def record_metadata_system(self, recording_requester):
"""
Record system metadata.
Parameters
----------
recording_requester : System
The System that would like to record its metadata.
"""
if self.connection:
# Cannot handle PETScVector yet
from openmdao.api import PETScVector
if PETScVector and isinstance(recording_requester._outputs,
PETScVector):
return # Cannot handle PETScVector yet
# collect scaling arrays
scaling_vecs = {}
for kind, odict in iteritems(recording_requester._vectors):
scaling_vecs[kind] = scaling = {}
for vecname, vec in iteritems(odict):
scaling[vecname] = vec._scaling
scaling_factors = pickle.dumps(scaling_vecs, self._pickle_version)
# create a copy of the system's metadata excluding what is in 'options_excludes'
user_options = OptionsDictionary()
excludes = recording_requester.recording_options[
'options_excludes']
for key in recording_requester.options._dict:
if check_path(key, [], excludes, True):
user_options._dict[
key] = recording_requester.options._dict[key]
user_options._read_only = recording_requester.options._read_only
# try to pickle the metadata, report if it failed
try:
pickled_metadata = pickle.dumps(user_options,
self._pickle_version)
except Exception:
pickled_metadata = pickle.dumps(OptionsDictionary(),
self._pickle_version)
warnings.warn(
"Trying to record options which cannot be pickled "
"on system with name: %s. Use the 'options_excludes' "
"recording option on system objects to avoid attempting "
"to record options which cannot be pickled. Skipping "
"recording options for this system." %
recording_requester.name, RuntimeWarning)
path = recording_requester.pathname
if not path:
path = 'root'
scaling_factors = sqlite3.Binary(scaling_factors)
pickled_metadata = sqlite3.Binary(pickled_metadata)
with self.connection as c:
c.execute(
"INSERT INTO system_metadata(id, scaling_factors, component_metadata) "
"VALUES(?,?,?)", (path, scaling_factors, pickled_metadata))
def __init__(self, grid, values, interp, **kwargs):
"""
Initialize table and subtables.
Parameters
----------
grid : tuple(ndarray)
Tuple containing ndarray of x grid locations for each table dimension.
values : ndarray
Array containing the values at all points in grid.
interp : class
Interpolation class to be used for subsequent table dimensions.
**kwargs : dict
Interpolator-specific options to pass onward.
"""
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.initialize()
self.options.update(kwargs)
self.subtable = None
self.grid = grid[0]
self.values = values
if len(grid) > 1:
self.subtable = interp(grid[1:], values, interp, **kwargs)
self.last_index = 0
self.k = None
self._name = None
self._vectorized = False
self.training_data_gradients = False
self._full_slice = None
def _get_metadata_system(self, system):
# Cannot handle PETScVector yet
from openmdao.api import PETScVector
if PETScVector and isinstance(system._outputs, PETScVector):
return None, None # Cannot handle PETScVector yet
# collect scaling arrays
scaling_vecs = {}
for kind, odict in system._vectors.items():
scaling_vecs[kind] = scaling = {}
for vecname, vec in odict.items():
scaling[vecname] = vec._scaling
# create a copy of the system's metadata excluding what is in 'options_excludes'
excludes = system.recording_options['options_excludes']
if excludes:
user_options = OptionsDictionary()
user_options._all_recordable = system.options._all_recordable
for key in system.options._dict:
if check_path(key, [], excludes, True):
user_options._dict[key] = system.options._dict[key]
user_options._read_only = system.options._read_only
return scaling_vecs, user_options
else:
return scaling_vecs, system.options
def __init__(self, grid, values, interp=None, **kwargs):
"""
Initialize table and subtables.
"""
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.initialize()
self.options.update(kwargs)
self._vectorized = True
interp_method = self.options['interp_method']
self._name = interp_method
self._full_slice = None
self.grid = grid
self.values = values
# InterpScipy supports automatic order reduction.
self._ki = []
# Order is the number of required points minus one.
k = SCIPY_ORDERS[interp_method] - 1
for p in grid:
n_p = len(p)
self._ki.append(k)
if n_p <= k:
self._ki[-1] = n_p - 1
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.
"""
self.trained = False
self.options = OptionsDictionary(parent_name=type(self).__name__)
self._declare_options()
self.options.update(kwargs)
def _declare_dynamic_parameter(self,
name,
targets,
shape=None,
units=None):
"""
Declare an input to the ODE.
Parameters
----------
name : str
The name of the dynamic parameter.
targets : string_types or Iterable or None
Paths to inputs in the ODE to which the incoming value of the dynamic parameter
needs to be connected.
shape : int or tuple or None
Shape of the parameter.
units : str or None
Units of the parameter.
"""
if name in self._dynamic_parameters:
raise ValueError(
'Dynamic parameter {0} has already been declared.'.format(
name))
options = OptionsDictionary()
options.declare('name', types=string_types)
options.declare('targets', default=[], types=Iterable)
options.declare('shape', default=(1, ), types=tuple)
options.declare('units',
default=None,
types=string_types,
allow_none=True)
options['name'] = name
if isinstance(targets, string_types):
options['targets'] = [targets]
elif isinstance(targets, Iterable):
options['targets'] = targets
elif targets is not None:
raise ValueError(
'targets must be of type string_types or Iterable or None')
if np.isscalar(shape):
options['shape'] = (shape, )
elif isinstance(shape, Iterable):
options['shape'] = tuple(shape)
elif shape is not None:
raise ValueError('shape must be of type int or Iterable or None')
if units is not None:
options['units'] = units
self._dynamic_parameters[name] = options
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict
options dictionary.
"""
self.trained = False
self.options = OptionsDictionary()
self._declare_options()
self.options.update(kwargs)
def record_metadata_system(self, recording_requester):
"""
Record system metadata.
Parameters
----------
recording_requester : System
The System that would like to record its metadata.
"""
if self.con:
# Cannot handle PETScVector yet
from openmdao.api import PETScVector
if PETScVector and isinstance(recording_requester._outputs,
PETScVector):
return # Cannot handle PETScVector yet
# collect scaling arrays
scaling_vecs = {}
for kind, odict in iteritems(recording_requester._vectors):
scaling_vecs[kind] = scaling = {}
for vecname, vec in iteritems(odict):
scaling[vecname] = vec._scaling
scaling_factors = pickle.dumps(scaling_vecs, self._pickle_version)
# create a copy of the system's metadata excluding what is in 'metadata_excludes'
user_metadata = OptionsDictionary()
excludes = recording_requester.recording_options[
'metadata_excludes']
for key in recording_requester.options._dict:
if check_path(key, [], excludes, True):
user_metadata._dict[
key] = recording_requester.options._dict[key]
user_metadata._read_only = recording_requester.options._read_only
pickled_metadata = pickle.dumps(user_metadata,
self._pickle_version)
path = recording_requester.pathname
if not path:
path = 'root'
with self.con:
self.con.execute(
"INSERT INTO system_metadata(id, scaling_factors, component_metadata) \
VALUES(?,?, ?)",
(path, sqlite3.Binary(scaling_factors),
sqlite3.Binary(pickled_metadata)))
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, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict
options dictionary.
"""
self._system = None
self._subjacs = {}
self._subjacs_info = {}
self.options = OptionsDictionary()
self.options.update(kwargs)
self._override_checks = False
def record_metadata_system(self, recording_requester, run_counter=None):
"""
Record system metadata.
Parameters
----------
recording_requester : System
The System that would like to record its metadata.
run_counter : int or None
The number of times run_driver or run_model has been called.
"""
if self.connection:
scaling_vecs, user_options = self._get_metadata_system(
recording_requester)
if scaling_vecs is None:
return
scaling_factors = pickle.dumps(scaling_vecs, self._pickle_version)
# try to pickle the metadata, report if it failed
try:
pickled_metadata = pickle.dumps(user_options,
self._pickle_version)
except Exception:
try:
for key, values in user_options._dict.items():
pickle.dumps(values, self._pickle_version)
except Exception:
pickled_metadata = pickle.dumps(OptionsDictionary(),
self._pickle_version)
simple_warning(
"Trying to record option '%s' which cannot be pickled on system "
"%s. Set 'recordable' to False. Skipping recording options for "
"this system." % (key, recording_requester.msginfo))
path = recording_requester.pathname
if not path:
path = 'root'
scaling_factors = sqlite3.Binary(scaling_factors)
pickled_metadata = sqlite3.Binary(pickled_metadata)
# Need to use OR IGNORE in here because if the user does run_driver more than once
# the current OpenMDAO code will call this function each time and there will be
# SQL errors for "UNIQUE constraint failed: system_metadata.id"
# Future versions of OpenMDAO will handle this better.
if run_counter is None:
name = path
else:
name = "{}_{}".format(path, str(run_counter))
with self.connection as c:
c.execute(
"INSERT OR IGNORE INTO system_metadata"
"(id, scaling_factors, component_metadata) "
"VALUES(?,?,?)", (name, scaling_factors, pickled_metadata))
def record_metadata_system(self, system, run_number=None):
"""
Record system metadata.
Parameters
----------
system : System
The System for which to record metadata.
run_number : int or None
Number indicating which run the metadata is associated with.
None for the first run, 1 for the second, etc.
"""
if self._record_metadata and self.metadata_connection:
scaling_vecs, user_options = self._get_metadata_system(system)
if scaling_vecs is None:
return
scaling_factors = pickle.dumps(scaling_vecs, self._pickle_version)
# try to pickle the metadata, report if it failed
try:
pickled_metadata = pickle.dumps(user_options,
self._pickle_version)
except Exception:
try:
for key, values in user_options._dict.items():
pickle.dumps(values, self._pickle_version)
except Exception:
pickled_metadata = pickle.dumps(OptionsDictionary(),
self._pickle_version)
msg = f"Trying to record option '{key}' which cannot be pickled on this " \
"system. Set option 'recordable' to False. Skipping recording options " \
"for this system."
issue_warning(msg,
prefix=system.msginfo,
category=CaseRecorderWarning)
path = system.pathname
if not path:
path = 'root'
scaling_factors = sqlite3.Binary(zlib.compress(scaling_factors))
pickled_metadata = sqlite3.Binary(zlib.compress(pickled_metadata))
if run_number is None:
name = path
else:
name = META_KEY_SEP.join([path, str(run_number)])
with self.metadata_connection as m:
m.execute(
"INSERT INTO system_metadata"
"(id, scaling_factors, component_metadata) "
"VALUES(?,?,?)", (name, scaling_factors, pickled_metadata))
def __init__(self, grid, values, interp, **kwargs):
"""
Initialize interp algorithm.
"""
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.initialize()
self.options.update(kwargs)
self.grid = grid
self.values = values
self.last_index = 0
self.k = None
self.dim = None
self._name = None
self._vectorized = True
self._compute_d_dvalues = False
self._supports_d_dvalues = False
self._compute_d_dx = True
def __init__(self, **kwargs):
"""
Initialize class attributes.
Parameters
----------
kwargs : dict
Keyword arguments that will be passed to the initialize method.
"""
self._system_class = None
self._system_init_kwargs = {}
time_options = OptionsDictionary()
time_options.declare('targets', default=[], types=Iterable)
time_options.declare('units',
default=None,
types=string_types,
allow_none=True)
self._time_options = time_options
self._states = {}
self._static_parameters = {}
self._dynamic_parameters = {}
self.initialize(**kwargs)
def _get_metadata_system(self, recording_requester):
# Cannot handle PETScVector yet
from openmdao.api import PETScVector
if PETScVector and isinstance(recording_requester._outputs, PETScVector):
return None, None # Cannot handle PETScVector yet
# collect scaling arrays
scaling_vecs = {}
for kind, odict in iteritems(recording_requester._vectors):
scaling_vecs[kind] = scaling = {}
for vecname, vec in iteritems(odict):
scaling[vecname] = vec._scaling
# create a copy of the system's metadata excluding what is in 'options_excludes'
user_options = OptionsDictionary()
excludes = recording_requester.recording_options['options_excludes']
for key in recording_requester.options._dict:
if check_path(key, [], excludes, True):
user_options._dict[key] = recording_requester.options._dict[key]
user_options._read_only = recording_requester.options._read_only
return scaling_vecs, user_options
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):
"""
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, grid, values, interp=None, **kwargs):
"""
Initialize table and subtables.
Parameters
----------
grid : tuple(ndarray)
Tuple containing x grid locations for this dimension and all subtable dimensions.
values : ndarray
Array containing the table values for all dimensions.
interp : class
Interpolation class to be used for subsequent table dimensions.
**kwargs : dict
Interpolator-specific options to pass onward.
"""
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.initialize()
self.options.update(kwargs)
self._vectorized = True
interp_method = self.options['interp_method']
self._name = interp_method
self._full_slice = None
self.grid = grid
self.values = values
# InterpScipy supports automatic order reduction.
self._ki = []
# Order is the number of required points minus one.
k = SCIPY_ORDERS[interp_method] - 1
for p in grid:
n_p = len(p)
self._ki.append(k)
if n_p <= k:
self._ki[-1] = n_p - 1
def __init__(self, grid, values, interp, **kwargs):
"""
Initialize table and subtables.
"""
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.initialize()
self.options.update(kwargs)
self.subtable = None
self.grid = grid[0]
self.values = values
if len(grid) > 1:
self.subtable = interp(grid[1:], values, interp, **kwargs)
self.last_index = 0
self.k = None
self._name = None
self._vectorized = False
self._compute_d_dvalues = False
self._compute_d_dx = True
self._full_slice = None
def record_metadata_system(self, recording_requester):
"""
Record system metadata.
Parameters
----------
recording_requester : System
The System that would like to record its metadata.
"""
if self.connection:
scaling_vecs, user_options = self._get_metadata_system(
recording_requester)
if scaling_vecs is None:
return
scaling_factors = pickle.dumps(scaling_vecs, self._pickle_version)
# try to pickle the metadata, report if it failed
try:
pickled_metadata = pickle.dumps(user_options,
self._pickle_version)
except Exception:
try:
for key, values in user_options._dict.items():
pickle.dumps(values, self._pickle_version)
except Exception:
pickled_metadata = pickle.dumps(OptionsDictionary(),
self._pickle_version)
simple_warning(
"Trying to record option '%s' which cannot be pickled on system "
"%s. Set 'recordable' to False. Skipping recording options for "
"this system." % (key, recording_requester.msginfo))
path = recording_requester.pathname
if not path:
path = 'root'
scaling_factors = sqlite3.Binary(scaling_factors)
pickled_metadata = sqlite3.Binary(pickled_metadata)
with self.connection as c:
# Because we can have a recorder attached to multiple Systems,
# and because we are now recording System metadata recursively,
# we can store System metadata multiple times. Need to ignore when that happens
# so we don't get database errors. So use OR IGNORE
c.execute(
"INSERT OR IGNORE INTO system_metadata"
"(id, scaling_factors, component_metadata) "
"VALUES(?,?,?)", (path, scaling_factors, pickled_metadata))
def record_metadata_system(self, recording_requester):
"""
Record system metadata.
Parameters
----------
recording_requester : System
The System that would like to record its metadata.
"""
if self.connection:
scaling_vecs, user_options = self._get_metadata_system(
recording_requester)
if scaling_vecs is None:
return
scaling_factors = pickle.dumps(scaling_vecs, self._pickle_version)
# try to pickle the metadata, report if it failed
try:
pickled_metadata = pickle.dumps(user_options,
self._pickle_version)
except Exception:
pickled_metadata = pickle.dumps(OptionsDictionary(),
self._pickle_version)
simple_warning(
"Trying to record options which cannot be pickled "
"on system with name: %s. Use the 'options_excludes' "
"recording option on system objects to avoid attempting "
"to record options which cannot be pickled. Skipping "
"recording options for this system." %
recording_requester.name, RuntimeWarning)
path = recording_requester.pathname
if not path:
path = 'root'
scaling_factors = sqlite3.Binary(scaling_factors)
pickled_metadata = sqlite3.Binary(pickled_metadata)
with self.connection as c:
# Because we can have a recorder attached to multiple Systems,
# and because we are now recording System metadata recursively,
# we can store System metadata multiple times. Need to ignore when that happens
# so we don't get database errors. So use OR IGNORE
c.execute(
"INSERT OR IGNORE INTO system_metadata"
"(id, scaling_factors, component_metadata) "
"VALUES(?,?,?)", (path, scaling_factors, pickled_metadata))
def _get_metadata_system(self, recording_requester):
# Cannot handle PETScVector yet
from openmdao.api import PETScVector
if PETScVector and isinstance(recording_requester._outputs,
PETScVector):
return None, None # Cannot handle PETScVector yet
# collect scaling arrays
scaling_vecs = {}
for kind, odict in iteritems(recording_requester._vectors):
scaling_vecs[kind] = scaling = {}
for vecname, vec in iteritems(odict):
scaling[vecname] = vec._scaling
# create a copy of the system's metadata excluding what is in 'options_excludes'
user_options = OptionsDictionary()
excludes = recording_requester.recording_options['options_excludes']
for key in recording_requester.options._dict:
if check_path(key, [], excludes, True):
user_options._dict[key] = recording_requester.options._dict[
key]
user_options._read_only = recording_requester.options._read_only
return scaling_vecs, user_options
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, **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):
"""
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()
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 SurrogateModel(object):
"""
Base class for surrogate models.
Parameters
----------
**kwargs : dict
Options dictionary.
Attributes
----------
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
trained : bool
True when surrogate has been trained.
"""
def __init__(self, **kwargs):
"""
Initialize all attributes.
"""
self.trained = False
self.options = OptionsDictionary(parent_name=type(self).__name__)
self._declare_options()
self.options.update(kwargs)
def train(self, x, y):
"""
Train the surrogate model with the given set of inputs and outputs.
Parameters
----------
x : array-like
Training input locations..
y : array-like
Model responses at given inputs.
"""
self.trained = True
def predict(self, x):
"""
Calculate a predicted value of the response based on the current trained model.
Parameters
----------
x : array-like
Point(s) at which the surrogate is evaluated.
"""
if not self.trained:
msg = "{0} has not been trained, so no prediction can be made."\
.format(type(self).__name__)
raise RuntimeError(msg)
def vectorized_predict(self, x):
"""
Calculate predicted values of the response based on the current trained model.
Parameters
----------
x : array-like
Vectorized point(s) at which the surrogate is evaluated.
"""
pass
def linearize(self, x):
"""
Calculate the jacobian of the interpolant at the requested point.
Parameters
----------
x : array-like
Point at which the surrogate Jacobian is evaluated.
"""
pass
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
"""
pass
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 InterpAlgorithm(object):
"""
Base class for interpolation over data in an n-dimensional table.
Attributes
----------
grid : tuple(ndarray)
Tuple containing x grid locations for this dimension.
k : int
Minimum number of points required for this algorithm.
last_index : integer
Index of previous evaluation, used to start search for current index.
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
subtable : <InterpAlgorithm>
Table interpolation that handles child dimensions.
training_data_gradients : bool
Flag that tells interpolation objects wether to compute gradients with respect to the
grid values.
values : ndarray
Array containing the table values for all dimensions.
_name : str
Algorithm name for error messages.
_full_slice : tuple of <Slice>
Used to cache the full slice if training derivatives are computed.
_vectorized :bool
If True, this method is vectorized and can simultaneously solve multiple interpolations.
"""
def __init__(self, grid, values, interp, **kwargs):
"""
Initialize table and subtables.
Parameters
----------
grid : tuple(ndarray)
Tuple containing ndarray of x grid locations for each table dimension.
values : ndarray
Array containing the values at all points in grid.
interp : class
Interpolation class to be used for subsequent table dimensions.
**kwargs : dict
Interpolator-specific options to pass onward.
"""
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.initialize()
self.options.update(kwargs)
self.subtable = None
self.grid = grid[0]
self.values = values
if len(grid) > 1:
self.subtable = interp(grid[1:], values, interp, **kwargs)
self.last_index = 0
self.k = None
self._name = None
self._vectorized = False
self.training_data_gradients = False
self._full_slice = None
def initialize(self):
"""
Declare options.
Override to add options.
"""
pass
def check_config(self):
"""
Verify that we have enough points for this interpolation algorithm.
"""
if self.subtable:
self.subtable.check_config()
k = self.k
n_p = len(self.grid)
if n_p < k:
raise ValueError("There are %d points in a data dimension,"
" but method %s requires at least %d "
"points per dimension."
"" % (n_p, self._name, k + 1))
def bracket(self, x):
"""
Locate the interval of the new independent.
Uses the following algorithm:
1. Determine if the value is above or below the value at last_index
2. Bracket the value between last_index and last_index +- inc, where
inc has an increasing value of 1,2,4,8, etc.
3. Once the value is bracketed, use bisection method within that bracket.
The grid is assumed to increase in a monotonic fashion.
Parameters
----------
x : float
Value of new independent to interpolate.
Returns
-------
integer
Grid interval index that contains x.
integer
Extrapolation flag, -1 if the bracket is below the first table element, 1 if the
bracket is above the last table element, 0 for normal interpolation.
"""
grid = self.grid
last_index = self.last_index
high = last_index + 1
highbound = len(grid) - 1
inc = 1
while x < grid[last_index]:
high = last_index
last_index -= inc
if last_index < 0:
last_index = 0
# Check if we're off of the bottom end.
if x < grid[0]:
return last_index, -1
break
inc += inc
if high > highbound:
high = highbound
while x > grid[high]:
last_index = high
high += inc
if high >= highbound:
# Check if we're off of the top end
if x > grid[highbound]:
last_index = highbound
return last_index, 1
high = highbound
break
inc += inc
# Bisection
while high - last_index > 1:
low = (high + last_index) // 2
if x < grid[low]:
high = low
else:
last_index = low
return last_index, 0
def evaluate(self, x, slice_idx=None):
"""
Interpolate across this and subsequent table dimensions.
Parameters
----------
x : ndarray
The coordinates to sample the gridded data at. First array element is the point to
interpolate here. Remaining elements are interpolated on sub tables.
slice_idx : List of <slice>
Slice object containing indices of data points requested by parent interpolating
tables.
Returns
-------
ndarray
Interpolated values.
ndarray
Derivative of interpolated values with respect to this independent and child
independents.
ndarray
Derivative of interpolated values with respect to values for this and subsequent table
dimensions.
ndarray
Derivative of interpolated values with respect to grid for this and subsequent table
dimensions.
"""
idx, _ = self.bracket(x[0])
self.last_index = idx
if slice_idx is None:
slice_idx = []
if self.subtable is not None:
self.subtable.training_data_gradients = self.training_data_gradients
result, d_dx, d_values, d_grid = self.interpolate(x, idx, slice_idx)
return result, d_dx, d_values, d_grid
def interpolate(self, x, idx, slice_idx):
"""
Compute the interpolated value over this grid dimension.
This method must be defined by child classes.
Parameters
----------
x : ndarray
The coordinates to sample the gridded data at. First array element is the point to
interpolate here. Remaining elements are interpolated on sub tables.
idx : integer
Interval index for x.
slice_idx : List of <slice>
Slice object containing indices of data points requested by parent interpolating
tables.
Returns
-------
ndarray
Interpolated values.
ndarray
Derivative of interpolated values with respect to this independent and child
independents.
ndarray
Derivative of interpolated values with respect to values for this and subsequent table
dimensions.
ndarray
Derivative of interpolated values with respect to grid for this and subsequent table
dimensions.
"""
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.
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()
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._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 Jacobian(object):
"""
Base Jacobian class.
This class provides a dictionary interface for sub-Jacobians and
performs matrix-vector products when apply_linear is called.
Attributes
----------
_system : <System>
Pointer to the system that is currently operating on this Jacobian.
_subjacs : dict
Dictionary of the user-supplied sub-Jacobians keyed by absolute names.
_subjacs_info : dict
Dictionary of the sub-Jacobian metadata keyed by absolute names.
options : <OptionsDictionary>
Options dictionary.
_override_checks : bool
If we are approximating a jacobian at the top level and we have specified indices on the
functions or designvars, then we need to disable the size checking temporarily so that we
can assign a jacobian with less rows or columns than the variable sizes.
"""
def __init__(self, **kwargs):
"""
Initialize all attributes.
Parameters
----------
**kwargs : dict
options dictionary.
"""
self._system = None
self._subjacs = {}
self._subjacs_info = {}
self.options = OptionsDictionary()
self.options.update(kwargs)
self._override_checks = False
def _abs_key2shape(self, abs_key):
"""
Return shape of sub-jacobian for variables making up the key tuple.
Parameters
----------
abs_key : (str, str)
Absolute name pair of sub-Jacobian.
Returns
-------
out_size : int
local size of the output variable.
in_size : int
local size of the input variable.
"""
abs2meta = self._system._var_allprocs_abs2meta
return (abs2meta[abs_key[0]]['size'], abs2meta[abs_key[1]]['size'])
def _multiply_subjac(self, abs_key, val):
"""
Multiply this sub-Jacobian by val.
Parameters
----------
abs_key : (str, str)
Absolute name pair of sub-Jacobian.
val : float
value to multiply by.
"""
jac = self._subjacs[abs_key]
if isinstance(jac, np.ndarray):
self._subjacs[abs_key] *= val
elif isinstance(jac, sparse_types):
self._subjacs[abs_key].data *= val # DOK not supported
elif len(jac) == 3:
self._subjacs[abs_key][0] *= val
else:
self._subjacs[abs_key] *= val
def __contains__(self, key):
"""
Return whether there is a subjac for the given promoted or relative name pair.
Parameters
----------
key : (str, str)
Promoted or relative name pair of sub-Jacobian.
Returns
-------
boolean
return whether sub-Jacobian has been defined.
"""
return key2abs_key(self._system, key) in self._subjacs
def __getitem__(self, key):
"""
Get sub-Jacobian.
Parameters
----------
key : (str, str)
Promoted or relative name pair of sub-Jacobian.
Returns
-------
ndarray or spmatrix or list[3]
sub-Jacobian as an array, sparse mtx, or AIJ/IJ list or tuple.
"""
abs_key = key2abs_key(self._system, key)
if abs_key in self._subjacs:
subjac = self._subjacs[abs_key]
if isinstance(subjac, list):
# Sparse AIJ format
return subjac[0]
return subjac
else:
msg = 'Variable name pair ("{}", "{}") not found.'
raise KeyError(msg.format(key[0], key[1]))
def __setitem__(self, key, subjac):
"""
Set sub-Jacobian.
Parameters
----------
key : (str, str)
Promoted or relative name pair of sub-Jacobian.
subjac : int or float or ndarray or sparse matrix
sub-Jacobian as a scalar, vector, array, or AIJ list or tuple.
"""
abs_key = key2abs_key(self._system, key)
if abs_key is not None:
# You can only set declared subjacobians.
if abs_key not in self._subjacs_info:
msg = 'Variable name pair ("{}", "{}") must first be declared.'
raise KeyError(msg.format(key[0], key[1]))
self._set_abs(abs_key, subjac)
else:
msg = 'Variable name pair ("{}", "{}") not found.'
raise KeyError(msg.format(key[0], key[1]))
def _set_abs(self, abs_key, subjac):
"""
Set sub-Jacobian.
Parameters
----------
abs_key : (str, str)
Absolute name pair of sub-Jacobian.
subjac : int or float or ndarray or sparse matrix
sub-Jacobian as a scalar, vector, array, or AIJ list or tuple.
"""
if not issparse(subjac):
# np.promote_types will choose the smallest dtype that can contain both arguments
subjac = np.atleast_1d(subjac)
safe_dtype = np.promote_types(subjac.dtype, float)
subjac = subjac.astype(safe_dtype, copy=False)
# Bail here so that we allow top level jacobians to be of reduced size when indices are
# specified on driver vars.
if self._override_checks:
self._subjacs[abs_key] = subjac
return
if abs_key in self._subjacs_info:
subjac_info = self._subjacs_info[abs_key][0]
rows = subjac_info['rows']
else:
rows = None
if rows is None:
# Dense subjac
shape = self._abs_key2shape(abs_key)
subjac = np.atleast_2d(subjac)
if subjac.shape == (1, 1):
subjac = subjac[0, 0] * np.ones(shape, dtype=safe_dtype)
else:
subjac = subjac.reshape(shape)
if abs_key in self._subjacs and self._subjacs[abs_key].shape == shape:
np.copyto(self._subjacs[abs_key], subjac)
else:
self._subjacs[abs_key] = subjac.copy()
else:
# Sparse subjac
if subjac.shape == (1,):
subjac = subjac[0] * np.ones(rows.shape, dtype=safe_dtype)
if subjac.shape != rows.shape:
raise ValueError("Sub-jacobian for key %s has "
"the wrong shape (%s), expected (%s)." %
(abs_key, subjac.shape, rows.shape))
if abs_key in self._subjacs and subjac.shape == self._subjacs[abs_key][0].shape:
np.copyto(self._subjacs[abs_key][0], subjac)
else:
self._subjacs[abs_key] = [subjac.copy(), rows, subjac_info['cols']]
else:
self._subjacs[abs_key] = subjac
def _initialize(self):
"""
Allocate the global matrices.
"""
pass
def _update(self):
"""
Read the user's sub-Jacobians and set into the global matrix.
"""
pass
def _apply(self, d_inputs, d_outputs, d_residuals, mode):
"""
Compute matrix-vector product.
Parameters
----------
d_inputs : Vector
inputs linear vector.
d_outputs : Vector
outputs linear vector.
d_residuals : Vector
residuals linear vector.
mode : str
'fwd' or 'rev'.
"""
pass
def _set_partials_meta(self, abs_key, meta, negate=False):
"""
Store subjacobian metadata.
Parameters
----------
abs_key : (str, str)
Absolute name pair of sub-Jacobian.
meta : dict
Metadata dictionary for the subjacobian.
negate : bool
If True negate the given value, if any.
"""
shape = self._abs_key2shape(abs_key)
self._subjacs_info[abs_key] = (meta, shape)
val = meta['value']
if val is not None:
self._set_abs(abs_key, val)
if negate:
self._multiply_subjac(abs_key, -1.0)
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
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 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.
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()
