class Driver(object):
""" Base class for drivers in OpenMDAO. Drivers can only be placed in a
Problem, and every problem has a Driver. Driver is the simplest driver that
runs (solves using solve_nonlinear) a problem once.
"""
def __init__(self):
super(Driver, self).__init__()
self.recorders = RecordingManager()
# What this driver supports
self.supports = OptionsDictionary(read_only=True)
self.supports.add_option('inequality_constraints', True)
self.supports.add_option('equality_constraints', True)
self.supports.add_option('linear_constraints', True)
self.supports.add_option('multiple_objectives', True)
self.supports.add_option('two_sided_constraints', True)
self.supports.add_option('integer_design_vars', True)
# inheriting Drivers should override this setting and set it to False
# if they don't use gradients.
self.supports.add_option('gradients', True)
# This driver's options
self.options = OptionsDictionary()
self._desvars = OrderedDict()
self._objs = OrderedDict()
self._cons = OrderedDict()
self._voi_sets = []
self._vars_to_record = None
# We take root during setup
self.root = None
self.iter_count = 0
self.dv_conversions = {}
self.fn_conversions = {}
def _setup(self):
""" Updates metadata for params, constraints and objectives, and
check for errors. Also determines all variables that need to be
gathered for case recording.
"""
root = self.root
desvars = OrderedDict()
objs = OrderedDict()
cons = OrderedDict()
if self.__class__ is Driver:
has_gradients = False
else:
has_gradients = self.supports['gradients']
item_tups = [
('Parameter', self._desvars, desvars),
('Objective', self._objs, objs),
('Constraint', self._cons, cons)
]
for item_name, item, newitem in item_tups:
for name, meta in iteritems(item):
# Check validity of variable
if name not in root.unknowns:
msg = "{} '{}' not found in unknowns."
msg = msg.format(item_name, name)
raise ValueError(msg)
rootmeta = root.unknowns.metadata(name)
if name in self._desvars:
rootmeta['is_desvar'] = True
if name in self._objs:
rootmeta['is_objective'] = True
if name in self._cons:
rootmeta['is_constraint'] = True
if MPI and 'src_indices' in rootmeta:
raise ValueError("'%s' is a distributed variable and may "
"not be used as a design var, objective, "
"or constraint." % name)
if has_gradients and rootmeta.get('pass_by_obj'):
if 'optimizer' in self.options:
oname = self.options['optimizer']
else:
oname = self.__class__.__name__
raise RuntimeError("%s '%s' is a 'pass_by_obj' variable "
"and can't be used with a gradient "
"based driver of type '%s'." %
(item_name, name, oname))
# Size is useful metadata to save
if 'indices' in meta:
meta['size'] = len(meta['indices'])
else:
meta['size'] = rootmeta['size']
newitem[name] = meta
self._desvars = desvars
self._objs = objs
self._cons = cons
# Cache scalers for derivative calculation
self.dv_conversions = OrderedDict()
for name, meta in iteritems(desvars):
scaler = meta.get('scaler')
if isinstance(scaler, np.ndarray):
if all(scaler == 1.0):
continue
elif scaler == 1.0:
continue
self.dv_conversions[name] = np.reciprocal(scaler)
self.fn_conversions = OrderedDict()
for name, meta in chain(iteritems(objs), iteritems(cons)):
scaler = meta.get('scaler')
if isinstance(scaler, np.ndarray):
if all(scaler == 1.0):
continue
elif scaler == 1.0:
continue
self.fn_conversions[name] = scaler
def _setup_communicators(self, comm, parent_dir):
"""
Assign a communicator to the root `System`.
Args
----
comm : an MPI communicator (real or fake)
The communicator being offered by the Problem.
parent_dir : str
Absolute directory of the Problem.
"""
self.root._setup_communicators(comm, parent_dir)
def get_req_procs(self):
"""
Returns
-------
tuple
A tuple of the form (min_procs, max_procs), indicating the
min and max processors usable by this `Driver`.
"""
return self.root.get_req_procs()
def cleanup(self):
""" Clean up resources prior to exit. """
self.recorders.close()
def _map_voi_indices(self):
poi_indices = OrderedDict()
qoi_indices = OrderedDict()
for name, meta in chain(iteritems(self._cons), iteritems(self._objs)):
# set indices of interest
if 'indices' in meta:
qoi_indices[name] = meta['indices']
for name, meta in iteritems(self._desvars):
# set indices of interest
if 'indices' in meta:
poi_indices[name] = meta['indices']
return poi_indices, qoi_indices
def _of_interest(self, voi_list):
"""Return a list of tuples, with the given voi_list organized
into tuples based on the previously defined grouping of VOIs.
"""
vois = []
remaining = set(voi_list)
for voi_set in self._voi_sets:
vois.append([])
for i, voi_set in enumerate(self._voi_sets):
for v in voi_list:
if v in voi_set:
vois[i].append(v)
remaining.remove(v)
vois = [tuple(x) for x in vois if x]
for v in voi_list:
if v in remaining:
vois.append((v,))
return vois
def desvars_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of design vars, organized into tuples according to
previously defined VOI groups.
"""
return self._of_interest(self._desvars)
def outputs_of_interest(self):
"""
Returns
-------
list of tuples of str
The list of constraints and objectives, organized into tuples
according to previously defined VOI groups.
"""
return self._of_interest(list(chain(self._objs, self._cons)))
def parallel_derivs(self, vnames):
"""
Specifies that the named variables of interest are to be grouped
together so that their derivatives can be solved for concurrently.
Args
----
vnames : iter of str
The names of variables of interest that are to be grouped.
"""
#make sure all vnames are desvars, constraints, or objectives
for n in vnames:
if not (n in self._desvars or n in self._objs or n in self._cons):
raise RuntimeError("'%s' is not a param, objective, or "
"constraint" % n)
for grp in self._voi_sets:
for vname in vnames:
if vname in grp:
msg = "'%s' cannot be added to VOI set %s because it " + \
"already exists in VOI set: %s"
raise RuntimeError(msg % (vname, tuple(vnames), grp))
param_intsect = set(vnames).intersection(self._desvars.keys())
if param_intsect and len(param_intsect) != len(vnames):
raise RuntimeError("%s cannot be grouped because %s are design "
"vars and %s are not." %
(vnames, list(param_intsect),
list(set(vnames).difference(param_intsect))))
if MPI:
self._voi_sets.append(tuple(vnames))
else:
warnings.warn("parallel derivs %s specified but not running under MPI")
def add_recorder(self, recorder):
"""
Adds a recorder to the driver.
Args
----
recorder : BaseRecorder
A recorder instance.
"""
self.recorders.append(recorder)
def add_desvar(self, name, lower=None, upper=None,
low=None, high=None,
indices=None, adder=0.0, scaler=1.0):
"""
Adds a design variable to this driver.
Args
----
name : string
Name of the design variable in the root system.
lower : float or ndarray, optional
Lower boundary for the param
upper : upper or ndarray, optional
Upper boundary for the param
indices : iter of int, optional
If a param is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if name in self._desvars:
msg = "Desvar '{}' already exists."
raise RuntimeError(msg.format(name))
if low is not None or high is not None:
warnings.simplefilter('always', DeprecationWarning)
warnings.warn("'low' and 'high' are deprecated. "
"Use 'lower' and 'upper' instead.",
DeprecationWarning,stacklevel=2)
warnings.simplefilter('ignore', DeprecationWarning)
if low is not None and lower is None:
lower = low
if high is not None and upper is None:
upper = high
if isinstance(lower, np.ndarray):
lower = lower.flatten()
elif lower is None or lower == -float('inf'):
lower = -sys.float_info.max
if isinstance(upper, np.ndarray):
upper = upper.flatten()
elif upper is None or upper == float('inf'):
upper = sys.float_info.max
if isinstance(adder, np.ndarray):
adder = adder.flatten().astype('float')
else:
adder = float(adder)
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten().astype('float')
else:
scaler = float(scaler)
# Scale the lower and upper values
lower = (lower + adder)*scaler
upper = (upper + adder)*scaler
param = OrderedDict()
param['lower'] = lower
param['upper'] = upper
param['adder'] = adder
param['scaler'] = scaler
if indices:
param['indices'] = np.array(indices, dtype=int)
self._desvars[name] = param
def add_param(self, name, lower=None, upper=None, indices=None, adder=0.0,
scaler=1.0):
"""
Deprecated. Use ``add_desvar`` instead.
"""
warnings.simplefilter('always', DeprecationWarning)
warnings.warn("Driver.add_param() is deprecated. Use add_desvar() instead.",
DeprecationWarning,stacklevel=2)
warnings.simplefilter('ignore', DeprecationWarning)
self.add_desvar(name, lower=lower, upper=upper, indices=indices, adder=adder,
scaler=scaler)
def get_desvars(self):
""" Returns a dict of possibly distributed design variables.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
desvars = OrderedDict()
for key, meta in iteritems(self._desvars):
desvars[key] = self._get_distrib_var(key, meta, 'design var')
return desvars
def _get_distrib_var(self, name, meta, voi_type):
uvec = self.root.unknowns
comm = self.root.comm
nproc = comm.size
iproc = comm.rank
if nproc > 1:
owner = self.root._owning_ranks[name]
if iproc == owner:
flatval = uvec._dat[name].val
else:
flatval = None
else:
owner = 0
flatval = uvec._dat[name].val
if 'indices' in meta and not (nproc > 1 and owner != iproc):
# Make sure our indices are valid
try:
flatval = flatval[meta['indices']]
except IndexError:
msg = "Index for {} '{}' is out of bounds. "
msg += "Requested index: {}, "
msg += "shape: {}."
raise IndexError(msg.format(voi_type, name, meta['indices'],
uvec.metadata(name)['shape']))
if nproc > 1:
# TODO: use Bcast for improved performance
if trace:
debug("%s.driver._get_distrib_var bcast: val=%s" % (self.root.pathname, flatval))
flatval = comm.bcast(flatval, root=owner)
if trace:
debug("%s.driver._get_distrib_var bcast DONE" % self.root.pathname)
scaler = meta['scaler']
adder = meta['adder']
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or scaler != 1.0 or adder != 0.0:
return (flatval + adder)*scaler
else:
return flatval
def get_desvar_metadata(self):
""" Returns a dict of design variable metadata.
Returns
-------
dict
Keys are the param object names, and the values are the param
values.
"""
return self._desvars
def set_desvar(self, name, value):
""" Sets a design variable.
Args
----
name : string
Name of the design variable in the root system.
val : ndarray or float
value to assign to the design variable.
"""
val = self.root.unknowns._dat[name].val
if not isinstance(val, _ByObjWrapper) and \
self.root.unknowns._dat[name].val.size == 0:
return
meta = self._desvars[name]
scaler = meta['scaler']
adder = meta['adder']
if isinstance(scaler, np.ndarray) or isinstance(adder, np.ndarray) \
or scaler != 1.0 or adder != 0.0:
value = value/scaler - adder
# Only set the indices we requested when we set the design variable.
idx = meta.get('indices')
if idx is not None:
self.root.unknowns[name][idx] = value
else:
self.root.unknowns[name] = value
def add_objective(self, name, indices=None, adder=0.0, scaler=1.0):
""" Adds an objective to this driver.
Args
----
name : string
Promoted pathname of the output that will serve as the objective.
indices : iter of int, optional
If an objective is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if len(self._objs) > 0 and not self.supports["multiple_objectives"]:
raise RuntimeError("Attempted to add multiple objectives to a "
"driver that does not support multiple "
"objectives.")
if name in self._objs:
msg = "Objective '{}' already exists."
raise RuntimeError(msg.format(name))
if isinstance(adder, np.ndarray):
adder = adder.flatten().astype('float')
else:
adder = float(adder)
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten().astype('float')
else:
scaler = float(scaler)
obj = OrderedDict()
obj['adder'] = adder
obj['scaler'] = scaler
if indices:
obj['indices'] = indices
if len(indices) > 1 and not self.supports['multiple_objectives']:
raise RuntimeError("Multiple objective indices specified for "
"variable '%s', but driver '%s' doesn't "
"support multiple objectives." %
(name, self.pathname))
self._objs[name] = obj
def get_objectives(self, return_type='dict'):
""" Gets all objectives of this driver.
Args
----
return_type : string
Set to 'dict' to return a dictionary, or set to 'array' to return a
flat ndarray.
Returns
-------
dict (for return_type 'dict')
Key is the objective name string, value is an ndarray with the values.
ndarray (for return_type 'array')
Array containing all objective values in the order they were added.
"""
objs = OrderedDict()
for key, meta in iteritems(self._objs):
objs[key] = self._get_distrib_var(key, meta, 'objective')
return objs
def add_constraint(self, name, lower=None, upper=None, equals=None,
linear=False, jacs=None, indices=None, adder=0.0,
scaler=1.0):
""" Adds a constraint to this driver. For inequality constraints,
`lower` or `upper` must be specified. For equality constraints, `equals`
must be specified.
Args
----
name : string
Promoted pathname of the output that will serve as the quantity to
constrain.
lower : float or ndarray, optional
Constrain the quantity to be greater than or equal to this value.
upper : float or ndarray, optional
Constrain the quantity to be less than or equal to this value.
equals : float or ndarray, optional
Constrain the quantity to be equal to this value.
linear : bool, optional
Set to True if this constraint is linear with respect to all design
variables so that it can be calculated once and cached.
jacs : dict of functions, optional
Dictionary of user-defined functions that return the flattened
Jacobian of this constraint with repsect to the design vars of
this driver, as indicated by the dictionary keys. Default is None
to let OpenMDAO calculate all derivatives. Note, this is currently
unsupported
indices : iter of int, optional
If a constraint is an array, these indicate which entries are of
interest for derivatives.
adder : float or ndarray, optional
Value to add to the model value to get the scaled value. Adder
is first in precedence.
scaler : float or ndarray, optional
value to multiply the model value to get the scaled value. Scaler
is second in precedence.
"""
if name in self._cons:
msg = "Constraint '{}' already exists."
raise RuntimeError(msg.format(name))
if equals is not None and (lower is not None or upper is not None):
msg = "Constraint '{}' cannot be both equality and inequality."
raise RuntimeError(msg.format(name))
if equals is not None and self.supports['equality_constraints'] is False:
msg = "Driver does not support equality constraint '{}'."
raise RuntimeError(msg.format(name))
if equals is None and self.supports['inequality_constraints'] is False:
msg = "Driver does not support inequality constraint '{}'."
raise RuntimeError(msg.format(name))
if lower is not None and upper is not None and self.supports['two_sided_constraints'] is False:
msg = "Driver does not support 2-sided constraint '{}'."
raise RuntimeError(msg.format(name))
if lower is None and upper is None and equals is None:
msg = "Constraint '{}' needs to define lower, upper, or equals."
raise RuntimeError(msg.format(name))
if isinstance(scaler, np.ndarray):
scaler = scaler.flatten().astype('float')
else:
scaler = float(scaler)
if isinstance(adder, np.ndarray):
adder = adder.flatten().astype('float')
else:
adder = float(adder)
if isinstance(lower, np.ndarray):
lower = lower.flatten()
if isinstance(upper, np.ndarray):
upper = upper.flatten()
if isinstance(equals, np.ndarray):
equals = equals.flatten()
# Scale the lower and upper values
if lower is not None:
lower = (lower + adder)*scaler
if upper is not None:
upper = (upper + adder)*scaler
if equals is not None:
equals = (equals + adder)*scaler
con = OrderedDict()
con['lower'] = lower
con['upper'] = upper
con['equals'] = equals
con['linear'] = linear
con['adder'] = adder
con['scaler'] = scaler
con['jacs'] = jacs
if indices:
con['indices'] = indices
self._cons[name] = con
def get_constraints(self, ctype='all', lintype='all'):
""" Gets all constraints for this driver.
Args
----
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
Returns
-------
dict
Key is the constraint name string, value is an ndarray with the values.
"""
cons = OrderedDict()
for key, meta in iteritems(self._cons):
if lintype == 'linear' and meta['linear'] is False:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
cons[key] = self._get_distrib_var(key, meta, 'constraint')
return cons
def get_constraint_metadata(self):
""" Returns a dict of constraint metadata.
Returns
-------
dict
Keys are the constraint object names, and the values are the param
values.
"""
return self._cons
def run(self, problem):
""" Runs the driver. This function should be overridden when inheriting.
Args
----
problem : `Problem`
Our parent `Problem`.
"""
self.run_once(problem)
def run_once(self, problem):
""" Runs root's solve_nonlinear one time
Args
----
problem : `Problem`
Our parent `Problem`.
"""
system = problem.root
# Metadata Setup
self.iter_count += 1
metadata = self.metadata = create_local_meta(None, 'Driver')
system.ln_solver.local_meta = metadata
update_local_meta(metadata, (self.iter_count,))
# Solve the system once and record results.
with system._dircontext:
system.solve_nonlinear(metadata=metadata)
self.recorders.record_iteration(system, metadata)
def calc_gradient(self, indep_list, unknown_list, mode='auto',
return_format='array', sparsity=None):
""" Returns the scaled gradient for the system that is contained in
self.root, scaled by all scalers that were specified when the desvars
and constraints were added.
Args
----
indep_list : list of strings
List of independent variable names that derivatives are to
be calculated with respect to. All params must have a IndepVarComp.
unknown_list : list of strings
List of output or state names that derivatives are to
be calculated for. All must be valid unknowns in OpenMDAO.
mode : string, optional
Deriviative direction, can be 'fwd', 'rev', 'fd', or 'auto'.
Default is 'auto', which uses mode specified on the linear solver
in root.
return_format : string, optional
Format for the derivatives, can be 'array' or 'dict'.
sparsity : dict, optional
Dictionary that gives the relevant design variables for each
constraint. This option is only supported in the `dict` return
format.
Returns
-------
ndarray or dict
Jacobian of unknowns with respect to params.
"""
J = self._problem.calc_gradient(indep_list, unknown_list, mode=mode,
return_format=return_format,
dv_scale=self.dv_conversions,
cn_scale=self.fn_conversions,
sparsity=sparsity)
self.recorders.record_derivatives(J, self.metadata)
return J
def generate_docstring(self):
"""
Generates a numpy-style docstring for a user-created Driver class.
Returns
-------
docstring : str
string that contains a basic numpy docstring.
"""
#start the docstring off
docstring = ' \"\"\"\n'
#Put options into docstring
firstTime = 1
for key, value in sorted(vars(self).items()):
if type(value)==OptionsDictionary:
if key == "supports":
continue
if firstTime: #start of Options docstring
docstring += '\n Options\n -------\n'
firstTime = 0
docstring += value._generate_docstring(key)
#finish up docstring
docstring += '\n \"\"\"\n'
return docstring
