def __init__(self):
super(Driver, self).__init__()
self.recorders = RecordingManager()
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option('inequality_constraints', True)
self.supports.add_option('equality_constraints', True)
self.supports.add_option('linear_constraints', True)
self.supports.add_option('multiple_objectives', True)
self.supports.add_option('two_sided_constraints', True)
self.supports.add_option('integer_design_vars', True)
# inheriting Drivers should override this setting and set it to False
# if they don't use gradients.
self.supports.add_option('gradients', True)
# This driver's options
self.options = OptionsDictionary()
self._desvars = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
self._vars_to_record = None
# We take root during setup
self.root = None
self.iter_count = 0
self.dv_conversions = {}
self.fn_conversions = {}
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict
options dictionary.
"""
self._system = None
self._depth = 0
self._vec_names = None
self._mode = 'fwd'
self._iter_count = 0
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
self.options.declare('maxiter', types=int, default=10,
desc='maximum number of iterations')
self.options.declare('atol', default=1e-10,
desc='absolute error tolerance')
self.options.declare('rtol', default=1e-10,
desc='relative error tolerance')
self.options.declare('iprint', types=int, default=1,
desc='whether to print output')
self.options.declare('err_on_maxiter', types=bool, default=False,
desc="When True, AnalysisError will be raised if we don't converge.")
# Case recording options
self.recording_options.declare('record_abs_error', types=bool, default=True,
desc='Set to True to record absolute error at the \
solver level')
self.recording_options.declare('record_rel_error', types=bool, default=True,
desc='Set to True to record relative error at the \
solver level')
self.recording_options.declare('record_solver_residuals', types=bool, default=False,
desc='Set to True to record residuals at the solver level')
self.recording_options.declare('record_metadata', types=bool, desc='Record metadata',
default=True)
self.recording_options.declare('includes', types=list, default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
# Case recording related
self._filtered_vars_to_record = {}
self._norm0 = 0.0
# What the solver supports.
self.supports = OptionsDictionary()
self.supports.declare('gradients', types=bool, default=False)
self._declare_options()
self.options.update(kwargs)
self.metadata = {}
self._rec_mgr = RecordingManager()
self.cite = ""
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.recorders = RecordingManager()
self.local_meta = None
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.options.add_option(
'err_on_maxiter',
False,
desc='If True, raise an AnalysisError if not converged at maxiter.'
)
self.recorders = RecordingManager()
self.local_meta = None
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = "Set to 0 to print only failures, set to 1 to print iteration totals to" + \
"stdout, set to 2 to print the residual each iteration to stdout," + \
"or -1 to suppress all printing."
self.options.add_option('iprint', 0, values=[-1, 0, 1, 2], desc=desc)
self.options.add_option(
'err_on_maxiter',
False,
desc='If True, raise an AnalysisError if not converged at maxiter.'
)
self.recorders = RecordingManager()
self.local_meta = None
def __init__(self):
super(Driver, self).__init__()
self.recorders = RecordingManager()
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option("inequality_constraints", True)
self.supports.add_option("equality_constraints", True)
self.supports.add_option("linear_constraints", True)
self.supports.add_option("multiple_objectives", True)
self.supports.add_option("two_sided_constraints", True)
self.supports.add_option("integer_design_vars", True)
# This driver's options
self.options = OptionsDictionary()
self._desvars = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
self._vars_to_record = None
# We take root during setup
self.root = None
self.iter_count = 0
self.dv_conversions = {}
self.fn_conversions = {}
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict
options dictionary.
"""
self._system = None
self._depth = 0
self._vec_names = None
self._mode = 'fwd'
self._iter_count = 0
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
self.options.declare('maxiter', types=int, default=10,
desc='maximum number of iterations')
self.options.declare('atol', default=1e-10,
desc='absolute error tolerance')
self.options.declare('rtol', default=1e-10,
desc='relative error tolerance')
self.options.declare('iprint', types=int, default=1,
desc='whether to print output')
self.options.declare('err_on_maxiter', types=bool, default=False,
desc="When True, AnalysisError will be raised if we don't converge.")
# Case recording options
self.recording_options.declare('record_abs_error', types=bool, default=True,
desc='Set to True to record absolute error at the \
solver level')
self.recording_options.declare('record_rel_error', types=bool, default=True,
desc='Set to True to record relative error at the \
solver level')
self.recording_options.declare('record_solver_residuals', types=bool, default=False,
desc='Set to True to record residuals at the solver level')
self.recording_options.declare('record_metadata', types=bool, desc='Record metadata',
default=True)
self.recording_options.declare('includes', types=list, default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
# Case recording related
self._filtered_vars_to_record = {}
self._norm0 = 0.0
# What the solver supports.
self.supports = OptionsDictionary()
self.supports.declare('gradients', types=bool, default=False)
self._declare_options()
self.options.update(kwargs)
self.metadata = {}
self._rec_mgr = RecordingManager()
self.cite = ""
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict
options dictionary.
"""
self._system = None
self._depth = 0
self._vec_names = None
self._mode = 'fwd'
self._iter_count = 0
self.options = OptionsDictionary()
self.options.declare('maxiter',
type_=int,
default=10,
desc='maximum number of iterations')
self.options.declare('atol',
default=1e-10,
desc='absolute error tolerance')
self.options.declare('rtol',
default=1e-10,
desc='relative error tolerance')
self.options.declare('iprint',
type_=int,
default=1,
desc='whether to print output')
self.options.declare(
'err_on_maxiter',
type_=bool,
default=False,
desc="When True, AnlysisError will be raised if we don't convege.")
# What the solver supports.
self.supports = OptionsDictionary()
self.supports.declare('gradients', type_=bool, default=False)
self._declare_options()
self.options.update(kwargs)
self.metadata = {}
self._rec_mgr = RecordingManager()
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.recorders = RecordingManager()
self.local_meta = None
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.options.add_option('err_on_maxiter', False,
desc='If True, raise an AnalysisError if not converged at maxiter.')
self.recorders = RecordingManager()
self.local_meta = None
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = "Set to 0 to print only failures, set to 1 to print iteration totals to" + \
"stdout, set to 2 to print the residual each iteration to stdout," + \
"or -1 to suppress all printing."
self.options.add_option('iprint', 0, values=[-1, 0, 1, 2], desc=desc)
self.options.add_option('err_on_maxiter', False,
desc='If True, raise an AnalysisError if not converged at maxiter.')
self.recorders = RecordingManager()
self.local_meta = None
def __init__(self):
"""
Initialize the driver.
"""
self._rec_mgr = RecordingManager()
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
self.options = OptionsDictionary()
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints',
type_=bool,
default=False)
self.supports.declare('equality_constraints',
type_=bool,
default=False)
self.supports.declare('linear_constraints', type_=bool, default=False)
self.supports.declare('two_sided_constraints',
type_=bool,
default=False)
self.supports.declare('multiple_objectives', type_=bool, default=False)
self.supports.declare('integer_design_vars', type_=bool, default=False)
self.supports.declare('gradients', type_=bool, default=False)
self.supports.declare('active_set', type_=bool, default=False)
self.iter_count = 0
self.metadata = None
self._model_viewer_data = None
# TODO, support these in Openmdao blue
self.supports.declare('integer_design_vars', type_=bool, default=False)
self.fail = False
def __init__(self):
"""
Initialize the driver.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
###########################
self.options.declare(
'debug_print',
types=list,
is_valid=_is_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars','ln_cons',"
"'nl_cons','objs'",
default=[])
###########################
self.recording_options.declare('record_metadata',
types=bool,
desc='Record metadata',
default=True)
self.recording_options.declare(
'record_desvars',
types=bool,
default=True,
desc='Set to True to record design variables at the \
driver level')
self.recording_options.declare(
'record_responses',
types=bool,
default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare(
'record_objectives',
types=bool,
default=True,
desc='Set to True to record objectives at the \
driver level')
self.recording_options.declare(
'record_constraints',
types=bool,
default=True,
desc='Set to True to record constraints at the \
driver level')
self.recording_options.declare(
'includes',
types=list,
default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare(
'record_derivatives',
types=bool,
default=False,
desc='Set to True to record derivatives at the driver \
level')
###########################
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints',
types=bool,
default=False)
self.supports.declare('equality_constraints',
types=bool,
default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints',
types=bool,
default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives',
types=bool,
default=False)
# Debug printing.
self.debug_print = OptionsDictionary()
self.debug_print.declare(
'debug_print',
types=bool,
default=False,
desc='Overall option to turn on Driver debug printing')
self.debug_print.declare('debug_print_desvars',
types=bool,
default=False,
desc='Print design variables')
self.debug_print.declare('debug_print_nl_con',
types=bool,
default=False,
desc='Print nonlinear constraints')
self.debug_print.declare('debug_print_ln_con',
types=bool,
default=False,
desc='Print linear constraints')
self.debug_print.declare('debug_print_objective',
types=bool,
default=False,
desc='Print objectives')
self.iter_count = 0
self.metadata = None
self._model_viewer_data = None
self.cite = ""
# TODO, support these in OpenMDAO
self.supports.declare('integer_design_vars', types=bool, default=False)
self._simul_coloring_info = None
self._res_jacs = {}
self.fail = False
class Driver(object):
"""
Top-level container for the systems and drivers.
Attributes
----------
fail : bool
Reports whether the driver ran successfully.
iter_count : int
Keep track of iterations for case recording.
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
recording_options : <OptionsDictionary>
Dictionary with driver recording options.
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
_problem : <Problem>
Pointer to the containing problem.
supports : <OptionsDictionary>
Provides a consistent way for drivers to declare what features they support.
_designvars : dict
Contains all design variable info.
_designvars_discrete : list
List of design variables that are discrete.
_cons : dict
Contains all constraint info.
_objs : dict
Contains all objective info.
_responses : dict
Contains all response info.
_remote_dvs : dict
Dict of design variables that are remote on at least one proc. Values are
(owning rank, size).
_remote_cons : dict
Dict of constraints that are remote on at least one proc. Values are
(owning rank, size).
_remote_objs : dict
Dict of objectives that are remote on at least one proc. Values are
(owning rank, size).
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this driver.
_coloring_info : dict
Metadata pertaining to total coloring.
_total_jac_sparsity : dict, str, or None
Specifies sparsity of sub-jacobians of the total jacobian. Only used by pyOptSparseDriver.
_res_jacs : dict
Dict of sparse subjacobians for use with certain optimizers, e.g. pyOptSparseDriver.
_total_jac : _TotalJacInfo or None
Cached total jacobian handling object.
"""
def __init__(self, **kwargs):
"""
Initialize the driver.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Driver options.
"""
self._rec_mgr = RecordingManager()
self._problem = None
self._designvars = None
self._designvars_discrete = []
self._cons = None
self._objs = None
self._responses = None
# Driver options
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.options.declare(
'debug_print',
types=list,
check_valid=_check_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars', 'ln_cons', "
"'nl_cons', 'objs', 'totals'",
default=[])
# Case recording options
self.recording_options = OptionsDictionary(
parent_name=type(self).__name__)
self.recording_options.declare(
'record_model_metadata',
types=bool,
default=True,
desc='Record metadata for all Systems in the model')
self.recording_options.declare(
'record_desvars',
types=bool,
default=True,
desc='Set to True to record design variables at the '
'driver level')
self.recording_options.declare(
'record_responses',
types=bool,
default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare(
'record_objectives',
types=bool,
default=True,
desc='Set to True to record objectives at the driver level')
self.recording_options.declare(
'record_constraints',
types=bool,
default=True,
desc='Set to True to record constraints at the '
'driver level')
self.recording_options.declare(
'includes',
types=list,
default=[],
desc='Patterns for variables to include in recording')
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare(
'record_derivatives',
types=bool,
default=False,
desc='Set to True to record derivatives at the driver '
'level')
self.recording_options.declare(
'record_inputs',
types=bool,
default=True,
desc='Set to True to record inputs at the driver level')
# What the driver supports.
self.supports = OptionsDictionary(parent_name=type(self).__name__)
self.supports.declare('inequality_constraints',
types=bool,
default=False)
self.supports.declare('equality_constraints',
types=bool,
default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints',
types=bool,
default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives',
types=bool,
default=False)
self.supports.declare('total_jac_sparsity', types=bool, default=False)
self.iter_count = 0
self.cite = ""
self._coloring_info = coloring_mod._DEF_COMP_SPARSITY_ARGS.copy()
self._coloring_info['coloring'] = None
self._coloring_info['dynamic'] = False
self._coloring_info['static'] = None
self._total_jac_sparsity = None
self._res_jacs = {}
self._total_jac = None
self.fail = False
self._declare_options()
self.options.update(kwargs)
@property
def msginfo(self):
"""
Return info to prepend to messages.
Returns
-------
str
Info to prepend to messages.
"""
return type(self).__name__
def add_recorder(self, recorder):
"""
Add a recorder to the driver.
Parameters
----------
recorder : CaseRecorder
A recorder instance.
"""
self._rec_mgr.append(recorder)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
# shut down all recorders
self._rec_mgr.shutdown()
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
This is optionally implemented by subclasses of Driver.
"""
pass
def _setup_comm(self, comm):
"""
Perform any driver-specific setup of communicators for the model.
Parameters
----------
comm : MPI.Comm or <FakeComm> or None
The communicator for the Problem.
Returns
-------
MPI.Comm or <FakeComm> or None
The communicator for the Problem model.
"""
return comm
def _setup_driver(self, problem):
"""
Prepare the driver for execution.
This is the final thing to run during setup.
Parameters
----------
problem : <Problem>
Pointer to the containing problem.
"""
self._problem = problem
self._recording_iter = problem._recording_iter
model = problem.model
self._total_jac = None
self._has_scaling = (np.any([
r['scaler'] is not None for r in itervalues(self._responses)
]) or np.any(
[dv['scaler'] is not None for dv in itervalues(self._designvars)]))
# Determine if any design variables are discrete.
self._designvars_discrete = [
dv for dv in self._designvars if dv in model._discrete_outputs
]
if not self.supports['integer_design_vars'] and len(
self._designvars_discrete) > 0:
msg = "Discrete design variables are not supported by this driver: "
msg += '.'.join(self._designvars_discrete)
raise RuntimeError(msg)
con_set = set()
obj_set = set()
dv_set = set()
self._remote_dvs = dv_dict = {}
self._remote_cons = con_dict = {}
self._remote_objs = obj_dict = {}
# Now determine if later we'll need to allgather cons, objs, or desvars.
if model.comm.size > 1 and model._subsystems_allprocs:
local_out_vars = set(model._outputs._views)
remote_dvs = set(self._designvars) - local_out_vars
remote_cons = set(self._cons) - local_out_vars
remote_objs = set(self._objs) - local_out_vars
all_remote_vois = model.comm.allgather(
(remote_dvs, remote_cons, remote_objs))
for rem_dvs, rem_cons, rem_objs in all_remote_vois:
con_set.update(rem_cons)
obj_set.update(rem_objs)
dv_set.update(rem_dvs)
# If we have remote VOIs, pick an owning rank for each and use that
# to bcast to others later
owning_ranks = model._owning_rank
sizes = model._var_sizes['nonlinear']['output']
for i, vname in enumerate(model._var_allprocs_abs_names['output']):
owner = owning_ranks[vname]
if vname in dv_set:
dv_dict[vname] = (owner, sizes[owner, i])
if vname in con_set:
con_dict[vname] = (owner, sizes[owner, i])
if vname in obj_set:
obj_dict[vname] = (owner, sizes[owner, i])
self._remote_responses = self._remote_cons.copy()
self._remote_responses.update(self._remote_objs)
# set up simultaneous deriv coloring
if coloring_mod._use_total_sparsity:
# reset the coloring
if self._coloring_info['dynamic'] or self._coloring_info[
'static'] is not None:
self._coloring_info['coloring'] = None
coloring = self._get_static_coloring()
if coloring is not None and self.supports[
'simultaneous_derivatives']:
if model._owns_approx_jac:
coloring._check_config_partial(model)
else:
coloring._check_config_total(self)
self._setup_simul_coloring()
def _check_for_missing_objective(self):
"""
Check for missing objective and raise error if no objectives found.
"""
if len(self._objs) == 0:
msg = "Driver requires objective to be declared"
raise RuntimeError(msg)
def _get_vars_to_record(self, recording_options):
"""
Get variables to record based on recording options.
Parameters
----------
recording_options : <OptionsDictionary>
Dictionary with recording options.
Returns
-------
dict
Dictionary containing lists of variables to record.
"""
problem = self._problem
model = problem.model
if MPI:
# TODO: Eventually, we think we can get rid of this next check.
# But to be safe, we are leaving it in there.
if not model.is_active():
raise RuntimeError(
"RecordingManager.startup should never be called when "
"running in parallel on an inactive System")
rrank = problem.comm.rank
rowned = model._owning_rank
incl = recording_options['includes']
excl = recording_options['excludes']
# includes and excludes for outputs are specified using promoted names
# NOTE: only local var names are in abs2prom, all will be gathered later
abs2prom = model._var_abs2prom['output']
all_desvars = {
n
for n in self._designvars
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)
}
all_objectives = {
n
for n in self._objs
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)
}
all_constraints = {
n
for n in self._cons
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)
}
# design variables, objectives and constraints are always in the options
mydesvars = myobjectives = myconstraints = set()
if recording_options['record_desvars']:
if MPI:
mydesvars = [n for n in all_desvars if rrank == rowned[n]]
else:
mydesvars = list(all_desvars)
if recording_options['record_objectives']:
if MPI:
myobjectives = [
n for n in all_objectives if rrank == rowned[n]
]
else:
myobjectives = list(all_objectives)
if recording_options['record_constraints']:
if MPI:
myconstraints = [
n for n in all_constraints if rrank == rowned[n]
]
else:
myconstraints = list(all_constraints)
filtered_vars_to_record = {
'des': mydesvars,
'obj': myobjectives,
'con': myconstraints
}
# responses (if in options)
if 'record_responses' in recording_options:
myresponses = set()
if recording_options['record_responses']:
myresponses = {
n
for n in self._responses if n in abs2prom
and check_path(abs2prom[n], incl, excl, True)
}
if MPI:
myresponses = [
n for n in myresponses if rrank == rowned[n]
]
filtered_vars_to_record['res'] = list(myresponses)
# inputs (if in options)
if 'record_inputs' in recording_options:
myinputs = set()
if recording_options['record_inputs']:
myinputs = {
n
for n in model._inputs if check_path(n, incl, excl)
}
if MPI:
# gather the variables from all ranks to rank 0
all_vars = model.comm.gather(myinputs, root=0)
if MPI.COMM_WORLD.rank == 0:
myinputs = all_vars[-1]
for d in all_vars[:-1]:
myinputs.update(d)
myinputs = [n for n in myinputs if rrank == rowned[n]]
filtered_vars_to_record['in'] = list(myinputs)
# system outputs
myoutputs = set()
if incl:
myoutputs = {
n
for n in model._outputs
if n in abs2prom and check_path(abs2prom[n], incl, excl)
}
if MPI:
# gather the variables from all ranks to rank 0
all_vars = model.comm.gather(myoutputs, root=0)
if MPI.COMM_WORLD.rank == 0:
myoutputs = all_vars[-1]
for d in all_vars[:-1]:
myoutputs.update(d)
# de-duplicate
myoutputs = myoutputs.difference(all_desvars, all_objectives,
all_constraints)
if MPI:
myoutputs = [n for n in myoutputs if rrank == rowned[n]]
filtered_vars_to_record['sys'] = list(myoutputs)
return filtered_vars_to_record
def _setup_recording(self):
"""
Set up case recording.
"""
self._filtered_vars_to_record = self._get_vars_to_record(
self.recording_options)
self._rec_mgr.startup(self)
# record the system metadata to the recorders attached to this Driver
if self.recording_options['record_model_metadata']:
for sub in self._problem.model.system_iter(recurse=True,
include_self=True):
self._rec_mgr.record_metadata(sub)
def _get_voi_val(self,
name,
meta,
remote_vois,
driver_scaling=True,
ignore_indices=False):
"""
Get the value of a variable of interest (objective, constraint, or design var).
This will retrieve the value if the VOI is remote.
Parameters
----------
name : str
Name of the variable of interest.
meta : dict
Metadata for the variable of interest.
remote_vois : dict
Dict containing (owning_rank, size) for all remote vois of a particular
type (design var, constraint, or objective).
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
float or ndarray
The value of the named variable of interest.
"""
model = self._problem.model
comm = model.comm
vec = model._outputs._views_flat
indices = meta['indices']
if name in remote_vois:
owner, size = remote_vois[name]
if owner == comm.rank:
if indices is None or ignore_indices:
val = vec[name].copy()
else:
val = vec[name][indices]
else:
if not (indices is None or ignore_indices):
size = len(indices)
val = np.empty(size)
comm.Bcast(val, root=owner)
else:
if name in self._designvars_discrete:
val = model._discrete_outputs[name]
# At present, only integers are supported by OpenMDAO drivers.
# We check the values here.
valid = True
msg = "Only integer scalars or ndarrays are supported as values for " + \
"discrete variables when used as a design variable. "
if np.isscalar(val) and not isinstance(val, int):
msg += "A value of type '{}' was specified.".format(
val.__class__.__name__)
valid = False
elif isinstance(val,
np.ndarray) and not np.issubdtype(val[0], int):
msg += "An array of type '{}' was specified.".format(
val[0].__class__.__name__)
valid = False
if valid is False:
raise ValueError(msg)
elif indices is None or ignore_indices:
val = vec[name].copy()
else:
val = vec[name][indices]
if self._has_scaling and driver_scaling:
# Scale design variable values
adder = meta['adder']
if adder is not None:
val += adder
scaler = meta['scaler']
if scaler is not None:
val *= scaler
return val
def get_design_var_values(self,
filter=None,
driver_scaling=True,
ignore_indices=False):
"""
Return the design variable values.
This is called to gather the initial design variable state.
Parameters
----------
filter : list
List of desvar names used by recorders.
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
dict
Dictionary containing values of each design variable.
"""
if filter:
dvs = filter
else:
# use all the designvars
dvs = self._designvars
return {
n: self._get_voi_val(n,
self._designvars[n],
self._remote_dvs,
driver_scaling=driver_scaling,
ignore_indices=ignore_indices)
for n in dvs
}
def set_design_var(self, name, value):
"""
Set the value of a design variable.
Parameters
----------
name : str
Global pathname of the design variable.
value : float or ndarray
Value for the design variable.
"""
problem = self._problem
if (name in self._remote_dvs
and problem.model._owning_rank[name] != problem.comm.rank):
return
meta = self._designvars[name]
indices = meta['indices']
if indices is None:
indices = slice(None)
if name in self._designvars_discrete:
problem.model._discrete_outputs[name] = int(value)
else:
desvar = problem.model._outputs._views_flat[name]
desvar[indices] = value
# Undo driver scaling when setting design var values into model.
if self._has_scaling:
scaler = meta['scaler']
if scaler is not None:
desvar[indices] *= 1.0 / scaler
adder = meta['adder']
if adder is not None:
desvar[indices] -= adder
def get_response_values(self, filter=None):
"""
Return response values.
Parameters
----------
filter : list
List of response names used by recorders.
Returns
-------
dict
Dictionary containing values of each response.
"""
if filter:
resps = filter
else:
resps = self._responses
return {
n: self._get_voi_val(n, self._responses[n], self._remote_objs)
for n in resps
}
def get_objective_values(self,
driver_scaling=True,
filter=None,
ignore_indices=False):
"""
Return objective values.
Parameters
----------
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
filter : list
List of objective names used by recorders.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
dict
Dictionary containing values of each objective.
"""
if filter:
objs = filter
else:
objs = self._objs
return {
n: self._get_voi_val(n,
self._objs[n],
self._remote_objs,
driver_scaling=driver_scaling,
ignore_indices=ignore_indices)
for n in objs
}
def get_constraint_values(self,
ctype='all',
lintype='all',
driver_scaling=True,
filter=None,
ignore_indices=False):
"""
Return constraint values.
Parameters
----------
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
filter : list
List of constraint names used by recorders.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
dict
Dictionary containing values of each constraint.
"""
if filter is not None:
cons = filter
else:
cons = self._cons
con_dict = {}
for name in cons:
meta = self._cons[name]
if lintype == 'linear' and not meta['linear']:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
con_dict[name] = self._get_voi_val(name,
meta,
self._remote_cons,
driver_scaling=driver_scaling,
ignore_indices=ignore_indices)
return con_dict
def _get_ordered_nl_responses(self):
"""
Return the names of nonlinear responses in the order used by the driver.
Default order is objectives followed by nonlinear constraints. This is used for
simultaneous derivative coloring and sparsity determination.
Returns
-------
list of str
The nonlinear response names in order.
"""
order = list(self._objs)
order.extend(n for n, meta in iteritems(self._cons)
if not ('linear' in meta and meta['linear']))
return order
def _update_voi_meta(self, model):
"""
Collect response and design var metadata from the model and size desvars and responses.
Parameters
----------
model : System
The System that represents the entire model.
Returns
-------
int
Total size of responses, with linear constraints excluded.
int
Total size of design vars.
"""
self._objs = objs = OrderedDict()
self._cons = cons = OrderedDict()
self._responses = resps = model.get_responses(recurse=True)
for name, data in iteritems(resps):
if data['type'] == 'con':
cons[name] = data
else:
objs[name] = data
response_size = sum(resps[n]['size']
for n in self._get_ordered_nl_responses())
# Gather up the information for design vars.
self._designvars = designvars = model.get_design_vars(recurse=True)
desvar_size = sum(data['size'] for data in itervalues(designvars))
return response_size, desvar_size
def run(self):
"""
Execute this driver.
The base `Driver` just runs the model. All other drivers overload
this method.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
with RecordingDebugging(self._get_name(), self.iter_count, self):
self._problem.model.run_solve_nonlinear()
self.iter_count += 1
return False
def _compute_totals(self,
of=None,
wrt=None,
return_format='flat_dict',
global_names=True):
"""
Compute derivatives of desired quantities with respect to desired inputs.
All derivatives are returned using driver scaling.
Parameters
----------
of : list of variable name strings or None
Variables whose derivatives will be computed. Default is None, which
uses the driver's objectives and constraints.
wrt : list of variable name strings or None
Variables with respect to which the derivatives will be computed.
Default is None, which uses the driver's desvars.
return_format : string
Format to return the derivatives. Default is a 'flat_dict', which
returns them in a dictionary whose keys are tuples of form (of, wrt). For
the scipy optimizer, 'array' is also supported.
global_names : bool
Set to True when passing in global names to skip some translation steps.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
problem = self._problem
total_jac = self._total_jac
debug_print = 'totals' in self.options['debug_print'] and (
not MPI or MPI.COMM_WORLD.rank == 0)
if debug_print:
header = 'Driver total derivatives for iteration: ' + str(
self.iter_count)
print(header)
print(len(header) * '-' + '\n')
if problem.model._owns_approx_jac:
self._recording_iter.stack.append(('_compute_totals_approx', 0))
try:
if total_jac is None:
total_jac = _TotalJacInfo(problem,
of,
wrt,
global_names,
return_format,
approx=True,
debug_print=debug_print)
# Don't cache linear constraint jacobian
if not total_jac.has_lin_cons:
self._total_jac = total_jac
totals = total_jac.compute_totals_approx(initialize=True)
else:
totals = total_jac.compute_totals_approx()
finally:
self._recording_iter.stack.pop()
else:
if total_jac is None:
total_jac = _TotalJacInfo(problem,
of,
wrt,
global_names,
return_format,
debug_print=debug_print)
# don't cache linear constraint jacobian
if not total_jac.has_lin_cons:
self._total_jac = total_jac
self._recording_iter.stack.append(('_compute_totals', 0))
try:
totals = total_jac.compute_totals()
finally:
self._recording_iter.stack.pop()
if self._rec_mgr._recorders and self.recording_options[
'record_derivatives']:
metadata = create_local_meta(self._get_name())
total_jac.record_derivatives(self, metadata)
return totals
def record_iteration(self):
"""
Record an iteration of the current Driver.
"""
if not self._rec_mgr._recorders:
return
# Get the data to record (collective calls that get across all ranks)
opts = self.recording_options
filt = self._filtered_vars_to_record
if opts['record_desvars']:
des_vars = self.get_design_var_values(driver_scaling=False,
ignore_indices=True)
else:
des_vars = {}
if opts['record_objectives']:
obj_vars = self.get_objective_values(driver_scaling=False,
ignore_indices=True)
else:
obj_vars = {}
if opts['record_constraints']:
con_vars = self.get_constraint_values(driver_scaling=False,
ignore_indices=True)
else:
con_vars = {}
if opts['record_responses']:
# res_vars = self.get_response_values() # not really working yet
res_vars = {}
else:
res_vars = {}
des_vars = {name: des_vars[name] for name in filt['des']}
obj_vars = {name: obj_vars[name] for name in filt['obj']}
con_vars = {name: con_vars[name] for name in filt['con']}
# res_vars = {name: res_vars[name] for name in filt['res']}
model = self._problem.model
names = model._outputs._names
views = model._outputs._views
sys_vars = {name: views[name] for name in names if name in filt['sys']}
if self.recording_options['record_inputs']:
names = model._inputs._names
views = model._inputs._views
in_vars = {
name: views[name]
for name in names if name in filt['in']
}
else:
in_vars = {}
if MPI:
des_vars = self._gather_vars(model, des_vars)
res_vars = self._gather_vars(model, res_vars)
obj_vars = self._gather_vars(model, obj_vars)
con_vars = self._gather_vars(model, con_vars)
sys_vars = self._gather_vars(model, sys_vars)
in_vars = self._gather_vars(model, in_vars)
outs = {}
if not MPI or model.comm.rank == 0:
outs.update(des_vars)
outs.update(res_vars)
outs.update(obj_vars)
outs.update(con_vars)
outs.update(sys_vars)
data = {'out': outs, 'in': in_vars}
metadata = create_local_meta(self._get_name())
self._rec_mgr.record_iteration(self, data, metadata)
def _gather_vars(self, root, local_vars):
"""
Gather and return only variables listed in `local_vars` from the `root` System.
Parameters
----------
root : <System>
the root System for the Problem
local_vars : dict
local variable names and values
Returns
-------
dct : dict
variable names and values.
"""
# if trace:
# debug("gathering vars for recording in %s" % root.pathname)
all_vars = root.comm.gather(local_vars, root=0)
# if trace:
# debug("DONE gathering rec vars for %s" % root.pathname)
if root.comm.rank == 0:
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
return dct
def _get_name(self):
"""
Get name of current Driver.
Returns
-------
str
Name of current Driver.
"""
return "Driver"
def set_total_jac_sparsity(self, sparsity):
"""
Set the sparsity of sub-jacobians of the total jacobian.
Note: This currently will have no effect if you are not using the pyOptSparseDriver.
Parameters
----------
sparsity : str or dict
::
# Sparsity is a nested dictionary where the outer keys are response
# names, the inner keys are design variable names, and the value is a tuple of
# the form (row_list, col_list, shape).
{
resp1: {
dv1: (rows, cols, shape), # for sub-jac d_resp1/d_dv1
dv2: (rows, cols, shape),
...
},
resp2: {
...
}
...
}
"""
if self.supports['total_jac_sparsity']:
self._total_jac_sparsity = sparsity
else:
raise RuntimeError(
"Driver '%s' does not support setting of total jacobian sparsity."
% self._get_name())
def declare_coloring(
self,
num_full_jacs=coloring_mod.
_DEF_COMP_SPARSITY_ARGS['num_full_jacs'],
tol=coloring_mod._DEF_COMP_SPARSITY_ARGS['tol'],
orders=coloring_mod._DEF_COMP_SPARSITY_ARGS['orders'],
perturb_size=coloring_mod._DEF_COMP_SPARSITY_ARGS['perturb_size'],
min_improve_pct=coloring_mod.
_DEF_COMP_SPARSITY_ARGS['min_improve_pct'],
show_summary=coloring_mod._DEF_COMP_SPARSITY_ARGS['show_summary'],
show_sparsity=coloring_mod._DEF_COMP_SPARSITY_ARGS['show_sparsity']
):
"""
Set options for total deriv coloring.
Parameters
----------
num_full_jacs : int
Number of times to repeat partial jacobian computation when computing sparsity.
tol : float
Tolerance used to determine if an array entry is nonzero during sparsity determination.
orders : int
Number of orders above and below the tolerance to check during the tolerance sweep.
perturb_size : float
Size of input/output perturbation during generation of sparsity.
min_improve_pct : float
If coloring does not improve (decrease) the number of solves more than the given
percentage, coloring will not be used.
show_summary : bool
If True, display summary information after generating coloring.
show_sparsity : bool
If True, display sparsity with coloring info after generating coloring.
"""
self._coloring_info['num_full_jacs'] = num_full_jacs
self._coloring_info['tol'] = tol
self._coloring_info['orders'] = orders
self._coloring_info['perturb_size'] = perturb_size
self._coloring_info['min_improve_pct'] = min_improve_pct
if self._coloring_info['static'] is None:
self._coloring_info['dynamic'] = True
else:
self._coloring_info['dynamic'] = False
self._coloring_info['coloring'] = None
self._coloring_info['show_summary'] = show_summary
self._coloring_info['show_sparsity'] = show_sparsity
def use_fixed_coloring(self, coloring=coloring_mod._STD_COLORING_FNAME):
"""
Tell the driver to use a precomputed coloring.
Parameters
----------
coloring : str
A coloring filename. If no arg is passed, filename will be determined
automatically.
"""
if self.supports['simultaneous_derivatives']:
if coloring_mod._force_dyn_coloring and coloring is coloring_mod._STD_COLORING_FNAME:
# force the generation of a dynamic coloring this time
self._coloring_info['dynamic'] = True
self._coloring_info['static'] = None
else:
self._coloring_info['static'] = coloring
self._coloring_info['dynamic'] = False
self._coloring_info['coloring'] = None
else:
raise RuntimeError(
"Driver '%s' does not support simultaneous derivatives." %
self._get_name())
def set_simul_deriv_color(self, coloring):
"""
See use_fixed_coloring. This method is deprecated.
Parameters
----------
coloring : str or Coloring
Information about simultaneous coloring for design vars and responses. If a
string, then coloring is assumed to be the name of a file that contains the
coloring information in pickle format. Otherwise it must be a Coloring object.
See the docstring for Coloring for details.
"""
warn_deprecation(
"set_simul_deriv_color is deprecated. Use use_fixed_coloring instead."
)
self.use_fixed_coloring(coloring)
def _setup_tot_jac_sparsity(self):
"""
Set up total jacobian subjac sparsity.
Drivers that can use subjac sparsity should override this.
"""
pass
def _get_static_coloring(self):
"""
Get the Coloring for this driver.
If necessary, load the Coloring from a file.
Returns
-------
Coloring or None
The pre-existing or loaded Coloring, or None
"""
info = self._coloring_info
static = info['static']
if isinstance(static, coloring_mod.Coloring):
coloring = static
info['coloring'] = coloring
else:
coloring = info['coloring']
if coloring is not None:
return coloring
if static is coloring_mod._STD_COLORING_FNAME or isinstance(
static, string_types):
if static is coloring_mod._STD_COLORING_FNAME:
fname = self._get_total_coloring_fname()
else:
fname = static
print("loading total coloring from file %s" % fname)
coloring = info['coloring'] = coloring_mod.Coloring.load(fname)
info.update(coloring._meta)
return coloring
def _get_total_coloring_fname(self):
return os.path.join(self._problem.options['coloring_dir'],
'total_coloring.pkl')
def _setup_simul_coloring(self):
"""
Set up metadata for coloring of total derivative solution.
If set_coloring was called with a filename, load the coloring file.
"""
# command line simul_coloring uses this env var to turn pre-existing coloring off
if not coloring_mod._use_total_sparsity:
return
problem = self._problem
if not problem.model._use_derivatives:
simple_warning(
"Derivatives are turned off. Skipping simul deriv coloring.")
return
total_coloring = self._get_static_coloring()
if total_coloring._rev and problem._orig_mode not in ('rev', 'auto'):
revcol = total_coloring._rev[0][0]
if revcol:
raise RuntimeError(
"Simultaneous coloring does reverse solves but mode has "
"been set to '%s'" % problem._orig_mode)
if total_coloring._fwd and problem._orig_mode not in ('fwd', 'auto'):
fwdcol = total_coloring._fwd[0][0]
if fwdcol:
raise RuntimeError(
"Simultaneous coloring does forward solves but mode has "
"been set to '%s'" % problem._orig_mode)
def _pre_run_model_debug_print(self):
"""
Optionally print some debugging information before the model runs.
"""
debug_opt = self.options['debug_print']
if not debug_opt or debug_opt == ['totals']:
return
if not MPI or MPI.COMM_WORLD.rank == 0:
header = 'Driver debug print for iter coord: {}'.format(
self._recording_iter.get_formatted_iteration_coordinate())
print(header)
print(len(header) * '-')
if 'desvars' in debug_opt:
desvar_vals = self.get_design_var_values(driver_scaling=False,
ignore_indices=True)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Design Vars")
if desvar_vals:
# Print desvars in non_flattened state.
meta = self._problem.model._var_allprocs_abs2meta
for name in desvar_vals:
shape = meta[name]['shape']
desvar_vals[name] = desvar_vals[name].reshape(shape)
pprint.pprint(desvar_vals)
else:
print("None")
print()
sys.stdout.flush()
def _post_run_model_debug_print(self):
"""
Optionally print some debugging information after the model runs.
"""
if 'nl_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='nonlinear',
driver_scaling=False)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Nonlinear constraints")
if cons:
pprint.pprint(cons)
else:
print("None")
print()
if 'ln_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='linear',
driver_scaling=False)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Linear constraints")
if cons:
pprint.pprint(cons)
else:
print("None")
print()
if 'objs' in self.options['debug_print']:
objs = self.get_objective_values(driver_scaling=False)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Objectives")
if objs:
pprint.pprint(objs)
else:
print("None")
print()
sys.stdout.flush()
def __init__(self, **kwargs):
"""
Initialize the driver.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Driver options.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
# Driver options
self.options = OptionsDictionary()
self.options.declare(
'debug_print',
types=list,
is_valid=_is_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars', 'ln_cons', "
"'nl_cons', 'objs'",
default=[])
# Case recording options
self.recording_options = OptionsDictionary()
self.recording_options.declare('record_metadata',
types=bool,
default=True,
desc='Record metadata')
self.recording_options.declare(
'record_desvars',
types=bool,
default=True,
desc='Set to True to record design variables at the '
'driver level')
self.recording_options.declare(
'record_responses',
types=bool,
default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare(
'record_objectives',
types=bool,
default=True,
desc='Set to True to record objectives at the driver level')
self.recording_options.declare(
'record_constraints',
types=bool,
default=True,
desc='Set to True to record constraints at the '
'driver level')
self.recording_options.declare(
'includes',
types=list,
default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare(
'record_derivatives',
types=bool,
default=False,
desc='Set to True to record derivatives at the driver '
'level')
self.recording_options.declare(
'record_inputs',
types=bool,
default=True,
desc='Set to True to record inputs at the driver level')
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints',
types=bool,
default=False)
self.supports.declare('equality_constraints',
types=bool,
default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints',
types=bool,
default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives',
types=bool,
default=False)
self.supports.declare('total_jac_sparsity', types=bool, default=False)
# TODO, support these in OpenMDAO
self.supports.declare('integer_design_vars', types=bool, default=False)
self.iter_count = 0
self._model_viewer_data = None
self.cite = ""
self._simul_coloring_info = None
self._total_jac_sparsity = None
self._res_jacs = {}
self._total_jac = None
self.fail = False
self._declare_options()
self.options.update(kwargs)
class Solver(object):
"""
Base solver class.
This class is subclassed by NonlinearSolver and LinearSolver,
which are in turn subclassed by actual solver implementations.
Attributes
----------
_system : <System>
Pointer to the owning system.
_depth : int
How many subsolvers deep this solver is (0 means not a subsolver).
_vec_names : [str, ...]
List of right-hand-side (RHS) vector names.
_mode : str
'fwd' or 'rev', applicable to linear solvers only.
_iter_count : int
Number of iterations for the current invocation of the solver.
_rec_mgr : <RecordingManager>
object that manages all recorders added to this solver
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
options : <OptionsDictionary>
Options dictionary.
recording_options : <OptionsDictionary>
Recording options dictionary.
supports : <OptionsDictionary>
Options dictionary describing what features are supported by this
solver.
_filtered_vars_to_record : Dict
Dict of list of var names to record
_norm0 : float
Normalization factor
_problem_meta : dict
Problem level metadata.
"""
# Object to store some formatting for iprint that is shared across all solvers.
SOLVER = 'base_solver'
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Solver options.
"""
self._system = None
self._depth = 0
self._vec_names = None
self._mode = 'fwd'
self._iter_count = 0
self._problem_meta = None
# Solver options
self.options = OptionsDictionary(parent_name=self.msginfo)
self.options.declare('maxiter',
types=int,
default=10,
desc='maximum number of iterations')
self.options.declare('atol',
default=1e-10,
desc='absolute error tolerance')
self.options.declare('rtol',
default=1e-10,
desc='relative error tolerance')
self.options.declare('iprint',
types=int,
default=1,
desc='whether to print output')
self.options.declare(
'err_on_non_converge',
types=bool,
default=False,
desc="When True, AnalysisError will be raised if we don't converge."
)
# Case recording options
self.recording_options = OptionsDictionary(parent_name=self.msginfo)
self.recording_options.declare(
'record_abs_error',
types=bool,
default=True,
desc='Set to True to record absolute error at the \
solver level')
self.recording_options.declare(
'record_rel_error',
types=bool,
default=True,
desc='Set to True to record relative error at the \
solver level')
self.recording_options.declare(
'record_inputs',
types=bool,
default=True,
desc='Set to True to record inputs at the solver level')
self.recording_options.declare(
'record_outputs',
types=bool,
default=True,
desc='Set to True to record outputs at the solver level')
self.recording_options.declare(
'record_solver_residuals',
types=bool,
default=False,
desc='Set to True to record residuals at the solver level')
self.recording_options.declare(
'record_metadata',
types=bool,
desc='Deprecated. Recording '
'of metadata will always be done',
deprecation="The recording option, record_metadata, on "
"Solver is "
"deprecated. Recording of metadata will always be done",
default=True)
self.recording_options.declare(
'includes',
types=list,
default=['*'],
desc="Patterns for variables to include in recording. \
Paths are relative to solver's Group. \
Uses fnmatch wildcards")
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc="Patterns for vars to exclude in recording. \
(processed post-includes) \
Paths are relative to solver's Group. \
Uses fnmatch wildcards")
# Case recording related
self._filtered_vars_to_record = {}
self._norm0 = 0.0
# What the solver supports.
self.supports = OptionsDictionary(parent_name=self.msginfo)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('implicit_components', types=bool, default=False)
self._declare_options()
self.options.update(kwargs)
self._rec_mgr = RecordingManager()
self.cite = ""
@property
def msginfo(self):
"""
Return info to prepend to messages.
Returns
-------
str
Info to prepend to messages.
"""
if self._system is None:
return type(self).__name__
return '{} in {}'.format(type(self).__name__, self._system().msginfo)
@property
def _recording_iter(self):
if self._problem_meta is None:
raise RuntimeError(
f"{self.msginfo}: Can't access recording_iter because "
"_setup_solvers has not been called.")
return self._problem_meta['recording_iter']
@property
def _solver_info(self):
if self._problem_meta is None:
raise RuntimeError(
f"{self.msginfo}: Can't access solver_info because _setup_solvers "
"has not been called.")
return self._problem_meta['solver_info']
def _assembled_jac_solver_iter(self):
"""
Return an empty generator of lin solvers using assembled jacs.
"""
for i in ():
yield
def add_recorder(self, recorder):
"""
Add a recorder to the solver's RecordingManager.
Parameters
----------
recorder : <CaseRecorder>
A recorder instance to be added to RecManager.
"""
if MPI:
raise RuntimeError(
"Recording of Solvers when running parallel code is not supported yet"
)
self._rec_mgr.append(recorder)
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
This is optionally implemented by subclasses of Solver.
"""
pass
def _setup_solvers(self, system, depth):
"""
Assign system instance, set depth, and optionally perform setup.
Parameters
----------
system : <System>
pointer to the owning system.
depth : int
depth of the current system (already incremented).
"""
self._system = weakref.ref(system)
self._depth = depth
self._problem_meta = system._problem_meta
if system.pathname:
parent_name = self.msginfo
self.options._parent_name = parent_name
self.recording_options._parent_name = parent_name
self.supports._parent_name = parent_name
if isinstance(self, LinearSolver) and not system._use_derivatives:
return
self._rec_mgr.startup(self)
myoutputs = myresiduals = myinputs = []
incl = self.recording_options['includes']
excl = self.recording_options['excludes']
# doesn't matter if we're a linear or nonlinear solver. The names for
# inputs, outputs, and residuals are the same for both the 'linear' and 'nonlinear'
# vectors.
if system.pathname:
incl = ['.'.join((system.pathname, i)) for i in incl]
excl = ['.'.join((system.pathname, i)) for i in excl]
if self.recording_options['record_solver_residuals']:
myresiduals = [
n for n in system._residuals._abs_iter()
if check_path(n, incl, excl)
]
if self.recording_options['record_outputs']:
myoutputs = [
n for n in system._outputs._abs_iter()
if check_path(n, incl, excl)
]
if self.recording_options['record_inputs']:
myinputs = [
n for n in system._inputs._abs_iter()
if check_path(n, incl, excl)
]
self._filtered_vars_to_record = {
'input': myinputs,
'output': myoutputs,
'residual': myresiduals
}
def _set_solver_print(self, level=2, type_='all'):
"""
Control printing for solvers and subsolvers in the model.
Parameters
----------
level : int
iprint level. Set to 2 to print residuals each iteration; set to 1
to print just the iteration totals; set to 0 to disable all printing
except for failures, and set to -1 to disable all printing including failures.
type_ : str
Type of solver to set: 'LN' for linear, 'NL' for nonlinear, or 'all' for all.
"""
self.options['iprint'] = level
def _mpi_print(self, iteration, abs_res, rel_res):
"""
Print residuals from an iteration.
Parameters
----------
iteration : int
iteration counter, 0-based.
abs_res : float
current absolute residual norm.
rel_res : float
current relative residual norm.
"""
if (self.options['iprint'] == 2
and (self._system().comm.rank == 0
or os.environ.get('USE_PROC_FILES'))):
prefix = self._solver_info.prefix
solver_name = self.SOLVER
if prefix.endswith('precon:'):
solver_name = solver_name[3:]
print_str = prefix + solver_name
print_str += ' %d ; %.9g %.9g' % (iteration, abs_res, rel_res)
print(print_str)
def _mpi_print_header(self):
"""
Print header text before solving.
"""
if (self.options['iprint'] > 0
and (self._system().comm.rank == 0
or os.environ.get('USE_PROC_FILES'))):
pathname = self._system().pathname
if pathname:
nchar = len(pathname)
prefix = self._solver_info.prefix
header = prefix + "\n"
header += prefix + nchar * "=" + "\n"
header += prefix + pathname + "\n"
header += prefix + nchar * "="
print(header)
def _iter_initialize(self):
"""
Perform any necessary pre-processing operations.
Returns
-------
float
initial error.
float
error at the first iteration.
"""
pass
def _run_apply(self):
"""
Run the appropriate apply method on the system.
"""
pass
def _linearize(self):
"""
Perform any required linearization operations such as matrix factorization.
"""
pass
def _linearize_children(self):
"""
Return a flag that is True when we need to call linearize on our subsystems' solvers.
Returns
-------
boolean
Flag for indicating child linerization
"""
return True
def __str__(self):
"""
Return a string representation of the solver.
Returns
-------
str
String representation of the solver.
"""
return self.SOLVER
def record_iteration(self, **kwargs):
"""
Record an iteration of the current Solver.
Parameters
----------
**kwargs : dict
Keyword arguments (used for abs and rel error).
"""
if not self._rec_mgr._recorders:
return
metadata = create_local_meta(self.SOLVER)
# Get the data
data = {
'abs':
kwargs.get('abs')
if self.recording_options['record_abs_error'] else None,
'rel':
kwargs.get('rel')
if self.recording_options['record_rel_error'] else None,
'input': {},
'output': {},
'residual': {}
}
system = self._system()
vec_name = 'nonlinear' if isinstance(self,
NonlinearSolver) else 'linear'
filt = self._filtered_vars_to_record
parallel = self._rec_mgr._check_parallel(
) if system.comm.size > 1 else False
if self.recording_options['record_outputs']:
data['output'] = system._retrieve_data_of_kind(
filt, 'output', vec_name, parallel)
if self.recording_options['record_inputs']:
data['input'] = system._retrieve_data_of_kind(
filt, 'input', vec_name, parallel)
if self.recording_options['record_solver_residuals']:
data['residual'] = system._retrieve_data_of_kind(
filt, 'residual', vec_name, parallel)
self._rec_mgr.record_iteration(self, data, metadata)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
# shut down all recorders
self._rec_mgr.shutdown()
def _set_complex_step_mode(self, active):
"""
Turn on or off complex stepping mode.
Recurses to turn on or off complex stepping mode in all subsystems and their vectors.
Parameters
----------
active : bool
Complex mode flag; set to True prior to commencing complex step.
"""
pass
def _disallow_distrib_solve(self):
"""
Raise an exception if our system or any subsystems are distributed or non-local.
"""
s = self._system()
if s.comm.size == 1:
return
from openmdao.core.group import Group
if s._has_distrib_vars or (isinstance(s, Group)
and s._contains_parallel_group):
msg = "{} linear solver in {} cannot be used in or above a ParallelGroup or a " + \
"distributed component."
raise RuntimeError(msg.format(type(self).__name__, s.msginfo))
def __init__(self):
"""
Initialize the driver.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
###########################
self.recording_options.declare('record_desvars', types=bool, default=True,
desc='Set to True to record design variables at the \
driver level')
self.recording_options.declare('record_responses', types=bool, default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare('record_objectives', types=bool, default=True,
desc='Set to True to record objectives at the \
driver level')
self.recording_options.declare('record_constraints', types=bool, default=True,
desc='Set to True to record constraints at the \
driver level')
self.recording_options.declare('includes', types=list, default=[],
desc='Patterns for variables to include in recording. \
Uses fnmatch wildcards')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes). Uses fnmatch wildcards')
self.recording_options.declare('record_derivatives', types=bool, default=False,
desc='Set to True to record derivatives at the driver \
level')
###########################
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints', types=bool, default=False)
self.supports.declare('equality_constraints', types=bool, default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints', types=bool, default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives', types=bool, default=False)
self.supports.declare('distributed_design_vars', types=bool, default=False)
self.iter_count = 0
self.options = None
self._model_viewer_data = None
self.cite = ""
# TODO, support these in OpenMDAO
self.supports.declare('integer_design_vars', types=bool, default=False)
self._res_jacs = {}
self.fail = False
class Driver(object):
""" Base class for drivers in OpenMDAO. Drivers can only be placed in a
Problem, and every problem has a Driver. Driver is the simplest driver that
runs (solves using solve_nonlinear) a problem once.
"""
def __init__(self):
super(Driver, self).__init__()
self.recorders = RecordingManager()
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option('inequality_constraints', True)
self.supports.add_option('equality_constraints', True)
self.supports.add_option('linear_constraints', True)
self.supports.add_option('multiple_objectives', True)
self.supports.add_option('two_sided_constraints', True)
self.supports.add_option('integer_design_vars', True)
# inheriting Drivers should override this setting and set it to False
# if they don't use gradients.
self.supports.add_option('gradients', True)
# This driver's options
self.options = OptionsDictionary()
self._desvars = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
self._vars_to_record = None
# We take root during setup
self.root = None
self.iter_count = 0
self.dv_conversions = {}
self.fn_conversions = {}
def _setup(self):
""" Updates metadata for params, constraints and objectives, and
check for errors. Also determines all variables that need to be
gathered for case recording.
"""
root = self.root
desvars = OrderedDict()
objs = OrderedDict()
cons = OrderedDict()
if self.__class__ is Driver:
has_gradients = False
else:
has_gradients = self.supports['gradients']
item_tups = [
('Parameter', self._desvars, desvars),
('Objective', self._objs, objs),
('Constraint', self._cons, cons)
]
for item_name, item, newitem in item_tups:
for name, meta in iteritems(item):
# Check validity of variable
if name not in root.unknowns:
msg = "{} '{}' not found in unknowns."
msg = msg.format(item_name, name)
raise ValueError(msg)
rootmeta = root.unknowns.metadata(name)
if name in self._desvars:
rootmeta['is_desvar'] = True
if name in self._objs:
rootmeta['is_objective'] = True
if name in self._cons:
rootmeta['is_constraint'] = True
if MPI and 'src_indices' in rootmeta:
raise ValueError("'%s' is a distributed variable and may "
"not be used as a design var, objective, "
"or constraint." % name)
if has_gradients and rootmeta.get('pass_by_obj'):
if 'optimizer' in self.options:
oname = self.options['optimizer']
else:
oname = self.__class__.__name__
raise RuntimeError("%s '%s' is a 'pass_by_obj' variable "
"and can't be used with a gradient "
"based driver of type '%s'." %
(item_name, name, oname))
# Size is useful metadata to save
if 'indices' in meta:
meta['size'] = len(meta['indices'])
else:
meta['size'] = rootmeta['size']
newitem[name] = meta
self._desvars = desvars
self._objs = objs
self._cons = cons
# Cache scalers for derivative calculation
self.dv_conversions = OrderedDict()
for name, meta in iteritems(desvars):
scaler = meta.get('scaler')
if isinstance(scaler, np.ndarray):
if all(scaler == 1.0):
continue
elif scaler == 1.0:
continue
self.dv_conversions[name] = np.reciprocal(scaler)
self.fn_conversions = OrderedDict()
for name, meta in chain(iteritems(objs), iteritems(cons)):
scaler = meta.get('scaler')
if isinstance(scaler, np.ndarray):
if all(scaler == 1.0):
continue
elif scaler == 1.0:
continue
self.fn_conversions[name] = scaler
def _setup_communicators(self, comm, parent_dir):
"""
Assign a communicator to the root `System`.
Args
----
comm : an MPI communicator (real or fake)
The communicator being offered by the Problem.
parent_dir : str
Absolute directory of the Problem.
"""
self.root._setup_communicators(comm, parent_dir)
def get_req_procs(self):
"""
Returns
-------
tuple
A tuple of the form (min_procs, max_procs), indicating the
min and max processors usable by this `Driver`.
"""
return self.root.get_req_procs()
def cleanup(self):
""" Clean up resources prior to exit. """
self.recorders.close()
def _map_voi_indices(self):
poi_indices = OrderedDict()
qoi_indices = OrderedDict()
for name, meta in chain(iteritems(self._cons), iteritems(self._objs)):
# set indices of interest
if 'indices' in meta:
qoi_indices[name] = meta['indices']
for name, meta in iteritems(self._desvars):
# set indices of interest
if 'indices' in meta:
poi_indices[name] = meta['indices']
return poi_indices, qoi_indices
def _of_interest(self, voi_list):
"""Return a list of tuples, with the given voi_list organized
into tuples based on the previously defined grouping of VOIs.
"""
vois = []
remaining = set(voi_list)
for voi_set in self._voi_sets:
vois.append([])
for i, voi_set in enumerate(self._voi_sets):
for v in voi_list:
if v in voi_set:
vois[i].append(v)
remaining.remove(v)
vois = [tuple(x) for x in vois if x]
for v in voi_list:
if v in remaining:
vois.append((v,))
return vois
def desvars_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of design vars, organized into tuples according to
previously defined VOI groups.
"""
return self._of_interest(self._desvars)
def outputs_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of constraints and objectives, organized into tuples
according to previously defined VOI groups.
"""
return self._of_interest(list(chain(self._objs, self._cons)))
def parallel_derivs(self, vnames):
"""
Specifies that the named variables of interest are to be grouped
together so that their derivatives can be solved for concurrently.
Args
----
vnames : iter of str
The names of variables of interest that are to be grouped.
"""
#make sure all vnames are desvars, constraints, or objectives
for n in vnames:
if not (n in self._desvars or n in self._objs or n in self._cons):
raise RuntimeError("'%s' is not a param, objective, or "
"constraint" % n)
for grp in self._voi_sets:
for vname in vnames:
if vname in grp:
msg = "'%s' cannot be added to VOI set %s because it " + \
"already exists in VOI set: %s"
raise RuntimeError(msg % (vname, tuple(vnames), grp))
param_intsect = set(vnames).intersection(self._desvars.keys())
if param_intsect and len(param_intsect) != len(vnames):
raise RuntimeError("%s cannot be grouped because %s are design "
"vars and %s are not." %
(vnames, list(param_intsect),
list(set(vnames).difference(param_intsect))))
if MPI:
self._voi_sets.append(tuple(vnames))
else:
warnings.warn("parallel derivs %s specified but not running under MPI")
def add_recorder(self, recorder):
"""
Adds a recorder to the driver.
Args
----
recorder : BaseRecorder
A recorder instance.
"""
self.recorders.append(recorder)
def add_desvar(self, name, lower=None, upper=None,
low=None, high=None,
indices=None, adder=0.0, scaler=1.0):
"""
Adds a design variable to this driver.
Args
----
name : string
Name of the design variable in the root system.
lower : float or ndarray, optional
Lower boundary for the param
upper : upper or ndarray, optional
Upper boundary for the param
indices : iter of int, optional
If a param is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if name in self._desvars:
msg = "Desvar '{}' already exists."
raise RuntimeError(msg.format(name))
if low is not None or high is not None:
warnings.simplefilter('always', DeprecationWarning)
warnings.warn("'low' and 'high' are deprecated. "
"Use 'lower' and 'upper' instead.",
DeprecationWarning,stacklevel=2)
warnings.simplefilter('ignore', DeprecationWarning)
if low is not None and lower is None:
lower = low
if high is not None and upper is None:
upper = high
if isinstance(lower, np.ndarray):
lower = lower.flatten()
elif lower is None or lower == -float('inf'):
lower = -sys.float_info.max
if isinstance(upper, np.ndarray):
upper = upper.flatten()
elif upper is None or upper == float('inf'):
upper = sys.float_info.max
if isinstance(adder, np.ndarray):
adder = adder.flatten().astype('float')
else:
adder = float(adder)
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten().astype('float')
else:
scaler = float(scaler)
# Scale the lower and upper values
lower = (lower + adder)*scaler
upper = (upper + adder)*scaler
param = OrderedDict()
param['lower'] = lower
param['upper'] = upper
param['adder'] = adder
param['scaler'] = scaler
if indices:
param['indices'] = np.array(indices, dtype=int)
self._desvars[name] = param
def add_param(self, name, lower=None, upper=None, indices=None, adder=0.0,
scaler=1.0):
"""
Deprecated. Use ``add_desvar`` instead.
"""
warnings.simplefilter('always', DeprecationWarning)
warnings.warn("Driver.add_param() is deprecated. Use add_desvar() instead.",
DeprecationWarning,stacklevel=2)
warnings.simplefilter('ignore', DeprecationWarning)
self.add_desvar(name, lower=lower, upper=upper, indices=indices, adder=adder,
scaler=scaler)
def get_desvars(self):
""" Returns a dict of possibly distributed design variables.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
desvars = OrderedDict()
for key, meta in iteritems(self._desvars):
desvars[key] = self._get_distrib_var(key, meta, 'design var')
return desvars
def _get_distrib_var(self, name, meta, voi_type):
uvec = self.root.unknowns
comm = self.root.comm
nproc = comm.size
iproc = comm.rank
if nproc > 1:
owner = self.root._owning_ranks[name]
if iproc == owner:
flatval = uvec._dat[name].val
else:
flatval = None
else:
owner = 0
flatval = uvec._dat[name].val
if 'indices' in meta and not (nproc > 1 and owner != iproc):
# Make sure our indices are valid
try:
flatval = flatval[meta['indices']]
except IndexError:
msg = "Index for {} '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "shape: {}."
raise IndexError(msg.format(voi_type, name, meta['indices'],
uvec.metadata(name)['shape']))
if nproc > 1:
# TODO: use Bcast for improved performance
if trace:
debug("%s.driver._get_distrib_var bcast: val=%s" % (self.root.pathname, flatval))
flatval = comm.bcast(flatval, root=owner)
if trace:
debug("%s.driver._get_distrib_var bcast DONE" % self.root.pathname)
scaler = meta['scaler']
adder = meta['adder']
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or scaler != 1.0 or adder != 0.0:
return (flatval + adder)*scaler
else:
return flatval
def get_desvar_metadata(self):
""" Returns a dict of design variable metadata.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
return self._desvars
def set_desvar(self, name, value):
""" Sets a design variable.
Args
----
name : string
Name of the design variable in the root system.
val : ndarray or float
value to assign to the design variable.
"""
val = self.root.unknowns._dat[name].val
if not isinstance(val, _ByObjWrapper) and \
self.root.unknowns._dat[name].val.size == 0:
return
meta = self._desvars[name]
scaler = meta['scaler']
adder = meta['adder']
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or scaler != 1.0 or adder != 0.0:
value = value/scaler - adder
# Only set the indices we requested when we set the design variable.
idx = meta.get('indices')
if idx is not None:
self.root.unknowns[name][idx] = value
else:
self.root.unknowns[name] = value
def add_objective(self, name, indices=None, adder=0.0, scaler=1.0):
""" Adds an objective to this driver.
Args
----
name : string
Promoted pathname of the output that will serve as the objective.
indices : iter of int, optional
If an objective is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if len(self._objs) > 0 and not self.supports["multiple_objectives"]:
raise RuntimeError("Attempted to add multiple objectives to a "
"driver that does not support multiple "
"objectives.")
if name in self._objs:
msg = "Objective '{}' already exists."
raise RuntimeError(msg.format(name))
if isinstance(adder, np.ndarray):
adder = adder.flatten().astype('float')
else:
adder = float(adder)
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten().astype('float')
else:
scaler = float(scaler)
obj = OrderedDict()
obj['adder'] = adder
obj['scaler'] = scaler
if indices:
obj['indices'] = indices
if len(indices) > 1 and not self.supports['multiple_objectives']:
raise RuntimeError("Multiple objective indices specified for "
"variable '%s', but driver '%s' doesn't "
"support multiple objectives." %
(name, self.pathname))
self._objs[name] = obj
def get_objectives(self, return_type='dict'):
""" Gets all objectives of this driver.
Args
----
return_type : string
Set to 'dict' to return a dictionary, or set to 'array' to return a
flat ndarray.
Returns
-------
dict (for return_type 'dict')
Key is the objective name string, value is an ndarray with the values.
ndarray (for return_type 'array')
Array containing all objective values in the order they were added.
"""
objs = OrderedDict()
for key, meta in iteritems(self._objs):
objs[key] = self._get_distrib_var(key, meta, 'objective')
return objs
def add_constraint(self, name, lower=None, upper=None, equals=None,
linear=False, jacs=None, indices=None, adder=0.0,
scaler=1.0):
""" Adds a constraint to this driver. For inequality constraints,
`lower` or `upper` must be specified. For equality constraints, `equals`
must be specified.
Args
----
name : string
Promoted pathname of the output that will serve as the quantity to
constrain.
lower : float or ndarray, optional
Constrain the quantity to be greater than or equal to this value.
upper : float or ndarray, optional
Constrain the quantity to be less than or equal to this value.
equals : float or ndarray, optional
Constrain the quantity to be equal to this value.
linear : bool, optional
Set to True if this constraint is linear with respect to all design
variables so that it can be calculated once and cached.
jacs : dict of functions, optional
Dictionary of user-defined functions that return the flattened
Jacobian of this constraint with repsect to the design vars of
this driver, as indicated by the dictionary keys. Default is None
to let OpenMDAO calculate all derivatives. Note, this is currently
unsupported
indices : iter of int, optional
If a constraint is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if name in self._cons:
msg = "Constraint '{}' already exists."
raise RuntimeError(msg.format(name))
if equals is not None and (lower is not None or upper is not None):
msg = "Constraint '{}' cannot be both equality and inequality."
raise RuntimeError(msg.format(name))
if equals is not None and self.supports['equality_constraints'] is False:
msg = "Driver does not support equality constraint '{}'."
raise RuntimeError(msg.format(name))
if equals is None and self.supports['inequality_constraints'] is False:
msg = "Driver does not support inequality constraint '{}'."
raise RuntimeError(msg.format(name))
if lower is not None and upper is not None and self.supports['two_sided_constraints'] is False:
msg = "Driver does not support 2-sided constraint '{}'."
raise RuntimeError(msg.format(name))
if lower is None and upper is None and equals is None:
msg = "Constraint '{}' needs to define lower, upper, or equals."
raise RuntimeError(msg.format(name))
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten().astype('float')
else:
scaler = float(scaler)
if isinstance(adder, np.ndarray):
adder = adder.flatten().astype('float')
else:
adder = float(adder)
if isinstance(lower, np.ndarray):
lower = lower.flatten()
if isinstance(upper, np.ndarray):
upper = upper.flatten()
if isinstance(equals, np.ndarray):
equals = equals.flatten()
# Scale the lower and upper values
if lower is not None:
lower = (lower + adder)*scaler
if upper is not None:
upper = (upper + adder)*scaler
if equals is not None:
equals = (equals + adder)*scaler
con = OrderedDict()
con['lower'] = lower
con['upper'] = upper
con['equals'] = equals
con['linear'] = linear
con['adder'] = adder
con['scaler'] = scaler
con['jacs'] = jacs
if indices:
con['indices'] = indices
self._cons[name] = con
def get_constraints(self, ctype='all', lintype='all'):
""" Gets all constraints for this driver.
Args
----
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
Returns
-------
dict
Key is the constraint name string, value is an ndarray with the values.
"""
cons = OrderedDict()
for key, meta in iteritems(self._cons):
if lintype == 'linear' and meta['linear'] is False:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
cons[key] = self._get_distrib_var(key, meta, 'constraint')
return cons
def get_constraint_metadata(self):
""" Returns a dict of constraint metadata.
Returns
-------
dict
Keys are the constraint object names, and the values are the param
values.
"""
return self._cons
def run(self, problem):
""" Runs the driver. This function should be overridden when inheriting.
Args
----
problem : `Problem`
Our parent `Problem`.
"""
self.run_once(problem)
def run_once(self, problem):
""" Runs root's solve_nonlinear one time
Args
----
problem : `Problem`
Our parent `Problem`.
"""
system = problem.root
# Metadata Setup
self.iter_count += 1
metadata = self.metadata = create_local_meta(None, 'Driver')
system.ln_solver.local_meta = metadata
update_local_meta(metadata, (self.iter_count,))
# Solve the system once and record results.
with system._dircontext:
system.solve_nonlinear(metadata=metadata)
self.recorders.record_iteration(system, metadata)
def calc_gradient(self, indep_list, unknown_list, mode='auto',
return_format='array', sparsity=None):
""" Returns the scaled gradient for the system that is contained in
self.root, scaled by all scalers that were specified when the desvars
and constraints were added.
Args
----
indep_list : list of strings
List of independent variable names that derivatives are to
be calculated with respect to. All params must have a IndepVarComp.
unknown_list : list of strings
List of output or state names that derivatives are to
be calculated for. All must be valid unknowns in OpenMDAO.
mode : string, optional
Deriviative direction, can be 'fwd', 'rev', 'fd', or 'auto'.
Default is 'auto', which uses mode specified on the linear solver
in root.
return_format : string, optional
Format for the derivatives, can be 'array' or 'dict'.
sparsity : dict, optional
Dictionary that gives the relevant design variables for each
constraint. This option is only supported in the `dict` return
format.
Returns
-------
ndarray or dict
Jacobian of unknowns with respect to params.
"""
J = self._problem.calc_gradient(indep_list, unknown_list, mode=mode,
return_format=return_format,
dv_scale=self.dv_conversions,
cn_scale=self.fn_conversions,
sparsity=sparsity)
self.recorders.record_derivatives(J, self.metadata)
return J
def generate_docstring(self):
"""
Generates a numpy-style docstring for a user-created Driver class.
Returns
-------
docstring : str
string that contains a basic numpy docstring.
"""
#start the docstring off
docstring = ' \"\"\"\n'
#Put options into docstring
firstTime = 1
for key, value in sorted(vars(self).items()):
if type(value)==OptionsDictionary:
if key == "supports":
continue
if firstTime: #start of Options docstring
docstring += '\n Options\n -------\n'
firstTime = 0
docstring += value._generate_docstring(key)
#finish up docstring
docstring += '\n \"\"\"\n'
return docstring
class SolverBase(object):
""" Common base class for Linear and Nonlinear solver. Should not be used
by users. Always inherit from `LinearSolver` or `NonlinearSolver`."""
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.recorders = RecordingManager()
self.local_meta = None
def setup(self, sub):
""" Solvers override to define post-setup initiailzation.
Args
----
sub: `System`
System that owns this solver.
"""
pass
def cleanup(self):
""" Clean up resources prior to exit. """
self.recorders.close()
def print_norm(self,
solver_string,
pathname,
iteration,
res,
res0,
msg=None,
indent=0,
solver='NL'):
""" Prints out the norm of the residual in a neat readable format.
Args
----
solver_string: string
Unique string to identify your solver type (e.g., 'LN_GS' or
'NEWTON').
pathname: dict
Parent system pathname.
iteration: int
Current iteration number
res: float
Absolute residual value.
res0: float
Baseline initial residual for relative comparison.
msg: string, optional
Message that indicates convergence.
ident: int, optional
Additional indentation levels for subiterations.
solver: string, optional
Solver type if not LN or NL (mostly for line search operations.)
"""
if pathname == '':
name = 'root'
else:
name = 'root.' + pathname
# Find indentation level
level = pathname.count('.')
# No indentation for driver; top solver is no indentation.
level = level + indent
indent = ' ' * level
if msg is not None:
form = indent + '[%s] %s: %s %d | %s'
print(form % (name, solver, solver_string, iteration, msg))
return
form = indent + '[%s] %s: %s %d | %.9g %.9g'
print(form % (name, solver, solver_string, iteration, res, res / res0))
def print_all_convergence(self):
""" Turns on iprint for this solver and all subsolvers. Override if
your solver has subsolvers."""
self.options['iprint'] = 1
def generate_docstring(self):
"""
Generates a numpy-style docstring for a user-created System class.
Returns
-------
docstring : str
string that contains a basic numpy docstring.
"""
#start the docstring off
docstring = ' \"\"\"\n'
#Put options into docstring
firstTime = 1
#for py3.4, items from vars must come out in same order.
from collections import OrderedDict
v = OrderedDict(sorted(vars(self).items()))
for key, value in v.items():
if type(value) == OptionsDictionary:
if firstTime: #start of Options docstring
docstring += '\n Options\n -------\n'
firstTime = 0
for (name, val) in sorted(value.items()):
docstring += " " + key + "['"
docstring += name + "']"
docstring += " : " + type(val).__name__
docstring += "("
if type(val).__name__ == 'str': docstring += "'"
docstring += str(val)
if type(val).__name__ == 'str': docstring += "'"
docstring += ")\n"
desc = value._options[name]['desc']
if (desc):
docstring += " " + desc + "\n"
#finish up docstring
docstring += '\n \"\"\"\n'
return docstring
def __init__(self, **kwargs):
"""
Initialize the driver.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Driver options.
"""
self._rec_mgr = RecordingManager()
self._problem = None
self._designvars = None
self._designvars_discrete = []
self._cons = None
self._objs = None
self._responses = None
# Driver options
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.options.declare(
'debug_print',
types=list,
check_valid=_check_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars', 'ln_cons', "
"'nl_cons', 'objs', 'totals'",
default=[])
# Case recording options
self.recording_options = OptionsDictionary(
parent_name=type(self).__name__)
self.recording_options.declare(
'record_model_metadata',
types=bool,
default=True,
desc='Deprecated. Recording of model metadata will always '
'be done',
deprecation="The recording option, record_model_metadata, "
"on Driver is "
"deprecated. Recording of model metadata will always "
"be done",
)
self.recording_options.declare(
'record_desvars',
types=bool,
default=True,
desc='Set to True to record design variables at the '
'driver level')
self.recording_options.declare(
'record_responses',
types=bool,
default=False,
desc='Set True to record constraints and objectives at the '
'driver level')
self.recording_options.declare(
'record_objectives',
types=bool,
default=True,
desc='Set to True to record objectives at the driver level')
self.recording_options.declare(
'record_constraints',
types=bool,
default=True,
desc='Set to True to record constraints at the '
'driver level')
self.recording_options.declare(
'includes',
types=list,
default=[],
desc='Patterns for variables to include in recording. '
'Uses fnmatch wildcards')
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes). Uses fnmatch wildcards')
self.recording_options.declare(
'record_derivatives',
types=bool,
default=False,
desc='Set to True to record derivatives at the driver '
'level')
self.recording_options.declare(
'record_inputs',
types=bool,
default=True,
desc='Set to True to record inputs at the driver level')
self.recording_options.declare(
'record_outputs',
types=bool,
default=True,
desc='Set True to record outputs at the '
'driver level.')
self.recording_options.declare(
'record_residuals',
types=bool,
default=False,
desc='Set True to record residuals at the '
'driver level.')
# What the driver supports.
self.supports = OptionsDictionary(parent_name=type(self).__name__)
self.supports.declare('inequality_constraints',
types=bool,
default=False)
self.supports.declare('equality_constraints',
types=bool,
default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints',
types=bool,
default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=True)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives',
types=bool,
default=False)
self.supports.declare('total_jac_sparsity', types=bool, default=False)
self.iter_count = 0
self.cite = ""
self._coloring_info = coloring_mod._get_coloring_meta()
self._total_jac_sparsity = None
self._res_jacs = {}
self._total_jac = None
self.fail = False
self._declare_options()
self.options.update(kwargs)
class SolverBase(object):
""" Common base class for Linear and Nonlinear solver. Should not be used
by users. Always inherit from `LinearSolver` or `NonlinearSolver`."""
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = "Set to 0 to print only failures, set to 1 to print iteration totals to" + \
"stdout, set to 2 to print the residual each iteration to stdout," + \
"or -1 to suppress all printing."
self.options.add_option('iprint', 0, values=[-1, 0, 1, 2], desc=desc)
self.options.add_option(
'err_on_maxiter',
False,
desc='If True, raise an AnalysisError if not converged at maxiter.'
)
self.recorders = RecordingManager()
self.local_meta = None
def setup(self, sub):
""" Solvers override to define post-setup initiailzation.
Args
----
sub: `System`
System that owns this solver.
"""
pass
def cleanup(self):
""" Clean up resources prior to exit. """
self.recorders.close()
def print_norm(self,
solver_string,
system,
iteration,
res,
res0,
msg=None,
indent=0,
solver='NL',
u_norm=None):
""" Prints out the norm of the residual in a neat readable format.
Args
----
solver_string: string
Unique string to identify your solver type (e.g., 'LN_GS' or
'NEWTON').
system: system
Parent system, which contains pathname and the preconditioning flag.
iteration: int
Current iteration number
res: float
Norm of the absolute residual value.
res0: float
Norm of the baseline initial residual for relative comparison.
msg: string, optional
Message that indicates convergence.
ident: int, optional
Additional indentation levels for subiterations.
solver: string, optional
Solver type if not LN or NL (mostly for line search operations.)
u_norm: float, optional
Norm of the u vector, if applicable.
"""
pathname = system.pathname
if pathname == '':
name = 'root'
else:
name = 'root.' + pathname
# Find indentation level
level = name.count('.')
# No indentation for driver; top solver is no indentation.
level = level + indent
indent = ' ' * level
if system._probdata.precon_level > 0:
solver_string = 'PRECON:' + solver_string
indent += ' ' * system._probdata.precon_level
if msg is not None:
form = indent + '[%s] %s: %s %d | %s'
if u_norm:
form += ' (%s)' % u_norm
print(form % (name, solver, solver_string, iteration, msg))
return
form = indent + '[%s] %s: %s %d | %.9g %.9g'
if u_norm:
form += ' (%s)' % u_norm
print(form % (name, solver, solver_string, iteration, res, res / res0))
def print_all_convergence(self, level=2):
""" Turns on iprint for this solver and all subsolvers. Override if
your solver has subsolvers.
Args
----
level : int(2)
iprint level. Set to 2 to print residuals each iteration; set to 1
to print just the iteration totals.
"""
self.options['iprint'] = level
def generate_docstring(self):
"""
Generates a numpy-style docstring for a user-created System class.
Returns
-------
docstring : str
string that contains a basic numpy docstring.
"""
#start the docstring off
docstrings = [' \"\"\"']
#Put options into docstring
firstTime = 1
for key, value in sorted(vars(self).items()):
if type(value) == OptionsDictionary:
if firstTime: #start of Options docstring
docstrings.extend(['', ' Options', ' -------'])
firstTime = 0
docstrings.append(value._generate_docstring(key))
#finish up docstring
docstrings.extend([' \"\"\"', ''])
return '\n'.join(docstrings)
class Driver(object):
"""
Top-level container for the systems and drivers.
Attributes
----------
fail : bool
Reports whether the driver ran successfully.
iter_count : int
Keep track of iterations for case recording.
metadata : list
List of metadata
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
_problem : <Problem>
Pointer to the containing problem.
supports : <OptionsDictionary>
Provides a consistant way for drivers to declare what features they support.
_designvars : dict
Contains all design variable info.
_cons : dict
Contains all constraint info.
_objs : dict
Contains all objective info.
_responses : dict
Contains all response info.
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this driver.
_model_viewer_data : dict
Structure of model, used to make n2 diagram.
_remote_dvs : dict
Dict of design variables that are remote on at least one proc. Values are
(owning rank, size).
_remote_cons : dict
Dict of constraints that are remote on at least one proc. Values are
(owning rank, size).
_remote_objs : dict
Dict of objectives that are remote on at least one proc. Values are
(owning rank, size).
_remote_responses : dict
A combined dict containing entries from _remote_cons and _remote_objs.
"""
def __init__(self):
"""
Initialize the driver.
"""
self._rec_mgr = RecordingManager()
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
self.options = OptionsDictionary()
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints',
type_=bool,
default=False)
self.supports.declare('equality_constraints',
type_=bool,
default=False)
self.supports.declare('linear_constraints', type_=bool, default=False)
self.supports.declare('two_sided_constraints',
type_=bool,
default=False)
self.supports.declare('multiple_objectives', type_=bool, default=False)
self.supports.declare('integer_design_vars', type_=bool, default=False)
self.supports.declare('gradients', type_=bool, default=False)
self.supports.declare('active_set', type_=bool, default=False)
self.iter_count = 0
self.metadata = None
self._model_viewer_data = None
# TODO, support these in Openmdao blue
self.supports.declare('integer_design_vars', type_=bool, default=False)
self.fail = False
def add_recorder(self, recorder):
"""
Add a recorder to the driver.
Parameters
----------
recorder : BaseRecorder
A recorder instance.
"""
self._rec_mgr.append(recorder)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
self._rec_mgr.close()
def _setup_driver(self, problem):
"""
Prepare the driver for execution.
This is the final thing to run during setup.
Parameters
----------
problem : <Problem>
Pointer to the containing problem.
"""
self._problem = problem
model = problem.model
self._objs = objs = OrderedDict()
self._cons = cons = OrderedDict()
self._responses = model.get_responses(recurse=True)
for name, data in iteritems(self._responses):
if data['type'] == 'con':
cons[name] = data
else:
objs[name] = data
# Gather up the information for design vars.
self._designvars = model.get_design_vars(recurse=True)
con_set = set()
obj_set = set()
dv_set = set()
self._remote_dvs = dv_dict = {}
self._remote_cons = con_dict = {}
self._remote_objs = obj_dict = {}
# Now determine if later we'll need to allgather cons, objs, or desvars.
if model.comm.size > 1 and model._subsystems_allprocs:
local_out_vars = set(model._outputs._views)
remote_dvs = set(self._designvars) - local_out_vars
remote_cons = set(self._cons) - local_out_vars
remote_objs = set(self._objs) - local_out_vars
all_remote_vois = model.comm.allgather(
(remote_dvs, remote_cons, remote_objs))
for rem_dvs, rem_cons, rem_objs in all_remote_vois:
con_set.update(rem_cons)
obj_set.update(rem_objs)
dv_set.update(rem_dvs)
# If we have remote VOIs, pick an owning rank for each and use that
# to bcast to others later
owning_ranks = model._owning_rank['output']
sizes = model._var_sizes['nonlinear']['output']
for i, vname in enumerate(model._var_allprocs_abs_names['output']):
owner = owning_ranks[vname]
if vname in dv_set:
dv_dict[vname] = (owner, sizes[owner, i])
if vname in con_set:
con_dict[vname] = (owner, sizes[owner, i])
if vname in obj_set:
obj_dict[vname] = (owner, sizes[owner, i])
self._remote_responses = self._remote_cons.copy()
self._remote_responses.update(self._remote_objs)
self._rec_mgr.startup(self)
if (self._rec_mgr._recorders):
from openmdao.devtools.problem_viewer.problem_viewer import _get_viewer_data
self._model_viewer_data = _get_viewer_data(problem)
self._rec_mgr.record_metadata(self)
def _get_voi_val(self, name, meta, remote_vois):
"""
Get the value of a variable of interest (objective, constraint, or design var).
This will retrieve the value if the VOI is remote.
Parameters
----------
name : str
Name of the variable of interest.
meta : dict
Metadata for the variable of interest.
remote_vois : dict
Dict containing (owning_rank, size) for all remote vois of a particular
type (design var, constraint, or objective).
Returns
-------
float or ndarray
The value of the named variable of interest.
"""
model = self._problem.model
comm = model.comm
vec = model._outputs._views_flat
indices = meta['indices']
if name in remote_vois:
owner, size = remote_vois[name]
if owner == comm.rank:
if indices is None:
val = vec[name].copy()
else:
val = vec[name][indices]
else:
if indices is not None:
size = len(indices)
val = np.empty(size)
comm.Bcast(val, root=owner)
else:
if indices is None:
val = vec[name].copy()
else:
val = vec[name][indices]
# Scale design variable values
adder = meta['adder']
if adder is not None:
val += adder
scaler = meta['scaler']
if scaler is not None:
val *= scaler
return val
def get_design_var_values(self, filter=None):
"""
Return the design variable values.
This is called to gather the initial design variable state.
Parameters
----------
filter : list
List of desvar names used by recorders.
Returns
-------
dict
Dictionary containing values of each design variable.
"""
if filter:
dvs = filter
else:
# use all the designvars
dvs = self._designvars
return {
n: self._get_voi_val(n, self._designvars[n], self._remote_dvs)
for n in dvs
}
def set_design_var(self, name, value):
"""
Set the value of a design variable.
Parameters
----------
name : str
Global pathname of the design variable.
value : float or ndarray
Value for the design variable.
"""
if (name in self._remote_dvs
and self._problem.model._owning_rank['output'][name] !=
self._problem.comm.rank):
return
meta = self._designvars[name]
indices = meta['indices']
if indices is None:
indices = slice(None)
desvar = self._problem.model._outputs._views_flat[name]
desvar[indices] = value
# Scale design variable values
scaler = meta['scaler']
if scaler is not None:
desvar[indices] *= 1.0 / scaler
adder = meta['adder']
if adder is not None:
desvar[indices] -= adder
def get_response_values(self, filter=None):
"""
Return response values.
Parameters
----------
filter : list
List of response names used by recorders.
Returns
-------
dict
Dictionary containing values of each response.
"""
# TODO: finish this method when we have a driver that requires it.
return {}
def get_objective_values(self, filter=None):
"""
Return objective values.
Parameters
----------
filter : list
List of objective names used by recorders.
Returns
-------
dict
Dictionary containing values of each objective.
"""
if filter:
objs = filter
else:
objs = self._objs
return {
n: self._get_voi_val(n, self._objs[n], self._remote_objs)
for n in objs
}
def get_constraint_values(self, ctype='all', lintype='all', filter=None):
"""
Return constraint values.
Parameters
----------
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
filter : list
List of constraint names used by recorders.
Returns
-------
dict
Dictionary containing values of each constraint.
"""
if filter is not None:
cons = filter
else:
cons = self._cons
con_dict = {}
for name in cons:
meta = self._cons[name]
if lintype == 'linear' and not meta['linear']:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
con_dict[name] = self._get_voi_val(name, meta, self._remote_cons)
return con_dict
def run(self):
"""
Execute this driver.
The base `Driver` just runs the model. All other drivers overload
this method.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
with Recording(self._get_name(), self.iter_count, self) as rec:
failure_flag = self._problem.model._solve_nonlinear()
self.iter_count += 1
return failure_flag
def _compute_totals(self,
of=None,
wrt=None,
return_format='flat_dict',
global_names=True):
"""
Compute derivatives of desired quantities with respect to desired inputs.
All derivatives are returned using driver scaling.
Parameters
----------
of : list of variable name strings or None
Variables whose derivatives will be computed. Default is None, which
uses the driver's objectives and constraints.
wrt : list of variable name strings or None
Variables with respect to which the derivatives will be computed.
Default is None, which uses the driver's desvars.
return_format : string
Format to return the derivatives. Default is a 'flat_dict', which
returns them in a dictionary whose keys are tuples of form (of, wrt). For
the scipy optimizer, 'array' is also supported.
global_names : bool
Set to True when passing in global names to skip some translation steps.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
prob = self._problem
# Compute the derivatives in dict format...
if prob.model._owns_approx_jac:
derivs = prob._compute_totals_approx(of=of,
wrt=wrt,
return_format='dict',
global_names=global_names)
else:
derivs = prob._compute_totals(of=of,
wrt=wrt,
return_format='dict',
global_names=global_names)
# ... then convert to whatever the driver needs.
if return_format == 'dict':
for okey, oval in iteritems(derivs):
for ikey, val in iteritems(oval):
imeta = self._designvars[ikey]
ometa = self._responses[okey]
iscaler = imeta['scaler']
oscaler = ometa['scaler']
# Scale response side
if oscaler is not None:
val[:] = (oscaler * val.T).T
# Scale design var side
if iscaler is not None:
val *= 1.0 / iscaler
elif return_format == 'array':
# Use sizes pre-computed in derivs for ease
osize = 0
isize = 0
do_wrt = True
islices = {}
oslices = {}
for okey, oval in iteritems(derivs):
if do_wrt:
for ikey, val in iteritems(oval):
istart = isize
isize += val.shape[1]
islices[ikey] = slice(istart, isize)
do_wrt = False
ostart = osize
osize += oval[ikey].shape[0]
oslices[okey] = slice(ostart, osize)
new_derivs = np.zeros((osize, isize))
relevant = prob.model._relevant
# Apply driver ref/ref0 and position subjac into array jacobian.
for okey, oval in iteritems(derivs):
oscaler = self._responses[okey]['scaler']
for ikey, val in iteritems(oval):
if okey in relevant[ikey] or ikey in relevant[okey]:
iscaler = self._designvars[ikey]['scaler']
# Scale response side
if oscaler is not None:
val[:] = (oscaler * val.T).T
# Scale design var side
if iscaler is not None:
val *= 1.0 / iscaler
new_derivs[oslices[okey], islices[ikey]] = val
derivs = new_derivs
else:
msg = "Derivative scaling by the driver only supports the 'dict' format at present."
raise RuntimeError(msg)
return derivs
def record_iteration(self):
"""
Record an iteration of the current Driver.
"""
metadata = create_local_meta(self._get_name())
self._rec_mgr.record_iteration(self, metadata)
def _get_name(self):
"""
Get name of current Driver.
Returns
-------
str
Name of current Driver.
"""
return "Driver"
class SolverBase(object):
""" Common base class for Linear and Nonlinear solver. Should not be used
by users. Always inherit from `LinearSolver` or `NonlinearSolver`."""
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = (
"Set to 0 to disable printing, set to 1 to print the "
"residual to stdout each iteration, set to 2 to print "
"subiteration residuals as well."
)
self.options.add_option("iprint", 0, values=[0, 1, 2], desc=desc)
self.recorders = RecordingManager()
self.local_meta = None
def setup(self, sub):
""" Solvers override to define post-setup initiailzation.
Args
----
sub: `System`
System that owns this solver.
"""
pass
def cleanup(self):
""" Clean up resources prior to exit. """
self.recorders.close()
def print_norm(self, solver_string, pathname, iteration, res, res0, msg=None, indent=0, solver="NL"):
""" Prints out the norm of the residual in a neat readable format.
Args
----
solver_string: string
Unique string to identify your solver type (e.g., 'LN_GS' or
'NEWTON').
pathname: dict
Parent system pathname.
iteration: int
Current iteration number
res: float
Absolute residual value.
res0: float
Baseline initial residual for relative comparison.
msg: string, optional
Message that indicates convergence.
ident: int, optional
Additional indentation levels for subiterations.
solver: string, optional
Solver type if not LN or NL (mostly for line search operations.)
"""
if pathname == "":
name = "root"
else:
name = "root." + pathname
# Find indentation level
level = pathname.count(".")
# No indentation for driver; top solver is no indentation.
level = level + indent
indent = " " * level
if msg is not None:
form = indent + "[%s] %s: %s %d | %s"
print(form % (name, solver, solver_string, iteration, msg))
return
form = indent + "[%s] %s: %s %d | %.9g %.9g"
print(form % (name, solver, solver_string, iteration, res, res / res0))
def print_all_convergence(self):
""" Turns on iprint for this solver and all subsolvers. Override if
your solver has subsolvers."""
self.options["iprint"] = 1
def generate_docstring(self):
"""
Generates a numpy-style docstring for a user-created System class.
Returns
-------
docstring : str
string that contains a basic numpy docstring.
"""
# start the docstring off
docstring = ' """\n'
# Put options into docstring
firstTime = 1
# for py3.4, items from vars must come out in same order.
from collections import OrderedDict
v = OrderedDict(sorted(vars(self).items()))
for key, value in v.items():
if type(value) == OptionsDictionary:
if firstTime: # start of Options docstring
docstring += "\n Options\n -------\n"
firstTime = 0
for (name, val) in sorted(value.items()):
docstring += " " + key + "['"
docstring += name + "']"
docstring += " : " + type(val).__name__
docstring += "("
if type(val).__name__ == "str":
docstring += "'"
docstring += str(val)
if type(val).__name__ == "str":
docstring += "'"
docstring += ")\n"
desc = value._options[name]["desc"]
if desc:
docstring += " " + desc + "\n"
# finish up docstring
docstring += '\n """\n'
return docstring
class Driver(object):
"""
Top-level container for the systems and drivers.
Options
-------
options['debug_print'] : list of strings([])
Indicates what variables to print at each iteration. The valid options are:
'desvars','ln_cons','nl_cons',and 'objs'.
recording_options['record_metadata'] : bool(True)
Tells recorder whether to record variable attribute metadata.
recording_options['record_desvars'] : bool(True)
Tells recorder whether to record the desvars of the Driver.
recording_options['record_responses'] : bool(False)
Tells recorder whether to record the responses of the Driver.
recording_options['record_objectives'] : bool(False)
Tells recorder whether to record the objectives of the Driver.
recording_options['record_constraints'] : bool(False)
Tells recorder whether to record the constraints of the Driver.
recording_options['includes'] : list of strings("*")
Patterns for variables to include in recording.
recording_options['excludes'] : list of strings('')
Patterns for variables to exclude in recording (processed after includes).
Attributes
----------
fail : bool
Reports whether the driver ran successfully.
iter_count : int
Keep track of iterations for case recording.
metadata : list
List of metadata
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
recording_options : <OptionsDictionary>
Dictionary with driver recording options.
debug_print : <OptionsDictionary>
Dictionary with debugging printing options.
cite : str
Listing of relevant citataions that should be referenced when
publishing work that uses this class.
_problem : <Problem>
Pointer to the containing problem.
supports : <OptionsDictionary>
Provides a consistant way for drivers to declare what features they support.
_designvars : dict
Contains all design variable info.
_cons : dict
Contains all constraint info.
_objs : dict
Contains all objective info.
_responses : dict
Contains all response info.
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this driver.
_vars_to_record: dict
Dict of lists of var names indicating what to record
_model_viewer_data : dict
Structure of model, used to make n2 diagram.
_remote_dvs : dict
Dict of design variables that are remote on at least one proc. Values are
(owning rank, size).
_remote_cons : dict
Dict of constraints that are remote on at least one proc. Values are
(owning rank, size).
_remote_objs : dict
Dict of objectives that are remote on at least one proc. Values are
(owning rank, size).
_remote_responses : dict
A combined dict containing entries from _remote_cons and _remote_objs.
_simul_coloring_info : tuple of dicts
A data structure describing coloring for simultaneous derivs.
_res_jacs : dict
Dict of sparse subjacobians for use with certain optimizers, e.g. pyOptSparseDriver.
"""
def __init__(self):
"""
Initialize the driver.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
###########################
self.options.declare('debug_print', types=list, is_valid=_is_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars','ln_cons',"
"'nl_cons','objs'",
default=[])
###########################
self.recording_options.declare('record_metadata', types=bool, desc='Record metadata',
default=True)
self.recording_options.declare('record_desvars', types=bool, default=True,
desc='Set to True to record design variables at the \
driver level')
self.recording_options.declare('record_responses', types=bool, default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare('record_objectives', types=bool, default=True,
desc='Set to True to record objectives at the \
driver level')
self.recording_options.declare('record_constraints', types=bool, default=True,
desc='Set to True to record constraints at the \
driver level')
self.recording_options.declare('includes', types=list, default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare('record_derivatives', types=bool, default=False,
desc='Set to True to record derivatives at the driver \
level')
###########################
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints', types=bool, default=False)
self.supports.declare('equality_constraints', types=bool, default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints', types=bool, default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives', types=bool, default=False)
# Debug printing.
self.debug_print = OptionsDictionary()
self.debug_print.declare('debug_print', types=bool, default=False,
desc='Overall option to turn on Driver debug printing')
self.debug_print.declare('debug_print_desvars', types=bool, default=False,
desc='Print design variables')
self.debug_print.declare('debug_print_nl_con', types=bool, default=False,
desc='Print nonlinear constraints')
self.debug_print.declare('debug_print_ln_con', types=bool, default=False,
desc='Print linear constraints')
self.debug_print.declare('debug_print_objective', types=bool, default=False,
desc='Print objectives')
self.iter_count = 0
self.metadata = None
self._model_viewer_data = None
self.cite = ""
# TODO, support these in OpenMDAO
self.supports.declare('integer_design_vars', types=bool, default=False)
self._simul_coloring_info = None
self._res_jacs = {}
self.fail = False
def add_recorder(self, recorder):
"""
Add a recorder to the driver.
Parameters
----------
recorder : BaseRecorder
A recorder instance.
"""
self._rec_mgr.append(recorder)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
self._rec_mgr.close()
def _setup_driver(self, problem):
"""
Prepare the driver for execution.
This is the final thing to run during setup.
Parameters
----------
problem : <Problem>
Pointer to the containing problem.
"""
self._problem = problem
model = problem.model
self._objs = objs = OrderedDict()
self._cons = cons = OrderedDict()
self._responses = model.get_responses(recurse=True)
response_size = 0
for name, data in iteritems(self._responses):
if data['type'] == 'con':
cons[name] = data
else:
objs[name] = data
response_size += data['size']
# Gather up the information for design vars.
self._designvars = model.get_design_vars(recurse=True)
desvar_size = np.sum(data['size'] for data in itervalues(self._designvars))
if ((problem._mode == 'fwd' and desvar_size > response_size) or
(problem._mode == 'rev' and response_size > desvar_size)):
warnings.warn("Inefficient choice of derivative mode. You chose '%s' for a "
"problem with %d design variables and %d response variables "
"(objectives and constraints)." %
(problem._mode, desvar_size, response_size), RuntimeWarning)
self._has_scaling = (
np.any([r['scaler'] is not None for r in self._responses.values()]) or
np.any([dv['scaler'] is not None for dv in self._designvars.values()])
)
con_set = set()
obj_set = set()
dv_set = set()
self._remote_dvs = dv_dict = {}
self._remote_cons = con_dict = {}
self._remote_objs = obj_dict = {}
# Now determine if later we'll need to allgather cons, objs, or desvars.
if model.comm.size > 1 and model._subsystems_allprocs:
local_out_vars = set(model._outputs._views)
remote_dvs = set(self._designvars) - local_out_vars
remote_cons = set(self._cons) - local_out_vars
remote_objs = set(self._objs) - local_out_vars
all_remote_vois = model.comm.allgather(
(remote_dvs, remote_cons, remote_objs))
for rem_dvs, rem_cons, rem_objs in all_remote_vois:
con_set.update(rem_cons)
obj_set.update(rem_objs)
dv_set.update(rem_dvs)
# If we have remote VOIs, pick an owning rank for each and use that
# to bcast to others later
owning_ranks = model._owning_rank['output']
sizes = model._var_sizes['nonlinear']['output']
for i, vname in enumerate(model._var_allprocs_abs_names['output']):
owner = owning_ranks[vname]
if vname in dv_set:
dv_dict[vname] = (owner, sizes[owner, i])
if vname in con_set:
con_dict[vname] = (owner, sizes[owner, i])
if vname in obj_set:
obj_dict[vname] = (owner, sizes[owner, i])
self._remote_responses = self._remote_cons.copy()
self._remote_responses.update(self._remote_objs)
# Case recording setup
mydesvars = myobjectives = myconstraints = myresponses = set()
mysystem_outputs = set()
incl = self.recording_options['includes']
excl = self.recording_options['excludes']
rec_desvars = self.recording_options['record_desvars']
rec_objectives = self.recording_options['record_objectives']
rec_constraints = self.recording_options['record_constraints']
rec_responses = self.recording_options['record_responses']
all_desvars = {n for n in self._designvars
if check_path(n, incl, excl, True)}
all_objectives = {n for n in self._objs
if check_path(n, incl, excl, True)}
all_constraints = {n for n in self._cons
if check_path(n, incl, excl, True)}
if rec_desvars:
mydesvars = all_desvars
if rec_objectives:
myobjectives = all_objectives
if rec_constraints:
myconstraints = all_constraints
if rec_responses:
myresponses = {n for n in self._responses
if check_path(n, incl, excl, True)}
# get the includes that were requested for this Driver recording
if incl:
prob = self._problem
root = prob.model
# The my* variables are sets
# First gather all of the desired outputs
# The following might only be the local vars if MPI
mysystem_outputs = {n for n in root._outputs
if check_path(n, incl, excl)}
# If MPI, and on rank 0, need to gather up all the variables
# even those not local to rank 0
if MPI:
all_vars = root.comm.gather(mysystem_outputs, root=0)
if MPI.COMM_WORLD.rank == 0:
mysystem_outputs = all_vars[-1]
for d in all_vars[:-1]:
mysystem_outputs.update(d)
# de-duplicate mysystem_outputs
mysystem_outputs = mysystem_outputs.difference(all_desvars, all_objectives,
all_constraints)
if MPI: # filter based on who owns the variables
# TODO Eventually, we think we can get rid of this next check. But to be safe,
# we are leaving it in there.
if not model.is_active():
raise RuntimeError(
"RecordingManager.startup should never be called when "
"running in parallel on an inactive System")
rrank = self._problem.comm.rank # root ( aka model ) rank.
rowned = model._owning_rank['output']
mydesvars = [n for n in mydesvars if rrank == rowned[n]]
myresponses = [n for n in myresponses if rrank == rowned[n]]
myobjectives = [n for n in myobjectives if rrank == rowned[n]]
myconstraints = [n for n in myconstraints if rrank == rowned[n]]
mysystem_outputs = [n for n in mysystem_outputs if rrank == rowned[n]]
self._filtered_vars_to_record = {
'des': mydesvars,
'obj': myobjectives,
'con': myconstraints,
'res': myresponses,
'sys': mysystem_outputs,
}
self._rec_mgr.startup(self)
if self._rec_mgr._recorders:
from openmdao.devtools.problem_viewer.problem_viewer import _get_viewer_data
self._model_viewer_data = _get_viewer_data(problem)
if self.recording_options['record_metadata']:
self._rec_mgr.record_metadata(self)
# set up simultaneous deriv coloring
if self._simul_coloring_info and self.supports['simultaneous_derivatives']:
if problem._mode == 'fwd':
self._setup_simul_coloring(problem._mode)
else:
raise RuntimeError("simultaneous derivs are currently not supported in rev mode.")
def _get_voi_val(self, name, meta, remote_vois):
"""
Get the value of a variable of interest (objective, constraint, or design var).
This will retrieve the value if the VOI is remote.
Parameters
----------
name : str
Name of the variable of interest.
meta : dict
Metadata for the variable of interest.
remote_vois : dict
Dict containing (owning_rank, size) for all remote vois of a particular
type (design var, constraint, or objective).
Returns
-------
float or ndarray
The value of the named variable of interest.
"""
model = self._problem.model
comm = model.comm
vec = model._outputs._views_flat
indices = meta['indices']
if name in remote_vois:
owner, size = remote_vois[name]
if owner == comm.rank:
if indices is None:
val = vec[name].copy()
else:
val = vec[name][indices]
else:
if indices is not None:
size = len(indices)
val = np.empty(size)
comm.Bcast(val, root=owner)
else:
if indices is None:
val = vec[name].copy()
else:
val = vec[name][indices]
if self._has_scaling:
# Scale design variable values
adder = meta['adder']
if adder is not None:
val += adder
scaler = meta['scaler']
if scaler is not None:
val *= scaler
return val
def get_design_var_values(self, filter=None):
"""
Return the design variable values.
This is called to gather the initial design variable state.
Parameters
----------
filter : list
List of desvar names used by recorders.
Returns
-------
dict
Dictionary containing values of each design variable.
"""
if filter:
dvs = filter
else:
# use all the designvars
dvs = self._designvars
return {n: self._get_voi_val(n, self._designvars[n], self._remote_dvs) for n in dvs}
def set_design_var(self, name, value):
"""
Set the value of a design variable.
Parameters
----------
name : str
Global pathname of the design variable.
value : float or ndarray
Value for the design variable.
"""
if (name in self._remote_dvs and
self._problem.model._owning_rank['output'][name] != self._problem.comm.rank):
return
meta = self._designvars[name]
indices = meta['indices']
if indices is None:
indices = slice(None)
desvar = self._problem.model._outputs._views_flat[name]
desvar[indices] = value
if self._has_scaling:
# Scale design variable values
scaler = meta['scaler']
if scaler is not None:
desvar[indices] *= 1.0 / scaler
adder = meta['adder']
if adder is not None:
desvar[indices] -= adder
def get_response_values(self, filter=None):
"""
Return response values.
Parameters
----------
filter : list
List of response names used by recorders.
Returns
-------
dict
Dictionary containing values of each response.
"""
if filter:
resps = filter
else:
resps = self._responses
return {n: self._get_voi_val(n, self._responses[n], self._remote_objs) for n in resps}
def get_objective_values(self, filter=None):
"""
Return objective values.
Parameters
----------
filter : list
List of objective names used by recorders.
Returns
-------
dict
Dictionary containing values of each objective.
"""
if filter:
objs = filter
else:
objs = self._objs
return {n: self._get_voi_val(n, self._objs[n], self._remote_objs) for n in objs}
def get_constraint_values(self, ctype='all', lintype='all', filter=None):
"""
Return constraint values.
Parameters
----------
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
filter : list
List of constraint names used by recorders.
Returns
-------
dict
Dictionary containing values of each constraint.
"""
if filter is not None:
cons = filter
else:
cons = self._cons
con_dict = {}
for name in cons:
meta = self._cons[name]
if lintype == 'linear' and not meta['linear']:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
con_dict[name] = self._get_voi_val(name, meta, self._remote_cons)
return con_dict
def run(self):
"""
Execute this driver.
The base `Driver` just runs the model. All other drivers overload
this method.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:
failure_flag, _, _ = self._problem.model._solve_nonlinear()
self.iter_count += 1
return failure_flag
def _dict2array_jac(self, derivs):
osize = 0
isize = 0
do_wrt = True
islices = {}
oslices = {}
for okey, oval in iteritems(derivs):
if do_wrt:
for ikey, val in iteritems(oval):
istart = isize
isize += val.shape[1]
islices[ikey] = slice(istart, isize)
do_wrt = False
ostart = osize
osize += oval[ikey].shape[0]
oslices[okey] = slice(ostart, osize)
new_derivs = np.zeros((osize, isize))
relevant = self._problem.model._relevant
for okey, odict in iteritems(derivs):
for ikey, val in iteritems(odict):
if okey in relevant[ikey] or ikey in relevant[okey]:
new_derivs[oslices[okey], islices[ikey]] = val
return new_derivs
def _compute_totals(self, of=None, wrt=None, return_format='flat_dict', global_names=True):
"""
Compute derivatives of desired quantities with respect to desired inputs.
All derivatives are returned using driver scaling.
Parameters
----------
of : list of variable name strings or None
Variables whose derivatives will be computed. Default is None, which
uses the driver's objectives and constraints.
wrt : list of variable name strings or None
Variables with respect to which the derivatives will be computed.
Default is None, which uses the driver's desvars.
return_format : string
Format to return the derivatives. Default is a 'flat_dict', which
returns them in a dictionary whose keys are tuples of form (of, wrt). For
the scipy optimizer, 'array' is also supported.
global_names : bool
Set to True when passing in global names to skip some translation steps.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
prob = self._problem
# Compute the derivatives in dict format...
if prob.model._owns_approx_jac:
derivs = prob._compute_totals_approx(of=of, wrt=wrt, return_format='dict',
global_names=global_names)
else:
derivs = prob._compute_totals(of=of, wrt=wrt, return_format='dict',
global_names=global_names)
# ... then convert to whatever the driver needs.
if return_format in ('dict', 'array'):
if self._has_scaling:
for okey, odict in iteritems(derivs):
for ikey, val in iteritems(odict):
iscaler = self._designvars[ikey]['scaler']
oscaler = self._responses[okey]['scaler']
# Scale response side
if oscaler is not None:
val[:] = (oscaler * val.T).T
# Scale design var side
if iscaler is not None:
val *= 1.0 / iscaler
else:
raise RuntimeError("Derivative scaling by the driver only supports the 'dict' and "
"'array' formats at present.")
if return_format == 'array':
derivs = self._dict2array_jac(derivs)
return derivs
def record_iteration(self):
"""
Record an iteration of the current Driver.
"""
if not self._rec_mgr._recorders:
return
metadata = create_local_meta(self._get_name())
# Get the data to record
data = {}
if self.recording_options['record_desvars']:
# collective call that gets across all ranks
desvars = self.get_design_var_values()
else:
desvars = {}
if self.recording_options['record_responses']:
# responses = self.get_response_values() # not really working yet
responses = {}
else:
responses = {}
if self.recording_options['record_objectives']:
objectives = self.get_objective_values()
else:
objectives = {}
if self.recording_options['record_constraints']:
constraints = self.get_constraint_values()
else:
constraints = {}
desvars = {name: desvars[name]
for name in self._filtered_vars_to_record['des']}
# responses not working yet
# responses = {name: responses[name] for name in self._filtered_vars_to_record['res']}
objectives = {name: objectives[name]
for name in self._filtered_vars_to_record['obj']}
constraints = {name: constraints[name]
for name in self._filtered_vars_to_record['con']}
if self.recording_options['includes']:
root = self._problem.model
outputs = root._outputs
# outputsinputs, outputs, residuals = root.get_nonlinear_vectors()
sysvars = {}
for name, value in iteritems(outputs._names):
if name in self._filtered_vars_to_record['sys']:
sysvars[name] = value
else:
sysvars = {}
if MPI:
root = self._problem.model
desvars = self._gather_vars(root, desvars)
responses = self._gather_vars(root, responses)
objectives = self._gather_vars(root, objectives)
constraints = self._gather_vars(root, constraints)
sysvars = self._gather_vars(root, sysvars)
data['des'] = desvars
data['res'] = responses
data['obj'] = objectives
data['con'] = constraints
data['sys'] = sysvars
self._rec_mgr.record_iteration(self, data, metadata)
def _gather_vars(self, root, local_vars):
"""
Gather and return only variables listed in `local_vars` from the `root` System.
Parameters
----------
root : <System>
the root System for the Problem
local_vars : dict
local variable names and values
Returns
-------
dct : dict
variable names and values.
"""
# if trace:
# debug("gathering vars for recording in %s" % root.pathname)
all_vars = root.comm.gather(local_vars, root=0)
# if trace:
# debug("DONE gathering rec vars for %s" % root.pathname)
if root.comm.rank == 0:
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
return dct
def _get_name(self):
"""
Get name of current Driver.
Returns
-------
str
Name of current Driver.
"""
return "Driver"
def set_simul_deriv_color(self, simul_info):
"""
Set the coloring for simultaneous derivatives.
Parameters
----------
simul_info : str or ({dv1: colors, ...}, {resp1: {dv1: {0: [res_idxs, dv_idxs]} ...} ...})
Information about simultaneous coloring for design vars and responses. If a string,
then simul_info is assumed to be the name of a file that contains the coloring
information in JSON format.
"""
if self.supports['simultaneous_derivatives']:
self._simul_coloring_info = simul_info
else:
raise RuntimeError("Driver '%s' does not support simultaneous derivatives." %
self._get_name())
def _setup_simul_coloring(self, mode='fwd'):
"""
Set up metadata for simultaneous derivative solution.
Parameters
----------
mode : str
Derivative direction, either 'fwd' or 'rev'.
"""
if mode == 'rev':
raise NotImplementedError("Simultaneous derivatives are currently not supported "
"in 'rev' mode")
# command line simul_coloring uses this env var to turn pre-existing coloring off
if not _use_simul_coloring:
return
prom2abs = self._problem.model._var_allprocs_prom2abs_list['output']
if isinstance(self._simul_coloring_info, string_types):
with open(self._simul_coloring_info, 'r') as f:
self._simul_coloring_info = json.load(f)
coloring, maps = self._simul_coloring_info
for dv, colors in iteritems(coloring):
if dv not in self._designvars:
# convert name from promoted to absolute
dv = prom2abs[dv][0]
self._designvars[dv]['simul_deriv_color'] = colors
for res, dvdict in iteritems(maps):
if res not in self._responses:
# convert name from promoted to absolute
res = prom2abs[res][0]
self._responses[res]['simul_map'] = dvdict
for dv, col_dict in dvdict.items():
col_dict = {int(k): v for k, v in iteritems(col_dict)}
if dv not in self._designvars:
# convert name from promoted to absolute and replace dictionary key
del dvdict[dv]
dv = prom2abs[dv][0]
dvdict[dv] = col_dict
def _pre_run_model_debug_print(self):
"""
Optionally print some debugging information before the model runs.
"""
if not self.options['debug_print']:
return
if not MPI or MPI.COMM_WORLD.rank == 0:
header = 'Driver debug print for iter coord: {}'.format(
get_formatted_iteration_coordinate())
print(header)
print(len(header) * '-')
if 'desvars' in self.options['debug_print']:
desvar_vals = self.get_design_var_values()
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Design Vars")
if desvar_vals:
for name, value in iteritems(desvar_vals):
print("{}: {}".format(name, repr(value)))
else:
print("None")
print()
def _post_run_model_debug_print(self):
"""
Optionally print some debugging information after the model runs.
"""
if 'nl_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='nonlinear')
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Nonlinear constraints")
if cons:
for name, value in iteritems(cons):
print("{}: {}".format(name, repr(value)))
else:
print("None")
print()
if 'ln_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='linear')
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Linear constraints")
if cons:
for name, value in iteritems(cons):
print("{}: {}".format(name, repr(value)))
else:
print("None")
print()
if 'objs' in self.options['debug_print']:
objs = self.get_objective_values()
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Objectives")
if objs:
for name, value in iteritems(objs):
print("{}: {}".format(name, repr(value)))
else:
print("None")
print()
def __init__(self):
"""
Initialize the driver.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
###########################
self.options.declare('debug_print', types=list, is_valid=_is_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars','ln_cons',"
"'nl_cons','objs'",
default=[])
###########################
self.recording_options.declare('record_metadata', types=bool, desc='Record metadata',
default=True)
self.recording_options.declare('record_desvars', types=bool, default=True,
desc='Set to True to record design variables at the \
driver level')
self.recording_options.declare('record_responses', types=bool, default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare('record_objectives', types=bool, default=True,
desc='Set to True to record objectives at the \
driver level')
self.recording_options.declare('record_constraints', types=bool, default=True,
desc='Set to True to record constraints at the \
driver level')
self.recording_options.declare('includes', types=list, default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare('record_derivatives', types=bool, default=False,
desc='Set to True to record derivatives at the driver \
level')
###########################
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints', types=bool, default=False)
self.supports.declare('equality_constraints', types=bool, default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints', types=bool, default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives', types=bool, default=False)
# Debug printing.
self.debug_print = OptionsDictionary()
self.debug_print.declare('debug_print', types=bool, default=False,
desc='Overall option to turn on Driver debug printing')
self.debug_print.declare('debug_print_desvars', types=bool, default=False,
desc='Print design variables')
self.debug_print.declare('debug_print_nl_con', types=bool, default=False,
desc='Print nonlinear constraints')
self.debug_print.declare('debug_print_ln_con', types=bool, default=False,
desc='Print linear constraints')
self.debug_print.declare('debug_print_objective', types=bool, default=False,
desc='Print objectives')
self.iter_count = 0
self.metadata = None
self._model_viewer_data = None
self.cite = ""
# TODO, support these in OpenMDAO
self.supports.declare('integer_design_vars', types=bool, default=False)
self._simul_coloring_info = None
self._res_jacs = {}
self.fail = False
def __init__(self, **kwargs):
"""
Initialize the driver.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Driver options.
"""
self._rec_mgr = RecordingManager()
self._problem = None
self._designvars = None
self._designvars_discrete = []
self._cons = None
self._objs = None
self._responses = None
# Driver options
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.options.declare(
'debug_print',
types=list,
check_valid=_check_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars', 'ln_cons', "
"'nl_cons', 'objs', 'totals'",
default=[])
# Case recording options
self.recording_options = OptionsDictionary(
parent_name=type(self).__name__)
self.recording_options.declare(
'record_model_metadata',
types=bool,
default=True,
desc='Record metadata for all Systems in the model')
self.recording_options.declare(
'record_desvars',
types=bool,
default=True,
desc='Set to True to record design variables at the '
'driver level')
self.recording_options.declare(
'record_responses',
types=bool,
default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare(
'record_objectives',
types=bool,
default=True,
desc='Set to True to record objectives at the driver level')
self.recording_options.declare(
'record_constraints',
types=bool,
default=True,
desc='Set to True to record constraints at the '
'driver level')
self.recording_options.declare(
'includes',
types=list,
default=[],
desc='Patterns for variables to include in recording')
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare(
'record_derivatives',
types=bool,
default=False,
desc='Set to True to record derivatives at the driver '
'level')
self.recording_options.declare(
'record_inputs',
types=bool,
default=True,
desc='Set to True to record inputs at the driver level')
# What the driver supports.
self.supports = OptionsDictionary(parent_name=type(self).__name__)
self.supports.declare('inequality_constraints',
types=bool,
default=False)
self.supports.declare('equality_constraints',
types=bool,
default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints',
types=bool,
default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives',
types=bool,
default=False)
self.supports.declare('total_jac_sparsity', types=bool, default=False)
self.iter_count = 0
self.cite = ""
self._coloring_info = coloring_mod._DEF_COMP_SPARSITY_ARGS.copy()
self._coloring_info['coloring'] = None
self._coloring_info['dynamic'] = False
self._coloring_info['static'] = None
self._total_jac_sparsity = None
self._res_jacs = {}
self._total_jac = None
self.fail = False
self._declare_options()
self.options.update(kwargs)
def __init__(self, **kwargs):
"""
Initialize the driver.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Driver options.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
# Driver options
self.options = OptionsDictionary()
self.options.declare('debug_print', types=list, check_valid=_check_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars', 'ln_cons', "
"'nl_cons', 'objs', 'totals'",
default=[])
# Case recording options
self.recording_options = OptionsDictionary()
self.recording_options.declare('record_model_metadata', types=bool, default=True,
desc='Record metadata for all Systems in the model')
self.recording_options.declare('record_desvars', types=bool, default=True,
desc='Set to True to record design variables at the '
'driver level')
self.recording_options.declare('record_responses', types=bool, default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare('record_objectives', types=bool, default=True,
desc='Set to True to record objectives at the driver level')
self.recording_options.declare('record_constraints', types=bool, default=True,
desc='Set to True to record constraints at the '
'driver level')
self.recording_options.declare('includes', types=list, default=[],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare('record_derivatives', types=bool, default=False,
desc='Set to True to record derivatives at the driver '
'level')
self.recording_options.declare('record_inputs', types=bool, default=True,
desc='Set to True to record inputs at the driver level')
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints', types=bool, default=False)
self.supports.declare('equality_constraints', types=bool, default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints', types=bool, default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives', types=bool, default=False)
self.supports.declare('total_jac_sparsity', types=bool, default=False)
self.iter_count = 0
self._model_viewer_data = None
self.cite = ""
self._simul_coloring_info = None
self._total_jac_sparsity = None
self._res_jacs = {}
self._total_jac = None
self.fail = False
self._declare_options()
self.options.update(kwargs)
class Driver(object):
"""
Top-level container for the systems and drivers.
Attributes
----------
fail : bool
Reports whether the driver ran successfully.
iter_count : int
Keep track of iterations for case recording.
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
recording_options : <OptionsDictionary>
Dictionary with driver recording options.
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
_problem : weakref to <Problem>
Pointer to the containing problem.
supports : <OptionsDictionary>
Provides a consistent way for drivers to declare what features they support.
_designvars : dict
Contains all design variable info.
_designvars_discrete : list
List of design variables that are discrete.
_dist_driver_vars : dict
Dict of constraints that are distributed outputs. Key is rank, values are
(local indices, local sizes).
_cons : dict
Contains all constraint info.
_objs : dict
Contains all objective info.
_responses : dict
Contains all response info.
_remote_dvs : dict
Dict of design variables that are remote on at least one proc. Values are
(owning rank, size).
_remote_cons : dict
Dict of constraints that are remote on at least one proc. Values are
(owning rank, size).
_remote_objs : dict
Dict of objectives that are remote on at least one proc. Values are
(owning rank, size).
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this driver.
_coloring_info : dict
Metadata pertaining to total coloring.
_total_jac_sparsity : dict, str, or None
Specifies sparsity of sub-jacobians of the total jacobian. Only used by pyOptSparseDriver.
_res_jacs : dict
Dict of sparse subjacobians for use with certain optimizers, e.g. pyOptSparseDriver.
_total_jac : _TotalJacInfo or None
Cached total jacobian handling object.
"""
def __init__(self, **kwargs):
"""
Initialize the driver.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Driver options.
"""
self._rec_mgr = RecordingManager()
self._problem = None
self._designvars = None
self._designvars_discrete = []
self._cons = None
self._objs = None
self._responses = None
# Driver options
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.options.declare(
'debug_print',
types=list,
check_valid=_check_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars', 'ln_cons', "
"'nl_cons', 'objs', 'totals'",
default=[])
# Case recording options
self.recording_options = OptionsDictionary(
parent_name=type(self).__name__)
self.recording_options.declare(
'record_model_metadata',
types=bool,
default=True,
desc='Deprecated. Recording of model metadata will always '
'be done',
deprecation="The recording option, record_model_metadata, "
"on Driver is "
"deprecated. Recording of model metadata will always "
"be done",
)
self.recording_options.declare(
'record_desvars',
types=bool,
default=True,
desc='Set to True to record design variables at the '
'driver level')
self.recording_options.declare(
'record_responses',
types=bool,
default=False,
desc='Set True to record constraints and objectives at the '
'driver level')
self.recording_options.declare(
'record_objectives',
types=bool,
default=True,
desc='Set to True to record objectives at the driver level')
self.recording_options.declare(
'record_constraints',
types=bool,
default=True,
desc='Set to True to record constraints at the '
'driver level')
self.recording_options.declare(
'includes',
types=list,
default=[],
desc='Patterns for variables to include in recording. '
'Uses fnmatch wildcards')
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes). Uses fnmatch wildcards')
self.recording_options.declare(
'record_derivatives',
types=bool,
default=False,
desc='Set to True to record derivatives at the driver '
'level')
self.recording_options.declare(
'record_inputs',
types=bool,
default=True,
desc='Set to True to record inputs at the driver level')
self.recording_options.declare(
'record_outputs',
types=bool,
default=True,
desc='Set True to record outputs at the '
'driver level.')
self.recording_options.declare(
'record_residuals',
types=bool,
default=False,
desc='Set True to record residuals at the '
'driver level.')
# What the driver supports.
self.supports = OptionsDictionary(parent_name=type(self).__name__)
self.supports.declare('inequality_constraints',
types=bool,
default=False)
self.supports.declare('equality_constraints',
types=bool,
default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints',
types=bool,
default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=True)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives',
types=bool,
default=False)
self.supports.declare('total_jac_sparsity', types=bool, default=False)
self.iter_count = 0
self.cite = ""
self._coloring_info = coloring_mod._get_coloring_meta()
self._total_jac_sparsity = None
self._res_jacs = {}
self._total_jac = None
self.fail = False
self._declare_options()
self.options.update(kwargs)
@property
def msginfo(self):
"""
Return info to prepend to messages.
Returns
-------
str
Info to prepend to messages.
"""
return type(self).__name__
def add_recorder(self, recorder):
"""
Add a recorder to the driver.
Parameters
----------
recorder : CaseRecorder
A recorder instance.
"""
self._rec_mgr.append(recorder)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
# shut down all recorders
self._rec_mgr.shutdown()
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
This is optionally implemented by subclasses of Driver.
"""
pass
def _setup_comm(self, comm):
"""
Perform any driver-specific setup of communicators for the model.
Parameters
----------
comm : MPI.Comm or <FakeComm> or None
The communicator for the Problem.
Returns
-------
MPI.Comm or <FakeComm> or None
The communicator for the Problem model.
"""
return comm
def _setup_driver(self, problem):
"""
Prepare the driver for execution.
This is the final thing to run during setup.
Parameters
----------
problem : <Problem>
Pointer to the containing problem.
"""
self._problem = weakref.ref(problem)
model = problem.model
self._total_jac = None
self._has_scaling = (np.any(
[r['total_scaler'] is not None for r in self._responses.values()])
or np.any([
dv['total_scaler'] is not None
for dv in self._designvars.values()
]))
# Determine if any design variables are discrete.
self._designvars_discrete = [
name for name, meta in self._designvars.items()
if meta['ivc_source'] in model._discrete_outputs
]
if not self.supports['integer_design_vars'] and len(
self._designvars_discrete) > 0:
msg = "Discrete design variables are not supported by this driver: "
msg += '.'.join(self._designvars_discrete)
raise RuntimeError(msg)
con_set = set()
obj_set = set()
dv_set = set()
self._remote_dvs = remote_dv_dict = {}
self._remote_cons = remote_con_dict = {}
self._dist_driver_vars = dist_dict = {}
self._remote_objs = remote_obj_dict = {}
src_design_vars = prom2ivc_src_dict(self._designvars)
src_cons = prom2ivc_src_dict(self._cons)
src_objs = prom2ivc_src_dict(self._objs)
responses = prom2ivc_src_dict(self._responses)
# Now determine if later we'll need to allgather cons, objs, or desvars.
if model.comm.size > 1 and model._subsystems_allprocs:
local_out_vars = set(model._outputs._abs_iter())
remote_dvs = set(src_design_vars) - local_out_vars
remote_cons = set(src_cons) - local_out_vars
remote_objs = set(src_objs) - local_out_vars
all_remote_vois = model.comm.allgather(
(remote_dvs, remote_cons, remote_objs))
for rem_dvs, rem_cons, rem_objs in all_remote_vois:
con_set.update(rem_cons)
obj_set.update(rem_objs)
dv_set.update(rem_dvs)
# If we have remote VOIs, pick an owning rank for each and use that
# to bcast to others later
owning_ranks = model._owning_rank
sizes = model._var_sizes['nonlinear']['output']
abs2meta = model._var_allprocs_abs2meta
rank = model.comm.rank
nprocs = model.comm.size
for i, vname in enumerate(model._var_allprocs_abs_names['output']):
if vname in responses:
indices = responses[vname].get('indices')
elif vname in src_design_vars:
indices = src_design_vars[vname].get('indices')
else:
continue
if abs2meta[vname]['distributed']:
idx = model._var_allprocs_abs2idx['nonlinear'][vname]
dist_sizes = model._var_sizes['nonlinear']['output'][:,
idx]
total_dist_size = np.sum(dist_sizes)
# Determine which indices are on our proc.
offsets = sizes2offsets(dist_sizes)
if indices is not None:
indices = convert_neg(indices, total_dist_size)
true_sizes = np.zeros(nprocs, dtype=INT_DTYPE)
for irank in range(nprocs):
dist_inds = indices[np.logical_and(
indices >= offsets[irank], indices <
(offsets[irank] + dist_sizes[irank]))]
if irank == rank:
local_indices = dist_inds - offsets[rank]
distrib_indices = dist_inds
true_sizes[irank] = dist_inds.size
dist_dict[vname] = (local_indices, true_sizes,
distrib_indices)
else:
dist_dict[vname] = (_full_slice, dist_sizes,
slice(
offsets[rank], offsets[rank] +
dist_sizes[rank]))
else:
owner = owning_ranks[vname]
sz = sizes[owner, i]
if vname in dv_set:
remote_dv_dict[vname] = (owner, sz)
if vname in con_set:
remote_con_dict[vname] = (owner, sz)
if vname in obj_set:
remote_obj_dict[vname] = (owner, sz)
self._remote_responses = self._remote_cons.copy()
self._remote_responses.update(self._remote_objs)
# set up simultaneous deriv coloring
if coloring_mod._use_total_sparsity:
# reset the coloring
if self._coloring_info['dynamic'] or self._coloring_info[
'static'] is not None:
self._coloring_info['coloring'] = None
coloring = self._get_static_coloring()
if coloring is not None and self.supports[
'simultaneous_derivatives']:
if model._owns_approx_jac:
coloring._check_config_partial(model)
else:
coloring._check_config_total(self)
self._setup_simul_coloring()
def _check_for_missing_objective(self):
"""
Check for missing objective and raise error if no objectives found.
"""
if len(self._objs) == 0:
msg = "Driver requires objective to be declared"
raise RuntimeError(msg)
def _get_vars_to_record(self, recording_options):
"""
Get variables to record based on recording options.
Parameters
----------
recording_options : <OptionsDictionary>
Dictionary with recording options.
Returns
-------
dict
Dictionary containing lists of variables to record.
"""
problem = self._problem()
model = problem.model
incl = recording_options['includes']
excl = recording_options['excludes']
# includes and excludes for outputs are specified using promoted names
abs2prom = model._var_allprocs_abs2prom['output']
# 1. If record_outputs is True, get the set of outputs
# 2. Filter those using includes and excludes to get the baseline set of variables to record
# 3. Add or remove from that set any desvars, objs, and cons based on the recording
# options of those
# includes and excludes for outputs are specified using _promoted_ names
# vectors are keyed on absolute name, discretes on relative/promoted name
myinputs = myoutputs = myresiduals = []
if recording_options['record_outputs']:
myoutputs = sorted([
n for n, prom in abs2prom.items()
if check_path(prom, incl, excl)
])
model_outs = model._outputs
if model._var_discrete['output']:
# if we have discrete outputs then residual name set doesn't match output one
if recording_options['record_residuals']:
myresiduals = [
n for n in myoutputs if model_outs._contains_abs(n)
]
elif recording_options['record_residuals']:
myresiduals = myoutputs
elif recording_options['record_residuals']:
myresiduals = [
n for n in model._residuals._abs_iter()
if check_path(abs2prom[n], incl, excl)
]
myoutputs = set(myoutputs)
if recording_options['record_desvars']:
myoutputs.update(self._designvars)
if recording_options['record_objectives'] or recording_options[
'record_responses']:
myoutputs.update(self._objs)
if recording_options['record_constraints'] or recording_options[
'record_responses']:
myoutputs.update(self._cons)
# inputs (if in options). inputs use _absolute_ names for includes/excludes
if 'record_inputs' in recording_options:
if recording_options['record_inputs']:
# sort the results since _var_allprocs_abs2prom isn't ordered
myinputs = sorted([
n for n in model._var_allprocs_abs2prom['input']
if check_path(n, incl, excl)
])
vars2record = {
'input': myinputs,
'output': list(myoutputs),
'residual': myresiduals
}
return vars2record
def _setup_recording(self):
"""
Set up case recording.
"""
self._filtered_vars_to_record = self._get_vars_to_record(
self.recording_options)
self._rec_mgr.startup(self)
def _get_voi_val(self,
name,
meta,
remote_vois,
driver_scaling=True,
rank=None):
"""
Get the value of a variable of interest (objective, constraint, or design var).
This will retrieve the value if the VOI is remote.
Parameters
----------
name : str
Name of the variable of interest.
meta : dict
Metadata for the variable of interest.
remote_vois : dict
Dict containing (owning_rank, size) for all remote vois of a particular
type (design var, constraint, or objective).
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
rank : int or None
If not None, gather value to this rank only.
Returns
-------
float or ndarray
The value of the named variable of interest.
"""
model = self._problem().model
comm = model.comm
get = model._outputs._abs_get_val
distributed_vars = self._dist_driver_vars
indices = meta['indices']
if meta.get('ivc_source') is not None:
src_name = meta['ivc_source']
else:
src_name = name
if MPI:
distributed = comm.size > 0 and src_name in distributed_vars
else:
distributed = False
if src_name in remote_vois:
owner, size = remote_vois[src_name]
# if var is distributed or only gathering to one rank
# TODO - support distributed var under a parallel group.
if owner is None or rank is not None:
val = model.get_val(src_name,
get_remote=True,
rank=rank,
flat=True)
if indices is not None:
val = val[indices]
else:
if owner == comm.rank:
if indices is None:
val = get(name).copy()
else:
val = get(name)[indices]
else:
if indices is not None:
size = len(indices)
val = np.empty(size)
comm.Bcast(val, root=owner)
elif distributed:
local_val = model.get_val(src_name, flat=True)
local_indices, sizes, _ = distributed_vars[src_name]
if local_indices is not None:
local_val = local_val[local_indices]
offsets = np.zeros(sizes.size, dtype=INT_DTYPE)
offsets[1:] = np.cumsum(sizes[:-1])
val = np.zeros(np.sum(sizes))
comm.Allgatherv(local_val, [val, sizes, offsets, MPI.DOUBLE])
else:
if name in self._designvars_discrete:
val = model._discrete_outputs[src_name]
# At present, only integers are supported by OpenMDAO drivers.
# We check the values here.
msg = "Only integer scalars or ndarrays are supported as values for " + \
"discrete variables when used as a design variable. "
if np.isscalar(val) and not isinstance(val, (int, np.integer)):
msg += "A value of type '{}' was specified.".format(
val.__class__.__name__)
raise ValueError(msg)
elif isinstance(val, np.ndarray) and not np.issubdtype(
val[0], np.integer):
msg += "An array of type '{}' was specified.".format(
val[0].__class__.__name__)
raise ValueError(msg)
elif indices is None:
val = get(src_name).copy()
else:
val = get(src_name)[indices]
if self._has_scaling and driver_scaling:
# Scale design variable values
adder = meta['total_adder']
if adder is not None:
val += adder
scaler = meta['total_scaler']
if scaler is not None:
val *= scaler
return val
def get_design_var_values(self):
"""
Return the design variable values.
Returns
-------
dict
Dictionary containing values of each design variable.
"""
return {
n: self._get_voi_val(n, dv, self._remote_dvs)
for n, dv in self._designvars.items()
}
def set_design_var(self, name, value):
"""
Set the value of a design variable.
Parameters
----------
name : str
Global pathname of the design variable.
value : float or ndarray
Value for the design variable.
"""
problem = self._problem()
meta = self._designvars[name]
src_name = meta['ivc_source']
# if the value is not local, don't set the value
if (src_name in self._remote_dvs
and problem.model._owning_rank[src_name] != problem.comm.rank):
return
indices = meta['indices']
if indices is None:
indices = _full_slice
if name in self._designvars_discrete:
# Note, drivers set values here and generally should know it is setting an integer.
# However, the DOEdriver may pull a non-integer value from its generator, so we
# convert it.
if isinstance(value, float):
value = int(value)
elif isinstance(value, np.ndarray):
if isinstance(problem.model._discrete_outputs[src_name], int):
# Setting an integer value with a 1D array - don't want to convert to array.
value = int(value)
else:
value = value.astype(np.int)
problem.model._discrete_outputs[src_name] = value
elif problem.model._outputs._contains_abs(src_name):
desvar = problem.model._outputs._abs_get_val(src_name)
if src_name in self._dist_driver_vars:
loc_idxs, _, dist_idxs = self._dist_driver_vars[src_name]
else:
loc_idxs = indices
dist_idxs = _full_slice
desvar[loc_idxs] = np.atleast_1d(value)[dist_idxs]
# Undo driver scaling when setting design var values into model.
if self._has_scaling:
scaler = meta['total_scaler']
if scaler is not None:
desvar[loc_idxs] *= 1.0 / scaler
adder = meta['total_adder']
if adder is not None:
desvar[loc_idxs] -= adder
def get_objective_values(self, driver_scaling=True):
"""
Return objective values.
Parameters
----------
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
Returns
-------
dict
Dictionary containing values of each objective.
"""
return {
n: self._get_voi_val(n,
obj,
self._remote_objs,
driver_scaling=driver_scaling)
for n, obj in self._objs.items()
}
def get_constraint_values(self,
ctype='all',
lintype='all',
driver_scaling=True):
"""
Return constraint values.
Parameters
----------
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
Returns
-------
dict
Dictionary containing values of each constraint.
"""
con_dict = {}
for name, meta in self._cons.items():
if lintype == 'linear' and not meta['linear']:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
con_dict[name] = self._get_voi_val(name,
meta,
self._remote_cons,
driver_scaling=driver_scaling)
return con_dict
def _get_ordered_nl_responses(self):
"""
Return the names of nonlinear responses in the order used by the driver.
Default order is objectives followed by nonlinear constraints. This is used for
simultaneous derivative coloring and sparsity determination.
Returns
-------
list of str
The nonlinear response names in order.
"""
order = list(self._objs)
order.extend(n for n, meta in self._cons.items()
if not ('linear' in meta and meta['linear']))
return order
def _update_voi_meta(self, model):
"""
Collect response and design var metadata from the model and size desvars and responses.
Parameters
----------
model : System
The System that represents the entire model.
Returns
-------
int
Total size of responses, with linear constraints excluded.
int
Total size of design vars.
"""
self._objs = objs = OrderedDict()
self._cons = cons = OrderedDict()
model._setup_driver_units()
self._responses = resps = model.get_responses(recurse=True,
use_prom_ivc=True)
for name, data in resps.items():
if data['type'] == 'con':
cons[name] = data
else:
objs[name] = data
response_size = sum(resps[n]['size']
for n in self._get_ordered_nl_responses())
# Gather up the information for design vars.
self._designvars = designvars = model.get_design_vars(
recurse=True, use_prom_ivc=True)
desvar_size = sum(data['size'] for data in designvars.values())
return response_size, desvar_size
def run(self):
"""
Execute this driver.
The base `Driver` just runs the model. All other drivers overload
this method.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
with RecordingDebugging(self._get_name(), self.iter_count, self):
self._problem().model.run_solve_nonlinear()
self.iter_count += 1
return False
@property
def _recording_iter(self):
return self._problem()._metadata['recording_iter']
def _compute_totals(self,
of=None,
wrt=None,
return_format='flat_dict',
global_names=None,
use_abs_names=True):
"""
Compute derivatives of desired quantities with respect to desired inputs.
All derivatives are returned using driver scaling.
Parameters
----------
of : list of variable name strings or None
Variables whose derivatives will be computed. Default is None, which
uses the driver's objectives and constraints.
wrt : list of variable name strings or None
Variables with respect to which the derivatives will be computed.
Default is None, which uses the driver's desvars.
return_format : string
Format to return the derivatives. Default is a 'flat_dict', which
returns them in a dictionary whose keys are tuples of form (of, wrt). For
the scipy optimizer, 'array' is also supported.
global_names : bool
Deprecated. Use 'use_abs_names' instead.
use_abs_names : bool
Set to True when passing in absolute names to skip some translation steps.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
problem = self._problem()
total_jac = self._total_jac
debug_print = 'totals' in self.options['debug_print'] and (
not MPI or problem.comm.rank == 0)
if debug_print:
header = 'Driver total derivatives for iteration: ' + str(
self.iter_count)
print(header)
print(len(header) * '-' + '\n')
if global_names is not None:
warn_deprecation(
"'global_names' is deprecated in calls to _compute_totals. "
"Use 'use_abs_names' instead.")
use_abs_names = global_names
if problem.model._owns_approx_jac:
self._recording_iter.push(('_compute_totals_approx', 0))
try:
if total_jac is None:
total_jac = _TotalJacInfo(problem,
of,
wrt,
use_abs_names,
return_format,
approx=True,
debug_print=debug_print)
# Don't cache linear constraint jacobian
if not total_jac.has_lin_cons:
self._total_jac = total_jac
totals = total_jac.compute_totals_approx(initialize=True)
else:
totals = total_jac.compute_totals_approx()
finally:
self._recording_iter.pop()
else:
if total_jac is None:
total_jac = _TotalJacInfo(problem,
of,
wrt,
use_abs_names,
return_format,
debug_print=debug_print)
# don't cache linear constraint jacobian
if not total_jac.has_lin_cons:
self._total_jac = total_jac
self._recording_iter.push(('_compute_totals', 0))
try:
totals = total_jac.compute_totals()
finally:
self._recording_iter.pop()
if self._rec_mgr._recorders and self.recording_options[
'record_derivatives']:
metadata = create_local_meta(self._get_name())
total_jac.record_derivatives(self, metadata)
return totals
def record_iteration(self):
"""
Record an iteration of the current Driver.
"""
record_iteration(self, self._problem(), self._get_name())
def _get_recorder_metadata(self, case_name):
"""
Return metadata from the latest iteration for use in the recorder.
Parameters
----------
case_name : str
Name of current case.
Returns
-------
dict
Metadata dictionary for the recorder.
"""
return create_local_meta(case_name)
def _get_name(self):
"""
Get name of current Driver.
Returns
-------
str
Name of current Driver.
"""
return "Driver"
def declare_coloring(
self,
num_full_jacs=coloring_mod.
_DEF_COMP_SPARSITY_ARGS['num_full_jacs'],
tol=coloring_mod._DEF_COMP_SPARSITY_ARGS['tol'],
orders=coloring_mod._DEF_COMP_SPARSITY_ARGS['orders'],
perturb_size=coloring_mod._DEF_COMP_SPARSITY_ARGS['perturb_size'],
min_improve_pct=coloring_mod.
_DEF_COMP_SPARSITY_ARGS['min_improve_pct'],
show_summary=coloring_mod._DEF_COMP_SPARSITY_ARGS['show_summary'],
show_sparsity=coloring_mod._DEF_COMP_SPARSITY_ARGS['show_sparsity']
):
"""
Set options for total deriv coloring.
Parameters
----------
num_full_jacs : int
Number of times to repeat partial jacobian computation when computing sparsity.
tol : float
Tolerance used to determine if an array entry is nonzero during sparsity determination.
orders : int
Number of orders above and below the tolerance to check during the tolerance sweep.
perturb_size : float
Size of input/output perturbation during generation of sparsity.
min_improve_pct : float
If coloring does not improve (decrease) the number of solves more than the given
percentage, coloring will not be used.
show_summary : bool
If True, display summary information after generating coloring.
show_sparsity : bool
If True, display sparsity with coloring info after generating coloring.
"""
self._coloring_info['num_full_jacs'] = num_full_jacs
self._coloring_info['tol'] = tol
self._coloring_info['orders'] = orders
self._coloring_info['perturb_size'] = perturb_size
self._coloring_info['min_improve_pct'] = min_improve_pct
if self._coloring_info['static'] is None:
self._coloring_info['dynamic'] = True
else:
self._coloring_info['dynamic'] = False
self._coloring_info['coloring'] = None
self._coloring_info['show_summary'] = show_summary
self._coloring_info['show_sparsity'] = show_sparsity
def use_fixed_coloring(self, coloring=coloring_mod._STD_COLORING_FNAME):
"""
Tell the driver to use a precomputed coloring.
Parameters
----------
coloring : str
A coloring filename. If no arg is passed, filename will be determined
automatically.
"""
if self.supports['simultaneous_derivatives']:
if coloring_mod._force_dyn_coloring and coloring is coloring_mod._STD_COLORING_FNAME:
# force the generation of a dynamic coloring this time
self._coloring_info['dynamic'] = True
self._coloring_info['static'] = None
else:
self._coloring_info['static'] = coloring
self._coloring_info['dynamic'] = False
self._coloring_info['coloring'] = None
else:
raise RuntimeError(
"Driver '%s' does not support simultaneous derivatives." %
self._get_name())
def _setup_tot_jac_sparsity(self, coloring=None):
"""
Set up total jacobian subjac sparsity.
Drivers that can use subjac sparsity should override this.
Parameters
----------
coloring : Coloring or None
Current coloring.
"""
pass
def _get_static_coloring(self):
"""
Get the Coloring for this driver.
If necessary, load the Coloring from a file.
Returns
-------
Coloring or None
The pre-existing or loaded Coloring, or None
"""
info = self._coloring_info
static = info['static']
if isinstance(static, coloring_mod.Coloring):
coloring = static
info['coloring'] = coloring
else:
coloring = info['coloring']
if coloring is not None:
return coloring
if static is coloring_mod._STD_COLORING_FNAME or isinstance(
static, str):
if static is coloring_mod._STD_COLORING_FNAME:
fname = self._get_total_coloring_fname()
else:
fname = static
print("loading total coloring from file %s" % fname)
coloring = info['coloring'] = coloring_mod.Coloring.load(fname)
info.update(coloring._meta)
return coloring
def _get_total_coloring_fname(self):
return os.path.join(self._problem().options['coloring_dir'],
'total_coloring.pkl')
def _setup_simul_coloring(self):
"""
Set up metadata for coloring of total derivative solution.
If set_coloring was called with a filename, load the coloring file.
"""
# command line simul_coloring uses this env var to turn pre-existing coloring off
if not coloring_mod._use_total_sparsity:
return
problem = self._problem()
if not problem.model._use_derivatives:
simple_warning(
"Derivatives are turned off. Skipping simul deriv coloring.")
return
total_coloring = self._get_static_coloring()
if total_coloring._rev and problem._orig_mode not in ('rev', 'auto'):
revcol = total_coloring._rev[0][0]
if revcol:
raise RuntimeError(
"Simultaneous coloring does reverse solves but mode has "
"been set to '%s'" % problem._orig_mode)
if total_coloring._fwd and problem._orig_mode not in ('fwd', 'auto'):
fwdcol = total_coloring._fwd[0][0]
if fwdcol:
raise RuntimeError(
"Simultaneous coloring does forward solves but mode has "
"been set to '%s'" % problem._orig_mode)
def _pre_run_model_debug_print(self):
"""
Optionally print some debugging information before the model runs.
"""
debug_opt = self.options['debug_print']
rank = self._problem().comm.rank
if not debug_opt or debug_opt == ['totals']:
return
if not MPI or rank == 0:
header = 'Driver debug print for iter coord: {}'.format(
self._recording_iter.get_formatted_iteration_coordinate())
print(header)
print(len(header) * '-')
if 'desvars' in debug_opt:
model = self._problem().model
desvar_vals = {
n: model.get_val(n, get_remote=True, rank=0)
for n in self._designvars
}
if not MPI or rank == 0:
print("Design Vars")
if desvar_vals:
pprint.pprint(desvar_vals)
else:
print("None")
print()
sys.stdout.flush()
def _post_run_model_debug_print(self):
"""
Optionally print some debugging information after the model runs.
"""
rank = self._problem().comm.rank
if 'nl_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='nonlinear',
driver_scaling=False)
if not MPI or rank == 0:
print("Nonlinear constraints")
if cons:
pprint.pprint(cons)
else:
print("None")
print()
if 'ln_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='linear',
driver_scaling=False)
if not MPI or rank == 0:
print("Linear constraints")
if cons:
pprint.pprint(cons)
else:
print("None")
print()
if 'objs' in self.options['debug_print']:
objs = self.get_objective_values(driver_scaling=False)
if not MPI or rank == 0:
print("Objectives")
if objs:
pprint.pprint(objs)
else:
print("None")
print()
sys.stdout.flush()
class SolverBase(object):
""" Common base class for Linear and Nonlinear solver. Should not be used
by users. Always inherit from `LinearSolver` or `NonlinearSolver`."""
def __init__(self):
self.iter_count = 0
self.options = OptionsDictionary()
desc = 'Set to 0 to disable printing, set to 1 to print the ' \
'residual to stdout each iteration, set to 2 to print ' \
'subiteration residuals as well.'
self.options.add_option('iprint', 0, values=[0, 1, 2], desc=desc)
self.options.add_option('err_on_maxiter', False,
desc='If True, raise an AnalysisError if not converged at maxiter.')
self.recorders = RecordingManager()
self.local_meta = None
def setup(self, sub):
""" Solvers override to define post-setup initiailzation.
Args
----
sub: `System`
System that owns this solver.
"""
pass
def cleanup(self):
""" Clean up resources prior to exit. """
self.recorders.close()
def print_norm(self, solver_string, pathname, iteration, res, res0,
msg=None, indent=0, solver='NL', u_norm=None):
""" Prints out the norm of the residual in a neat readable format.
Args
----
solver_string: string
Unique string to identify your solver type (e.g., 'LN_GS' or
'NEWTON').
pathname: dict
Parent system pathname.
iteration: int
Current iteration number
res: float
Norm of the absolute residual value.
res0: float
Norm of the baseline initial residual for relative comparison.
msg: string, optional
Message that indicates convergence.
ident: int, optional
Additional indentation levels for subiterations.
solver: string, optional
Solver type if not LN or NL (mostly for line search operations.)
u_norm: float, optional
Norm of the u vector, if applicable.
"""
if pathname=='':
name = 'root'
else:
name = 'root.' + pathname
# Find indentation level
level = pathname.count('.')
# No indentation for driver; top solver is no indentation.
level = level + indent
indent = ' ' * level
if msg is not None:
form = indent + '[%s] %s: %s %d | %s'
if u_norm:
form += ' (%s)' % u_norm
print(form % (name, solver, solver_string, iteration, msg))
return
form = indent + '[%s] %s: %s %d | %.9g %.9g'
if u_norm:
form += ' (%s)' % u_norm
print(form % (name, solver, solver_string, iteration, res, res/res0))
def print_all_convergence(self):
""" Turns on iprint for this solver and all subsolvers. Override if
your solver has subsolvers."""
self.options['iprint'] = 1
def generate_docstring(self):
"""
Generates a numpy-style docstring for a user-created System class.
Returns
-------
docstring : str
string that contains a basic numpy docstring.
"""
#start the docstring off
docstrings = [' \"\"\"']
#Put options into docstring
firstTime = 1
for key, value in sorted(vars(self).items()):
if type(value)==OptionsDictionary:
if firstTime: #start of Options docstring
docstrings.extend(['',' Options',' -------'])
firstTime = 0
docstrings.append(value._generate_docstring(key))
#finish up docstring
docstrings.extend([' \"\"\"',''])
return '\n'.join(docstrings)
class ExperimentalDriver(object):
"""
A fake driver class used for doc generation testing.
Attributes
----------
fail : bool
Reports whether the driver ran successfully.
iter_count : int
Keep track of iterations for case recording.
options : list
List of options
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
recording_options : <OptionsDictionary>
Dictionary with driver recording options.
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
_problem : <Problem>
Pointer to the containing problem.
supports : <OptionsDictionary>
Provides a consistant way for drivers to declare what features they support.
_designvars : dict
Contains all design variable info.
_cons : dict
Contains all constraint info.
_objs : dict
Contains all objective info.
_responses : dict
Contains all response info.
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this driver.
_vars_to_record : dict
Dict of lists of var names indicating what to record
_model_viewer_data : dict
Structure of model, used to make n2 diagram.
_remote_dvs : dict
Dict of design variables that are remote on at least one proc. Values are
(owning rank, size).
_remote_cons : dict
Dict of constraints that are remote on at least one proc. Values are
(owning rank, size).
_remote_objs : dict
Dict of objectives that are remote on at least one proc. Values are
(owning rank, size).
_remote_responses : dict
A combined dict containing entries from _remote_cons and _remote_objs.
_total_coloring : tuple of dicts
A data structure describing coloring for simultaneous derivs.
_res_jacs : dict
Dict of sparse subjacobians for use with certain optimizers, e.g. pyOptSparseDriver.
"""
def __init__(self):
"""
Initialize the driver.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
###########################
self.recording_options.declare('record_desvars', types=bool, default=True,
desc='Set to True to record design variables at the \
driver level')
self.recording_options.declare('record_responses', types=bool, default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare('record_objectives', types=bool, default=True,
desc='Set to True to record objectives at the \
driver level')
self.recording_options.declare('record_constraints', types=bool, default=True,
desc='Set to True to record constraints at the \
driver level')
self.recording_options.declare('includes', types=list, default=[],
desc='Patterns for variables to include in recording. \
Uses fnmatch wildcards')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes). Uses fnmatch wildcards')
self.recording_options.declare('record_derivatives', types=bool, default=False,
desc='Set to True to record derivatives at the driver \
level')
###########################
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints', types=bool, default=False)
self.supports.declare('equality_constraints', types=bool, default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints', types=bool, default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives', types=bool, default=False)
self.supports.declare('distributed_design_vars', types=bool, default=False)
self.iter_count = 0
self.options = None
self._model_viewer_data = None
self.cite = ""
# TODO, support these in OpenMDAO
self.supports.declare('integer_design_vars', types=bool, default=False)
self._res_jacs = {}
self.fail = False
def add_recorder(self, recorder):
"""
Add a recorder to the driver.
Parameters
----------
recorder : CaseRecorder
A recorder instance.
"""
self._rec_mgr.append(recorder)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
self._rec_mgr.close()
def _setup_driver(self, problem):
"""
Prepare the driver for execution.
This is the final thing to run during setup.
Parameters
----------
problem : <Problem>
Pointer to the containing problem.
"""
pass
def _get_voi_val(self, name, meta, remote_vois):
"""
Get the value of a variable of interest (objective, constraint, or design var).
This will retrieve the value if the VOI is remote.
Parameters
----------
name : str
Name of the variable of interest.
meta : dict
Metadata for the variable of interest.
remote_vois : dict
Dict containing (owning_rank, size) for all remote vois of a particular
type (design var, constraint, or objective).
Returns
-------
float or ndarray
The value of the named variable of interest.
"""
model = self._problem.model
comm = model.comm
vec = model._outputs._views_flat
indices = meta['indices']
if name in remote_vois:
owner, size = remote_vois[name]
if owner == comm.rank:
if indices is None:
val = vec[name].copy()
else:
val = vec[name][indices]
else:
if indices is not None:
size = indices.indexed_src_size
val = np.empty(size)
comm.Bcast(val, root=owner)
else:
if indices is None:
val = vec[name].copy()
else:
val = vec[name][indices]
if self._has_scaling:
# Scale design variable values
adder = meta['adder']
if adder is not None:
val += adder
scaler = meta['scaler']
if scaler is not None:
val *= scaler
return val
def get_design_var_values(self, filter=None):
"""
Return the design variable values.
This is called to gather the initial design variable state.
Parameters
----------
filter : list
List of desvar names used by recorders.
Returns
-------
dict
Dictionary containing values of each design variable.
"""
if filter:
dvs = filter
else:
# use all the designvars
dvs = self._designvars
return {n: self._get_voi_val(n, self._designvars[n], self._remote_dvs) for n in dvs}
def set_design_var(self, name, value):
"""
Set the value of a design variable.
Parameters
----------
name : str
Global pathname of the design variable.
value : float or ndarray
Value for the design variable.
"""
if (name in self._remote_dvs and
self._problem.model._owning_rank['output'][name] != self._problem.comm.rank):
return
meta = self._designvars[name]
indices = meta['indices']
if indices is None:
indices = slice(None)
desvar = self._problem.model._outputs._views_flat[name]
desvar[indices] = value
if self._has_scaling:
# Scale design variable values
scaler = meta['scaler']
if scaler is not None:
desvar[indices] *= 1.0 / scaler
adder = meta['adder']
if adder is not None:
desvar[indices] -= adder
def get_response_values(self, filter=None):
"""
Return response values.
Parameters
----------
filter : list
List of response names used by recorders.
Returns
-------
dict
Dictionary containing values of each response.
"""
if filter:
resps = filter
else:
resps = self._responses
return {n: self._get_voi_val(n, self._responses[n], self._remote_objs) for n in resps}
def get_objective_values(self, filter=None):
"""
Return objective values.
Parameters
----------
filter : list
List of objective names used by recorders.
Returns
-------
dict
Dictionary containing values of each objective.
"""
if filter:
objs = filter
else:
objs = self._objs
return {n: self._get_voi_val(n, self._objs[n], self._remote_objs) for n in objs}
def get_constraint_values(self, ctype='all', lintype='all', filter=None):
"""
Return constraint values.
Parameters
----------
ctype : str
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : str
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
filter : list
List of constraint names used by recorders.
Returns
-------
dict
Dictionary containing values of each constraint.
"""
if filter is not None:
cons = filter
else:
cons = self._cons
con_dict = {}
for name in cons:
meta = self._cons[name]
if lintype == 'linear' and not meta['linear']:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
con_dict[name] = self._get_voi_val(name, meta, self._remote_cons)
return con_dict
def run(self):
"""
Execute this driver.
The base `Driver` just runs the model. All other drivers overload
this method.
Returns
-------
bool
Failure flag; True if failed to converge, False is successful.
"""
with Recording(self._get_name(), self.iter_count, self) as rec:
self._problem.model.run_solve_nonlinear()
self.iter_count += 1
return False
def _dict2array_jac(self, derivs):
osize = 0
isize = 0
do_wrt = True
islices = {}
oslices = {}
for okey, oval in derivs.items():
if do_wrt:
for ikey, val in oval.items():
istart = isize
isize += val.shape[1]
islices[ikey] = slice(istart, isize)
do_wrt = False
ostart = osize
osize += oval[ikey].shape[0]
oslices[okey] = slice(ostart, osize)
new_derivs = np.zeros((osize, isize))
relevant = self._problem.model._relevant
for okey, odict in derivs.items():
for ikey, val in odict.items():
if okey in relevant[ikey] or ikey in relevant[okey]:
new_derivs[oslices[okey], islices[ikey]] = val
return new_derivs
def _compute_totals(self, of=None, wrt=None, return_format='flat_dict', use_abs_names=True):
"""
Compute derivatives of desired quantities with respect to desired inputs.
All derivatives are returned using driver scaling.
Parameters
----------
of : list of variable name str or None
Variables whose derivatives will be computed. Default is None, which
uses the driver's objectives and constraints.
wrt : list of variable name str or None
Variables with respect to which the derivatives will be computed.
Default is None, which uses the driver's desvars.
return_format : str
Format to return the derivatives. Default is a 'flat_dict', which
returns them in a dictionary whose keys are tuples of form (of, wrt). For
the scipy optimizer, 'array' is also supported.
use_abs_names : bool
Set to True when passing in global names to skip some translation steps.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
prob = self._problem
# Compute the derivatives in dict format...
if prob.model._owns_approx_jac:
derivs = prob._compute_totals_approx(of=of, wrt=wrt, return_format='dict',
use_abs_names=use_abs_names)
else:
derivs = prob._compute_totals(of=of, wrt=wrt, return_format='dict',
use_abs_names=use_abs_names)
# ... then convert to whatever the driver needs.
if return_format in ('dict', 'array'):
if self._has_scaling:
for okey, odict in derivs.items():
for ikey, val in odict.items():
iscaler = self._designvars[ikey]['scaler']
oscaler = self._responses[okey]['scaler']
# Scale response side
if oscaler is not None:
val[:] = (oscaler * val.T).T
# Scale design var side
if iscaler is not None:
val *= 1.0 / iscaler
else:
raise RuntimeError("Derivative scaling by the driver only supports the 'dict' and "
"'array' formats at present.")
if return_format == 'array':
derivs = self._dict2array_jac(derivs)
return derivs
def record_iteration(self):
"""
Record an iteration of the current Driver.
"""
if not self._rec_mgr._recorders:
return
metadata = create_local_meta(self._get_name())
# Get the data to record
data = {}
if self.recording_options['record_desvars']:
# collective call that gets across all ranks
desvars = self.get_design_var_values()
else:
desvars = {}
if self.recording_options['record_responses']:
# responses = self.get_response_values() # not really working yet
responses = {}
else:
responses = {}
if self.recording_options['record_objectives']:
objectives = self.get_objective_values()
else:
objectives = {}
if self.recording_options['record_constraints']:
constraints = self.get_constraint_values()
else:
constraints = {}
desvars = {name: desvars[name]
for name in self._filtered_vars_to_record['des']}
# responses not working yet
# responses = {name: responses[name] for name in self._filtered_vars_to_record['res']}
objectives = {name: objectives[name]
for name in self._filtered_vars_to_record['obj']}
constraints = {name: constraints[name]
for name in self._filtered_vars_to_record['con']}
if self.recording_options['includes']:
root = self._problem.model
outputs = root._outputs
# outputsinputs, outputs, residuals = root.get_nonlinear_vectors()
sysvars = {}
for name, value in outputs._names.items():
if name in self._filtered_vars_to_record['sys']:
sysvars[name] = value
else:
sysvars = {}
if MPI:
root = self._problem.model
desvars = self._gather_vars(root, desvars)
responses = self._gather_vars(root, responses)
objectives = self._gather_vars(root, objectives)
constraints = self._gather_vars(root, constraints)
sysvars = self._gather_vars(root, sysvars)
data['des'] = desvars
data['res'] = responses
data['obj'] = objectives
data['con'] = constraints
data['sys'] = sysvars
self._rec_mgr.record_iteration(self, data, metadata)
def _gather_vars(self, root, local_vars):
"""
Gather and return only variables listed in `local_vars` from the `root` System.
Parameters
----------
root : <System>
the root System for the Problem
local_vars : dict
local variable names and values
Returns
-------
dct : dict
variable names and values.
"""
# if trace:
# debug("gathering vars for recording in %s" % root.pathname)
all_vars = root.comm.gather(local_vars, root=0)
# if trace:
# debug("DONE gathering rec vars for %s" % root.pathname)
if root.comm.rank == 0:
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
return dct
def _get_name(self):
"""
Get name of current Driver.
Returns
-------
str
Name of current Driver.
"""
return "Driver"
class Driver(object):
"""
Top-level container for the systems and drivers.
Attributes
----------
fail : bool
Reports whether the driver ran successfully.
iter_count : int
Keep track of iterations for case recording.
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
recording_options : <OptionsDictionary>
Dictionary with driver recording options.
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
_problem : <Problem>
Pointer to the containing problem.
supports : <OptionsDictionary>
Provides a consistent way for drivers to declare what features they support.
_designvars : dict
Contains all design variable info.
_cons : dict
Contains all constraint info.
_objs : dict
Contains all objective info.
_responses : dict
Contains all response info.
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this driver.
_vars_to_record: dict
Dict of lists of var names indicating what to record
_model_viewer_data : dict
Structure of model, used to make n2 diagram.
_simul_coloring_info : tuple of dicts
A data structure describing coloring for simultaneous derivs.
_total_jac_sparsity : dict, str, or None
Specifies sparsity of sub-jacobians of the total jacobian. Only used by pyOptSparseDriver.
_res_jacs : dict
Dict of sparse subjacobians for use with certain optimizers, e.g. pyOptSparseDriver.
_total_jac : _TotalJacInfo or None
Cached total jacobian handling object.
"""
def __init__(self, **kwargs):
"""
Initialize the driver.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Driver options.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
# Driver options
self.options = OptionsDictionary()
self.options.declare('debug_print', types=list, check_valid=_check_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars', 'ln_cons', "
"'nl_cons', 'objs', 'totals'",
default=[])
# Case recording options
self.recording_options = OptionsDictionary()
self.recording_options.declare('record_model_metadata', types=bool, default=True,
desc='Record metadata for all Systems in the model')
self.recording_options.declare('record_desvars', types=bool, default=True,
desc='Set to True to record design variables at the '
'driver level')
self.recording_options.declare('record_responses', types=bool, default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare('record_objectives', types=bool, default=True,
desc='Set to True to record objectives at the driver level')
self.recording_options.declare('record_constraints', types=bool, default=True,
desc='Set to True to record constraints at the '
'driver level')
self.recording_options.declare('includes', types=list, default=[],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare('record_derivatives', types=bool, default=False,
desc='Set to True to record derivatives at the driver '
'level')
self.recording_options.declare('record_inputs', types=bool, default=True,
desc='Set to True to record inputs at the driver level')
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints', types=bool, default=False)
self.supports.declare('equality_constraints', types=bool, default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints', types=bool, default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives', types=bool, default=False)
self.supports.declare('total_jac_sparsity', types=bool, default=False)
self.iter_count = 0
self._model_viewer_data = None
self.cite = ""
self._simul_coloring_info = None
self._total_jac_sparsity = None
self._res_jacs = {}
self._total_jac = None
self.fail = False
self._declare_options()
self.options.update(kwargs)
def add_recorder(self, recorder):
"""
Add a recorder to the driver.
Parameters
----------
recorder : CaseRecorder
A recorder instance.
"""
self._rec_mgr.append(recorder)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
# shut down all recorders
self._rec_mgr.shutdown()
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
This is optionally implemented by subclasses of Driver.
"""
pass
def _setup_comm(self, comm):
"""
Perform any driver-specific setup of communicators for the model.
Parameters
----------
comm : MPI.Comm or <FakeComm> or None
The communicator for the Problem.
Returns
-------
MPI.Comm or <FakeComm> or None
The communicator for the Problem model.
"""
return comm
def _setup_driver(self, problem):
"""
Prepare the driver for execution.
This is the final thing to run during setup.
Parameters
----------
problem : <Problem>
Pointer to the containing problem.
"""
self._problem = problem
self._recording_iter = problem._recording_iter
model = problem.model
self._total_jac = None
self._has_scaling = (
np.any([r['scaler'] is not None for r in itervalues(self._responses)]) or
np.any([dv['scaler'] is not None for dv in itervalues(self._designvars)])
)
con_set = set()
obj_set = set()
dv_set = set()
self._remote_dvs = dv_dict = {}
self._remote_cons = con_dict = {}
self._remote_objs = obj_dict = {}
# Now determine if later we'll need to allgather cons, objs, or desvars.
if model.comm.size > 1 and model._subsystems_allprocs:
local_out_vars = set(model._outputs._views)
remote_dvs = set(self._designvars) - local_out_vars
remote_cons = set(self._cons) - local_out_vars
remote_objs = set(self._objs) - local_out_vars
all_remote_vois = model.comm.allgather(
(remote_dvs, remote_cons, remote_objs))
for rem_dvs, rem_cons, rem_objs in all_remote_vois:
con_set.update(rem_cons)
obj_set.update(rem_objs)
dv_set.update(rem_dvs)
# If we have remote VOIs, pick an owning rank for each and use that
# to bcast to others later
owning_ranks = model._owning_rank
sizes = model._var_sizes['nonlinear']['output']
for i, vname in enumerate(model._var_allprocs_abs_names['output']):
owner = owning_ranks[vname]
if vname in dv_set:
dv_dict[vname] = (owner, sizes[owner, i])
if vname in con_set:
con_dict[vname] = (owner, sizes[owner, i])
if vname in obj_set:
obj_dict[vname] = (owner, sizes[owner, i])
self._remote_responses = self._remote_cons.copy()
self._remote_responses.update(self._remote_objs)
# set up simultaneous deriv coloring
if (coloring_mod._use_sparsity and self._simul_coloring_info and
self.supports['simultaneous_derivatives']):
self._setup_simul_coloring()
def _get_vars_to_record(self, recording_options):
"""
Get variables to record based on recording options.
Parameters
----------
recording_options : <OptionsDictionary>
Dictionary with recording options.
Returns
-------
dict
Dictionary containing lists of variables to record.
"""
problem = self._problem
model = problem.model
if MPI:
# TODO: Eventually, we think we can get rid of this next check.
# But to be safe, we are leaving it in there.
if not model.is_active():
raise RuntimeError("RecordingManager.startup should never be called when "
"running in parallel on an inactive System")
rrank = problem.comm.rank
rowned = model._owning_rank
incl = recording_options['includes']
excl = recording_options['excludes']
# includes and excludes for outputs are specified using promoted names
# NOTE: only local var names are in abs2prom, all will be gathered later
abs2prom = model._var_abs2prom['output']
all_desvars = {n for n in self._designvars
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)}
all_objectives = {n for n in self._objs
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)}
all_constraints = {n for n in self._cons
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)}
# design variables, objectives and constraints are always in the options
mydesvars = myobjectives = myconstraints = set()
if recording_options['record_desvars']:
if MPI:
mydesvars = [n for n in all_desvars if rrank == rowned[n]]
else:
mydesvars = list(all_desvars)
if recording_options['record_objectives']:
if MPI:
myobjectives = [n for n in all_objectives if rrank == rowned[n]]
else:
myobjectives = list(all_objectives)
if recording_options['record_constraints']:
if MPI:
myconstraints = [n for n in all_constraints if rrank == rowned[n]]
else:
myconstraints = list(all_constraints)
filtered_vars_to_record = {
'des': mydesvars,
'obj': myobjectives,
'con': myconstraints
}
# responses (if in options)
if 'record_responses' in recording_options:
myresponses = set()
if recording_options['record_responses']:
myresponses = {n for n in self._responses
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)}
if MPI:
myresponses = [n for n in myresponses if rrank == rowned[n]]
filtered_vars_to_record['res'] = list(myresponses)
# inputs (if in options)
if 'record_inputs' in recording_options:
myinputs = set()
if recording_options['record_inputs']:
myinputs = {n for n in model._inputs if check_path(n, incl, excl)}
if MPI:
# gather the variables from all ranks to rank 0
all_vars = model.comm.gather(myinputs, root=0)
if MPI.COMM_WORLD.rank == 0:
myinputs = all_vars[-1]
for d in all_vars[:-1]:
myinputs.update(d)
myinputs = [n for n in myinputs if rrank == rowned[n]]
filtered_vars_to_record['in'] = list(myinputs)
# system outputs (if the options being processed are for the driver itself)
if recording_options is self.recording_options:
myoutputs = set()
if incl:
myoutputs = {n for n in model._outputs
if n in abs2prom and check_path(abs2prom[n], incl, excl)}
if MPI:
# gather the variables from all ranks to rank 0
all_vars = model.comm.gather(myoutputs, root=0)
if MPI.COMM_WORLD.rank == 0:
myoutputs = all_vars[-1]
for d in all_vars[:-1]:
myoutputs.update(d)
# de-duplicate
myoutputs = myoutputs.difference(all_desvars, all_objectives, all_constraints)
if MPI:
myoutputs = [n for n in myoutputs if rrank == rowned[n]]
filtered_vars_to_record['sys'] = list(myoutputs)
return filtered_vars_to_record
def _setup_recording(self):
"""
Set up case recording.
"""
self._filtered_vars_to_record = self._get_vars_to_record(self.recording_options)
self._rec_mgr.startup(self)
# record the system metadata to the recorders attached to this Driver
if self.recording_options['record_model_metadata']:
for sub in self._problem.model.system_iter(recurse=True, include_self=True):
self._rec_mgr.record_metadata(sub)
def _get_voi_val(self, name, meta, remote_vois, unscaled=False, ignore_indices=False):
"""
Get the value of a variable of interest (objective, constraint, or design var).
This will retrieve the value if the VOI is remote.
Parameters
----------
name : str
Name of the variable of interest.
meta : dict
Metadata for the variable of interest.
remote_vois : dict
Dict containing (owning_rank, size) for all remote vois of a particular
type (design var, constraint, or objective).
unscaled : bool
Set to True if unscaled (physical) design variables are desired.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
float or ndarray
The value of the named variable of interest.
"""
model = self._problem.model
comm = model.comm
vec = model._outputs._views_flat
indices = meta['indices']
if name in remote_vois:
owner, size = remote_vois[name]
if owner == comm.rank:
if indices is None or ignore_indices:
val = vec[name].copy()
else:
val = vec[name][indices]
else:
if not (indices is None or ignore_indices):
size = len(indices)
val = np.empty(size)
comm.Bcast(val, root=owner)
else:
if indices is None or ignore_indices:
val = vec[name].copy()
else:
val = vec[name][indices]
if self._has_scaling and not unscaled:
# Scale design variable values
adder = meta['adder']
if adder is not None:
val += adder
scaler = meta['scaler']
if scaler is not None:
val *= scaler
return val
def get_design_var_values(self, filter=None, unscaled=False, ignore_indices=False):
"""
Return the design variable values.
This is called to gather the initial design variable state.
Parameters
----------
filter : list
List of desvar names used by recorders.
unscaled : bool
Set to True if unscaled (physical) design variables are desired.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
dict
Dictionary containing values of each design variable.
"""
if filter:
dvs = filter
else:
# use all the designvars
dvs = self._designvars
return {n: self._get_voi_val(n, self._designvars[n], self._remote_dvs, unscaled=unscaled,
ignore_indices=ignore_indices) for n in dvs}
def set_design_var(self, name, value):
"""
Set the value of a design variable.
Parameters
----------
name : str
Global pathname of the design variable.
value : float or ndarray
Value for the design variable.
"""
problem = self._problem
if (name in self._remote_dvs and
problem.model._owning_rank[name] != problem.comm.rank):
return
meta = self._designvars[name]
indices = meta['indices']
if indices is None:
indices = slice(None)
desvar = problem.model._outputs._views_flat[name]
desvar[indices] = value
if self._has_scaling:
# Scale design variable values
scaler = meta['scaler']
if scaler is not None:
desvar[indices] *= 1.0 / scaler
adder = meta['adder']
if adder is not None:
desvar[indices] -= adder
def get_response_values(self, filter=None):
"""
Return response values.
Parameters
----------
filter : list
List of response names used by recorders.
Returns
-------
dict
Dictionary containing values of each response.
"""
if filter:
resps = filter
else:
resps = self._responses
return {n: self._get_voi_val(n, self._responses[n], self._remote_objs) for n in resps}
def get_objective_values(self, unscaled=False, filter=None, ignore_indices=False):
"""
Return objective values.
Parameters
----------
unscaled : bool
Set to True if unscaled (physical) design variables are desired.
filter : list
List of objective names used by recorders.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
dict
Dictionary containing values of each objective.
"""
if filter:
objs = filter
else:
objs = self._objs
return {n: self._get_voi_val(n, self._objs[n], self._remote_objs, unscaled=unscaled,
ignore_indices=ignore_indices)
for n in objs}
def get_constraint_values(self, ctype='all', lintype='all', unscaled=False, filter=None,
ignore_indices=False):
"""
Return constraint values.
Parameters
----------
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
unscaled : bool
Set to True if unscaled (physical) design variables are desired.
filter : list
List of constraint names used by recorders.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
dict
Dictionary containing values of each constraint.
"""
if filter is not None:
cons = filter
else:
cons = self._cons
con_dict = {}
for name in cons:
meta = self._cons[name]
if lintype == 'linear' and not meta['linear']:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
con_dict[name] = self._get_voi_val(name, meta, self._remote_cons, unscaled=unscaled,
ignore_indices=ignore_indices)
return con_dict
def _get_ordered_nl_responses(self):
"""
Return the names of nonlinear responses in the order used by the driver.
Default order is objectives followed by nonlinear constraints. This is used for
simultaneous derivative coloring and sparsity determination.
Returns
-------
list of str
The nonlinear response names in order.
"""
order = list(self._objs)
order.extend(n for n, meta in iteritems(self._cons)
if not ('linear' in meta and meta['linear']))
return order
def _update_voi_meta(self, model):
"""
Collect response and design var metadata from the model and size desvars and responses.
Parameters
----------
model : System
The System that represents the entire model.
Returns
-------
int
Total size of responses, with linear constraints excluded.
int
Total size of design vars.
"""
self._objs = objs = OrderedDict()
self._cons = cons = OrderedDict()
self._responses = resps = model.get_responses(recurse=True)
for name, data in iteritems(resps):
if data['type'] == 'con':
cons[name] = data
else:
objs[name] = data
response_size = sum(resps[n]['size'] for n in self._get_ordered_nl_responses())
# Gather up the information for design vars.
self._designvars = designvars = model.get_design_vars(recurse=True)
desvar_size = sum(data['size'] for data in itervalues(designvars))
return response_size, desvar_size
def run(self):
"""
Execute this driver.
The base `Driver` just runs the model. All other drivers overload
this method.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
with RecordingDebugging(self._get_name(), self.iter_count, self):
self._problem.model._solve_nonlinear()
self.iter_count += 1
return False
def _compute_totals(self, of=None, wrt=None, return_format='flat_dict', global_names=True):
"""
Compute derivatives of desired quantities with respect to desired inputs.
All derivatives are returned using driver scaling.
Parameters
----------
of : list of variable name strings or None
Variables whose derivatives will be computed. Default is None, which
uses the driver's objectives and constraints.
wrt : list of variable name strings or None
Variables with respect to which the derivatives will be computed.
Default is None, which uses the driver's desvars.
return_format : string
Format to return the derivatives. Default is a 'flat_dict', which
returns them in a dictionary whose keys are tuples of form (of, wrt). For
the scipy optimizer, 'array' is also supported.
global_names : bool
Set to True when passing in global names to skip some translation steps.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
problem = self._problem
total_jac = self._total_jac
debug_print = 'totals' in self.options['debug_print'] and (not MPI or
MPI.COMM_WORLD.rank == 0)
if debug_print:
header = 'Driver total derivatives for iteration: ' + str(self.iter_count)
print(header)
print(len(header) * '-' + '\n')
if problem.model._owns_approx_jac:
self._recording_iter.stack.append(('_compute_totals_approx', 0))
try:
if total_jac is None:
total_jac = _TotalJacInfo(problem, of, wrt, global_names,
return_format, approx=True, debug_print=debug_print)
self._total_jac = total_jac
totals = total_jac.compute_totals_approx(initialize=True)
else:
totals = total_jac.compute_totals_approx()
finally:
self._recording_iter.stack.pop()
else:
if total_jac is None:
total_jac = _TotalJacInfo(problem, of, wrt, global_names, return_format,
debug_print=debug_print)
# don't cache linear constraint jacobian
if not total_jac.has_lin_cons:
self._total_jac = total_jac
self._recording_iter.stack.append(('_compute_totals', 0))
try:
totals = total_jac.compute_totals()
finally:
self._recording_iter.stack.pop()
if self._rec_mgr._recorders and self.recording_options['record_derivatives']:
metadata = create_local_meta(self._get_name())
total_jac.record_derivatives(self, metadata)
return totals
def record_iteration(self):
"""
Record an iteration of the current Driver.
"""
if not self._rec_mgr._recorders:
return
# Get the data to record (collective calls that get across all ranks)
opts = self.recording_options
filt = self._filtered_vars_to_record
if opts['record_desvars']:
des_vars = self.get_design_var_values(unscaled=True, ignore_indices=True)
else:
des_vars = {}
if opts['record_objectives']:
obj_vars = self.get_objective_values(unscaled=True, ignore_indices=True)
else:
obj_vars = {}
if opts['record_constraints']:
con_vars = self.get_constraint_values(unscaled=True, ignore_indices=True)
else:
con_vars = {}
if opts['record_responses']:
# res_vars = self.get_response_values() # not really working yet
res_vars = {}
else:
res_vars = {}
des_vars = {name: des_vars[name] for name in filt['des']}
obj_vars = {name: obj_vars[name] for name in filt['obj']}
con_vars = {name: con_vars[name] for name in filt['con']}
# res_vars = {name: res_vars[name] for name in filt['res']}
model = self._problem.model
names = model._outputs._names
views = model._outputs._views
sys_vars = {name: views[name] for name in names if name in filt['sys']}
if self.recording_options['record_inputs']:
names = model._inputs._names
views = model._inputs._views
in_vars = {name: views[name] for name in names if name in filt['in']}
else:
in_vars = {}
if MPI:
des_vars = self._gather_vars(model, des_vars)
res_vars = self._gather_vars(model, res_vars)
obj_vars = self._gather_vars(model, obj_vars)
con_vars = self._gather_vars(model, con_vars)
sys_vars = self._gather_vars(model, sys_vars)
in_vars = self._gather_vars(model, in_vars)
outs = {}
if not MPI or model.comm.rank == 0:
outs.update(des_vars)
outs.update(res_vars)
outs.update(obj_vars)
outs.update(con_vars)
outs.update(sys_vars)
data = {
'out': outs,
'in': in_vars
}
metadata = create_local_meta(self._get_name())
self._rec_mgr.record_iteration(self, data, metadata)
def _gather_vars(self, root, local_vars):
"""
Gather and return only variables listed in `local_vars` from the `root` System.
Parameters
----------
root : <System>
the root System for the Problem
local_vars : dict
local variable names and values
Returns
-------
dct : dict
variable names and values.
"""
# if trace:
# debug("gathering vars for recording in %s" % root.pathname)
all_vars = root.comm.gather(local_vars, root=0)
# if trace:
# debug("DONE gathering rec vars for %s" % root.pathname)
if root.comm.rank == 0:
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
return dct
def _get_name(self):
"""
Get name of current Driver.
Returns
-------
str
Name of current Driver.
"""
return "Driver"
def set_simul_deriv_color(self, simul_info):
"""
Set the coloring (and possibly the sub-jac sparsity) for simultaneous total derivatives.
Parameters
----------
simul_info : str or dict
::
# Information about simultaneous coloring for design vars and responses. If a
# string, then simul_info is assumed to be the name of a file that contains the
# coloring information in JSON format. If a dict, the structure looks like this:
{
"fwd": [
# First, a list of column index lists, each index list representing columns
# having the same color, except for the very first index list, which contains
# indices of all columns that are not colored.
[
[i1, i2, i3, ...] # list of non-colored columns
[ia, ib, ...] # list of columns in first color
[ic, id, ...] # list of columns in second color
... # remaining color lists, one list of columns per color
],
# Next is a list of lists, one for each column, containing the nonzero rows for
# that column. If a column is not colored, then it will have a None entry
# instead of a list.
[
[r1, rn, ...] # list of nonzero rows for column 0
None, # column 1 is not colored
[ra, rb, ...] # list of nonzero rows for column 2
...
],
],
# This example is not a bidirectional coloring, so the opposite direction, "rev"
# in this case, has an empty row index list. It could also be removed entirely.
"rev": [[[]], []],
"sparsity":
# The sparsity entry can be absent, indicating that no sparsity structure is
# specified, or it can be a nested dictionary where the outer keys are response
# names, the inner keys are design variable names, and the value is a tuple of
# the form (row_list, col_list, shape).
{
resp1_name: {
dv1_name: (rows, cols, shape), # for sub-jac d_resp1/d_dv1
dv2_name: (rows, cols, shape),
...
},
resp2_name: {
...
}
...
}
}
"""
if self.supports['simultaneous_derivatives']:
self._simul_coloring_info = simul_info
else:
raise RuntimeError("Driver '%s' does not support simultaneous derivatives." %
self._get_name())
def set_total_jac_sparsity(self, sparsity):
"""
Set the sparsity of sub-jacobians of the total jacobian.
Note: This currently will have no effect if you are not using the pyOptSparseDriver.
Parameters
----------
sparsity : str or dict
::
# Sparsity is a nested dictionary where the outer keys are response
# names, the inner keys are design variable names, and the value is a tuple of
# the form (row_list, col_list, shape).
{
resp1: {
dv1: (rows, cols, shape), # for sub-jac d_resp1/d_dv1
dv2: (rows, cols, shape),
...
},
resp2: {
...
}
...
}
"""
if self.supports['total_jac_sparsity']:
self._total_jac_sparsity = sparsity
else:
raise RuntimeError("Driver '%s' does not support setting of total jacobian sparsity." %
self._get_name())
def _setup_simul_coloring(self):
"""
Set up metadata for simultaneous derivative solution.
"""
# command line simul_coloring uses this env var to turn pre-existing coloring off
if not coloring_mod._use_sparsity:
return
problem = self._problem
if not problem.model._use_derivatives:
simple_warning("Derivatives are turned off. Skipping simul deriv coloring.")
return
if isinstance(self._simul_coloring_info, string_types):
with open(self._simul_coloring_info, 'r') as f:
self._simul_coloring_info = coloring_mod._json2coloring(json.load(f))
if 'rev' in self._simul_coloring_info and problem._orig_mode not in ('rev', 'auto'):
revcol = self._simul_coloring_info['rev'][0][0]
if revcol:
raise RuntimeError("Simultaneous coloring does reverse solves but mode has "
"been set to '%s'" % problem._orig_mode)
if 'fwd' in self._simul_coloring_info and problem._orig_mode not in ('fwd', 'auto'):
fwdcol = self._simul_coloring_info['fwd'][0][0]
if fwdcol:
raise RuntimeError("Simultaneous coloring does forward solves but mode has "
"been set to '%s'" % problem._orig_mode)
# simul_coloring_info can contain data for either fwd, rev, or both, along with optional
# sparsity patterns
if 'sparsity' in self._simul_coloring_info:
sparsity = self._simul_coloring_info['sparsity']
del self._simul_coloring_info['sparsity']
else:
sparsity = None
if sparsity is not None and self._total_jac_sparsity is not None:
raise RuntimeError("Total jac sparsity was set in both _simul_coloring_info"
" and _total_jac_sparsity.")
self._total_jac_sparsity = sparsity
def _pre_run_model_debug_print(self):
"""
Optionally print some debugging information before the model runs.
"""
debug_opt = self.options['debug_print']
if not debug_opt or debug_opt == ['totals']:
return
if not MPI or MPI.COMM_WORLD.rank == 0:
header = 'Driver debug print for iter coord: {}'.format(
self._recording_iter.get_formatted_iteration_coordinate())
print(header)
print(len(header) * '-')
if 'desvars' in debug_opt:
desvar_vals = self.get_design_var_values(unscaled=True, ignore_indices=True)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Design Vars")
if desvar_vals:
pprint.pprint(desvar_vals)
else:
print("None")
print()
sys.stdout.flush()
def _post_run_model_debug_print(self):
"""
Optionally print some debugging information after the model runs.
"""
if 'nl_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='nonlinear', unscaled=True)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Nonlinear constraints")
if cons:
pprint.pprint(cons)
else:
print("None")
print()
if 'ln_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='linear', unscaled=True)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Linear constraints")
if cons:
pprint.pprint(cons)
else:
print("None")
print()
if 'objs' in self.options['debug_print']:
objs = self.get_objective_values(unscaled=True)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Objectives")
if objs:
pprint.pprint(objs)
else:
print("None")
print()
sys.stdout.flush()
class Solver(object):
"""
Base solver class.
This class is subclassed by NonlinearSolver and LinearSolver,
which are in turn subclassed by actual solver implementations.
Attributes
----------
_system : <System>
Pointer to the owning system.
_depth : int
How many subsolvers deep this solver is (0 means not a subsolver).
_vec_names : [str, ...]
List of right-hand-side (RHS) vector names.
_mode : str
'fwd' or 'rev', applicable to linear solvers only.
_iter_count : int
Number of iterations for the current invocation of the solver.
_rec_mgr : <RecordingManager>
object that manages all recorders added to this solver
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
options : <OptionsDictionary>
Options dictionary.
recording_options : <OptionsDictionary>
Recording options dictionary.
supports : <OptionsDictionary>
Options dictionary describing what features are supported by this
solver.
_filtered_vars_to_record: Dict
Dict of list of var names to record
_norm0: float
Normalization factor
_solver_info : SolverInfo
A stack-like object shared by all Solvers in the model.
"""
# Object to store some formatting for iprint that is shared across all solvers.
SOLVER = 'base_solver'
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Solver options.
"""
self._system = None
self._depth = 0
self._vec_names = None
self._mode = 'fwd'
self._iter_count = 0
self._solver_info = None
# Solver options
self.options = OptionsDictionary(parent_name=self.msginfo)
self.options.declare('maxiter', types=int, default=10,
desc='maximum number of iterations')
self.options.declare('atol', default=1e-10,
desc='absolute error tolerance')
self.options.declare('rtol', default=1e-10,
desc='relative error tolerance')
self.options.declare('iprint', types=int, default=1,
desc='whether to print output')
self.options.declare('err_on_maxiter', types=bool, default=None, allow_none=True,
desc="Deprecated. Use 'err_on_non_converge'.")
self.options.declare('err_on_non_converge', types=bool, default=False,
desc="When True, AnalysisError will be raised if we don't converge.")
# Case recording options
self.recording_options = OptionsDictionary(parent_name=self.msginfo)
self.recording_options.declare('record_abs_error', types=bool, default=True,
desc='Set to True to record absolute error at the \
solver level')
self.recording_options.declare('record_rel_error', types=bool, default=True,
desc='Set to True to record relative error at the \
solver level')
self.recording_options.declare('record_inputs', types=bool, default=True,
desc='Set to True to record inputs at the solver level')
self.recording_options.declare('record_outputs', types=bool, default=True,
desc='Set to True to record outputs at the solver level')
self.recording_options.declare('record_solver_residuals', types=bool, default=False,
desc='Set to True to record residuals at the solver level')
self.recording_options.declare('record_metadata', types=bool, desc='Record metadata',
default=True)
self.recording_options.declare('includes', types=list, default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
# Case recording related
self._filtered_vars_to_record = {}
self._norm0 = 0.0
# What the solver supports.
self.supports = OptionsDictionary(parent_name=self.msginfo)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('implicit_components', types=bool, default=False)
self._declare_options()
self.options.update(kwargs)
self._rec_mgr = RecordingManager()
self.cite = ""
@property
def msginfo(self):
"""
Return info to prepend to messages.
Returns
-------
str
Info to prepend to messages.
"""
if self._system is None:
return type(self).__name__
return '{} in {}'.format(type(self).__name__, self._system().msginfo)
def _assembled_jac_solver_iter(self):
"""
Return an empty generator of lin solvers using assembled jacs.
"""
for i in ():
yield
def add_recorder(self, recorder):
"""
Add a recorder to the solver's RecordingManager.
Parameters
----------
recorder : <CaseRecorder>
A recorder instance to be added to RecManager.
"""
if MPI:
raise RuntimeError(
"Recording of Solvers when running parallel code is not supported yet")
self._rec_mgr.append(recorder)
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
This is optionally implemented by subclasses of Solver.
"""
pass
def _setup_solvers(self, system, depth):
"""
Assign system instance, set depth, and optionally perform setup.
Parameters
----------
system : <System>
pointer to the owning system.
depth : int
depth of the current system (already incremented).
"""
self._system = weakref.ref(system)
self._depth = depth
self._solver_info = system._solver_info
self._recording_iter = system._recording_iter
if system.pathname:
parent_name = self.msginfo
self.options._parent_name = parent_name
self.recording_options._parent_name = parent_name
self.supports._parent_name = parent_name
if isinstance(self, LinearSolver) and not system._use_derivatives:
return
self._rec_mgr.startup(self)
self._rec_mgr.record_metadata(self)
myoutputs = myresiduals = myinputs = set()
incl = self.recording_options['includes']
excl = self.recording_options['excludes']
if self.recording_options['record_solver_residuals']:
if isinstance(self, NonlinearSolver):
residuals = system._residuals
else: # it's a LinearSolver
residuals = system._vectors['residual']['linear']
myresiduals = {n for n in residuals._names if check_path(n, incl, excl)}
if self.recording_options['record_outputs']:
if isinstance(self, NonlinearSolver):
outputs = system._outputs
else: # it's a LinearSolver
outputs = system._vectors['output']['linear']
myoutputs = {n for n in outputs._names if check_path(n, incl, excl)}
if self.recording_options['record_inputs']:
if isinstance(self, NonlinearSolver):
inputs = system._inputs
else:
inputs = system._vectors['input']['linear']
myinputs = {n for n in inputs._names if check_path(n, incl, excl)}
self._filtered_vars_to_record = {
'in': myinputs,
'out': myoutputs,
'res': myresiduals
}
# Raise a deprecation warning for changed option.
if 'err_on_maxiter' in self.options and self.options['err_on_maxiter'] is not None:
self.options['err_on_non_converge'] = self.options['err_on_maxiter']
warn_deprecation("The 'err_on_maxiter' option provides backwards compatibility "
"with earlier version of OpenMDAO; use options['err_on_non_converge'] "
"instead.")
def _set_solver_print(self, level=2, type_='all'):
"""
Control printing for solvers and subsolvers in the model.
Parameters
----------
level : int
iprint level. Set to 2 to print residuals each iteration; set to 1
to print just the iteration totals; set to 0 to disable all printing
except for failures, and set to -1 to disable all printing including failures.
type_ : str
Type of solver to set: 'LN' for linear, 'NL' for nonlinear, or 'all' for all.
"""
self.options['iprint'] = level
def _mpi_print(self, iteration, abs_res, rel_res):
"""
Print residuals from an iteration.
Parameters
----------
iteration : int
iteration counter, 0-based.
abs_res : float
current absolute residual norm.
rel_res : float
current relative residual norm.
"""
if (self.options['iprint'] == 2 and self._system().comm.rank == 0):
prefix = self._solver_info.prefix
solver_name = self.SOLVER
if prefix.endswith('precon:'):
solver_name = solver_name[3:]
print_str = prefix + solver_name
print_str += ' %d ; %.9g %.9g' % (iteration, abs_res, rel_res)
print(print_str)
def _mpi_print_header(self):
"""
Print header text before solving.
"""
pass
def _solve(self):
"""
Run the iterative solver.
"""
maxiter = self.options['maxiter']
atol = self.options['atol']
rtol = self.options['rtol']
iprint = self.options['iprint']
self._mpi_print_header()
self._iter_count = 0
norm0, norm = self._iter_initialize()
self._norm0 = norm0
self._mpi_print(self._iter_count, norm, norm / norm0)
while self._iter_count < maxiter and norm > atol and norm / norm0 > rtol:
with Recording(type(self).__name__, self._iter_count, self) as rec:
self._single_iteration()
self._iter_count += 1
self._run_apply()
norm = self._iter_get_norm()
# With solvers, we want to record the norm AFTER the call, but the call needs to
# be wrapped in the with for stack purposes, so we locally assign norm & norm0
# into the class.
rec.abs = norm
rec.rel = norm / norm0
if norm0 == 0:
norm0 = 1
self._mpi_print(self._iter_count, norm, norm / norm0)
system = self._system()
if system.comm.rank == 0 or os.environ.get('USE_PROC_FILES'):
prefix = self._solver_info.prefix + self.SOLVER
# Solver terminated early because a Nan in the norm doesn't satisfy the while-loop
# conditionals.
if np.isinf(norm) or np.isnan(norm):
msg = "Solver '{}' on system '{}': residuals contain 'inf' or 'NaN' after {} " + \
"iterations."
if iprint > -1:
print(prefix + msg.format(self.SOLVER, system.pathname,
self._iter_count))
# Raise AnalysisError if requested.
if self.options['err_on_non_converge']:
raise AnalysisError(msg.format(self.SOLVER, system.pathname,
self._iter_count))
# Solver hit maxiter without meeting desired tolerances.
elif (norm > atol and norm / norm0 > rtol):
msg = "Solver '{}' on system '{}' failed to converge in {} iterations."
if iprint > -1:
print(prefix + msg.format(self.SOLVER, system.pathname,
self._iter_count))
# Raise AnalysisError if requested.
if self.options['err_on_non_converge']:
raise AnalysisError(msg.format(self.SOLVER, system.pathname,
self._iter_count))
# Solver converged
elif iprint == 1:
print(prefix + ' Converged in {} iterations'.format(self._iter_count))
elif iprint == 2:
print(prefix + ' Converged')
def _iter_initialize(self):
"""
Perform any necessary pre-processing operations.
Returns
-------
float
initial error.
float
error at the first iteration.
"""
pass
def _run_apply(self):
"""
Run the appropriate apply method on the system.
"""
pass
def _linearize(self):
"""
Perform any required linearization operations such as matrix factorization.
"""
pass
def _linearize_children(self):
"""
Return a flag that is True when we need to call linearize on our subsystems' solvers.
Returns
-------
boolean
Flag for indicating child linerization
"""
return True
def __str__(self):
"""
Return a string representation of the solver.
Returns
-------
str
String representation of the solver.
"""
return self.SOLVER
def record_iteration(self, **kwargs):
"""
Record an iteration of the current Solver.
Parameters
----------
**kwargs : dict
Keyword arguments (used for abs and rel error).
"""
if not self._rec_mgr._recorders:
return
metadata = create_local_meta(self.SOLVER)
# Get the data
data = {}
if self.recording_options['record_abs_error']:
data['abs'] = kwargs.get('abs')
else:
data['abs'] = None
if self.recording_options['record_rel_error']:
data['rel'] = kwargs.get('rel')
else:
data['rel'] = None
system = self._system()
if isinstance(self, NonlinearSolver):
outputs = system._outputs
inputs = system._inputs
residuals = system._residuals
else: # it's a LinearSolver
outputs = system._vectors['output']['linear']
inputs = system._vectors['input']['linear']
residuals = system._vectors['residual']['linear']
if self.recording_options['record_outputs']:
data['o'] = {}
if 'out' in self._filtered_vars_to_record:
for out in self._filtered_vars_to_record['out']:
if out in outputs._names:
data['o'][out] = outputs._views[out]
else:
data['o'] = outputs
else:
data['o'] = None
if self.recording_options['record_inputs']:
data['i'] = {}
if 'in' in self._filtered_vars_to_record:
for inp in self._filtered_vars_to_record['in']:
if inp in inputs._names:
data['i'][inp] = inputs._views[inp]
else:
data['i'] = inputs
else:
data['i'] = None
if self.recording_options['record_solver_residuals']:
data['r'] = {}
if 'res' in self._filtered_vars_to_record:
for res in self._filtered_vars_to_record['res']:
if res in residuals._names:
data['r'][res] = residuals._views[res]
else:
data['r'] = residuals
else:
data['r'] = None
self._rec_mgr.record_iteration(self, data, metadata)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
# shut down all recorders
self._rec_mgr.shutdown()
def _set_complex_step_mode(self, active):
"""
Turn on or off complex stepping mode.
Recurses to turn on or off complex stepping mode in all subsystems and their vectors.
Parameters
----------
active : bool
Complex mode flag; set to True prior to commencing complex step.
"""
pass
class Solver(object):
"""
Base solver class.
This class is subclassed by NonlinearSolver and LinearSolver,
which are in turn subclassed by actual solver implementations.
Attributes
----------
_system : <System>
Pointer to the owning system.
_depth : int
How many subsolvers deep this solver is (0 means not a subsolver).
_vec_names : [str, ...]
List of right-hand-side (RHS) vector names.
_mode : str
'fwd' or 'rev', applicable to linear solvers only.
_iter_count : int
Number of iterations for the current invocation of the solver.
_rec_mgr : <RecordingManager>
object that manages all recorders added to this solver
_solver_info : <SolverInfo>
Object to store some formatting for iprint that is shared across all solvers.
cite : str
Listing of relevant citataions that should be referenced when
publishing work that uses this class.
options : <OptionsDictionary>
Options dictionary.
recording_options : <OptionsDictionary>
Recording options dictionary.
metadata : dict
Dictionary holding data about this solver.
supports : <OptionsDictionary>
Options dictionary describing what features are supported by this
solver.
_filtered_vars_to_record: Dict
Dict of list of var names to record
_norm0: float
Normalization factor
"""
SOLVER = 'base_solver'
_solver_info = SolverInfo()
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict
options dictionary.
"""
self._system = None
self._depth = 0
self._vec_names = None
self._mode = 'fwd'
self._iter_count = 0
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
self.options.declare('maxiter', types=int, default=10,
desc='maximum number of iterations')
self.options.declare('atol', default=1e-10,
desc='absolute error tolerance')
self.options.declare('rtol', default=1e-10,
desc='relative error tolerance')
self.options.declare('iprint', types=int, default=1,
desc='whether to print output')
self.options.declare('err_on_maxiter', types=bool, default=False,
desc="When True, AnalysisError will be raised if we don't converge.")
# Case recording options
self.recording_options.declare('record_abs_error', types=bool, default=True,
desc='Set to True to record absolute error at the \
solver level')
self.recording_options.declare('record_rel_error', types=bool, default=True,
desc='Set to True to record relative error at the \
solver level')
self.recording_options.declare('record_solver_residuals', types=bool, default=False,
desc='Set to True to record residuals at the solver level')
self.recording_options.declare('record_metadata', types=bool, desc='Record metadata',
default=True)
self.recording_options.declare('includes', types=list, default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
# Case recording related
self._filtered_vars_to_record = {}
self._norm0 = 0.0
# What the solver supports.
self.supports = OptionsDictionary()
self.supports.declare('gradients', types=bool, default=False)
self._declare_options()
self.options.update(kwargs)
self.metadata = {}
self._rec_mgr = RecordingManager()
self.cite = ""
def add_recorder(self, recorder):
"""
Add a recorder to the driver's RecordingManager.
Parameters
----------
recorder : <BaseRecorder>
A recorder instance to be added to RecManager.
"""
if MPI:
raise RuntimeError(
"Recording of Solvers when running parallel code is not supported yet")
self._rec_mgr.append(recorder)
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
This is optionally implemented by subclasses of Solver.
"""
pass
def _setup_solvers(self, system, depth):
"""
Assign system instance, set depth, and optionally perform setup.
Parameters
----------
system : <System>
pointer to the owning system.
depth : int
depth of the current system (already incremented).
"""
self._system = system
self._depth = depth
self._rec_mgr.startup(self)
self._rec_mgr.record_metadata(self)
myoutputs = myresiduals = set()
incl = self.recording_options['includes']
excl = self.recording_options['excludes']
if self.recording_options['record_solver_residuals']:
if isinstance(self, NonlinearSolver):
residuals = self._system._residuals
else: # it's a LinearSolver
residuals = self._system._vectors['residual']['linear']
myresiduals = {n for n in residuals._names
if check_path(n, incl, excl)}
if isinstance(self, NonlinearSolver):
outputs = self._system._outputs
else: # it's a LinearSolver
outputs = self._system._vectors['output']['linear']
myoutputs = {n for n in outputs._names
if check_path(n, incl, excl)}
self._filtered_vars_to_record = {
'out': myoutputs,
'res': myresiduals
}
def _set_solver_print(self, level=2, type_='all'):
"""
Control printing for solvers and subsolvers in the model.
Parameters
----------
level : int
iprint level. Set to 2 to print residuals each iteration; set to 1
to print just the iteration totals; set to 0 to disable all printing
except for failures, and set to -1 to disable all printing including failures.
type_ : str
Type of solver to set: 'LN' for linear, 'NL' for nonlinear, or 'all' for all.
"""
self.options['iprint'] = level
def _mpi_print(self, iteration, abs_res, rel_res):
"""
Print residuals from an iteration.
Parameters
----------
iteration : int
iteration counter, 0-based.
abs_res : float
current absolute residual norm.
rel_res : float
current relative residual norm.
"""
if (self.options['iprint'] == 2 and self._system.comm.rank == 0):
prefix = self._solver_info.prefix
solver_name = self.SOLVER
if prefix.endswith('precon:'):
solver_name = solver_name[3:]
print_str = prefix + solver_name
print_str += ' %d ; %.9g %.9g' % (iteration, abs_res, rel_res)
print(print_str)
def _mpi_print_header(self):
"""
Print header text before solving.
"""
pass
def _run_iterator(self):
"""
Run the iterative solver.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
float
absolute error.
float
relative error.
"""
maxiter = self.options['maxiter']
atol = self.options['atol']
rtol = self.options['rtol']
iprint = self.options['iprint']
self._mpi_print_header()
self._iter_count = 0
norm0, norm = self._iter_initialize()
self._norm0 = norm0
self._mpi_print(self._iter_count, norm, norm / norm0)
while self._iter_count < maxiter and \
norm > atol and norm / norm0 > rtol:
with Recording(type(self).__name__, self._iter_count, self) as rec:
self._iter_execute()
self._iter_count += 1
self._run_apply()
norm = self._iter_get_norm()
# With solvers, we want to record the norm AFTER the call, but the call needs to
# be wrapped in the with for stack purposes, so we locally assign norm & norm0
# into the class.
rec.abs = norm
rec.rel = norm / norm0
if norm0 == 0:
norm0 = 1
self._mpi_print(self._iter_count, norm, norm / norm0)
fail = (np.isinf(norm) or np.isnan(norm) or
(norm > atol and norm / norm0 > rtol))
if self._system.comm.rank == 0 or os.environ.get('USE_PROC_FILES'):
prefix = self._solver_info.prefix + self.SOLVER
if fail:
if iprint > -1:
msg = ' Failed to Converge in {} iterations'.format(self._iter_count)
print(prefix + msg)
# Raise AnalysisError if requested.
if self.options['err_on_maxiter']:
msg = "Solver '{}' on system '{}' failed to converge."
raise AnalysisError(msg.format(self.SOLVER, self._system.pathname))
elif iprint == 1:
print(prefix + ' Converged in {} iterations'.format(self._iter_count))
elif iprint == 2:
print(prefix + ' Converged')
return fail, norm, norm / norm0
def _iter_initialize(self):
"""
Perform any necessary pre-processing operations.
Returns
-------
float
initial error.
float
error at the first iteration.
"""
pass
def _iter_execute(self):
"""
Perform the operations in the iteration loop.
"""
pass
def _run_apply(self):
"""
Run the appropriate apply method on the system.
"""
pass
def _iter_get_norm(self):
"""
Return the norm of the residual.
Returns
-------
float
norm.
"""
pass
def _linearize(self):
"""
Perform any required linearization operations such as matrix factorization.
"""
pass
def _linearize_children(self):
"""
Return a flag that is True when we need to call linearize on our subsystems' solvers.t.
Returns
-------
boolean
Flag for indicating child linerization
"""
return True
def solve(self):
"""
Run the solver.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
float
absolute error.
float
relative error.
"""
pass
def __str__(self):
"""
Return a string representation of the solver.
Returns
-------
str
String representation of the solver.
"""
return self.SOLVER
def record_iteration(self, **kwargs):
"""
Record an iteration of the current Solver.
Parameters
----------
**kwargs : dict
Keyword arguments (used for abs and rel error).
"""
if not self._rec_mgr._recorders:
return
metadata = create_local_meta(self.SOLVER)
# Get the data
data = {}
# if self.options['record_abs_error'] or self.options['record_rel_error']:
# norm = self._iter_get_norm()
if self.recording_options['record_abs_error']:
# data['abs'] = norm
data['abs'] = kwargs.get('abs')
else:
data['abs'] = None
if self.recording_options['record_rel_error']:
# data['rel'] = norm / self._norm0
data['rel'] = kwargs.get('rel')
else:
data['rel'] = None
if isinstance(self, NonlinearSolver):
outputs = self._system._outputs
else: # it's a LinearSolver
outputs = self._system._vectors['output']['linear']
data['o'] = {}
if 'out' in self._filtered_vars_to_record:
for out in self._filtered_vars_to_record['out']:
if out in outputs._names:
data['o'][out] = outputs._names[out]
else:
data['o'] = outputs
if self.recording_options['record_solver_residuals']:
if isinstance(self, NonlinearSolver):
residuals = self._system._residuals
else: # it's a LinearSolver
residuals = self._system._vectors['residual']['linear']
data['r'] = {}
if 'res' in self._filtered_vars_to_record:
for res in self._filtered_vars_to_record['res']:
if res in residuals._names:
data['r'][res] = residuals._names[res]
else:
data['r'] = residuals
else:
data['r'] = None
self._rec_mgr.record_iteration(self, data, metadata)
def __init__(self):
"""
Initialize the driver.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
###########################
self.recording_options.declare('record_metadata', types=bool, desc='Record metadata',
default=True)
self.recording_options.declare('record_desvars', types=bool, default=True,
desc='Set to True to record design variables at the \
driver level')
self.recording_options.declare('record_responses', types=bool, default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare('record_objectives', types=bool, default=True,
desc='Set to True to record objectives at the \
driver level')
self.recording_options.declare('record_constraints', types=bool, default=True,
desc='Set to True to record constraints at the \
driver level')
self.recording_options.declare('includes', types=list, default=[],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare('record_derivatives', types=bool, default=False,
desc='Set to True to record derivatives at the driver \
level')
###########################
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints', types=bool, default=False)
self.supports.declare('equality_constraints', types=bool, default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints', types=bool, default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives', types=bool, default=False)
self.iter_count = 0
self.options = None
self._model_viewer_data = None
self.cite = ""
# TODO, support these in OpenMDAO
self.supports.declare('integer_design_vars', types=bool, default=False)
self._simul_coloring_info = None
self._res_jacs = {}
self.fail = False
class Solver(object):
"""
Base solver class.
This class is subclassed by NonlinearSolver and LinearSolver,
which are in turn subclassed by actual solver implementations.
Attributes
----------
_system : <System>
Pointer to the owning system.
_depth : int
How many subsolvers deep this solver is (0 means not a subsolver).
_vec_names : [str, ...]
List of right-hand-side (RHS) vector names.
_mode : str
'fwd' or 'rev', applicable to linear solvers only.
_iter_count : int
Number of iterations for the current invocation of the solver.
_rec_mgr : <RecordingManager>
object that manages all recorders added to this solver
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
options : <OptionsDictionary>
Options dictionary.
recording_options : <OptionsDictionary>
Recording options dictionary.
supports : <OptionsDictionary>
Options dictionary describing what features are supported by this
solver.
_filtered_vars_to_record: Dict
Dict of list of var names to record
_norm0: float
Normalization factor
_solver_info : SolverInfo
A stack-like object shared by all Solvers in the model.
"""
# Object to store some formatting for iprint that is shared across all solvers.
SOLVER = 'base_solver'
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Solver options.
"""
self._system = None
self._depth = 0
self._vec_names = None
self._mode = 'fwd'
self._iter_count = 0
self._solver_info = None
# Solver options
self.options = OptionsDictionary()
self.options.declare('maxiter', types=int, default=10,
desc='maximum number of iterations')
self.options.declare('atol', default=1e-10,
desc='absolute error tolerance')
self.options.declare('rtol', default=1e-10,
desc='relative error tolerance')
self.options.declare('iprint', types=int, default=1,
desc='whether to print output')
self.options.declare('err_on_maxiter', types=bool, default=False,
desc="When True, AnalysisError will be raised if we don't converge.")
# Case recording options
self.recording_options = OptionsDictionary()
self.recording_options.declare('record_abs_error', types=bool, default=True,
desc='Set to True to record absolute error at the \
solver level')
self.recording_options.declare('record_rel_error', types=bool, default=True,
desc='Set to True to record relative error at the \
solver level')
self.recording_options.declare('record_inputs', types=bool, default=True,
desc='Set to True to record inputs at the solver level')
self.recording_options.declare('record_outputs', types=bool, default=True,
desc='Set to True to record outputs at the solver level')
self.recording_options.declare('record_solver_residuals', types=bool, default=False,
desc='Set to True to record residuals at the solver level')
self.recording_options.declare('record_metadata', types=bool, desc='Record metadata',
default=True)
self.recording_options.declare('includes', types=list, default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
# Case recording related
self._filtered_vars_to_record = {}
self._norm0 = 0.0
# What the solver supports.
self.supports = OptionsDictionary()
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('implicit_components', types=bool, default=False)
self._declare_options()
self.options.update(kwargs)
self._rec_mgr = RecordingManager()
self.cite = ""
def _assembled_jac_solver_iter(self):
"""
Return an empty generator of lin solvers using assembled jacs.
"""
for i in ():
yield
def add_recorder(self, recorder):
"""
Add a recorder to the solver's RecordingManager.
Parameters
----------
recorder : <CaseRecorder>
A recorder instance to be added to RecManager.
"""
if MPI:
raise RuntimeError(
"Recording of Solvers when running parallel code is not supported yet")
self._rec_mgr.append(recorder)
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
This is optionally implemented by subclasses of Solver.
"""
pass
def _setup_solvers(self, system, depth):
"""
Assign system instance, set depth, and optionally perform setup.
Parameters
----------
system : <System>
pointer to the owning system.
depth : int
depth of the current system (already incremented).
"""
self._system = system
self._depth = depth
self._solver_info = system._solver_info
self._recording_iter = system._recording_iter
if isinstance(self, LinearSolver) and not system._use_derivatives:
return
self._rec_mgr.startup(self)
self._rec_mgr.record_metadata(self)
myoutputs = myresiduals = myinputs = set()
incl = self.recording_options['includes']
excl = self.recording_options['excludes']
if self.recording_options['record_solver_residuals']:
if isinstance(self, NonlinearSolver):
residuals = system._residuals
else: # it's a LinearSolver
residuals = system._vectors['residual']['linear']
myresiduals = {n for n in residuals._names if check_path(n, incl, excl)}
if self.recording_options['record_outputs']:
if isinstance(self, NonlinearSolver):
outputs = system._outputs
else: # it's a LinearSolver
outputs = system._vectors['output']['linear']
myoutputs = {n for n in outputs._names if check_path(n, incl, excl)}
if self.recording_options['record_inputs']:
if isinstance(self, NonlinearSolver):
inputs = system._inputs
else:
inputs = system._vectors['input']['linear']
myinputs = {n for n in inputs._names if check_path(n, incl, excl)}
self._filtered_vars_to_record = {
'in': myinputs,
'out': myoutputs,
'res': myresiduals
}
def _set_solver_print(self, level=2, type_='all'):
"""
Control printing for solvers and subsolvers in the model.
Parameters
----------
level : int
iprint level. Set to 2 to print residuals each iteration; set to 1
to print just the iteration totals; set to 0 to disable all printing
except for failures, and set to -1 to disable all printing including failures.
type_ : str
Type of solver to set: 'LN' for linear, 'NL' for nonlinear, or 'all' for all.
"""
self.options['iprint'] = level
def _mpi_print(self, iteration, abs_res, rel_res):
"""
Print residuals from an iteration.
Parameters
----------
iteration : int
iteration counter, 0-based.
abs_res : float
current absolute residual norm.
rel_res : float
current relative residual norm.
"""
if (self.options['iprint'] == 2 and self._system.comm.rank == 0):
prefix = self._solver_info.prefix
solver_name = self.SOLVER
if prefix.endswith('precon:'):
solver_name = solver_name[3:]
print_str = prefix + solver_name
print_str += ' %d ; %.9g %.9g' % (iteration, abs_res, rel_res)
print(print_str)
def _mpi_print_header(self):
"""
Print header text before solving.
"""
pass
def _solve(self):
"""
Run the iterative solver.
"""
maxiter = self.options['maxiter']
atol = self.options['atol']
rtol = self.options['rtol']
iprint = self.options['iprint']
self._mpi_print_header()
self._iter_count = 0
norm0, norm = self._iter_initialize()
self._norm0 = norm0
self._mpi_print(self._iter_count, norm, norm / norm0)
while self._iter_count < maxiter and norm > atol and norm / norm0 > rtol:
with Recording(type(self).__name__, self._iter_count, self) as rec:
self._single_iteration()
self._iter_count += 1
self._run_apply()
norm = self._iter_get_norm()
# With solvers, we want to record the norm AFTER the call, but the call needs to
# be wrapped in the with for stack purposes, so we locally assign norm & norm0
# into the class.
rec.abs = norm
rec.rel = norm / norm0
if norm0 == 0:
norm0 = 1
self._mpi_print(self._iter_count, norm, norm / norm0)
if self._system.comm.rank == 0 or os.environ.get('USE_PROC_FILES'):
prefix = self._solver_info.prefix + self.SOLVER
if np.isinf(norm) or np.isnan(norm) or (norm > atol and norm / norm0 > rtol):
if iprint > -1:
msg = ' Failed to Converge in {} iterations'.format(self._iter_count)
print(prefix + msg)
# Raise AnalysisError if requested.
if self.options['err_on_maxiter']:
msg = "Solver '{}' on system '{}' failed to converge."
raise AnalysisError(msg.format(self.SOLVER, self._system.pathname))
elif iprint == 1:
print(prefix + ' Converged in {} iterations'.format(self._iter_count))
elif iprint == 2:
print(prefix + ' Converged')
def _iter_initialize(self):
"""
Perform any necessary pre-processing operations.
Returns
-------
float
initial error.
float
error at the first iteration.
"""
pass
def _run_apply(self):
"""
Run the appropriate apply method on the system.
"""
pass
def _linearize(self):
"""
Perform any required linearization operations such as matrix factorization.
"""
pass
def _linearize_children(self):
"""
Return a flag that is True when we need to call linearize on our subsystems' solvers.
Returns
-------
boolean
Flag for indicating child linerization
"""
return True
def __str__(self):
"""
Return a string representation of the solver.
Returns
-------
str
String representation of the solver.
"""
return self.SOLVER
def record_iteration(self, **kwargs):
"""
Record an iteration of the current Solver.
Parameters
----------
**kwargs : dict
Keyword arguments (used for abs and rel error).
"""
if not self._rec_mgr._recorders:
return
metadata = create_local_meta(self.SOLVER)
# Get the data
data = {}
if self.recording_options['record_abs_error']:
data['abs'] = kwargs.get('abs')
else:
data['abs'] = None
if self.recording_options['record_rel_error']:
data['rel'] = kwargs.get('rel')
else:
data['rel'] = None
system = self._system
if isinstance(self, NonlinearSolver):
outputs = system._outputs
inputs = system._inputs
residuals = system._residuals
else: # it's a LinearSolver
outputs = system._vectors['output']['linear']
inputs = system._vectors['input']['linear']
residuals = system._vectors['residual']['linear']
if self.recording_options['record_outputs']:
data['o'] = {}
if 'out' in self._filtered_vars_to_record:
for out in self._filtered_vars_to_record['out']:
if out in outputs._names:
data['o'][out] = outputs._views[out]
else:
data['o'] = outputs
else:
data['o'] = None
if self.recording_options['record_inputs']:
data['i'] = {}
if 'in' in self._filtered_vars_to_record:
for inp in self._filtered_vars_to_record['in']:
if inp in inputs._names:
data['i'][inp] = inputs._views[inp]
else:
data['i'] = inputs
else:
data['i'] = None
if self.recording_options['record_solver_residuals']:
data['r'] = {}
if 'res' in self._filtered_vars_to_record:
for res in self._filtered_vars_to_record['res']:
if res in residuals._names:
data['r'][res] = residuals._views[res]
else:
data['r'] = residuals
else:
data['r'] = None
self._rec_mgr.record_iteration(self, data, metadata)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
# shut down all recorders
self._rec_mgr.shutdown()
def _set_complex_step_mode(self, active):
"""
Turn on or off complex stepping mode.
Recurses to turn on or off complex stepping mode in all subsystems and their vectors.
Parameters
----------
active : bool
Complex mode flag; set to True prior to commencing complex step.
"""
pass
class Driver(object):
"""
Top-level container for the systems and drivers.
Attributes
----------
fail : bool
Reports whether the driver ran successfully.
iter_count : int
Keep track of iterations for case recording.
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
recording_options : <OptionsDictionary>
Dictionary with driver recording options.
debug_print : <OptionsDictionary>
Dictionary with debugging printing options.
cite : str
Listing of relevant citataions that should be referenced when
publishing work that uses this class.
_problem : <Problem>
Pointer to the containing problem.
supports : <OptionsDictionary>
Provides a consistant way for drivers to declare what features they support.
_designvars : dict
Contains all design variable info.
_cons : dict
Contains all constraint info.
_objs : dict
Contains all objective info.
_responses : dict
Contains all response info.
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this driver.
_vars_to_record: dict
Dict of lists of var names indicating what to record
_model_viewer_data : dict
Structure of model, used to make n2 diagram.
_remote_dvs : dict
Dict of design variables that are remote on at least one proc. Values are
(owning rank, size).
_remote_cons : dict
Dict of constraints that are remote on at least one proc. Values are
(owning rank, size).
_remote_objs : dict
Dict of objectives that are remote on at least one proc. Values are
(owning rank, size).
_remote_responses : dict
A combined dict containing entries from _remote_cons and _remote_objs.
_simul_coloring_info : tuple of dicts
A data structure describing coloring for simultaneous derivs.
_total_jac_sparsity : dict, str, or None
Specifies sparsity of sub-jacobians of the total jacobian. Only used by pyOptSparseDriver.
_res_jacs : dict
Dict of sparse subjacobians for use with certain optimizers, e.g. pyOptSparseDriver.
_total_jac : _TotalJacInfo or None
Cached total jacobian handling object.
"""
def __init__(self, **kwargs):
"""
Initialize the driver.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Driver options.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
# Driver options
self.options = OptionsDictionary()
self.options.declare(
'debug_print',
types=list,
is_valid=_is_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars', 'ln_cons', "
"'nl_cons', 'objs'",
default=[])
# Case recording options
self.recording_options = OptionsDictionary()
self.recording_options.declare('record_metadata',
types=bool,
default=True,
desc='Record metadata')
self.recording_options.declare(
'record_desvars',
types=bool,
default=True,
desc='Set to True to record design variables at the '
'driver level')
self.recording_options.declare(
'record_responses',
types=bool,
default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare(
'record_objectives',
types=bool,
default=True,
desc='Set to True to record objectives at the driver level')
self.recording_options.declare(
'record_constraints',
types=bool,
default=True,
desc='Set to True to record constraints at the '
'driver level')
self.recording_options.declare(
'includes',
types=list,
default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare(
'record_derivatives',
types=bool,
default=False,
desc='Set to True to record derivatives at the driver '
'level')
self.recording_options.declare(
'record_inputs',
types=bool,
default=True,
desc='Set to True to record inputs at the driver level')
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints',
types=bool,
default=False)
self.supports.declare('equality_constraints',
types=bool,
default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints',
types=bool,
default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives',
types=bool,
default=False)
self.supports.declare('total_jac_sparsity', types=bool, default=False)
# TODO, support these in OpenMDAO
self.supports.declare('integer_design_vars', types=bool, default=False)
self.iter_count = 0
self._model_viewer_data = None
self.cite = ""
self._simul_coloring_info = None
self._total_jac_sparsity = None
self._res_jacs = {}
self._total_jac = None
self.fail = False
self._declare_options()
self.options.update(kwargs)
def add_recorder(self, recorder):
"""
Add a recorder to the driver.
Parameters
----------
recorder : BaseRecorder
A recorder instance.
"""
self._rec_mgr.append(recorder)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
self._rec_mgr.close()
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
This is optionally implemented by subclasses of Driver.
"""
pass
def _setup_comm(self, comm):
"""
Perform any driver-specific setup of communicators for the model.
Parameters
----------
comm : MPI.Comm or <FakeComm> or None
The communicator for the Problem.
Returns
-------
MPI.Comm or <FakeComm> or None
The communicator for the Problem model.
"""
return comm
def _setup_driver(self, problem):
"""
Prepare the driver for execution.
This is the final thing to run during setup.
Parameters
----------
problem : <Problem>
Pointer to the containing problem.
"""
self._problem = problem
model = problem.model
self._total_jac = None
self._objs = objs = OrderedDict()
self._cons = cons = OrderedDict()
self._responses = model.get_responses(recurse=True)
response_size = 0
for name, data in iteritems(self._responses):
if data['type'] == 'con':
cons[name] = data
else:
objs[name] = data
response_size += data['size']
# Gather up the information for design vars.
self._designvars = model.get_design_vars(recurse=True)
self._has_scaling = (
np.any([r['scaler'] is not None for r in self._responses.values()])
or np.any(
[dv['scaler'] is not None
for dv in self._designvars.values()]))
con_set = set()
obj_set = set()
dv_set = set()
self._remote_dvs = dv_dict = {}
self._remote_cons = con_dict = {}
self._remote_objs = obj_dict = {}
# Now determine if later we'll need to allgather cons, objs, or desvars.
if model.comm.size > 1 and model._subsystems_allprocs:
local_out_vars = set(model._outputs._views)
remote_dvs = set(self._designvars) - local_out_vars
remote_cons = set(self._cons) - local_out_vars
remote_objs = set(self._objs) - local_out_vars
all_remote_vois = model.comm.allgather(
(remote_dvs, remote_cons, remote_objs))
for rem_dvs, rem_cons, rem_objs in all_remote_vois:
con_set.update(rem_cons)
obj_set.update(rem_objs)
dv_set.update(rem_dvs)
# If we have remote VOIs, pick an owning rank for each and use that
# to bcast to others later
owning_ranks = model._owning_rank
sizes = model._var_sizes['nonlinear']['output']
for i, vname in enumerate(model._var_allprocs_abs_names['output']):
owner = owning_ranks[vname]
if vname in dv_set:
dv_dict[vname] = (owner, sizes[owner, i])
if vname in con_set:
con_dict[vname] = (owner, sizes[owner, i])
if vname in obj_set:
obj_dict[vname] = (owner, sizes[owner, i])
self._remote_responses = self._remote_cons.copy()
self._remote_responses.update(self._remote_objs)
# set up case recording
self._setup_recording()
desvar_size = np.sum(data['size']
for data in itervalues(self._designvars))
# set up simultaneous deriv coloring
if (coloring_mod._use_sparsity and self._simul_coloring_info
and self.supports['simultaneous_derivatives']):
if problem._mode == 'fwd':
self._setup_simul_coloring(problem._mode)
else:
raise RuntimeError(
"simultaneous derivs are currently not supported in rev mode."
)
# if we're using simultaneous derivatives then our effective design var size is less
# than the full design var size
if self._simul_coloring_info:
col_lists = self._simul_coloring_info[0]
if col_lists:
desvar_size = len(col_lists[0])
desvar_size += len(col_lists) - 1
if ((problem._mode == 'fwd' and desvar_size > response_size)
or (problem._mode == 'rev' and response_size > desvar_size)):
warnings.warn(
"Inefficient choice of derivative mode. You chose '%s' for a "
"problem with %d design variables and %d response variables "
"(objectives and constraints)." %
(problem._mode, desvar_size, response_size), RuntimeWarning)
def _setup_recording(self):
"""
Set up case recording.
"""
problem = self._problem
model = problem.model
mydesvars = myobjectives = myconstraints = myresponses = set()
myinputs = set()
mysystem_outputs = set()
incl = self.recording_options['includes']
excl = self.recording_options['excludes']
rec_desvars = self.recording_options['record_desvars']
rec_objectives = self.recording_options['record_objectives']
rec_constraints = self.recording_options['record_constraints']
rec_responses = self.recording_options['record_responses']
rec_inputs = self.recording_options['record_inputs']
all_desvars = {
n
for n in self._designvars if check_path(n, incl, excl, True)
}
all_objectives = {
n
for n in self._objs if check_path(n, incl, excl, True)
}
all_constraints = {
n
for n in self._cons if check_path(n, incl, excl, True)
}
if rec_desvars:
mydesvars = all_desvars
if rec_objectives:
myobjectives = all_objectives
if rec_constraints:
myconstraints = all_constraints
if rec_responses:
myresponses = {
n
for n in self._responses if check_path(n, incl, excl, True)
}
# get the includes that were requested for this Driver recording
if incl:
# The my* variables are sets
# First gather all of the desired outputs
# The following might only be the local vars if MPI
mysystem_outputs = {
n
for n in model._outputs if check_path(n, incl, excl)
}
# If MPI, and on rank 0, need to gather up all the variables
# even those not local to rank 0
if MPI:
all_vars = model.comm.gather(mysystem_outputs, root=0)
if MPI.COMM_WORLD.rank == 0:
mysystem_outputs = all_vars[-1]
for d in all_vars[:-1]:
mysystem_outputs.update(d)
# de-duplicate mysystem_outputs
mysystem_outputs = mysystem_outputs.difference(
all_desvars, all_objectives, all_constraints)
if rec_inputs:
prob = self._problem
root = prob.model
myinputs = {n for n in root._inputs if check_path(n, incl, excl)}
if MPI:
all_vars = root.comm.gather(myinputs, root=0)
if MPI.COMM_WORLD.rank == 0:
myinputs = all_vars[-1]
for d in all_vars[:-1]:
myinputs.update(d)
if MPI: # filter based on who owns the variables
# TODO Eventually, we think we can get rid of this next check. But to be safe,
# we are leaving it in there.
if not model.is_active():
raise RuntimeError(
"RecordingManager.startup should never be called when "
"running in parallel on an inactive System")
rrank = problem.comm.rank
rowned = model._owning_rank
mydesvars = [n for n in mydesvars if rrank == rowned[n]]
myresponses = [n for n in myresponses if rrank == rowned[n]]
myobjectives = [n for n in myobjectives if rrank == rowned[n]]
myconstraints = [n for n in myconstraints if rrank == rowned[n]]
mysystem_outputs = [
n for n in mysystem_outputs if rrank == rowned[n]
]
myinputs = [n for n in myinputs if rrank == rowned[n]]
self._filtered_vars_to_record = {
'des': mydesvars,
'obj': myobjectives,
'con': myconstraints,
'res': myresponses,
'sys': mysystem_outputs,
'in': myinputs
}
self._rec_mgr.startup(self)
if self._rec_mgr._recorders:
from openmdao.devtools.problem_viewer.problem_viewer import _get_viewer_data
self._model_viewer_data = _get_viewer_data(problem)
if self.recording_options['record_metadata']:
self._rec_mgr.record_metadata(self)
def _get_voi_val(self,
name,
meta,
remote_vois,
unscaled=False,
ignore_indices=False):
"""
Get the value of a variable of interest (objective, constraint, or design var).
This will retrieve the value if the VOI is remote.
Parameters
----------
name : str
Name of the variable of interest.
meta : dict
Metadata for the variable of interest.
remote_vois : dict
Dict containing (owning_rank, size) for all remote vois of a particular
type (design var, constraint, or objective).
unscaled : bool
Set to True if unscaled (physical) design variables are desired.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
float or ndarray
The value of the named variable of interest.
"""
model = self._problem.model
comm = model.comm
vec = model._outputs._views_flat
indices = meta['indices']
if name in remote_vois:
owner, size = remote_vois[name]
if owner == comm.rank:
if indices is None or ignore_indices:
val = vec[name].copy()
else:
val = vec[name][indices]
else:
if indices is not None:
size = len(indices)
val = np.empty(size)
comm.Bcast(val, root=owner)
else:
if indices is None or ignore_indices:
val = vec[name].copy()
else:
val = vec[name][indices]
if self._has_scaling and not unscaled:
# Scale design variable values
adder = meta['adder']
if adder is not None:
val += adder
scaler = meta['scaler']
if scaler is not None:
val *= scaler
return val
def get_design_var_values(self,
filter=None,
unscaled=False,
ignore_indices=False):
"""
Return the design variable values.
This is called to gather the initial design variable state.
Parameters
----------
filter : list
List of desvar names used by recorders.
unscaled : bool
Set to True if unscaled (physical) design variables are desired.
ignore_indices : bool
Set to True if the full array is desired, not just those indicated by indices.
Returns
-------
dict
Dictionary containing values of each design variable.
"""
if filter:
dvs = filter
else:
# use all the designvars
dvs = self._designvars
return {
n: self._get_voi_val(n,
self._designvars[n],
self._remote_dvs,
unscaled=unscaled,
ignore_indices=ignore_indices)
for n in dvs
}
def set_design_var(self, name, value):
"""
Set the value of a design variable.
Parameters
----------
name : str
Global pathname of the design variable.
value : float or ndarray
Value for the design variable.
"""
if (name in self._remote_dvs and self._problem.model._owning_rank[name]
!= self._problem.comm.rank):
return
meta = self._designvars[name]
indices = meta['indices']
if indices is None:
indices = slice(None)
desvar = self._problem.model._outputs._views_flat[name]
desvar[indices] = value
if self._has_scaling:
# Scale design variable values
scaler = meta['scaler']
if scaler is not None:
desvar[indices] *= 1.0 / scaler
adder = meta['adder']
if adder is not None:
desvar[indices] -= adder
def get_response_values(self, filter=None):
"""
Return response values.
Parameters
----------
filter : list
List of response names used by recorders.
Returns
-------
dict
Dictionary containing values of each response.
"""
if filter:
resps = filter
else:
resps = self._responses
return {
n: self._get_voi_val(n, self._responses[n], self._remote_objs)
for n in resps
}
def get_objective_values(self, unscaled=False, filter=None):
"""
Return objective values.
Parameters
----------
unscaled : bool
Set to True if unscaled (physical) design variables are desired.
filter : list
List of objective names used by recorders.
Returns
-------
dict
Dictionary containing values of each objective.
"""
if filter:
objs = filter
else:
objs = self._objs
return {
n: self._get_voi_val(n,
self._objs[n],
self._remote_objs,
unscaled=unscaled)
for n in objs
}
def get_constraint_values(self,
ctype='all',
lintype='all',
unscaled=False,
filter=None):
"""
Return constraint values.
Parameters
----------
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
unscaled : bool
Set to True if unscaled (physical) design variables are desired.
filter : list
List of constraint names used by recorders.
Returns
-------
dict
Dictionary containing values of each constraint.
"""
if filter is not None:
cons = filter
else:
cons = self._cons
con_dict = {}
for name in cons:
meta = self._cons[name]
if lintype == 'linear' and not meta['linear']:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
con_dict[name] = self._get_voi_val(name,
meta,
self._remote_cons,
unscaled=unscaled)
return con_dict
def _get_ordered_nl_responses(self):
"""
Return the names of nonlinear responses in the order used by the driver.
Default order is objectives followed by nonlinear constraints. This is used for
simultaneous derivative coloring and sparsity determination.
Returns
-------
list of str
The nonlinear response names in order.
"""
order = list(self._objs)
order.extend(n for n, meta in iteritems(self._cons)
if not ('linear' in meta and meta['linear']))
return order
def run(self):
"""
Execute this driver.
The base `Driver` just runs the model. All other drivers overload
this method.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
with RecordingDebugging(self._get_name(), self.iter_count,
self) as rec:
failure_flag, _, _ = self._problem.model._solve_nonlinear()
self.iter_count += 1
return failure_flag
def _compute_totals(self,
of=None,
wrt=None,
return_format='flat_dict',
global_names=True):
"""
Compute derivatives of desired quantities with respect to desired inputs.
All derivatives are returned using driver scaling.
Parameters
----------
of : list of variable name strings or None
Variables whose derivatives will be computed. Default is None, which
uses the driver's objectives and constraints.
wrt : list of variable name strings or None
Variables with respect to which the derivatives will be computed.
Default is None, which uses the driver's desvars.
return_format : string
Format to return the derivatives. Default is a 'flat_dict', which
returns them in a dictionary whose keys are tuples of form (of, wrt). For
the scipy optimizer, 'array' is also supported.
global_names : bool
Set to True when passing in global names to skip some translation steps.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
total_jac = self._total_jac
debug_print = 'totals' in self.options['debug_print'] and (
not MPI or MPI.COMM_WORLD.rank == 0)
if debug_print:
header = 'Driver total derivatives for iteration: ' + str(
self.iter_count)
print(header)
print(len(header) * '-' + '\n')
if self._problem.model._owns_approx_jac:
if total_jac is None:
self._total_jac = total_jac = _TotalJacInfo(
self._problem,
of,
wrt,
global_names,
return_format,
approx=True,
debug_print=debug_print)
return total_jac.compute_totals_approx()
else:
if total_jac is None:
total_jac = _TotalJacInfo(self._problem,
of,
wrt,
global_names,
return_format,
debug_print=debug_print)
# don't cache linear constraint jacobian
if not total_jac.has_lin_cons:
self._total_jac = total_jac
return total_jac.compute_totals()
def record_iteration(self):
"""
Record an iteration of the current Driver.
"""
if not self._rec_mgr._recorders:
return
# Get the data to record (collective calls that get across all ranks)
opts = self.recording_options
filt = self._filtered_vars_to_record
if opts['record_desvars']:
des_vars = self.get_design_var_values()
else:
des_vars = {}
if opts['record_objectives']:
obj_vars = self.get_objective_values()
else:
obj_vars = {}
if opts['record_constraints']:
con_vars = self.get_constraint_values()
else:
con_vars = {}
if opts['record_responses']:
# res_vars = self.get_response_values() # not really working yet
res_vars = {}
else:
res_vars = {}
des_vars = {name: des_vars[name] for name in filt['des']}
obj_vars = {name: obj_vars[name] for name in filt['obj']}
con_vars = {name: con_vars[name] for name in filt['con']}
# res_vars = {name: res_vars[name] for name in filt['res']}
model = self._problem.model
sys_vars = {}
in_vars = {}
outputs = model._outputs
inputs = model._inputs
views = outputs._views
views_in = inputs._views
sys_vars = {
name: views[name]
for name in outputs._names if name in filt['sys']
}
if self.recording_options['record_inputs']:
in_vars = {
name: views_in[name]
for name in inputs._names if name in filt['in']
}
if MPI:
des_vars = self._gather_vars(model, des_vars)
res_vars = self._gather_vars(model, res_vars)
obj_vars = self._gather_vars(model, obj_vars)
con_vars = self._gather_vars(model, con_vars)
sys_vars = self._gather_vars(model, sys_vars)
in_vars = self._gather_vars(model, in_vars)
outs = {}
if not MPI or model.comm.rank == 0:
outs.update(des_vars)
outs.update(res_vars)
outs.update(obj_vars)
outs.update(con_vars)
outs.update(sys_vars)
data = {'out': outs, 'in': in_vars}
metadata = create_local_meta(self._get_name())
self._rec_mgr.record_iteration(self, data, metadata)
def _gather_vars(self, root, local_vars):
"""
Gather and return only variables listed in `local_vars` from the `root` System.
Parameters
----------
root : <System>
the root System for the Problem
local_vars : dict
local variable names and values
Returns
-------
dct : dict
variable names and values.
"""
# if trace:
# debug("gathering vars for recording in %s" % root.pathname)
all_vars = root.comm.gather(local_vars, root=0)
# if trace:
# debug("DONE gathering rec vars for %s" % root.pathname)
if root.comm.rank == 0:
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
return dct
def _get_name(self):
"""
Get name of current Driver.
Returns
-------
str
Name of current Driver.
"""
return "Driver"
def set_simul_deriv_color(self, simul_info):
"""
Set the coloring (and possibly the sub-jac sparsity) for simultaneous total derivatives.
Parameters
----------
simul_info : str or tuple
::
# Information about simultaneous coloring for design vars and responses. If a
# string, then simul_info is assumed to be the name of a file that contains the
# coloring information in JSON format. If a tuple, the structure looks like this:
(
# First, a list of column index lists, each index list representing columns
# having the same color, except for the very first index list, which contains
# indices of all columns that are not colored.
[
[i1, i2, i3, ...] # list of non-colored columns
[ia, ib, ...] # list of columns in first color
[ic, id, ...] # list of columns in second color
... # remaining color lists, one list of columns per color
],
# Next is a list of lists, one for each column, containing the nonzero rows for
# that column. If a column is not colored, then it will have a None entry
# instead of a list.
[
[r1, rn, ...] # list of nonzero rows for column 0
None, # column 1 is not colored
[ra, rb, ...] # list of nonzero rows for column 2
...
],
# The last tuple entry can be None, indicating that no sparsity structure is
# specified, or it can be a nested dictionary where the outer keys are response
# names, the inner keys are design variable names, and the value is a tuple of
# the form (row_list, col_list, shape).
{
resp1_name: {
dv1_name: (rows, cols, shape), # for sub-jac d_resp1/d_dv1
dv2_name: (rows, cols, shape),
...
},
resp2_name: {
...
}
...
}
)
"""
if self.supports['simultaneous_derivatives']:
self._simul_coloring_info = simul_info
else:
raise RuntimeError(
"Driver '%s' does not support simultaneous derivatives." %
self._get_name())
def set_total_jac_sparsity(self, sparsity):
"""
Set the sparsity of sub-jacobians of the total jacobian.
Note: This currently will have no effect if you are not using the pyOptSparseDriver.
Parameters
----------
sparsity : str or dict
::
# Sparsity is a nested dictionary where the outer keys are response
# names, the inner keys are design variable names, and the value is a tuple of
# the form (row_list, col_list, shape).
{
resp1: {
dv1: (rows, cols, shape), # for sub-jac d_resp1/d_dv1
dv2: (rows, cols, shape),
...
},
resp2: {
...
}
...
}
"""
if self.supports['total_jac_sparsity']:
self._total_jac_sparsity = sparsity
else:
raise RuntimeError(
"Driver '%s' does not support setting of total jacobian sparsity."
% self._get_name())
def _setup_simul_coloring(self, mode='fwd'):
"""
Set up metadata for simultaneous derivative solution.
Parameters
----------
mode : str
Derivative direction, either 'fwd' or 'rev'.
"""
if mode == 'rev':
raise NotImplementedError(
"Simultaneous derivatives are currently not supported "
"in 'rev' mode")
# command line simul_coloring uses this env var to turn pre-existing coloring off
if not coloring_mod._use_sparsity:
return
if isinstance(self._simul_coloring_info, string_types):
with open(self._simul_coloring_info, 'r') as f:
self._simul_coloring_info = json.load(f)
tup = self._simul_coloring_info
column_lists, row_map = tup[:2]
if len(tup) > 2:
sparsity = tup[2]
if self._total_jac_sparsity is not None:
raise RuntimeError(
"Total jac sparsity was set in both _simul_coloring_info"
" and _total_jac_sparsity.")
self._total_jac_sparsity = sparsity
self._simul_coloring_info = column_lists, row_map
def _pre_run_model_debug_print(self):
"""
Optionally print some debugging information before the model runs.
"""
debug_opt = self.options['debug_print']
if not debug_opt or debug_opt == ['totals']:
return
if not MPI or MPI.COMM_WORLD.rank == 0:
header = 'Driver debug print for iter coord: {}'.format(
get_formatted_iteration_coordinate())
print(header)
print(len(header) * '-')
if 'desvars' in debug_opt:
desvar_vals = self.get_design_var_values(unscaled=True,
ignore_indices=True)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Design Vars")
if desvar_vals:
pprint.pprint(desvar_vals)
else:
print("None")
print()
sys.stdout.flush()
def _post_run_model_debug_print(self):
"""
Optionally print some debugging information after the model runs.
"""
if 'nl_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='nonlinear',
unscaled=True)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Nonlinear constraints")
if cons:
pprint.pprint(cons)
else:
print("None")
print()
if 'ln_cons' in self.options['debug_print']:
cons = self.get_constraint_values(lintype='linear', unscaled=True)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Linear constraints")
if cons:
pprint.pprint(cons)
else:
print("None")
print()
if 'objs' in self.options['debug_print']:
objs = self.get_objective_values(unscaled=True)
if not MPI or MPI.COMM_WORLD.rank == 0:
print("Objectives")
if objs:
pprint.pprint(objs)
else:
print("None")
print()
sys.stdout.flush()
class ExperimentalDriver(object):
"""
A fake driver class used for doc generation testing.
Attributes
----------
fail : bool
Reports whether the driver ran successfully.
iter_count : int
Keep track of iterations for case recording.
options : list
List of options
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
recording_options : <OptionsDictionary>
Dictionary with driver recording options.
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
_problem : <Problem>
Pointer to the containing problem.
supports : <OptionsDictionary>
Provides a consistant way for drivers to declare what features they support.
_designvars : dict
Contains all design variable info.
_cons : dict
Contains all constraint info.
_objs : dict
Contains all objective info.
_responses : dict
Contains all response info.
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this driver.
_vars_to_record: dict
Dict of lists of var names indicating what to record
_model_viewer_data : dict
Structure of model, used to make n2 diagram.
_remote_dvs : dict
Dict of design variables that are remote on at least one proc. Values are
(owning rank, size).
_remote_cons : dict
Dict of constraints that are remote on at least one proc. Values are
(owning rank, size).
_remote_objs : dict
Dict of objectives that are remote on at least one proc. Values are
(owning rank, size).
_remote_responses : dict
A combined dict containing entries from _remote_cons and _remote_objs.
_total_coloring : tuple of dicts
A data structure describing coloring for simultaneous derivs.
_res_jacs : dict
Dict of sparse subjacobians for use with certain optimizers, e.g. pyOptSparseDriver.
"""
def __init__(self):
"""
Initialize the driver.
"""
self._rec_mgr = RecordingManager()
self._vars_to_record = {
'desvarnames': set(),
'responsenames': set(),
'objectivenames': set(),
'constraintnames': set(),
'sysinclnames': set(),
}
self._problem = None
self._designvars = None
self._cons = None
self._objs = None
self._responses = None
self.options = OptionsDictionary()
self.recording_options = OptionsDictionary()
###########################
self.recording_options.declare('record_metadata',
types=bool,
desc='Record metadata',
default=True)
self.recording_options.declare(
'record_desvars',
types=bool,
default=True,
desc='Set to True to record design variables at the \
driver level')
self.recording_options.declare(
'record_responses',
types=bool,
default=False,
desc='Set to True to record responses at the driver level')
self.recording_options.declare(
'record_objectives',
types=bool,
default=True,
desc='Set to True to record objectives at the \
driver level')
self.recording_options.declare(
'record_constraints',
types=bool,
default=True,
desc='Set to True to record constraints at the \
driver level')
self.recording_options.declare(
'includes',
types=list,
default=[],
desc='Patterns for variables to include in recording')
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
self.recording_options.declare(
'record_derivatives',
types=bool,
default=False,
desc='Set to True to record derivatives at the driver \
level')
###########################
# What the driver supports.
self.supports = OptionsDictionary()
self.supports.declare('inequality_constraints',
types=bool,
default=False)
self.supports.declare('equality_constraints',
types=bool,
default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints',
types=bool,
default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=False)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives',
types=bool,
default=False)
self.iter_count = 0
self.options = None
self._model_viewer_data = None
self.cite = ""
# TODO, support these in OpenMDAO
self.supports.declare('integer_design_vars', types=bool, default=False)
self._res_jacs = {}
self.fail = False
def add_recorder(self, recorder):
"""
Add a recorder to the driver.
Parameters
----------
recorder : CaseRecorder
A recorder instance.
"""
self._rec_mgr.append(recorder)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
self._rec_mgr.close()
def _setup_driver(self, problem):
"""
Prepare the driver for execution.
This is the final thing to run during setup.
Parameters
----------
problem : <Problem>
Pointer to the containing problem.
"""
self._problem = problem
model = problem.model
self._objs = objs = OrderedDict()
self._cons = cons = OrderedDict()
self._responses = model.get_responses(recurse=True)
response_size = 0
for name, data in iteritems(self._responses):
if data['type'] == 'con':
cons[name] = data
else:
objs[name] = data
response_size += data['size']
# Gather up the information for design vars.
self._designvars = model.get_design_vars(recurse=True)
desvar_size = np.sum(data['size']
for data in itervalues(self._designvars))
if ((problem._mode == 'fwd' and desvar_size > response_size)
or (problem._mode == 'rev' and response_size > desvar_size)):
warnings.warn(
"Inefficient choice of derivative mode. You chose '%s' for a "
"problem with %d design variables and %d response variables "
"(objectives and constraints)." %
(problem._mode, desvar_size, response_size), RuntimeWarning)
self._has_scaling = (
np.any([r['scaler'] is not None for r in self._responses.values()])
or np.any(
[dv['scaler'] is not None
for dv in self._designvars.values()]))
con_set = set()
obj_set = set()
dv_set = set()
self._remote_dvs = dv_dict = {}
self._remote_cons = con_dict = {}
self._remote_objs = obj_dict = {}
# Now determine if later we'll need to allgather cons, objs, or desvars.
if model.comm.size > 1 and model._subsystems_allprocs:
local_out_vars = set(model._outputs._views)
remote_dvs = set(self._designvars) - local_out_vars
remote_cons = set(self._cons) - local_out_vars
remote_objs = set(self._objs) - local_out_vars
all_remote_vois = model.comm.allgather(
(remote_dvs, remote_cons, remote_objs))
for rem_dvs, rem_cons, rem_objs in all_remote_vois:
con_set.update(rem_cons)
obj_set.update(rem_objs)
dv_set.update(rem_dvs)
# If we have remote VOIs, pick an owning rank for each and use that
# to bcast to others later
owning_ranks = model._owning_rank['output']
sizes = model._var_sizes['nonlinear']['output']
for i, vname in enumerate(model._var_allprocs_abs_names['output']):
owner = owning_ranks[vname]
if vname in dv_set:
dv_dict[vname] = (owner, sizes[owner, i])
if vname in con_set:
con_dict[vname] = (owner, sizes[owner, i])
if vname in obj_set:
obj_dict[vname] = (owner, sizes[owner, i])
self._remote_responses = self._remote_cons.copy()
self._remote_responses.update(self._remote_objs)
# Case recording setup
mydesvars = myobjectives = myconstraints = myresponses = set()
mysystem_outputs = set()
incl = self.recording_options['includes']
excl = self.recording_options['excludes']
rec_desvars = self.recording_options['record_desvars']
rec_objectives = self.recording_options['record_objectives']
rec_constraints = self.recording_options['record_constraints']
rec_responses = self.recording_options['record_responses']
# includes and excludes for outputs are specified using promoted names
# NOTE: only local var names are in abs2prom, all will be gathered later
abs2prom = model._var_abs2prom['output']
all_desvars = {
n
for n in self._designvars
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)
}
all_objectives = {
n
for n in self._objs
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)
}
all_constraints = {
n
for n in self._cons
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)
}
if rec_desvars:
mydesvars = all_desvars
if rec_objectives:
myobjectives = all_objectives
if rec_constraints:
myconstraints = all_constraints
if rec_responses:
myresponses = {
n
for n in self._responses
if n in abs2prom and check_path(abs2prom[n], incl, excl, True)
}
# get the includes that were requested for this Driver recording
if incl:
prob = self._problem
root = prob.model
# The my* variables are sets
# First gather all of the desired outputs
# The following might only be the local vars if MPI
mysystem_outputs = {
n
for n in root._outputs
if n in abs2prom and check_path(abs2prom[n], incl, excl)
}
# If MPI, and on rank 0, need to gather up all the variables
# even those not local to rank 0
if MPI:
all_vars = root.comm.gather(mysystem_outputs, root=0)
if MPI.COMM_WORLD.rank == 0:
mysystem_outputs = all_vars[-1]
for d in all_vars[:-1]:
mysystem_outputs.update(d)
# de-duplicate mysystem_outputs
mysystem_outputs = mysystem_outputs.difference(
all_desvars, all_objectives, all_constraints)
if MPI: # filter based on who owns the variables
# TODO Eventually, we think we can get rid of this next check. But to be safe,
# we are leaving it in there.
if not model.is_active():
raise RuntimeError(
"RecordingManager.startup should never be called when "
"running in parallel on an inactive System")
rrank = self._problem.comm.rank # root ( aka model ) rank.
rowned = model._owning_rank['output']
mydesvars = [n for n in mydesvars if rrank == rowned[n]]
myresponses = [n for n in myresponses if rrank == rowned[n]]
myobjectives = [n for n in myobjectives if rrank == rowned[n]]
myconstraints = [n for n in myconstraints if rrank == rowned[n]]
mysystem_outputs = [
n for n in mysystem_outputs if rrank == rowned[n]
]
self._filtered_vars_to_record = {
'des': mydesvars,
'obj': myobjectives,
'con': myconstraints,
'res': myresponses,
'sys': mysystem_outputs,
}
self._rec_mgr.startup(self)
def _get_voi_val(self, name, meta, remote_vois):
"""
Get the value of a variable of interest (objective, constraint, or design var).
This will retrieve the value if the VOI is remote.
Parameters
----------
name : str
Name of the variable of interest.
meta : dict
Metadata for the variable of interest.
remote_vois : dict
Dict containing (owning_rank, size) for all remote vois of a particular
type (design var, constraint, or objective).
Returns
-------
float or ndarray
The value of the named variable of interest.
"""
model = self._problem.model
comm = model.comm
vec = model._outputs._views_flat
indices = meta['indices']
if name in remote_vois:
owner, size = remote_vois[name]
if owner == comm.rank:
if indices is None:
val = vec[name].copy()
else:
val = vec[name][indices]
else:
if indices is not None:
size = len(indices)
val = np.empty(size)
comm.Bcast(val, root=owner)
else:
if indices is None:
val = vec[name].copy()
else:
val = vec[name][indices]
if self._has_scaling:
# Scale design variable values
adder = meta['adder']
if adder is not None:
val += adder
scaler = meta['scaler']
if scaler is not None:
val *= scaler
return val
def get_design_var_values(self, filter=None):
"""
Return the design variable values.
This is called to gather the initial design variable state.
Parameters
----------
filter : list
List of desvar names used by recorders.
Returns
-------
dict
Dictionary containing values of each design variable.
"""
if filter:
dvs = filter
else:
# use all the designvars
dvs = self._designvars
return {
n: self._get_voi_val(n, self._designvars[n], self._remote_dvs)
for n in dvs
}
def set_design_var(self, name, value):
"""
Set the value of a design variable.
Parameters
----------
name : str
Global pathname of the design variable.
value : float or ndarray
Value for the design variable.
"""
if (name in self._remote_dvs
and self._problem.model._owning_rank['output'][name] !=
self._problem.comm.rank):
return
meta = self._designvars[name]
indices = meta['indices']
if indices is None:
indices = slice(None)
desvar = self._problem.model._outputs._views_flat[name]
desvar[indices] = value
if self._has_scaling:
# Scale design variable values
scaler = meta['scaler']
if scaler is not None:
desvar[indices] *= 1.0 / scaler
adder = meta['adder']
if adder is not None:
desvar[indices] -= adder
def get_response_values(self, filter=None):
"""
Return response values.
Parameters
----------
filter : list
List of response names used by recorders.
Returns
-------
dict
Dictionary containing values of each response.
"""
if filter:
resps = filter
else:
resps = self._responses
return {
n: self._get_voi_val(n, self._responses[n], self._remote_objs)
for n in resps
}
def get_objective_values(self, filter=None):
"""
Return objective values.
Parameters
----------
filter : list
List of objective names used by recorders.
Returns
-------
dict
Dictionary containing values of each objective.
"""
if filter:
objs = filter
else:
objs = self._objs
return {
n: self._get_voi_val(n, self._objs[n], self._remote_objs)
for n in objs
}
def get_constraint_values(self, ctype='all', lintype='all', filter=None):
"""
Return constraint values.
Parameters
----------
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
filter : list
List of constraint names used by recorders.
Returns
-------
dict
Dictionary containing values of each constraint.
"""
if filter is not None:
cons = filter
else:
cons = self._cons
con_dict = {}
for name in cons:
meta = self._cons[name]
if lintype == 'linear' and not meta['linear']:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
con_dict[name] = self._get_voi_val(name, meta, self._remote_cons)
return con_dict
def run(self):
"""
Execute this driver.
The base `Driver` just runs the model. All other drivers overload
this method.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
with Recording(self._get_name(), self.iter_count, self) as rec:
self._problem.model.run_solve_nonlinear()
self.iter_count += 1
return False
def _dict2array_jac(self, derivs):
osize = 0
isize = 0
do_wrt = True
islices = {}
oslices = {}
for okey, oval in iteritems(derivs):
if do_wrt:
for ikey, val in iteritems(oval):
istart = isize
isize += val.shape[1]
islices[ikey] = slice(istart, isize)
do_wrt = False
ostart = osize
osize += oval[ikey].shape[0]
oslices[okey] = slice(ostart, osize)
new_derivs = np.zeros((osize, isize))
relevant = self._problem.model._relevant
for okey, odict in iteritems(derivs):
for ikey, val in iteritems(odict):
if okey in relevant[ikey] or ikey in relevant[okey]:
new_derivs[oslices[okey], islices[ikey]] = val
return new_derivs
def _compute_totals(self,
of=None,
wrt=None,
return_format='flat_dict',
global_names=True):
"""
Compute derivatives of desired quantities with respect to desired inputs.
All derivatives are returned using driver scaling.
Parameters
----------
of : list of variable name strings or None
Variables whose derivatives will be computed. Default is None, which
uses the driver's objectives and constraints.
wrt : list of variable name strings or None
Variables with respect to which the derivatives will be computed.
Default is None, which uses the driver's desvars.
return_format : string
Format to return the derivatives. Default is a 'flat_dict', which
returns them in a dictionary whose keys are tuples of form (of, wrt). For
the scipy optimizer, 'array' is also supported.
global_names : bool
Set to True when passing in global names to skip some translation steps.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
prob = self._problem
# Compute the derivatives in dict format...
if prob.model._owns_approx_jac:
derivs = prob._compute_totals_approx(of=of,
wrt=wrt,
return_format='dict',
global_names=global_names)
else:
derivs = prob._compute_totals(of=of,
wrt=wrt,
return_format='dict',
global_names=global_names)
# ... then convert to whatever the driver needs.
if return_format in ('dict', 'array'):
if self._has_scaling:
for okey, odict in iteritems(derivs):
for ikey, val in iteritems(odict):
iscaler = self._designvars[ikey]['scaler']
oscaler = self._responses[okey]['scaler']
# Scale response side
if oscaler is not None:
val[:] = (oscaler * val.T).T
# Scale design var side
if iscaler is not None:
val *= 1.0 / iscaler
else:
raise RuntimeError(
"Derivative scaling by the driver only supports the 'dict' and "
"'array' formats at present.")
if return_format == 'array':
derivs = self._dict2array_jac(derivs)
return derivs
def record_iteration(self):
"""
Record an iteration of the current Driver.
"""
if not self._rec_mgr._recorders:
return
metadata = create_local_meta(self._get_name())
# Get the data to record
data = {}
if self.recording_options['record_desvars']:
# collective call that gets across all ranks
desvars = self.get_design_var_values()
else:
desvars = {}
if self.recording_options['record_responses']:
# responses = self.get_response_values() # not really working yet
responses = {}
else:
responses = {}
if self.recording_options['record_objectives']:
objectives = self.get_objective_values()
else:
objectives = {}
if self.recording_options['record_constraints']:
constraints = self.get_constraint_values()
else:
constraints = {}
desvars = {
name: desvars[name]
for name in self._filtered_vars_to_record['des']
}
# responses not working yet
# responses = {name: responses[name] for name in self._filtered_vars_to_record['res']}
objectives = {
name: objectives[name]
for name in self._filtered_vars_to_record['obj']
}
constraints = {
name: constraints[name]
for name in self._filtered_vars_to_record['con']
}
if self.recording_options['includes']:
root = self._problem.model
outputs = root._outputs
# outputsinputs, outputs, residuals = root.get_nonlinear_vectors()
sysvars = {}
for name, value in iteritems(outputs._names):
if name in self._filtered_vars_to_record['sys']:
sysvars[name] = value
else:
sysvars = {}
if MPI:
root = self._problem.model
desvars = self._gather_vars(root, desvars)
responses = self._gather_vars(root, responses)
objectives = self._gather_vars(root, objectives)
constraints = self._gather_vars(root, constraints)
sysvars = self._gather_vars(root, sysvars)
data['des'] = desvars
data['res'] = responses
data['obj'] = objectives
data['con'] = constraints
data['sys'] = sysvars
self._rec_mgr.record_iteration(self, data, metadata)
def _gather_vars(self, root, local_vars):
"""
Gather and return only variables listed in `local_vars` from the `root` System.
Parameters
----------
root : <System>
the root System for the Problem
local_vars : dict
local variable names and values
Returns
-------
dct : dict
variable names and values.
"""
# if trace:
# debug("gathering vars for recording in %s" % root.pathname)
all_vars = root.comm.gather(local_vars, root=0)
# if trace:
# debug("DONE gathering rec vars for %s" % root.pathname)
if root.comm.rank == 0:
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
return dct
def _get_name(self):
"""
Get name of current Driver.
Returns
-------
str
Name of current Driver.
"""
return "Driver"
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Solver options.
"""
self._system = None
self._depth = 0
self._vec_names = None
self._mode = 'fwd'
self._iter_count = 0
self._problem_meta = None
# Solver options
self.options = OptionsDictionary(parent_name=self.msginfo)
self.options.declare('maxiter',
types=int,
default=10,
desc='maximum number of iterations')
self.options.declare('atol',
default=1e-10,
desc='absolute error tolerance')
self.options.declare('rtol',
default=1e-10,
desc='relative error tolerance')
self.options.declare('iprint',
types=int,
default=1,
desc='whether to print output')
self.options.declare(
'err_on_non_converge',
types=bool,
default=False,
desc="When True, AnalysisError will be raised if we don't converge."
)
# Case recording options
self.recording_options = OptionsDictionary(parent_name=self.msginfo)
self.recording_options.declare(
'record_abs_error',
types=bool,
default=True,
desc='Set to True to record absolute error at the \
solver level')
self.recording_options.declare(
'record_rel_error',
types=bool,
default=True,
desc='Set to True to record relative error at the \
solver level')
self.recording_options.declare(
'record_inputs',
types=bool,
default=True,
desc='Set to True to record inputs at the solver level')
self.recording_options.declare(
'record_outputs',
types=bool,
default=True,
desc='Set to True to record outputs at the solver level')
self.recording_options.declare(
'record_solver_residuals',
types=bool,
default=False,
desc='Set to True to record residuals at the solver level')
self.recording_options.declare(
'record_metadata',
types=bool,
desc='Deprecated. Recording '
'of metadata will always be done',
deprecation="The recording option, record_metadata, on "
"Solver is "
"deprecated. Recording of metadata will always be done",
default=True)
self.recording_options.declare(
'includes',
types=list,
default=['*'],
desc="Patterns for variables to include in recording. \
Paths are relative to solver's Group. \
Uses fnmatch wildcards")
self.recording_options.declare(
'excludes',
types=list,
default=[],
desc="Patterns for vars to exclude in recording. \
(processed post-includes) \
Paths are relative to solver's Group. \
Uses fnmatch wildcards")
# Case recording related
self._filtered_vars_to_record = {}
self._norm0 = 0.0
# What the solver supports.
self.supports = OptionsDictionary(parent_name=self.msginfo)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('implicit_components', types=bool, default=False)
self._declare_options()
self.options.update(kwargs)
self._rec_mgr = RecordingManager()
self.cite = ""
class Driver(object):
""" Base class for drivers in OpenMDAO. Drivers can only be placed in a
Problem, and every problem has a Driver. Driver is the simplest driver that
runs (solves using solve_nonlinear) a problem once.
"""
def __init__(self):
super(Driver, self).__init__()
self.recorders = RecordingManager()
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option("inequality_constraints", True)
self.supports.add_option("equality_constraints", True)
self.supports.add_option("linear_constraints", True)
self.supports.add_option("multiple_objectives", True)
self.supports.add_option("two_sided_constraints", True)
self.supports.add_option("integer_design_vars", True)
# This driver's options
self.options = OptionsDictionary()
self._desvars = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
self._vars_to_record = None
# We take root during setup
self.root = None
self.iter_count = 0
def _setup(self, root):
""" Updates metadata for params, constraints and objectives, and
check for errors. Also determines all variables that need to be
gathered for case recording.
"""
self.root = root
desvars = OrderedDict()
objs = OrderedDict()
cons = OrderedDict()
item_tups = [
("Parameter", self._desvars, desvars),
("Objective", self._objs, objs),
("Constraint", self._cons, cons),
]
for item_name, item, newitem in item_tups:
for name, meta in iteritems(item):
rootmeta = root.unknowns.metadata(name)
if MPI and "src_indices" in rootmeta: # pragma: no cover
raise ValueError(
"'%s' is a distributed variable and may "
"not be used as a design var, objective, "
"or constraint." % name
)
# Check validity of variable
if name not in root.unknowns:
msg = "{} '{}' not found in unknowns."
msg = msg.format(item_name, name)
raise ValueError(msg)
# Size is useful metadata to save
if "indices" in meta:
meta["size"] = len(meta["indices"])
else:
meta["size"] = rootmeta["size"]
newitem[name] = meta
self._desvars = desvars
self._objs = objs
self._cons = cons
def _map_voi_indices(self):
poi_indices = {}
qoi_indices = {}
for name, meta in chain(iteritems(self._cons), iteritems(self._objs)):
# set indices of interest
if "indices" in meta:
qoi_indices[name] = meta["indices"]
for name, meta in iteritems(self._desvars):
# set indices of interest
if "indices" in meta:
poi_indices[name] = meta["indices"]
return poi_indices, qoi_indices
def _of_interest(self, voi_list):
"""Return a list of tuples, with the given voi_list organized
into tuples based on the previously defined grouping of VOIs.
"""
vois = []
remaining = set(voi_list)
for voi_set in self._voi_sets:
vois.append([])
for i, voi_set in enumerate(self._voi_sets):
for v in voi_list:
if v in voi_set:
vois[i].append(v)
remaining.remove(v)
vois = [tuple(x) for x in vois if x]
for v in voi_list:
if v in remaining:
vois.append((v,))
return vois
def desvars_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of design vars, organized into tuples according to
previously defined VOI groups.
"""
return self._of_interest(self._desvars)
def outputs_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of constraints and objectives, organized into tuples
according to previously defined VOI groups.
"""
return self._of_interest(list(chain(self._objs, self._cons)))
def parallel_derivs(self, vnames):
"""
Specifies that the named variables of interest are to be grouped
together so that their derivatives can be solved for concurrently.
Args
----
vnames : iter of str
The names of variables of interest that are to be grouped.
"""
# make sure all vnames are desvars, constraints, or objectives
found = set()
for n in vnames:
if not (n in self._desvars or n in self._objs or n in self._cons):
raise RuntimeError("'%s' is not a param, objective, or " "constraint" % n)
for grp in self._voi_sets:
for vname in vnames:
if vname in grp:
msg = "'%s' cannot be added to VOI set %s because it " + "already exists in VOI set: %s"
raise RuntimeError(msg % (vname, tuple(vnames), grp))
param_intsect = set(vnames).intersection(self._desvars.keys())
if param_intsect and len(param_intsect) != len(vnames):
raise RuntimeError(
"%s cannot be grouped because %s are design "
"vars and %s are not." % (vnames, list(param_intsect), list(set(vnames).difference(param_intsect)))
)
if MPI: # pragma: no cover
self._voi_sets.append(tuple(vnames))
else:
warnings.warn("parallel derivs %s specified but not running under MPI")
def add_recorder(self, recorder):
"""
Adds a recorder to the driver.
Args
----
recorder : BaseRecorder
A recorder instance.
"""
self.recorders.append(recorder)
def add_desvar(self, name, low=None, high=None, indices=None, adder=0.0, scaler=1.0):
"""
Adds a parameter to this driver.
Args
----
name : string
Name of the IndepVarComp in the root system.
low : float or ndarray, optional
Lower boundary for the param
high : upper or ndarray, optional
Lower boundary for the param
indices : iter of int, optional
If a param is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if low is None:
low = -1e99
elif isinstance(low, np.ndarray):
low = low.flatten()
if high is None:
high = 1e99
elif isinstance(high, np.ndarray):
high = high.flatten()
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
# Scale the low and high values
low = (low + adder) * scaler
high = (high + adder) * scaler
param = {}
param["low"] = low
param["high"] = high
param["adder"] = adder
param["scaler"] = scaler
if indices:
param["indices"] = np.array(indices, dtype=int)
self._desvars[name] = param
def add_param(self, name, low=None, high=None, indices=None, adder=0.0, scaler=1.0):
"""
Deprecated. Use ``add_desvar`` instead.
"""
warnings.simplefilter("always", DeprecationWarning)
warnings.warn("Driver.add_param() is deprecated. Use add_desvar() instead.", DeprecationWarning, stacklevel=2)
warnings.simplefilter("ignore", DeprecationWarning)
self.add_desvar(name, low=low, high=high, indices=indices, adder=adder, scaler=scaler)
def get_desvars(self):
""" Returns a dict of possibly distributed parameters.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
uvec = self.root.unknowns
desvars = OrderedDict()
for key, meta in iteritems(self._desvars):
desvars[key] = self._get_distrib_var(key, meta, "design var")
return desvars
def _get_distrib_var(self, name, meta, voi_type):
uvec = self.root.unknowns
comm = self.root.comm
nproc = comm.size
iproc = comm.rank
if nproc > 1:
owner = self.root._owning_ranks[name]
if iproc == owner:
flatval = uvec.flat[name]
else:
flatval = None
else:
owner = 0
flatval = uvec.flat[name]
if "indices" in meta and not (nproc > 1 and owner != iproc):
# Make sure our indices are valid
try:
flatval = flatval[meta["indices"]]
except IndexError:
msg = "Index for {} '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "shape: {}."
raise IndexError(msg.format(voi_type, name, meta["indices"], uvec.metadata(name)["shape"]))
if nproc > 1:
flatval = comm.bcast(flatval, root=owner)
scaler = meta["scaler"]
adder = meta["adder"]
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) or scaler != 1.0 or adder != 0.0:
return (flatval + adder) * scaler
else:
return flatval
def get_desvar_metadata(self):
""" Returns a dict of parameter metadata.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
return self._desvars
def set_desvar(self, name, value):
""" Sets a parameter.
Args
----
name : string
Name of the IndepVarComp in the root system.
val : ndarray or float
value to set the parameter
"""
if self.root.unknowns.flat[name].size == 0:
return
scaler = self._desvars[name]["scaler"]
adder = self._desvars[name]["adder"]
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) or scaler != 1.0 or adder != 0.0:
value = value / scaler - adder
else:
value = value
# Only set the indices we requested when we set the parameter.
idx = self._desvars[name].get("indices")
if idx is not None:
self.root.unknowns[name][idx] = value
else:
self.root.unknowns[name] = value
def add_objective(self, name, indices=None, adder=0.0, scaler=1.0):
""" Adds an objective to this driver.
Args
----
name : string
Promoted pathname of the output that will serve as the objective.
indices : iter of int, optional
If an objective is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
obj = {}
obj["adder"] = adder
obj["scaler"] = scaler
if indices:
obj["indices"] = indices
if len(indices) > 1 and not self.supports["multiple_objectives"]:
raise RuntimeError(
"Multiple objective indices specified for "
"variable '%s', but driver '%s' doesn't "
"support multiple objectives." % (name, self.pathname)
)
self._objs[name] = obj
def get_objectives(self, return_type="dict"):
""" Gets all objectives of this driver.
Args
----
return_type : string
Set to 'dict' to return a dictionary, or set to 'array' to return a
flat ndarray.
Returns
-------
dict (for return_type 'dict')
Key is the objective name string, value is an ndarray with the values.
ndarray (for return_type 'array')
Array containing all objective values in the order they were added.
"""
uvec = self.root.unknowns
objs = OrderedDict()
for key, meta in iteritems(self._objs):
objs[key] = self._get_distrib_var(key, meta, "objective")
return objs
def add_constraint(
self, name, lower=None, upper=None, equals=None, linear=False, jacs=None, indices=None, adder=0.0, scaler=1.0
):
""" Adds a constraint to this driver. For inequality constraints,
`lower` or `upper` must be specified. For equality constraints, `equals`
must be specified.
Args
----
name : string
Promoted pathname of the output that will serve as the quantity to
constrain.
lower : float or ndarray, optional
Constrain the quantity to be greater than this value.
upper : float or ndarray, optional
Constrain the quantity to be less than this value.
equals : float or ndarray, optional
Constrain the quantity to be equal to this value.
linear : bool, optional
Set to True if this constraint is linear with respect to all design
variables so that it can be calculated once and cached.
jacs : dict of functions, optional
Dictionary of user-defined functions that return the flattened
Jacobian of this constraint with repsect to the design vars of
this driver, as indicated by the dictionary keys. Default is None
to let OpenMDAO calculate all derivatives. Note, this is currently
unsupported
indices : iter of int, optional
If a constraint is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if equals is not None and (lower is not None or upper is not None):
msg = "Constraint '{}' cannot be both equality and inequality."
raise RuntimeError(msg.format(name))
if equals is not None and self.supports["equality_constraints"] is False:
msg = "Driver does not support equality constraint '{}'."
raise RuntimeError(msg.format(name))
if equals is None and self.supports["inequality_constraints"] is False:
msg = "Driver does not support inequality constraint '{}'."
raise RuntimeError(msg.format(name))
if lower is not None and upper is not None and self.supports["two_sided_constraints"] is False:
msg = "Driver does not support 2-sided constraint '{}'."
raise RuntimeError(msg.format(name))
if lower is None and upper is None and equals is None:
msg = "Constraint '{}' needs to define lower, upper, or equals."
raise RuntimeError(msg.format(name))
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten()
if isinstance(adder, np.ndarray):
adder = adder.flatten()
if isinstance(lower, np.ndarray):
lower = lower.flatten()
if isinstance(upper, np.ndarray):
upper = upper.flatten()
if isinstance(equals, np.ndarray):
equals = equals.flatten()
con = {}
con["lower"] = lower
con["upper"] = upper
con["equals"] = equals
con["linear"] = linear
con["adder"] = adder
con["scaler"] = scaler
con["jacs"] = jacs
if indices:
con["indices"] = indices
self._cons[name] = con
def get_constraints(self, ctype="all", lintype="all"):
""" Gets all constraints for this driver.
Args
----
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
Returns
-------
dict
Key is the constraint name string, value is an ndarray with the values.
"""
uvec = self.root.unknowns
cons = OrderedDict()
for key, meta in iteritems(self._cons):
if lintype == "linear" and meta["linear"] == False:
continue
if lintype == "nonlinear" and meta["linear"]:
continue
if ctype == "eq" and meta["equals"] is None:
continue
if ctype == "ineq" and meta["equals"] is not None:
continue
scaler = meta["scaler"]
adder = meta["adder"]
cons[key] = self._get_distrib_var(key, meta, "constraint")
return cons
def get_constraint_metadata(self):
""" Returns a dict of constraint metadata.
Returns
-------
dict
Keys are the constraint object names, and the values are the param
values.
"""
return self._cons
def run(self, problem):
""" Runs the driver. This function should be overriden when inheriting.
Args
----
problem : `Problem`
Our parent `Problem`.
"""
system = problem.root
# Metadata Setup
self.iter_count += 1
metadata = create_local_meta(None, "Driver")
system.ln_solver.local_meta = metadata
update_local_meta(metadata, (self.iter_count,))
# Solve the system once and record results.
system.solve_nonlinear(metadata=metadata)
self.recorders.record(system, metadata)
def generate_docstring(self):
"""
Generates a numpy-style docstring for a user-created Driver class.
Returns
-------
docstring : str
string that contains a basic numpy docstring.
"""
# start the docstring off
docstring = ' """\n'
# Put options into docstring
from openmdao.core.options import OptionsDictionary
firstTime = 1
# for py3.4, items from vars must come out in same order.
v = OrderedDict(sorted(vars(self).items()))
for key, value in v.items():
if type(value) == OptionsDictionary:
if key == "supports":
continue
if firstTime: # start of Options docstring
docstring += "\n Options\n -------\n"
firstTime = 0
for (name, val) in sorted(value.items()):
docstring += " " + key + "['"
docstring += name + "']"
docstring += " : " + type(val).__name__
docstring += "("
if type(val).__name__ == "str":
docstring += "'"
docstring += str(val)
if type(val).__name__ == "str":
docstring += "'"
docstring += ")\n"
desc = value._options[name]["desc"]
if desc:
docstring += " " + desc + "\n"
# finish up docstring
docstring += '\n """\n'
return docstring
