical error(s)" % nfixed)
nfixed = flow.fix_abicritical()
if nfixed: print("Fixed %d AbiCritical error(s)" % nfixed)
# update database
flow.pickle_dump()
# Submit the tasks that are ready.
try:
nlaunch = PyLauncher(flow).rapidfire(max_nlaunch=max_nlaunch,
sleep_time=10)
self.nlaunch += nlaunch
if nlaunch:
print("[%s] Number of launches: %d" %
(time.asctime(), nlaunch))
except Exception:
excs.append(straceback())
# check status.
flow.show_status()
if excs:
logger.critical("*** Scheduler exceptions:\n *** %s" %
"\n".join(excs))
self.exceptions.extend(excs)
def create(self):
"""
create single abinit G0W0 flow
"""
# manager = 'slurm' if 'ceci' in self.spec['mode'] else 'shell'
# an AbiStructure object has an overwritten version of get_sorted_structure that sorts according to Z
# this could also be pulled into the constructor of Abistructure
# abi_structure = self.structure.get_sorted_structure()
from abipy import abilab
item = copy.copy(self.structure.item)
self.structure.__class__ = abilab.Structure
self.structure = self.structure.get_sorted_structure_z()
self.structure.item = item
abi_structure = self.structure
manager = TaskManager.from_user_config()
# Initialize the flow.
flow = Flow(self.work_dir, manager, pickle_protocol=0)
# flow = Flow(self.work_dir, manager)
# kpoint grid defined over density 40 > ~ 3 3 3
if self.spec['converge'] and not self.all_converged:
# (2x2x2) gamma centered mesh for the convergence test on nbands and ecuteps
# if kp_in is present in the specs a kp_in X kp_in x kp_in mesh is used for the convergence study
if 'kp_in' in self.spec.data.keys():
if self.spec['kp_in'] > 9:
print(
'WARNING:\nkp_in should be < 13 to generate an n x n x n mesh\nfor larger values a grid with '
'density kp_in will be generated')
kppa = self.spec['kp_in']
else:
kppa = 2
else:
# use the specified density for the final calculation with the converged nbands and ecuteps of other
# stand alone calculations
kppa = self.spec['kp_grid_dens']
gamma = True
# 'standard' parameters for stand alone calculation
scf_nband = self.get_bands(self.structure) + 20
# additional bands to accommodate for nbdbuf and a bit extra
nscf_nband = [10 * self.get_bands(self.structure)]
nksmall = None
ecuteps = [8]
extra_abivars = dict()
# read user defined extra abivars from file 'extra_abivars' should be dictionary
extra_abivars.update(read_extra_abivars())
# self.bands_fac = 0.5 if 'gwcomp' in extra_abivars.keys() else 1
# self.convs['nscf_nbands']['test_range'] =
# tuple([self.bands_fac*x for x in self.convs['nscf_nbands']['test_range']])
ecut = extra_abivars.pop('ecut', 44)
ecutsigx = extra_abivars.pop('ecutsigx', 44)
if ecutsigx > ecut:
raise RuntimeError('ecutsigx can not be largen than ecut')
if ecutsigx < max(ecuteps):
raise RuntimeError('ecutsigx < ecuteps this is not realistic')
response_models = ['godby']
if 'ppmodel' in extra_abivars.keys():
response_models = [extra_abivars.pop('ppmodel')]
if self.option is not None:
for k in self.option.keys():
if k == 'ecut':
ecut = self.option[k]
if k in ['ecuteps', 'nscf_nbands']:
pass
else:
extra_abivars.update({k: self.option[k]})
try:
grid = read_grid_from_file(s_name(self.structure) +
".full_res")['grid']
all_done = read_grid_from_file(
s_name(self.structure) + ".full_res")['all_done']
workdir = os.path.join(s_name(self.structure), 'w' + str(grid))
except (IOError, OSError):
grid = 0
all_done = False
workdir = None
if not all_done:
if (self.spec['test']
or self.spec['converge']) and not self.all_converged:
if self.spec['test']:
print('| setting test calculation')
tests = SingleAbinitGWWork(self.structure, self.spec).tests
response_models = []
else:
if grid == 0:
print('| setting convergence calculations for grid 0')
# tests = SingleAbinitGWWorkFlow(self.structure, self.spec).convs
tests = self.convs
else:
print('| extending grid')
# tests = expand(SingleAbinitGWWorkFlow(self.structure, self.spec).convs, grid)
tests = expand(self.convs, grid)
ecuteps = []
nscf_nband = []
for test in tests:
if tests[test]['level'] == 'scf':
if self.option is None:
extra_abivars.update(
{test + '_s': tests[test]['test_range']})
elif test in self.option:
extra_abivars.update({test: self.option[test]})
else:
extra_abivars.update(
{test + '_s': tests[test]['test_range']})
else:
for value in tests[test]['test_range']:
if test == 'nscf_nbands':
nscf_nband.append(
value * self.get_bands(self.structure))
# scr_nband takes nscf_nbands if not specified
# sigma_nband takes scr_nbands if not specified
if test == 'ecuteps':
ecuteps.append(value)
if test == 'response_model':
response_models.append(value)
elif self.all_converged:
print(
'| setting up for testing the converged values at the high kp grid '
)
# add a bandstructure and dos calculation
if os.path.isfile('bands'):
nksmall = -30
# negative value > only bandstructure
else:
nksmall = 30
# in this case a convergence study has already been performed.
# The resulting parameters are passed as option
ecuteps = [
self.option['ecuteps'], self.option['ecuteps'] +
self.convs['ecuteps']['test_range'][1] -
self.convs['ecuteps']['test_range'][0]
]
nscf_nband = [
self.option['nscf_nbands'], self.option['nscf_nbands'] +
self.convs['nscf_nbands']['test_range'][1] -
self.convs['nscf_nbands']['test_range'][0]
]
# for option in self.option:
# if option not in ['ecuteps', 'nscf_nband']:
# extra_abivars.update({option + '_s': self.option[option]})
else:
print('| all is done for this material')
return
logger.info('ecuteps : %s ' % str(ecuteps))
logger.info('extra : %s ' % str(extra_abivars))
logger.info('nscf_nb : %s ' % str(nscf_nband))
inputs = g0w0_convergence_inputs(abi_structure,
self.pseudo_table,
kppa,
nscf_nband,
ecuteps,
ecutsigx,
scf_nband,
ecut,
accuracy="normal",
spin_mode="unpolarized",
smearing=None,
response_models=response_models,
charge=0.0,
sigma_nband=None,
scr_nband=None,
gamma=gamma,
nksmall=nksmall,
extra_abivars=extra_abivars)
work = G0W0Work(scf_inputs=inputs[0],
nscf_inputs=inputs[1],
scr_inputs=inputs[2],
sigma_inputs=inputs[3])
# work = g0w0_extended_work(abi_structure, self.pseudo_table, kppa, nscf_nband, ecuteps, ecutsigx, scf_nband,
# accuracy="normal", spin_mode="unpolarized", smearing=None, response_models=response_models,
# charge=0.0, sigma_nband=None, scr_nband=None, gamma=gamma, nksmall=nksmall, **extra_abivars)
print(workdir)
flow.register_work(work, workdir=workdir)
return flow.allocate()
