class Driver(Component):
""" A Driver iterates over a workflow of Components until some condition
is met. """
implements(IDriver, IHasEvents)
# set factory here so we see a default value in the docs, even
# though we replace it with a new Dataflow in __init__
workflow = Slot(Workflow, allow_none=True, required=True,
factory=Dataflow, hidden=True)
gradient_options = VarTree(GradientOptions(), iotype='in',
framework_var=True)
def __init__(self):
self._iter = None
super(Driver, self).__init__()
self.workflow = Dataflow(self)
self._required_compnames = None
# clean up unwanted trait from Component
self.remove_trait('missing_deriv_policy')
def __deepcopy__(self, memo):
"""For some reason `missing_deriv_policy` gets resurrected."""
result = super(Driver, self).__deepcopy__(memo)
result.remove_trait('missing_deriv_policy')
return result
def _workflow_changed(self, oldwf, newwf):
"""callback when new workflow is slotted"""
if newwf is not None:
newwf.parent = self
def get_expr_scope(self):
"""Return the scope to be used to evaluate ExprEvaluators."""
return self.parent
def check_config(self, strict=False):
"""Verify that our workflow is able to resolve all of its components."""
# workflow will raise an exception if it can't resolve a Component
super(Driver, self).check_config(strict=strict)
self.workflow.check_config(strict=strict)
def iteration_set(self, solver_only=False):
"""Return a set of all Components in our workflow and
recursively in any workflow in any Driver in our workflow.
solver_only: Bool
Only recurse into solver drivers. These are the only kinds
of drivers whose derivatives get absorbed into the parent
driver's graph.
"""
allcomps = set()
for child in self.workflow.get_components(full=True):
allcomps.add(child)
if has_interface(child, IDriver):
if solver_only and not has_interface(child, ISolver):
continue
allcomps.update(child.iteration_set())
return allcomps
@rbac(('owner', 'user'))
def get_expr_depends(self):
"""Returns a list of tuples of the form (src_comp_name,
dest_comp_name) for each dependency introduced by any ExprEvaluators
in this Driver, ignoring any dependencies on components that are
inside of this Driver's iteration set.
"""
iternames = set([c.name for c in self.iteration_set()])
conn_list = super(Driver, self).get_expr_depends()
new_list = []
for src, dest in conn_list:
if src not in iternames and dest not in iternames:
new_list.append((src, dest))
return new_list
@rbac(('owner', 'user'))
def get_expr_var_depends(self, recurse=True):
"""Returns a tuple of sets of the form (src_set, dest_set)
containing all dependencies introduced by any parameters,
objectives, or constraints in this Driver. If recurse is True,
include any refs from subdrivers.
"""
srcset = set()
destset = set()
if hasattr(self, '_delegates_'):
for dname in self._delegates_:
delegate = getattr(self, dname)
if isinstance(delegate, HasParameters):
destset.update(delegate.get_referenced_varpaths())
elif isinstance(delegate, (HasConstraints,
HasEqConstraints, HasIneqConstraints,
HasObjective, HasObjectives)):
srcset.update(delegate.get_referenced_varpaths())
if recurse:
for sub in self.subdrivers():
srcs, dests = sub.get_expr_var_depends(recurse)
srcset.update(srcs)
destset.update(dests)
return srcset, destset
@rbac(('owner', 'user'))
def subdrivers(self):
"""Returns a generator of of subdrivers of this driver."""
for d in self.iteration_set():
if has_interface(d, IDriver):
yield d
def _get_required_compnames(self):
"""Returns a set of names of components that are required by
this Driver in order to evaluate parameters, objectives
and constraints. This list will include any intermediate
components in the data flow between components referenced by
parameters and those referenced by objectives and/or constraints.
"""
if self._required_compnames is None:
conns = super(Driver, self).get_expr_depends()
getcomps = set([u for u, v in conns if u != self.name])
setcomps = set([v for u, v in conns if v != self.name])
full = set(setcomps)
full.update(getcomps)
full.update(self.list_pseudocomps())
compgraph = self.parent._depgraph.component_graph()
for end in getcomps:
for start in setcomps:
full.update(find_all_connecting(compgraph, start, end))
self._required_compnames = full
return self._required_compnames
@rbac(('owner', 'user'))
def list_pseudocomps(self):
"""Return a list of names of pseudocomps resulting from
our objectives, and constraints.
"""
pcomps = []
if hasattr(self, '_delegates_'):
for name in self._delegates_:
delegate = getattr(self, name)
if hasattr(delegate, 'list_pseudocomps'):
pcomps.extend(delegate.list_pseudocomps())
return pcomps
def get_references(self, name):
"""Return a dict of parameter, constraint, and objective
references to component `name` in preparation for
subsequent :meth:`restore_references` call.
name: string
Name of component being referenced.
"""
refs = {}
if hasattr(self, '_delegates_'):
for dname in self._delegates_:
inst = getattr(self, dname)
if isinstance(inst, (HasParameters, HasConstraints,
HasEqConstraints, HasIneqConstraints,
HasObjective, HasObjectives)):
refs[inst] = inst.get_references(name)
return refs
def remove_references(self, name):
"""Remove parameter, constraint, objective and workflow
references to component `name`.
name: string
Name of component being removed.
"""
if hasattr(self, '_delegates_'):
for dname in self._delegates_:
inst = getattr(self, dname)
if isinstance(inst, (HasParameters, HasConstraints,
HasEqConstraints, HasIneqConstraints,
HasObjective, HasObjectives)):
inst.remove_references(name)
self.workflow.remove(name)
def restore_references(self, refs):
"""Restore parameter, constraint, and objective references to component
`name` from `refs`.
refs: object
Value returned by :meth:`get_references`.
"""
for inst, inst_refs in refs.items():
inst.restore_references(inst_refs)
@rbac('*', 'owner')
def run(self, force=False, ffd_order=0, case_uuid=''):
"""Run this object. This should include fetching input variables if
necessary, executing, and updating output variables. Do not override
this function.
force: bool
If True, force component to execute even if inputs have not
changed. (Default is False)
ffd_order: int
Order of the derivatives to be used when finite differencing (1
for first derivatives, 2 for second derivatives). During regular
execution, ffd_order should be 0. (Default is 0)
case_uuid: str
Identifier for the Case that is associated with this run.
"""
# (Re)configure parameters.
if hasattr(self, 'config_parameters'):
self.config_parameters()
# force param pseudocomps to get updated values to start
# KTM1 - probably don't need this anymore
self.update_parameters()
# Reset the workflow.
self.workflow.reset()
super(Driver, self).run(ffd_order, case_uuid)
@rbac(('owner', 'user'))
def configure_recording(self, includes, excludes):
"""Called at start of top-level run to configure case recording.
Returns set of paths for changing inputs."""
return self.workflow.configure_recording(includes, excludes)
def update_parameters(self):
if hasattr(self, 'get_parameters'):
for param in self.get_parameters().values():
param.initialize(self.get_expr_scope())
def execute(self):
""" Iterate over a workflow of Components until some condition
is met. If you don't want to structure your driver to use
*pre_iteration*, *post_iteration*, etc., just override this function.
As a result, none of the ``<start/pre/post/continue>_iteration()``
functions will be called.
"""
self._iter = None
self.start_iteration()
while self.continue_iteration():
self.pre_iteration()
self.run_iteration()
self.post_iteration()
def stop(self):
"""Stop the workflow."""
self._stop = True
self.workflow.stop()
def start_iteration(self):
"""Called just prior to the beginning of an iteration loop. This can
be overridden by inherited classes. It can be used to perform any
necessary pre-iteration initialization.
"""
self._continue = True
def continue_iteration(self):
"""Return False to stop iterating."""
return self._continue
def pre_iteration(self):
"""Called prior to each iteration.
This is where iteration events are set."""
self.set_events()
def run_iteration(self):
"""Runs workflow."""
wf = self.workflow
if len(wf) == 0:
self._logger.warning("'%s': workflow is empty!"
% self.get_pathname())
wf.run(ffd_order=self.ffd_order)
def calc_derivatives(self, first=False, second=False, savebase=False,
required_inputs=None, required_outputs=None):
""" Calculate derivatives and save baseline states for all components
in this workflow."""
self.workflow.calc_derivatives(first, second, savebase,
required_inputs, required_outputs)
def calc_gradient(self, inputs=None, outputs=None):
"""Returns the gradient of the passed outputs with respect to
all passed inputs. The basic driver behavior is to call calc_gradient
on its workflow. However, some driver (optimizers in particular) may
want to define their own behavior.
"""
return self.workflow.calc_gradient(inputs, outputs, upscope=True)
def post_iteration(self):
"""Called after each iteration."""
self._continue = False # by default, stop after one iteration
def config_changed(self, update_parent=True):
"""Call this whenever the configuration of this Component changes,
for example, children are added or removed or dependencies may have
changed.
"""
super(Driver, self).config_changed(update_parent)
self._required_compnames = None
if self.workflow is not None:
self.workflow.config_changed()
def get_workflow(self):
""" Get the driver info and the list of components that make up the
driver's workflow; recurse on nested drivers.
"""
from openmdao.main.assembly import Assembly
ret = {}
ret['pathname'] = self.get_pathname()
ret['type'] = type(self).__module__ + '.' + type(self).__name__
ret['workflow'] = []
comps = [comp for comp in self.workflow]
for comp in comps:
# Skip pseudo-comps
if hasattr(comp, '_pseudo_type'):
continue
pathname = comp.get_pathname()
if is_instance(comp, Assembly) and comp.driver:
inames = [cls.__name__
for cls in list(implementedBy(comp.__class__))]
ret['workflow'].append({
'pathname': pathname,
'type': type(comp).__module__ + '.' + type(comp).__name__,
'interfaces': inames,
'driver': comp.driver.get_workflow(),
})
elif is_instance(comp, Driver):
ret['workflow'].append(comp.get_workflow())
else:
inames = [cls.__name__
for cls in list(implementedBy(comp.__class__))]
ret['workflow'].append({
'pathname': pathname,
'type': type(comp).__module__ + '.' + type(comp).__name__,
'interfaces': inames,
})
return ret
class Driver(Component):
""" A Driver iterates over a workflow of Components until some condition
is met. """
implements(IDriver, IHasEvents)
# set factory here so we see a default value in the docs, even
# though we replace it with a new Dataflow in __init__
workflow = Slot(Workflow,
allow_none=True,
required=True,
factory=Dataflow,
hidden=True)
gradient_options = VarTree(GradientOptions(),
iotype='in',
framework_var=True)
# flag to determine partitioning of our workflow's System
system_type = Enum('auto', ['auto', 'serial', 'parallel'],
desc="Determines the partitioning of this driver's "
"workflow components into Systems. Default is "
"'auto', where a hierarchy of serial and parallel "
"systems is automatically determined. 'serial' "
"and 'parallel' may be specified to force the"
"workflow components into a single serial or "
"parallel System. Note that when not running "
"under MPI, this option is ignored and the "
"resulting System will always be serial.",
framework_var=True)
def __init__(self):
self._iter = None
super(Driver, self).__init__()
self.workflow = Dataflow(self)
self._required_compnames = None
self._reduced_graph = None
# clean up unwanted trait from Component
self.remove_trait('missing_deriv_policy')
def __deepcopy__(self, memo):
"""For some reason `missing_deriv_policy` gets resurrected."""
result = super(Driver, self).__deepcopy__(memo)
result.remove_trait('missing_deriv_policy')
return result
def _workflow_changed(self, oldwf, newwf):
"""callback when new workflow is slotted"""
if newwf is not None:
newwf.parent = self
def requires_derivs(self):
return False
def get_expr_scope(self):
"""Return the scope to be used to evaluate ExprEvaluators."""
return self.parent
def _collapse_subdrivers(self, g):
"""collapse subdriver iteration sets into single nodes."""
# collapse all subdrivers in our graph
itercomps = {}
itercomps['#parent'] = self.workflow.get_names(full=True)
for child_drv in self.subdrivers(recurse=False):
itercomps[child_drv.name] = [
c.name for c in child_drv.iteration_set()
]
for child_drv in self.subdrivers(recurse=False):
excludes = set()
for name, comps in itercomps.items():
if name != child_drv.name:
for cname in comps:
if cname not in itercomps[child_drv.name]:
excludes.add(cname)
collapse_driver(g, child_drv, excludes)
# now remove any comps that are shared by subdrivers but are not found
# in our workflow
to_remove = set()
for name, comps in itercomps.items():
if name != '#parent':
for comp in comps:
if comp not in itercomps['#parent']:
to_remove.add(comp)
g.remove_nodes_from(to_remove)
def get_depgraph(self):
return self.parent._depgraph # May change this to use a smaller graph later
def get_reduced_graph(self):
if self._reduced_graph is None:
parent_graph = self.parent.get_reduced_graph()
# copy parent graph
g = parent_graph.subgraph(parent_graph.nodes_iter())
nodes = set([c.name for c in self.workflow])
g.collapse_subdrivers(nodes, self.workflow.subdrivers())
nodes.add(self.name)
g = g.full_subgraph(nodes)
nodes.remove(self.name)
# create fake edges to/from the driver and each of its
# components so we can get everything that's relevant
# by getting all nodes that are strongly connected to the
# driver in the graph.
to_add = []
for name in nodes:
if not g.has_edge(self.name, name):
to_add.append((self.name, name))
if not g.has_edge(name, self.name):
to_add.append((name, self.name))
g.add_edges_from(to_add)
comps = []
for comps in strongly_connected_components(g):
if self.name in comps:
break
g.remove_edges_from(to_add)
self._reduced_graph = g.subgraph(comps)
return self._reduced_graph
def check_config(self, strict=False):
"""Verify that our workflow is able to resolve all of its components."""
# workflow will raise an exception if it can't resolve a Component
super(Driver, self).check_config(strict=strict)
self.workflow.check_config(strict=strict)
@rbac(('owner', 'user'))
def get_itername(self):
"""Return current 'iteration coordinates'."""
if self.parent._top_driver is self:
return self.parent.get_itername()
return self.itername
def iteration_set(self, solver_only=False):
"""Return a set of all Components in our workflow and
recursively in any workflow in any Driver in our workflow.
solver_only: Bool
Only recurse into solver drivers. These are the only kinds
of drivers whose derivatives get absorbed into the parent
driver's graph.
"""
allcomps = set()
for child in self.workflow:
allcomps.add(child)
if has_interface(child, IDriver):
if solver_only and not has_interface(child, ISolver):
continue
allcomps.update(child.iteration_set())
return allcomps
@rbac(('owner', 'user'))
def get_expr_depends(self):
"""Returns a list of tuples of the form (src_comp_name,
dest_comp_name) for each dependency introduced by any ExprEvaluators
in this Driver, ignoring any dependencies on components that are
inside of this Driver's iteration set.
"""
iternames = set([c.name for c in self.iteration_set()])
deps = []
for src, dest in super(Driver, self).get_expr_depends():
if src not in iternames and dest not in iternames:
deps.add((src, dest))
return list(deps)
@rbac(('owner', 'user'))
def get_expr_var_depends(self, recurse=True):
"""Returns a tuple of sets of the form (src_set, dest_set)
containing all dependencies introduced by any parameters,
objectives, or constraints in this Driver. If recurse is True,
include any refs from subdrivers.
"""
srcset = set()
destset = set()
if hasattr(self, '_delegates_'):
for dname in self._delegates_:
delegate = getattr(self, dname)
if isinstance(delegate, HasParameters):
destset.update(delegate.get_referenced_varpaths(refs=True))
elif isinstance(
delegate,
(HasConstraints, HasEqConstraints, HasIneqConstraints)):
srcset.update(delegate.list_constraint_targets())
elif isinstance(delegate, (HasObjective, HasObjectives)):
srcset.update(delegate.list_objective_targets())
if recurse:
for sub in self.subdrivers(recurse=True):
srcs, dests = sub.get_expr_var_depends(recurse=True)
srcset.update(srcs)
destset.update(dests)
return srcset, destset
@rbac(('owner', 'user'))
def subdrivers(self, recurse=False):
"""Returns a generator of all subdrivers
contained in this driver's workflow. If recurse is True,
include all subdrivers in our entire iteration set.
"""
if recurse:
itercomps = self.iteration_set()
else:
itercomps = list(self.workflow)
for comp in itercomps:
if has_interface(comp, IDriver):
yield comp
def _get_required_compnames(self):
"""Returns a set of names of components that are required by
this Driver in order to evaluate parameters, objectives
and constraints. This list will include any intermediate
components in the data flow between components referenced by
parameters and those referenced by objectives and/or constraints.
"""
if self._required_compnames is None:
# call base class version of get_expr_depends so we don't filter out
# comps in our iterset. We want required names to be everything between
# and including comps that we reference in any parameter, objective, or
# constraint.
conns = super(Driver, self).get_expr_depends()
getcomps = set([u for u, v in conns if u != self.name])
setcomps = set([v for u, v in conns if v != self.name])
full = set(setcomps)
full.update(getcomps)
full.update(self.list_pseudocomps())
compgraph = self.get_depgraph().component_graph()
for end in getcomps:
for start in setcomps:
full.update(find_all_connecting(compgraph, start, end))
self._required_compnames = full
return self._required_compnames
@rbac(('owner', 'user'))
def list_pseudocomps(self):
"""Return a list of names of pseudocomps resulting from
our objectives, and constraints.
"""
pcomps = []
if hasattr(self, '_delegates_'):
for name in self._delegates_:
delegate = getattr(self, name)
if hasattr(delegate, 'list_pseudocomps'):
pcomps.extend(delegate.list_pseudocomps())
return pcomps
def get_references(self, name):
"""Return a dict of parameter, constraint, and objective
references to component `name` in preparation for
subsequent :meth:`restore_references` call.
name: string
Name of component being referenced.
"""
refs = {}
if hasattr(self, '_delegates_'):
for dname in self._delegates_:
inst = getattr(self, dname)
if isinstance(inst,
(HasParameters, HasConstraints, HasEqConstraints,
HasIneqConstraints, HasObjective, HasObjectives,
HasResponses)):
refs[inst] = inst.get_references(name)
return refs
def remove_references(self, name):
"""Remove parameter, constraint, objective and workflow
references to component `name`.
name: string
Name of component being removed.
"""
if hasattr(self, '_delegates_'):
for dname in self._delegates_:
inst = getattr(self, dname)
if isinstance(inst,
(HasParameters, HasConstraints, HasEqConstraints,
HasIneqConstraints, HasObjective, HasObjectives,
HasResponses)):
inst.remove_references(name)
self.workflow.remove(name)
def restore_references(self, refs):
"""Restore parameter, constraint, and objective references to component
`name` from `refs`.
refs: object
Value returned by :meth:`get_references`.
"""
for inst, inst_refs in refs.items():
inst.restore_references(inst_refs)
@rbac('*', 'owner')
def run(self, force=False, case_uuid=''):
"""Run this object. This should include fetching input variables if
necessary, executing, and updating output variables. Do not override
this function.
force: bool
If True, force component to execute even if inputs have not
changed. (Default is False)
case_uuid: str
Identifier for the Case that is associated with this run.
"""
# (Re)configure parameters.
if hasattr(self, 'config_parameters'):
self.config_parameters()
# force param pseudocomps to get updated values to start
self.update_parameters()
# Reset the workflow.
self.workflow.reset()
super(Driver, self).run(case_uuid)
@rbac(('owner', 'user'))
def configure_recording(self, recording_options=None):
"""Called at start of top-level run to configure case recording.
Returns set of paths for changing inputs."""
return self.workflow.configure_recording(recording_options)
def update_parameters(self):
if hasattr(self, 'get_parameters'):
params = self.get_parameters()
for param in params.values():
param.initialize(self.get_expr_scope(), self)
if 'u' in self.workflow._system.vec:
self.workflow._system.vec['u'].set_to_scope(
self.parent, params.keys())
def execute(self):
""" Iterate over a workflow of Components until some condition
is met. If you don't want to structure your driver to use
*pre_iteration*, *post_iteration*, etc., just override this function.
As a result, none of the ``<start/pre/post/continue>_iteration()``
functions will be called.
"""
self._iter = None
self.start_iteration()
while self.continue_iteration():
self.pre_iteration()
self.run_iteration()
self.post_iteration()
self.end_iteration()
def stop(self):
"""Stop the workflow."""
self._stop = True
self.workflow.stop()
def start_iteration(self):
"""Called just prior to the beginning of an iteration loop. This can
be overridden by inherited classes. It can be used to perform any
necessary pre-iteration initialization.
"""
self._continue = True
def end_iteration(self):
"""Called at the end of the iteraton loop. Override this in
inherited classes to perform some action after iteration is complete.
"""
pass
def continue_iteration(self):
"""Return False to stop iterating."""
return self._continue
def pre_iteration(self):
"""Called prior to each iteration.
This is where iteration events are set."""
self.set_events()
def run_iteration(self):
"""Runs workflow."""
wf = self.workflow
if len(wf) == 0:
self._logger.warning("'%s': workflow is empty!" %
self.get_pathname())
wf.run()
def calc_derivatives(self, first=False, second=False):
""" Calculate derivatives and save baseline states for all components
in this workflow."""
self.workflow.calc_derivatives(first, second)
def post_iteration(self):
"""Called after each iteration."""
self._continue = False # by default, stop after one iteration
def config_changed(self, update_parent=True):
"""Call this whenever the configuration of this Component changes,
for example, children are added or removed or dependencies may have
changed.
"""
super(Driver, self).config_changed(update_parent)
self._required_compnames = None
self._depgraph = None
if self.workflow is not None:
self.workflow.config_changed()
def _get_param_constraint_pairs(self):
"""Returns a list of tuples of the form (param, constraint)."""
pairs = []
if hasattr(self, 'list_param_group_targets'):
pgroups = self.list_param_group_targets()
for key, cnst in self.get_eq_constraints().iteritems():
for params in pgroups:
if params[0] == cnst.rhs.text:
pairs.append((params[0], cnst.pcomp_name + '.out0'))
elif params[0] == cnst.lhs.text:
pairs.append((params[0], cnst.pcomp_name + '.out0'))
return pairs
@rbac(('owner', 'user'))
def setup_systems(self):
"""Set up system trees from here down to all of our
child Components.
"""
if self.name in self.parent._reduced_graph:
self._system = self.parent._reduced_graph.node[self.name]['system']
self.workflow.setup_systems(self.system_type)
#### MPI related methods ####
@rbac(('owner', 'user'))
def get_req_cpus(self):
"""Return requested_cpus."""
return self.workflow.get_req_cpus()
def setup_communicators(self, comm):
"""Allocate communicators from here down to all of our
child Components.
"""
self.workflow.setup_communicators(comm)
def setup_scatters(self):
self.workflow.setup_scatters()
@rbac(('owner', 'user'))
def get_full_nodeset(self):
"""Return the full set of nodes in the depgraph
belonging to this driver (includes full iteration set).
"""
names = super(Driver, self).get_full_nodeset()
names.update(self.workflow.get_full_nodeset())
return names
def calc_gradient(self,
inputs=None,
outputs=None,
mode='auto',
return_format='array',
force_regen=True):
"""Returns the Jacobian of derivatives between inputs and outputs.
inputs: list of strings
List of OpenMDAO inputs to take derivatives with respect to.
outputs: list of strings
Lis of OpenMDAO outputs to take derivatives of.
mode: string in ['forward', 'adjoint', 'auto', 'fd']
Mode for gradient calculation. Set to 'auto' to let OpenMDAO choose
forward or adjoint based on problem dimensions. Set to 'fd' to
finite difference the entire workflow.
return_format: string in ['array', 'dict']
Format for return value. Default is array, but some optimizers may
want a dictionary instead.
force_regen: boolean
Set to True to force a regeneration of the system hierarchy. This
is set to True because this function is meant for manual testing.
"""
return self.workflow.calc_gradient(inputs=inputs,
outputs=outputs,
mode=mode,
return_format=return_format,
force_regen=force_regen)
@rbac(('owner', 'user'))
def setup_depgraph(self, dgraph):
self._reduced_graph = None
# # add connections for params, constraints, etc.
# pass
if self.workflow._calc_gradient_inputs is not None:
for param in self.workflow._calc_gradient_inputs:
dgraph.add_param(self.name, param)
else: # add connections for our params/constraints/objectives
# if hasattr(self, 'list_param_group_targets'):
# params = self.list_param_group_targets()
# for now do nothing here because params are already
# in the depgraph
pass
# add connections for calc gradient outputs
if self.workflow._calc_gradient_outputs is not None:
for vname in self.workflow._calc_gradient_outputs:
dgraph.add_driver_input(self.name, vname)
else:
pass # for now, do nothing
@rbac(('owner', 'user'))
def pre_setup(self):
self._reduced_graph = None
self.workflow.pre_setup()
class Driver(Component):
""" A Driver iterates over a workflow of Components until some condition
is met. """
implements(IDriver, IHasEvents)
# set factory here so we see a default value in the docs, even
# though we replace it with a new Dataflow in __init__
workflow = Slot(Workflow, allow_none=True, required=True,
factory=Dataflow, hidden=True)
gradient_options = VarTree(GradientOptions(), iotype='in',
framework_var=True)
def __init__(self):
self._iter = None
super(Driver, self).__init__()
self.workflow = Dataflow(self)
self._required_compnames = None
# clean up unwanted trait from Component
self.remove_trait('missing_deriv_policy')
def __deepcopy__(self, memo):
"""For some reason `missing_deriv_policy` gets resurrected."""
result = super(Driver, self).__deepcopy__(memo)
result.remove_trait('missing_deriv_policy')
return result
def _workflow_changed(self, oldwf, newwf):
"""callback when new workflow is slotted"""
if newwf is not None:
newwf.parent = self
def get_expr_scope(self):
"""Return the scope to be used to evaluate ExprEvaluators."""
return self.parent
def check_config(self, strict=False):
"""Verify that our workflow is able to resolve all of its components."""
# workflow will raise an exception if it can't resolve a Component
super(Driver, self).check_config(strict=strict)
self.workflow.check_config(strict=strict)
def iteration_set(self, solver_only=False):
"""Return a set of all Components in our workflow and
recursively in any workflow in any Driver in our workflow.
solver_only: Bool
Only recurse into solver drivers. These are the only kinds
of drivers whose derivatives get absorbed into the parent
driver's graph.
"""
allcomps = set()
for child in self.workflow.get_components(full=True):
allcomps.add(child)
if has_interface(child, IDriver):
if solver_only and not has_interface(child, ISolver):
continue
allcomps.update(child.iteration_set())
return allcomps
@rbac(('owner', 'user'))
def get_expr_depends(self):
"""Returns a list of tuples of the form (src_comp_name,
dest_comp_name) for each dependency introduced by any ExprEvaluators
in this Driver, ignoring any dependencies on components that are
inside of this Driver's iteration set.
"""
iternames = set([c.name for c in self.iteration_set()])
conn_list = super(Driver, self).get_expr_depends()
new_list = []
for src, dest in conn_list:
if src not in iternames and dest not in iternames:
new_list.append((src, dest))
return new_list
@rbac(('owner', 'user'))
def get_expr_var_depends(self, recurse=True):
"""Returns a tuple of sets of the form (src_set, dest_set)
containing all dependencies introduced by any parameters,
objectives, or constraints in this Driver. If recurse is True,
include any refs from subdrivers.
"""
srcset = set()
destset = set()
if hasattr(self, '_delegates_'):
for dname in self._delegates_:
delegate = getattr(self, dname)
if isinstance(delegate, HasParameters):
destset.update(delegate.get_referenced_varpaths())
elif isinstance(delegate, (HasConstraints,
HasEqConstraints, HasIneqConstraints,
HasObjective, HasObjectives)):
srcset.update(delegate.get_referenced_varpaths())
if recurse:
for sub in self.subdrivers():
srcs, dests = sub.get_expr_var_depends(recurse)
srcset.update(srcs)
destset.update(dests)
return srcset, destset
@rbac(('owner', 'user'))
def subdrivers(self):
"""Returns a generator of of subdrivers of this driver."""
for d in self.iteration_set():
if has_interface(d, IDriver):
yield d
def _get_required_compnames(self):
"""Returns a set of names of components that are required by
this Driver in order to evaluate parameters, objectives
and constraints. This list will include any intermediate
components in the data flow between components referenced by
parameters and those referenced by objectives and/or constraints.
"""
if self._required_compnames is None:
boundary_vars = self.parent.list_vars()
conns = super(Driver, self).get_expr_depends()
getcomps = set([u for u, v in conns if u != self.name \
if u not in boundary_vars and v not in boundary_vars])
setcomps = set([v for u, v in conns if v != self.name \
if u not in boundary_vars and v not in boundary_vars])
full = set(setcomps)
full.update(getcomps)
full.update(self.list_pseudocomps())
compgraph = self.parent._depgraph.component_graph()
for end in getcomps:
for start in setcomps:
full.update(find_all_connecting(compgraph, start, end))
self._required_compnames = full
return self._required_compnames
@rbac(('owner', 'user'))
def list_pseudocomps(self):
"""Return a list of names of pseudocomps resulting from
our objectives, and constraints.
"""
pcomps = []
if hasattr(self, '_delegates_'):
for name in self._delegates_:
delegate = getattr(self, name)
if hasattr(delegate, 'list_pseudocomps'):
pcomps.extend(delegate.list_pseudocomps())
return pcomps
def get_references(self, name):
"""Return a dict of parameter, constraint, and objective
references to component `name` in preparation for
subsequent :meth:`restore_references` call.
name: string
Name of component being referenced.
"""
refs = {}
if hasattr(self, '_delegates_'):
for dname in self._delegates_:
inst = getattr(self, dname)
if isinstance(inst, (HasParameters, HasConstraints,
HasEqConstraints, HasIneqConstraints,
HasObjective, HasObjectives)):
refs[inst] = inst.get_references(name)
return refs
def remove_references(self, name):
"""Remove parameter, constraint, objective and workflow
references to component `name`.
name: string
Name of component being removed.
"""
if hasattr(self, '_delegates_'):
for dname in self._delegates_:
inst = getattr(self, dname)
if isinstance(inst, (HasParameters, HasConstraints,
HasEqConstraints, HasIneqConstraints,
HasObjective, HasObjectives)):
inst.remove_references(name)
self.workflow.remove(name)
def restore_references(self, refs):
"""Restore parameter, constraint, and objective references to component
`name` from `refs`.
refs: object
Value returned by :meth:`get_references`.
"""
for inst, inst_refs in refs.items():
inst.restore_references(inst_refs)
@rbac('*', 'owner')
def run(self, force=False, ffd_order=0, case_uuid=''):
"""Run this object. This should include fetching input variables if
necessary, executing, and updating output variables. Do not override
this function.
force: bool
If True, force component to execute even if inputs have not
changed. (Default is False)
ffd_order: int
Order of the derivatives to be used when finite differencing (1
for first derivatives, 2 for second derivatives). During regular
execution, ffd_order should be 0. (Default is 0)
case_uuid: str
Identifier for the Case that is associated with this run.
"""
# (Re)configure parameters.
if hasattr(self, 'config_parameters'):
self.config_parameters()
# force param pseudocomps to get updated values to start
# KTM1 - probably don't need this anymore
self.update_parameters()
# Reset the workflow.
self.workflow.reset()
super(Driver, self).run(ffd_order, case_uuid)
@rbac(('owner', 'user'))
def configure_recording(self, recording_options=None):
"""Called at start of top-level run to configure case recording.
Returns set of paths for changing inputs."""
return self.workflow.configure_recording(recording_options)
def update_parameters(self):
if hasattr(self, 'get_parameters'):
for param in self.get_parameters().values():
param.initialize(self.get_expr_scope())
def execute(self):
""" Iterate over a workflow of Components until some condition
is met. If you don't want to structure your driver to use
*pre_iteration*, *post_iteration*, etc., just override this function.
As a result, none of the ``<start/pre/post/continue>_iteration()``
functions will be called.
"""
self._iter = None
self.start_iteration()
while self.continue_iteration():
self.pre_iteration()
self.run_iteration()
self.post_iteration()
def stop(self):
"""Stop the workflow."""
self._stop = True
self.workflow.stop()
def start_iteration(self):
"""Called just prior to the beginning of an iteration loop. This can
be overridden by inherited classes. It can be used to perform any
necessary pre-iteration initialization.
"""
self._continue = True
def continue_iteration(self):
"""Return False to stop iterating."""
return self._continue
def pre_iteration(self):
"""Called prior to each iteration.
This is where iteration events are set."""
self.set_events()
def run_iteration(self):
"""Runs workflow."""
wf = self.workflow
if len(wf) == 0:
self._logger.warning("'%s': workflow is empty!"
% self.get_pathname())
wf.run(ffd_order=self.ffd_order)
def calc_derivatives(self, first=False, second=False, savebase=False,
required_inputs=None, required_outputs=None):
""" Calculate derivatives and save baseline states for all components
in this workflow."""
self.workflow.calc_derivatives(first, second, savebase,
required_inputs, required_outputs)
def calc_gradient(self, inputs=None, outputs=None):
"""Returns the gradient of the passed outputs with respect to
all passed inputs. The basic driver behavior is to call calc_gradient
on its workflow. However, some driver (optimizers in particular) may
want to define their own behavior.
"""
return self.workflow.calc_gradient(inputs, outputs, upscope=True)
def post_iteration(self):
"""Called after each iteration."""
self._continue = False # by default, stop after one iteration
def config_changed(self, update_parent=True):
"""Call this whenever the configuration of this Component changes,
for example, children are added or removed or dependencies may have
changed.
"""
super(Driver, self).config_changed(update_parent)
self._required_compnames = None
if self.workflow is not None:
self.workflow.config_changed()
def get_workflow(self):
""" Get the driver info and the list of components that make up the
driver's workflow; recurse on nested drivers.
"""
from openmdao.main.assembly import Assembly
ret = {}
ret['pathname'] = self.get_pathname()
ret['type'] = type(self).__module__ + '.' + type(self).__name__
ret['workflow'] = []
comps = [comp for comp in self.workflow]
for comp in comps:
# Skip pseudo-comps
if hasattr(comp, '_pseudo_type'):
continue
pathname = comp.get_pathname()
if is_instance(comp, Assembly) and comp.driver:
inames = [cls.__name__
for cls in list(implementedBy(comp.__class__))]
ret['workflow'].append({
'pathname': pathname,
'type': type(comp).__module__ + '.' + type(comp).__name__,
'interfaces': inames,
'driver': comp.driver.get_workflow(),
})
elif is_instance(comp, Driver):
ret['workflow'].append(comp.get_workflow())
else:
inames = [cls.__name__
for cls in list(implementedBy(comp.__class__))]
ret['workflow'].append({
'pathname': pathname,
'type': type(comp).__module__ + '.' + type(comp).__name__,
'interfaces': inames,
})
return ret