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driver.py
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driver.py
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"""Define a base class for all Drivers in OpenMDAO."""
from collections import OrderedDict
import pprint
import sys
import os
import weakref
import numpy as np
from openmdao.core.total_jac import _TotalJacInfo
from openmdao.recorders.recording_manager import RecordingManager
from openmdao.recorders.recording_iteration_stack import Recording
from openmdao.utils.record_util import create_local_meta, check_path
from openmdao.utils.general_utils import simple_warning, warn_deprecation
from openmdao.utils.mpi import MPI
from openmdao.utils.options_dictionary import OptionsDictionary
import openmdao.utils.coloring as coloring_mod
from openmdao.vectors.vector import INT_DTYPE
def _check_debug_print_opts_valid(name, opts):
"""
Check validity of debug_print option for Driver.
Parameters
----------
name : str
The name of the option.
opts : list
The value of the debug_print option set by the user.
"""
if not isinstance(opts, list):
raise ValueError("Option '%s' with value %s is not a list." % (name, opts))
_valid_opts = ['desvars', 'nl_cons', 'ln_cons', 'objs', 'totals']
for opt in opts:
if opt not in _valid_opts:
raise ValueError("Option '%s' contains value '%s' which is not one of %s." %
(name, opt, _valid_opts))
class Driver(object):
"""
Top-level container for the systems and drivers.
Attributes
----------
fail : bool
Reports whether the driver ran successfully.
iter_count : int
Keep track of iterations for case recording.
options : <OptionsDictionary>
Dictionary with general pyoptsparse options.
recording_options : <OptionsDictionary>
Dictionary with driver recording options.
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
_problem : weakref to <Problem>
Pointer to the containing problem.
supports : <OptionsDictionary>
Provides a consistent way for drivers to declare what features they support.
_designvars : dict
Contains all design variable info.
_designvars_discrete : list
List of design variables that are discrete.
_distributed_resp : dict
Dict of constraints that are distributed outputs. Key is rank, values are
(local indices, local sizes).
_cons : dict
Contains all constraint info.
_objs : dict
Contains all objective info.
_responses : dict
Contains all response info.
_remote_dvs : dict
Dict of design variables that are remote on at least one proc. Values are
(owning rank, size).
_remote_cons : dict
Dict of constraints that are remote on at least one proc. Values are
(owning rank, size).
_remote_objs : dict
Dict of objectives that are remote on at least one proc. Values are
(owning rank, size).
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this driver.
_coloring_info : dict
Metadata pertaining to total coloring.
_total_jac_sparsity : dict, str, or None
Specifies sparsity of sub-jacobians of the total jacobian. Only used by pyOptSparseDriver.
_res_jacs : dict
Dict of sparse subjacobians for use with certain optimizers, e.g. pyOptSparseDriver.
_total_jac : _TotalJacInfo or None
Cached total jacobian handling object.
"""
def __init__(self, **kwargs):
"""
Initialize the driver.
Parameters
----------
**kwargs : dict of keyword arguments
Keyword arguments that will be mapped into the Driver options.
"""
self._rec_mgr = RecordingManager()
self._problem = None
self._designvars = None
self._designvars_discrete = []
self._cons = None
self._objs = None
self._responses = None
# Driver options
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.options.declare('debug_print', types=list, check_valid=_check_debug_print_opts_valid,
desc="List of what type of Driver variables to print at each "
"iteration. Valid items in list are 'desvars', 'ln_cons', "
"'nl_cons', 'objs', 'totals'",
default=[])
# Case recording options
self.recording_options = OptionsDictionary(parent_name=type(self).__name__)
self.recording_options.declare('record_model_metadata', types=bool, default=True,
desc='Deprecated. Recording of model metadata will always '
'be done',
deprecation="The recording option, record_model_metadata, "
"on Driver is "
"deprecated. Recording of model metadata will always "
"be done",
)
self.recording_options.declare('record_desvars', types=bool, default=True,
desc='Set to True to record design variables at the '
'driver level')
self.recording_options.declare('record_responses', types=bool, default=False,
desc='Set True to record constraints and objectives at the '
'driver level')
self.recording_options.declare('record_objectives', types=bool, default=True,
desc='Set to True to record objectives at the driver level')
self.recording_options.declare('record_constraints', types=bool, default=True,
desc='Set to True to record constraints at the '
'driver level')
self.recording_options.declare('includes', types=list, default=[],
desc='Patterns for variables to include in recording. '
'Uses fnmatch wildcards')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes). Uses fnmatch wildcards')
self.recording_options.declare('record_derivatives', types=bool, default=False,
desc='Set to True to record derivatives at the driver '
'level')
self.recording_options.declare('record_inputs', types=bool, default=True,
desc='Set to True to record inputs at the driver level')
self.recording_options.declare('record_outputs', types=bool, default=True,
desc='Set True to record outputs at the '
'driver level.')
self.recording_options.declare('record_residuals', types=bool, default=False,
desc='Set True to record residuals at the '
'driver level.')
# What the driver supports.
self.supports = OptionsDictionary(parent_name=type(self).__name__)
self.supports.declare('inequality_constraints', types=bool, default=False)
self.supports.declare('equality_constraints', types=bool, default=False)
self.supports.declare('linear_constraints', types=bool, default=False)
self.supports.declare('two_sided_constraints', types=bool, default=False)
self.supports.declare('multiple_objectives', types=bool, default=False)
self.supports.declare('integer_design_vars', types=bool, default=True)
self.supports.declare('gradients', types=bool, default=False)
self.supports.declare('active_set', types=bool, default=False)
self.supports.declare('simultaneous_derivatives', types=bool, default=False)
self.supports.declare('total_jac_sparsity', types=bool, default=False)
self.iter_count = 0
self.cite = ""
self._coloring_info = coloring_mod._get_coloring_meta()
self._total_jac_sparsity = None
self._res_jacs = {}
self._total_jac = None
self.fail = False
self._declare_options()
self.options.update(kwargs)
@property
def msginfo(self):
"""
Return info to prepend to messages.
Returns
-------
str
Info to prepend to messages.
"""
return type(self).__name__
def add_recorder(self, recorder):
"""
Add a recorder to the driver.
Parameters
----------
recorder : CaseRecorder
A recorder instance.
"""
self._rec_mgr.append(recorder)
def cleanup(self):
"""
Clean up resources prior to exit.
"""
# shut down all recorders
self._rec_mgr.shutdown()
def _declare_options(self):
"""
Declare options before kwargs are processed in the init method.
This is optionally implemented by subclasses of Driver.
"""
pass
def _setup_comm(self, comm):
"""
Perform any driver-specific setup of communicators for the model.
Parameters
----------
comm : MPI.Comm or <FakeComm> or None
The communicator for the Problem.
Returns
-------
MPI.Comm or <FakeComm> or None
The communicator for the Problem model.
"""
return comm
def _setup_driver(self, problem):
"""
Prepare the driver for execution.
This is the final thing to run during setup.
Parameters
----------
problem : <Problem>
Pointer to the containing problem.
"""
self._problem = weakref.ref(problem)
self._recording_iter = problem._recording_iter
model = problem.model
self._total_jac = None
self._has_scaling = (
np.any([r['total_scaler'] is not None for r in self._responses.values()]) or
np.any([dv['total_scaler'] is not None for dv in self._designvars.values()])
)
# Determine if any design variables are discrete.
self._designvars_discrete = [dv for dv in self._designvars
if dv in model._discrete_outputs]
if not self.supports['integer_design_vars'] and len(self._designvars_discrete) > 0:
msg = "Discrete design variables are not supported by this driver: "
msg += '.'.join(self._designvars_discrete)
raise RuntimeError(msg)
con_set = set()
obj_set = set()
dv_set = set()
self._remote_dvs = remote_dv_dict = {}
self._remote_cons = remote_con_dict = {}
self._distributed_resp = dist_resp_dict = {}
self._remote_objs = remote_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']
abs2meta = model._var_allprocs_abs2meta
for i, vname in enumerate(model._var_allprocs_abs_names['output']):
distributed = abs2meta[vname]['distributed']
if distributed:
owner = sz = None
else:
owner = owning_ranks[vname]
sz = sizes[owner, i]
if vname in dv_set:
remote_dv_dict[vname] = (owner, sz)
# Note that design vars are not distributed.
elif distributed and vname in self._responses:
idx = model._var_allprocs_abs2idx['nonlinear'][vname]
dist_sizes = model._var_sizes['nonlinear']['output'][:, idx]
# Determine which indices are on our proc.
rank = model.comm.rank
size = dist_sizes.size
offsets = np.cumsum(dist_sizes)
resp_dict = self._responses[vname]
indices = resp_dict['indices']
if indices is not None:
local_indices = []
true_sizes = np.zeros(size, dtype=INT_DTYPE)
for index in indices:
if index < 0:
# Support for negative indices. Convert to positive index.
index = index + np.sum(dist_sizes)
irank = np.argwhere(offsets > index)[0][0]
true_sizes[irank] += 1
if rank == irank:
new_index = index - offsets[irank] + dist_sizes[irank]
local_indices.append(new_index)
indices = local_indices
dist_sizes = true_sizes
dist_resp_dict[vname] = (indices, dist_sizes)
if vname in con_set:
remote_con_dict[vname] = (owner, sz)
if vname in obj_set:
remote_obj_dict[vname] = (owner, sz)
self._remote_responses = self._remote_cons.copy()
self._remote_responses.update(self._remote_objs)
# set up simultaneous deriv coloring
if coloring_mod._use_total_sparsity:
# reset the coloring
if self._coloring_info['dynamic'] or self._coloring_info['static'] is not None:
self._coloring_info['coloring'] = None
coloring = self._get_static_coloring()
if coloring is not None and self.supports['simultaneous_derivatives']:
if model._owns_approx_jac:
coloring._check_config_partial(model)
else:
coloring._check_config_total(self)
self._setup_simul_coloring()
def _check_for_missing_objective(self):
"""
Check for missing objective and raise error if no objectives found.
"""
if len(self._objs) == 0:
msg = "Driver requires objective to be declared"
raise RuntimeError(msg)
def _get_vars_to_record(self, recording_options):
"""
Get variables to record based on recording options.
Parameters
----------
recording_options : <OptionsDictionary>
Dictionary with recording options.
Returns
-------
dict
Dictionary containing lists of variables to record.
"""
problem = self._problem()
model = problem.model
incl = recording_options['includes']
excl = recording_options['excludes']
# includes and excludes for outputs are specified using promoted names
abs2prom = model._var_allprocs_abs2prom['output']
# 1. If record_outputs is True, get the set of outputs
# 2. Filter those using includes and excludes to get the baseline set of variables to record
# 3. Add or remove from that set any desvars, objs, and cons based on the recording
# options of those
# includes and excludes for outputs are specified using _promoted_ names
# vectors are keyed on absolute name, discretes on relative/promoted name
myinputs = myoutputs = myresiduals = []
if recording_options['record_outputs']:
myoutputs = sorted([n for n, prom in abs2prom.items() if check_path(prom, incl, excl)])
views = model._outputs._views
if model._var_discrete['output']:
# if we have discrete outputs then residual name set doesn't match output one
if recording_options['record_residuals']:
myresiduals = [n for n in myoutputs if n in views]
elif recording_options['record_residuals']:
myresiduals = myoutputs
elif recording_options['record_residuals']:
myresiduals = [n for n in model._residuals._views
if check_path(abs2prom[n], incl, excl)]
myoutputs = set(myoutputs)
if recording_options['record_desvars']:
myoutputs.update(self._designvars)
if recording_options['record_objectives'] or recording_options['record_responses']:
myoutputs.update(self._objs)
if recording_options['record_constraints'] or recording_options['record_responses']:
myoutputs.update(self._cons)
# inputs (if in options). inputs use _absolute_ names for includes/excludes
if 'record_inputs' in recording_options:
if recording_options['record_inputs']:
# sort the results since _var_allprocs_abs2prom isn't ordered
myinputs = sorted([n for n in model._var_allprocs_abs2prom['input']
if check_path(n, incl, excl)])
vars2record = {
'input': myinputs,
'output': list(myoutputs),
'residual': myresiduals
}
return vars2record
def _setup_recording(self):
"""
Set up case recording.
"""
self._filtered_vars_to_record = self._get_vars_to_record(self.recording_options)
self._rec_mgr.startup(self)
def _get_voi_val(self, name, meta, remote_vois, driver_scaling=True, rank=None):
"""
Get the value of a variable of interest (objective, constraint, or design var).
This will retrieve the value if the VOI is remote.
Parameters
----------
name : str
Name of the variable of interest.
meta : dict
Metadata for the variable of interest.
remote_vois : dict
Dict containing (owning_rank, size) for all remote vois of a particular
type (design var, constraint, or objective).
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
rank : int or None
If not None, gather value to this rank only.
Returns
-------
float or ndarray
The value of the named variable of interest.
"""
model = self._problem().model
comm = model.comm
vec = model._outputs._views_flat
distributed_vars = self._distributed_resp
indices = meta['indices']
if MPI:
distributed = comm.size > 0 and name in distributed_vars
else:
distributed = False
if name in remote_vois:
owner, size = remote_vois[name]
# if var is distributed or only gathering to one rank
# TODO - support distributed var under a parallel group.
if owner is None or rank is not None:
val = model._get_val(name, get_remote=True, rank=rank, flat=True)
if indices is not None:
val = val[indices]
else:
if owner == comm.rank:
if indices is None:
val = 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)
elif distributed:
local_val = model._get_val(name, flat=True)
local_indices, sizes = distributed_vars[name]
if local_indices is not None:
local_val = local_val[local_indices]
offsets = np.zeros(sizes.size, dtype=INT_DTYPE)
offsets[1:] = np.cumsum(sizes[:-1])
val = np.zeros(np.sum(sizes))
comm.Allgatherv(local_val, [val, sizes, offsets, MPI.DOUBLE])
else:
if name in self._designvars_discrete:
val = model._discrete_outputs[name]
# At present, only integers are supported by OpenMDAO drivers.
# We check the values here.
msg = "Only integer scalars or ndarrays are supported as values for " + \
"discrete variables when used as a design variable. "
if np.isscalar(val) and not isinstance(val, int):
msg += "A value of type '{}' was specified.".format(val.__class__.__name__)
raise ValueError(msg)
elif isinstance(val, np.ndarray) and not np.issubdtype(val[0], int):
msg += "An array of type '{}' was specified.".format(val[0].__class__.__name__)
raise ValueError(msg)
elif indices is None:
val = vec[name].copy()
else:
val = vec[name][indices]
if self._has_scaling and driver_scaling:
# Scale design variable values
adder = meta['total_adder']
if adder is not None:
val += adder
scaler = meta['total_scaler']
if scaler is not None:
val *= scaler
return val
def get_design_var_values(self):
"""
Return the design variable values.
Returns
-------
dict
Dictionary containing values of each design variable.
"""
return {n: self._get_voi_val(n, dv, self._remote_dvs)
for n, dv in self._designvars.items()}
def set_design_var(self, name, value):
"""
Set the value of a design variable.
Parameters
----------
name : str
Global pathname of the design variable.
value : float or ndarray
Value for the design variable.
"""
problem = self._problem()
# if the value is not local, don't set the value
if (name in self._remote_dvs and
problem.model._owning_rank[name] != problem.comm.rank):
return
meta = self._designvars[name]
indices = meta['indices']
if indices is None:
indices = slice(None)
if name in self._designvars_discrete:
# Note, drivers set values here and generally should know it is setting an integer.
# However, the DOEdriver may pull a non-integer value from its generator, so we
# convert it.
if isinstance(value, float):
value = int(value)
elif isinstance(value, np.ndarray):
if isinstance(problem.model._discrete_outputs[name], int):
# Setting an integer value with a 1D array - don't want to convert to array.
value = int(value)
else:
value = value.astype(np.int)
problem.model._discrete_outputs[name] = value
else:
desvar = problem.model._outputs._views_flat[name]
desvar[indices] = value
# Undo driver scaling when setting design var values into model.
if self._has_scaling:
scaler = meta['total_scaler']
if scaler is not None:
desvar[indices] *= 1.0 / scaler
adder = meta['total_adder']
if adder is not None:
desvar[indices] -= adder
def get_objective_values(self, driver_scaling=True):
"""
Return objective values.
Parameters
----------
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
Returns
-------
dict
Dictionary containing values of each objective.
"""
return {n: self._get_voi_val(n, obj, self._remote_objs,
driver_scaling=driver_scaling)
for n, obj in self._objs.items()}
def get_constraint_values(self, ctype='all', lintype='all', driver_scaling=True):
"""
Return constraint values.
Parameters
----------
ctype : string
Default is 'all'. Optionally return just the inequality constraints
with 'ineq' or the equality constraints with 'eq'.
lintype : string
Default is 'all'. Optionally return just the linear constraints
with 'linear' or the nonlinear constraints with 'nonlinear'.
driver_scaling : bool
When True, return values that are scaled according to either the adder and scaler or
the ref and ref0 values that were specified when add_design_var, add_objective, and
add_constraint were called on the model. Default is True.
Returns
-------
dict
Dictionary containing values of each constraint.
"""
con_dict = {}
for name, meta in self._cons.items():
if lintype == 'linear' and not meta['linear']:
continue
if lintype == 'nonlinear' and meta['linear']:
continue
if ctype == 'eq' and meta['equals'] is None:
continue
if ctype == 'ineq' and meta['equals'] is not None:
continue
con_dict[name] = self._get_voi_val(name, meta, self._remote_cons,
driver_scaling=driver_scaling)
return con_dict
def _get_ordered_nl_responses(self):
"""
Return the names of nonlinear responses in the order used by the driver.
Default order is objectives followed by nonlinear constraints. This is used for
simultaneous derivative coloring and sparsity determination.
Returns
-------
list of str
The nonlinear response names in order.
"""
order = list(self._objs)
order.extend(n for n, meta in self._cons.items()
if not ('linear' in meta and meta['linear']))
return order
def _update_voi_meta(self, model):
"""
Collect response and design var metadata from the model and size desvars and responses.
Parameters
----------
model : System
The System that represents the entire model.
Returns
-------
int
Total size of responses, with linear constraints excluded.
int
Total size of design vars.
"""
self._objs = objs = OrderedDict()
self._cons = cons = OrderedDict()
model._setup_driver_units()
self._responses = resps = model.get_responses(recurse=True)
for name, data in resps.items():
if data['type'] == 'con':
cons[name] = data
else:
objs[name] = data
response_size = sum(resps[n]['size'] for n in self._get_ordered_nl_responses())
# Gather up the information for design vars.
self._designvars = designvars = model.get_design_vars(recurse=True)
desvar_size = sum(data['size'] for data in designvars.values())
return response_size, desvar_size
def run(self):
"""
Execute this driver.
The base `Driver` just runs the model. All other drivers overload
this method.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
with RecordingDebugging(self._get_name(), self.iter_count, self):
self._problem().model.run_solve_nonlinear()
self.iter_count += 1
return False
def _compute_totals(self, of=None, wrt=None, return_format='flat_dict', global_names=None,
use_abs_names=True):
"""
Compute derivatives of desired quantities with respect to desired inputs.
All derivatives are returned using driver scaling.
Parameters
----------
of : list of variable name strings or None
Variables whose derivatives will be computed. Default is None, which
uses the driver's objectives and constraints.
wrt : list of variable name strings or None
Variables with respect to which the derivatives will be computed.
Default is None, which uses the driver's desvars.
return_format : string
Format to return the derivatives. Default is a 'flat_dict', which
returns them in a dictionary whose keys are tuples of form (of, wrt). For
the scipy optimizer, 'array' is also supported.
global_names : bool
Deprecated. Use 'use_abs_names' instead.
use_abs_names : bool
Set to True when passing in absolute names to skip some translation steps.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
problem = self._problem()
total_jac = self._total_jac
debug_print = 'totals' in self.options['debug_print'] and (not MPI or
problem.comm.rank == 0)
if debug_print:
header = 'Driver total derivatives for iteration: ' + str(self.iter_count)
print(header)
print(len(header) * '-' + '\n')
if global_names is not None:
warn_deprecation("'global_names' is deprecated in calls to _compute_totals. "
"Use 'use_abs_names' instead.")
use_abs_names = global_names
if problem.model._owns_approx_jac:
self._recording_iter.push(('_compute_totals_approx', 0))
try:
if total_jac is None:
total_jac = _TotalJacInfo(problem, of, wrt, use_abs_names,
return_format, approx=True, debug_print=debug_print)
# Don't cache linear constraint jacobian
if not total_jac.has_lin_cons:
self._total_jac = total_jac
totals = total_jac.compute_totals_approx(initialize=True)
else:
totals = total_jac.compute_totals_approx()
finally:
self._recording_iter.pop()
else:
if total_jac is None:
total_jac = _TotalJacInfo(problem, of, wrt, use_abs_names, return_format,
debug_print=debug_print)
# don't cache linear constraint jacobian
if not total_jac.has_lin_cons:
self._total_jac = total_jac
self._recording_iter.push(('_compute_totals', 0))
try:
totals = total_jac.compute_totals()
finally:
self._recording_iter.pop()
if self._rec_mgr._recorders and self.recording_options['record_derivatives']:
metadata = create_local_meta(self._get_name())
total_jac.record_derivatives(self, metadata)
return totals
def record_iteration(self):
"""
Record an iteration of the current Driver.
"""
record_iteration(self, self._problem(), self._get_name())
def _get_recorder_metadata(self, case_name):
"""
Return metadata from the latest iteration for use in the recorder.
Parameters
----------
case_name : str
Name of current case.
Returns
-------
dict
Metadata dictionary for the recorder.
"""
return create_local_meta(case_name)
def _get_name(self):
"""
Get name of current Driver.
Returns
-------
str
Name of current Driver.
"""
return "Driver"
def declare_coloring(self, num_full_jacs=coloring_mod._DEF_COMP_SPARSITY_ARGS['num_full_jacs'],
tol=coloring_mod._DEF_COMP_SPARSITY_ARGS['tol'],
orders=coloring_mod._DEF_COMP_SPARSITY_ARGS['orders'],
perturb_size=coloring_mod._DEF_COMP_SPARSITY_ARGS['perturb_size'],
min_improve_pct=coloring_mod._DEF_COMP_SPARSITY_ARGS['min_improve_pct'],
show_summary=coloring_mod._DEF_COMP_SPARSITY_ARGS['show_summary'],
show_sparsity=coloring_mod._DEF_COMP_SPARSITY_ARGS['show_sparsity']):
"""
Set options for total deriv coloring.
Parameters
----------
num_full_jacs : int
Number of times to repeat partial jacobian computation when computing sparsity.
tol : float
Tolerance used to determine if an array entry is nonzero during sparsity determination.
orders : int
Number of orders above and below the tolerance to check during the tolerance sweep.
perturb_size : float
Size of input/output perturbation during generation of sparsity.
min_improve_pct : float
If coloring does not improve (decrease) the number of solves more than the given
percentage, coloring will not be used.
show_summary : bool
If True, display summary information after generating coloring.
show_sparsity : bool
If True, display sparsity with coloring info after generating coloring.
"""
self._coloring_info['num_full_jacs'] = num_full_jacs
self._coloring_info['tol'] = tol
self._coloring_info['orders'] = orders
self._coloring_info['perturb_size'] = perturb_size
self._coloring_info['min_improve_pct'] = min_improve_pct
if self._coloring_info['static'] is None:
self._coloring_info['dynamic'] = True
else:
self._coloring_info['dynamic'] = False
self._coloring_info['coloring'] = None
self._coloring_info['show_summary'] = show_summary
self._coloring_info['show_sparsity'] = show_sparsity
def use_fixed_coloring(self, coloring=coloring_mod._STD_COLORING_FNAME):
"""
Tell the driver to use a precomputed coloring.
Parameters
----------
coloring : str
A coloring filename. If no arg is passed, filename will be determined
automatically.
"""
if self.supports['simultaneous_derivatives']:
if coloring_mod._force_dyn_coloring and coloring is coloring_mod._STD_COLORING_FNAME:
# force the generation of a dynamic coloring this time
self._coloring_info['dynamic'] = True
self._coloring_info['static'] = None
else:
self._coloring_info['static'] = coloring
self._coloring_info['dynamic'] = False
self._coloring_info['coloring'] = None
else:
raise RuntimeError("Driver '%s' does not support simultaneous derivatives." %
self._get_name())
def _setup_tot_jac_sparsity(self, coloring=None):
"""
Set up total jacobian subjac sparsity.
Drivers that can use subjac sparsity should override this.
Parameters
----------
coloring : Coloring or None
Current coloring.
"""
pass
def _get_static_coloring(self):
"""
Get the Coloring for this driver.
If necessary, load the Coloring from a file.
Returns
-------
Coloring or None
The pre-existing or loaded Coloring, or None
"""
info = self._coloring_info
static = info['static']
if isinstance(static, coloring_mod.Coloring):
coloring = static
info['coloring'] = coloring
else:
coloring = info['coloring']
if coloring is not None:
return coloring
if static is coloring_mod._STD_COLORING_FNAME or isinstance(static, str):
if static is coloring_mod._STD_COLORING_FNAME:
fname = self._get_total_coloring_fname()
else:
fname = static
print("loading total coloring from file %s" % fname)
coloring = info['coloring'] = coloring_mod.Coloring.load(fname)
info.update(coloring._meta)
return coloring
def _get_total_coloring_fname(self):
return os.path.join(self._problem().options['coloring_dir'], 'total_coloring.pkl')
def _setup_simul_coloring(self):
"""
Set up metadata for coloring of total derivative solution.
If set_coloring was called with a filename, load the coloring file.
"""
# command line simul_coloring uses this env var to turn pre-existing coloring off
if not coloring_mod._use_total_sparsity:
return
problem = self._problem()
if not problem.model._use_derivatives:
simple_warning("Derivatives are turned off. Skipping simul deriv coloring.")
return
total_coloring = self._get_static_coloring()