Source code for openmdao.core.driver

"""Define a base class for all Drivers in OpenMDAO."""
from __future__ import print_function

from collections import OrderedDict
import pprint
import sys
import os
import weakref

from six import iteritems, itervalues, string_types

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

def _check_debug_print_opts_valid(name, opts):
    Check validity of debug_print option for Driver.

    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))

[docs]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. _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. """
[docs] def __init__(self, **kwargs): """ Initialize the driver. Parameters ---------- **kwargs : dict of keyword arguments Keyword arguments that will be mapped into the Driver options. """ self._rec_mgr = RecordingManager() self._problem = None self._designvars = None self._designvars_discrete = [] self._cons = None self._objs = None self._responses = None # Driver options self.options = OptionsDictionary(parent_name=type(self).__name__) self.options.declare('debug_print', types=list, check_valid=_check_debug_print_opts_valid, desc="List of what type of Driver variables to print at each " "iteration. Valid items in list are 'desvars', 'ln_cons', " "'nl_cons', 'objs', 'totals'", default=[]) # Case recording options self.recording_options = OptionsDictionary(parent_name=type(self).__name__) self.recording_options.declare('record_model_metadata', types=bool, default=True, desc='Record metadata for all Systems in the model') self.recording_options.declare('record_desvars', types=bool, default=True, desc='Set to True to record design variables at the ' 'driver level') self.recording_options.declare('record_responses', types=bool, default=False, desc='Set 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') # What the driver supports. self.supports = OptionsDictionary(parent_name=type(self).__name__) self.supports.declare('inequality_constraints', types=bool, default=False) self.supports.declare('equality_constraints', types=bool, default=False) self.supports.declare('linear_constraints', types=bool, default=False) self.supports.declare('two_sided_constraints', types=bool, default=False) self.supports.declare('multiple_objectives', types=bool, default=False) self.supports.declare('integer_design_vars', types=bool, default=False) self.supports.declare('gradients', types=bool, default=False) self.supports.declare('active_set', types=bool, default=False) self.supports.declare('simultaneous_derivatives', types=bool, default=False) self.supports.declare('total_jac_sparsity', types=bool, default=False) self.iter_count = 0 self.cite = "" self._coloring_info = coloring_mod._DEF_COMP_SPARSITY_ARGS.copy() self._coloring_info['coloring'] = None self._coloring_info['dynamic'] = False self._coloring_info['static'] = None self._total_jac_sparsity = None self._res_jacs = {} self._total_jac = None = 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__
[docs] def add_recorder(self, recorder): """ Add a recorder to the driver. Parameters ---------- recorder : CaseRecorder A recorder instance. """ self._rec_mgr.append(recorder)
[docs] 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['scaler'] is not None for r in itervalues(self._responses)]) or np.any([dv['scaler'] is not None for dv in itervalues(self._designvars)]) ) # Determine if any design variables are discrete. self._designvars_discrete = [dv for dv in self._designvars if dv in model._discrete_outputs] if not self.supports['integer_design_vars'] and len(self._designvars_discrete) > 0: msg = "Discrete design variables are not supported by this driver: " msg += '.'.join(self._designvars_discrete) raise RuntimeError(msg) con_set = set() obj_set = set() dv_set = set() self._remote_dvs = dv_dict = {} self._remote_cons = con_dict = {} self._remote_objs = obj_dict = {} # Now determine if later we'll need to allgather cons, objs, or desvars. if model.comm.size > 1 and model._subsystems_allprocs: local_out_vars = set(model._outputs._views) remote_dvs = set(self._designvars) - local_out_vars remote_cons = set(self._cons) - local_out_vars remote_objs = set(self._objs) - local_out_vars all_remote_vois = model.comm.allgather( (remote_dvs, remote_cons, remote_objs)) for rem_dvs, rem_cons, rem_objs in all_remote_vois: con_set.update(rem_cons) obj_set.update(rem_objs) dv_set.update(rem_dvs) # If we have remote VOIs, pick an owning rank for each and use that # to bcast to others later owning_ranks = model._owning_rank sizes = model._var_sizes['nonlinear']['output'] abs2meta = model._var_allprocs_abs2meta for i, vname in enumerate(model._var_allprocs_abs_names['output']): if abs2meta[vname]['distributed']: owner = sz = None else: owner = owning_ranks[vname] sz = sizes[owner, i] if vname in dv_set: dv_dict[vname] = (owner, sz) if vname in con_set: con_dict[vname] = (owner, sz) if vname in obj_set: 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 rrank = problem.comm.rank incl = recording_options['includes'] excl = recording_options['excludes'] # includes and excludes for outputs are specified using promoted names abs2prom = model._var_allprocs_abs2prom['output'] allvars = [] # if desvars, etc. are not wanted, we need to exclude them from the outputs even if # they match the includes list. skip = set() if recording_options['record_desvars']: allvars.extend(self._designvars) else: skip.update(self._designvars) if recording_options['record_objectives'] or recording_options['record_responses']: allvars.extend(self._objs) else: skip.update(self._objs) if recording_options['record_constraints'] or recording_options['record_responses']: allvars.extend(self._cons) else: skip.update(self._cons) vars2record = { 'output': [n for n in allvars if n in abs2prom and check_path(abs2prom[n], incl, excl, True)] } # 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 vars2record['input'] = sorted([n for n in model._var_allprocs_abs2prom['input'] if check_path(n, incl, excl)]) else: vars2record['input'] = [] if incl: # loop over abs2prom (which includes both continuous and discrete outputs) since # the order doesn't matter (we're sorting it at the end). vars2record['output'].extend(n for n, prom in abs2prom.items() if n not in skip and check_path(prom, incl, excl)) # remove dups and make sure order is the same on all procs vars2record['output'] = sorted(set(vars2record['output'])) 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) # record the system metadata to the recorders attached to this Driver if self.recording_options['record_model_metadata']: for sub in self._problem().model.system_iter(recurse=True, include_self=True): self._rec_mgr.record_metadata(sub) def _get_voi_val(self, name, meta, remote_vois, driver_scaling=True, 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 indices = meta['indices'] if name in remote_vois: owner, size = remote_vois[name] # if var is distributed or only gathering to one rank 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) 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['adder'] if adder is not None: val += adder scaler = meta['scaler'] if scaler is not None: val *= scaler return val
[docs] 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()}
[docs] 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: problem.model._discrete_outputs[name] = int(value) else: desvar = problem.model._outputs._views_flat[name] desvar[indices] = value # Undo driver scaling when setting design var values into model. if self._has_scaling: scaler = meta['scaler'] if scaler is not None: desvar[indices] *= 1.0 / scaler adder = meta['adder'] if adder is not None: desvar[indices] -= adder
[docs] 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()}
[docs] 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 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
[docs] def run(self): """ Execute this driver. The base `Driver` just runs the model. All other drivers overload this method. Returns ------- boolean Failure flag; True if failed to converge, False is successful. """ with RecordingDebugging(self._get_name(), self.iter_count, self): self._problem().model.run_solve_nonlinear() self.iter_count += 1 return False
def _compute_totals(self, of=None, wrt=None, return_format='flat_dict', global_names=True): """ Compute derivatives of desired quantities with respect to desired inputs. All derivatives are returned using driver scaling. Parameters ---------- of : list of variable name strings or None Variables whose derivatives will be computed. Default is None, which uses the driver's objectives and constraints. wrt : list of variable name strings or None Variables with respect to which the derivatives will be computed. Default is None, which uses the driver's desvars. return_format : string Format to return the derivatives. Default is a 'flat_dict', which returns them in a dictionary whose keys are tuples of form (of, wrt). For the scipy optimizer, 'array' is also supported. global_names : bool Set to True when passing in global names to skip some translation steps. Returns ------- derivs : object Derivatives in form requested by 'return_format'. """ problem = self._problem() total_jac = self._total_jac debug_print = 'totals' in self.options['debug_print'] and (not MPI or MPI.COMM_WORLD.rank == 0) if debug_print: header = 'Driver total derivatives for iteration: ' + str(self.iter_count) print(header) print(len(header) * '-' + '\n') if problem.model._owns_approx_jac: self._recording_iter.push(('_compute_totals_approx', 0)) try: if total_jac is None: total_jac = _TotalJacInfo(problem, of, wrt, global_names, return_format, approx=True, debug_print=debug_print) # Don't cache linear constraint jacobian if not total_jac.has_lin_cons: self._total_jac = total_jac totals = total_jac.compute_totals_approx(initialize=True) else: totals = total_jac.compute_totals_approx() finally: self._recording_iter.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.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
[docs] 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"
[docs] 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
[docs] 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())
[docs] def set_simul_deriv_color(self, coloring): """ See use_fixed_coloring. This method is deprecated. Parameters ---------- coloring : str or Coloring Information about simultaneous coloring for design vars and responses. If a string, then coloring is assumed to be the name of a file that contains the coloring information in pickle format. Otherwise it must be a Coloring object. See the docstring for Coloring for details. """ warn_deprecation("set_simul_deriv_color is deprecated. Use use_fixed_coloring instead.") self.use_fixed_coloring(coloring)
def _setup_tot_jac_sparsity(self): """ Set up total jacobian subjac sparsity. Drivers that can use subjac sparsity should override this. """ pass def _get_static_coloring(self): """ Get the Coloring for this driver. If necessary, load the Coloring from a file. Returns ------- Coloring or None The pre-existing or loaded Coloring, or None """ info = self._coloring_info static = info['static'] if isinstance(static, coloring_mod.Coloring): coloring = static info['coloring'] = coloring else: coloring = info['coloring'] if coloring is not None: return coloring if static is coloring_mod._STD_COLORING_FNAME or isinstance(static, string_types): if static is coloring_mod._STD_COLORING_FNAME: fname = self._get_total_coloring_fname() else: fname = static print("loading total coloring from file %s" % fname) coloring = info['coloring'] = coloring_mod.Coloring.load(fname) info.update(coloring._meta) return coloring def _get_total_coloring_fname(self): return os.path.join(self._problem().options['coloring_dir'], 'total_coloring.pkl') def _setup_simul_coloring(self): """ Set up metadata for coloring of total derivative solution. If set_coloring was called with a filename, load the coloring file. """ # command line simul_coloring uses this env var to turn pre-existing coloring off if not coloring_mod._use_total_sparsity: return problem = self._problem() if not problem.model._use_derivatives: simple_warning("Derivatives are turned off. Skipping simul deriv coloring.") return total_coloring = self._get_static_coloring() if total_coloring._rev and problem._orig_mode not in ('rev', 'auto'): revcol = total_coloring._rev[0][0] if revcol: raise RuntimeError("Simultaneous coloring does reverse solves but mode has " "been set to '%s'" % problem._orig_mode) if total_coloring._fwd and problem._orig_mode not in ('fwd', 'auto'): fwdcol = total_coloring._fwd[0][0] if fwdcol: raise RuntimeError("Simultaneous coloring does forward solves but mode has " "been set to '%s'" % problem._orig_mode) def _pre_run_model_debug_print(self): """ Optionally print some debugging information before the model runs. """ debug_opt = self.options['debug_print'] if not debug_opt or debug_opt == ['totals']: return if not MPI or MPI.COMM_WORLD.rank == 0: header = 'Driver debug print for iter coord: {}'.format( self._recording_iter.get_formatted_iteration_coordinate()) print(header) print(len(header) * '-') if 'desvars' in debug_opt: model = self._problem().model desvar_vals = {n: model._get_val(n, get_remote=True, rank=0) for n in self._designvars} if not MPI or 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', driver_scaling=False) if not MPI or MPI.COMM_WORLD.rank == 0: print("Nonlinear constraints") if cons: pprint.pprint(cons) else: print("None") print() if 'ln_cons' in self.options['debug_print']: cons = self.get_constraint_values(lintype='linear', driver_scaling=False) if not MPI or MPI.COMM_WORLD.rank == 0: print("Linear constraints") if cons: pprint.pprint(cons) else: print("None") print() if 'objs' in self.options['debug_print']: objs = self.get_objective_values(driver_scaling=False) if not MPI or MPI.COMM_WORLD.rank == 0: print("Objectives") if objs: pprint.pprint(objs) else: print("None") print() sys.stdout.flush()
[docs]class RecordingDebugging(Recording): """ A class that acts as a context manager. Handles doing the case recording and also the Driver debugging printing. """ def __enter__(self): """ Do things before the code inside the 'with RecordingDebugging' block. Returns ------- self : object self """ super(RecordingDebugging, self).__enter__() self.recording_requester()._pre_run_model_debug_print() return self def __exit__(self, *args): """ Do things after the code inside the 'with RecordingDebugging' block. Parameters ---------- *args : array Solver recording requires extra args. """ self.recording_requester()._post_run_model_debug_print() super(RecordingDebugging, self).__exit__()
[docs]def record_iteration(requester, prob, case_name): """ Record an iteration of the current Problem or Driver. Parameters ---------- requester : Problem or Driver The recording requester. prob : Problem The Problem. case_name : str The name of this case. """ if not requester._rec_mgr._recorders: return # Get the data to record (collective calls that get across all ranks) filt = requester._filtered_vars_to_record model = prob.model parallel = requester._rec_mgr._check_parallel() if model.comm.size > 1 else False outs = model._retrieve_data_of_kind(filt, 'output', 'nonlinear', parallel) ins = model._retrieve_data_of_kind(filt, 'input', 'nonlinear', parallel) data = { 'output': outs, 'input': ins } requester._rec_mgr.record_iteration(requester, data, requester._get_recorder_metadata(case_name))