Source code for openmdao.drivers.pyoptsparse_driver

"""
OpenMDAO Wrapper for pyoptsparse.

pyoptsparse is based on pyOpt, which is an object-oriented framework for
formulating and solving nonlinear constrained optimization problems, with
additional MPI capability.
"""

from collections import OrderedDict
import json
import signal
from distutils.version import LooseVersion

import numpy as np
from scipy.sparse import coo_matrix

try:
    import pyoptsparse
    Optimization = pyoptsparse.Optimization
except ImportError:
    Optimization = None
    pyoptsparse = None

from openmdao.core.constants import INT_DTYPE
from openmdao.core.analysis_error import AnalysisError
from openmdao.core.driver import Driver, RecordingDebugging
import openmdao.utils.coloring as c_mod
from openmdao.utils.class_util import WeakMethodWrapper
from openmdao.utils.mpi import FakeComm
from openmdao.utils.om_warnings import issue_warning, DerivativesWarning


# names of optimizers that use gradients
grad_drivers = {'CONMIN', 'FSQP', 'IPOPT', 'NLPQLP',
                'PSQP', 'SLSQP', 'SNOPT', 'NLPY_AUGLAG', 'ParOpt'}

# names of optimizers that allow multiple objectives
multi_obj_drivers = {'NSGA2'}

# All optimizers in pyoptsparse
optlist = ['ALPSO', 'CONMIN', 'FSQP', 'IPOPT', 'NLPQLP',
           'NSGA2', 'PSQP', 'SLSQP', 'SNOPT', 'NLPY_AUGLAG', 'NOMAD', 'ParOpt']

# All optimizers that require an initial run
run_required = ['NSGA2', 'ALPSO']

DEFAULT_OPT_SETTINGS = {}
DEFAULT_OPT_SETTINGS['IPOPT'] = {
    'hessian_approximation': 'limited-memory',
    'nlp_scaling_method': 'user-scaling',
    'linear_solver': 'mumps'
}

CITATIONS = """@article{Wu_pyoptsparse_2020,
    author = {Neil Wu and Gaetan Kenway and Charles A. Mader and John Jasa and
     Joaquim R. R. A. Martins},
    title = {{pyOptSparse:} A {Python} framework for large-scale constrained
     nonlinear optimization of sparse systems},
    journal = {Journal of Open Source Software},
    volume = {5},
    number = {54},
    month = {October},
    year = {2020},
    pages = {2564},
    doi = {10.21105/joss.02564},
    publisher = {The Open Journal},
}

@article{Hwang_maud_2018
    author = {Hwang, John T. and Martins, Joaquim R.R.A.},
    title = "{A Computational Architecture for Coupling Heterogeneous
             Numerical Models and Computing Coupled Derivatives}",
    journal = "{ACM Trans. Math. Softw.}",
    volume = {44},
    number = {4},
    month = jun,
    year = {2018},
    pages = {37:1--37:39},
    articleno = {37},
    numpages = {39},
    doi = {10.1145/3182393},
    publisher = {ACM},
}
"""

DEFAULT_SIGNAL = None


[docs]class UserRequestedException(Exception): """ User Requested Exception. This exception indicates that the user has requested that SNOPT/pyoptsparse ceases model execution and reports to SNOPT that execution should be terminated. """ pass
[docs]class pyOptSparseDriver(Driver): """ Driver wrapper for pyoptsparse. Pyoptsparse is based on pyOpt, which is an object-oriented framework for formulating and solving nonlinear constrained optimization problems, with additional MPI capability. pypptsparse has interfaces to the following optimizers: ALPSO, CONMIN, FSQP, IPOPT, NLPQLP, NSGA2, PSQP, SLSQP, SNOPT, NLPY_AUGLAG, NOMAD, ParOpt. Note that some of these are not open source and therefore not included in the pyoptsparse source code. pyOptSparseDriver supports the following: equality_constraints inequality_constraints two_sided_constraints Parameters ---------- **kwargs : dict of keyword arguments Keyword arguments that will be mapped into the Driver options. Attributes ---------- fail : bool Flag that indicates failure of most recent optimization. hist_file : str or None File location for saving pyopt_sparse optimization history. Default is None for no output. hotstart_file : str Optional file to hot start the optimization. opt_settings : dict Dictionary for setting optimizer-specific options. pyopt_solution : Solution Pyopt_sparse solution object. _check_jac : bool Used internally to control when to perform singular checks on computed total derivs. _exc_info : None or <Exception> Cached exception that was raised in the _objfunc or _gradfunc callbacks. _in_user_function :bool This is set to True at the start of a pyoptsparse callback to _objfunc and _gradfunc, and restored to False at the finish of each callback. _indep_list : list List of design variables. _quantities : list Contains the objectives plus nonlinear constraints. _signal_cache : <Function> Cached function pointer that was assigned as handler for signal defined in option user_terminate_signal. _user_termination_flag : bool This is set to True when the user sends a signal to terminate the job. """
[docs] def __init__(self, **kwargs): """ Initialize pyopt. """ if Optimization is None: raise RuntimeError('pyOptSparseDriver is not available, pyOptsparse is not installed.') super().__init__(**kwargs) # What we support self.supports['inequality_constraints'] = True self.supports['equality_constraints'] = True self.supports['multiple_objectives'] = True self.supports['two_sided_constraints'] = True self.supports['linear_constraints'] = True self.supports['simultaneous_derivatives'] = True self.supports['total_jac_sparsity'] = True # What we don't support yet self.supports['active_set'] = False self.supports['integer_design_vars'] = False self.supports['distributed_design_vars'] = False self.supports._read_only = True # The user places optimizer-specific settings in here. self.opt_settings = {} # The user can set a file name here to store history self.hist_file = None # The user can set a file here to hot start the optimization # with a history file self.hotstart_file = None # We save the pyopt_solution so that it can be queried later on. self.pyopt_solution = None self._indep_list = [] self._quantities = [] self.fail = False self._signal_cache = None self._user_termination_flag = False self._in_user_function = False self._check_jac = False self._exc_info = None self.cite = CITATIONS
def _declare_options(self): """ Declare options before kwargs are processed in the init method. """ self.options.declare('optimizer', default='SLSQP', values=optlist, desc='Name of optimizers to use') self.options.declare('title', default='Optimization using pyOpt_sparse', desc='Title of this optimization run') self.options.declare('print_results', types=bool, default=True, desc='Print pyOpt results if True') self.options.declare('gradient method', default='openmdao', values={'openmdao', 'pyopt_fd', 'snopt_fd'}, desc='Finite difference implementation to use') self.options.declare('user_terminate_signal', default=DEFAULT_SIGNAL, allow_none=True, desc='OS signal that triggers a clean user-termination. Only SNOPT' 'supports this option.') self.options.declare('singular_jac_behavior', default='warn', values=['error', 'warn', 'ignore'], desc='Defines behavior of a zero row/col check after first call to' 'compute_totals:' 'error - raise an error.' 'warn - raise a warning.' "ignore - don't perform check.") self.options.declare('singular_jac_tol', default=1e-16, desc='Tolerance for zero row/column check.') # Deprecated option self.options.declare('user_teriminate_signal', default=None, allow_none=True, desc='OS signal that triggers a clean user-termination. Only SNOPT' 'supports this option.', deprecation="The option 'user_teriminate_signal' was misspelled and " "will be deprecated. Please use 'user_terminate_signal' instead.") 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. """ super()._setup_driver(problem) self.supports._read_only = False self.supports['gradients'] = self.options['optimizer'] in grad_drivers self.supports._read_only = True if len(self._objs) > 1 and self.options['optimizer'] not in multi_obj_drivers: raise RuntimeError('Multiple objectives have been added to pyOptSparseDriver' ' but the selected optimizer ({0}) does not support' ' multiple objectives.'.format(self.options['optimizer'])) self._setup_tot_jac_sparsity() # Handle deprecated option. if self.options._dict['user_teriminate_signal']['val'] is not None: self.options['user_terminate_signal'] = \ self.options._dict['user_teriminate_signal']['val']
[docs] def run(self): """ Excute pyOptsparse. Note that pyOpt controls the execution, and the individual optimizers (e.g., SNOPT) control the iteration. Returns ------- bool Failure flag; True if failed to converge, False is successful. """ problem = self._problem() model = problem.model relevant = model._relevant self.pyopt_solution = None self._total_jac = None self.iter_count = 0 fwd = problem._mode == 'fwd' optimizer = self.options['optimizer'] self._quantities = [] self._check_for_missing_objective() self._check_jac = self.options['singular_jac_behavior'] in ['error', 'warn'] # Only need initial run if we have linear constraints or if we are using an optimizer that # doesn't perform one initially. con_meta = self._cons model_ran = False if optimizer in run_required or np.any([con['linear'] for con in self._cons.values()]): with RecordingDebugging(self._get_name(), self.iter_count, self) as rec: # Initial Run model.run_solve_nonlinear() rec.abs = 0.0 rec.rel = 0.0 model_ran = True self.iter_count += 1 # compute dynamic simul deriv coloring or just sparsity if option is set if c_mod._use_total_sparsity: coloring = None if self._coloring_info['coloring'] is None and self._coloring_info['dynamic']: coloring = c_mod.dynamic_total_coloring(self, run_model=not model_ran, fname=self._get_total_coloring_fname()) if coloring is not None: # if the improvement wasn't large enough, don't use coloring pct = coloring._solves_info()[-1] info = self._coloring_info if info['min_improve_pct'] > pct: info['coloring'] = info['static'] = None msg = f"Coloring was deactivated. Improvement of {pct:.1f}% was less " \ f"than min allowed ({info['min_improve_pct']:.1f}%)." issue_warning(msg, prefix=self.msginfo, category=DerivativesWarning) comm = None if isinstance(problem.comm, FakeComm) else problem.comm opt_prob = Optimization(self.options['title'], WeakMethodWrapper(self, '_objfunc'), comm=comm) # Add all design variables input_meta = self._designvars self._indep_list = indep_list = list(input_meta) input_vals = self.get_design_var_values() for name, meta in input_meta.items(): size = meta['global_size'] if meta['distributed'] else meta['size'] opt_prob.addVarGroup(name, size, type='c', value=input_vals[name], lower=meta['lower'], upper=meta['upper']) if not hasattr(pyoptsparse, '__version__') or \ LooseVersion(pyoptsparse.__version__) < LooseVersion('2.5.1'): opt_prob.finalizeDesignVariables() else: opt_prob.finalize() # Add all objectives objs = self.get_objective_values() for name in objs: opt_prob.addObj(name) self._quantities.append(name) # Calculate and save derivatives for any linear constraints. lcons = [key for (key, con) in con_meta.items() if con['linear']] if len(lcons) > 0: _lin_jacs = self._compute_totals(of=lcons, wrt=indep_list, return_format='dict') # convert all of our linear constraint jacs to COO format. Otherwise pyoptsparse will # do it for us and we'll end up with a fully dense COO matrix and very slow evaluation # of linear constraints! to_remove = [] for jacdct in _lin_jacs.values(): for n, subjac in jacdct.items(): if isinstance(subjac, np.ndarray): # we can safely use coo_matrix to automatically convert the ndarray # since our linear constraint jacs are constant, so zeros won't become # nonzero during the optimization. mat = coo_matrix(subjac) if mat.row.size > 0: # convert to 'coo' format here to avoid an emphatic warning # by pyoptsparse. jacdct[n] = {'coo': [mat.row, mat.col, mat.data], 'shape': mat.shape} # Add all equality constraints for name, meta in con_meta.items(): if meta['equals'] is None: continue size = meta['global_size'] if meta['distributed'] else meta['size'] lower = upper = meta['equals'] if fwd: wrt = [v for v in indep_list if name in relevant[input_meta[v]['ivc_source']]] else: rels = relevant[name] wrt = [v for v in indep_list if input_meta[v]['ivc_source'] in rels] if meta['linear']: jac = {w: _lin_jacs[name][w] for w in wrt} opt_prob.addConGroup(name, size, lower=lower, upper=upper, linear=True, wrt=wrt, jac=jac) else: if name in self._res_jacs: resjac = self._res_jacs[name] jac = {n: resjac[input_meta[n]['ivc_source']] for n in wrt} else: jac = None opt_prob.addConGroup(name, size, lower=lower, upper=upper, wrt=wrt, jac=jac) self._quantities.append(name) # Add all inequality constraints for name, meta in con_meta.items(): if meta['equals'] is not None: continue size = meta['global_size'] if meta['distributed'] else meta['size'] # Bounds - double sided is supported lower = meta['lower'] upper = meta['upper'] if fwd: wrt = [v for v in indep_list if name in relevant[input_meta[v]['ivc_source']]] else: rels = relevant[name] wrt = [v for v in indep_list if input_meta[v]['ivc_source'] in rels] if meta['linear']: jac = {w: _lin_jacs[name][w] for w in wrt} opt_prob.addConGroup(name, size, upper=upper, lower=lower, linear=True, wrt=wrt, jac=jac) else: if name in self._res_jacs: resjac = self._res_jacs[name] jac = {n: resjac[input_meta[n]['ivc_source']] for n in wrt} else: jac = None opt_prob.addConGroup(name, size, upper=upper, lower=lower, wrt=wrt, jac=jac) self._quantities.append(name) # Instantiate the requested optimizer try: _tmp = __import__('pyoptsparse', globals(), locals(), [optimizer], 0) opt = getattr(_tmp, optimizer)() except Exception as err: # Change whatever pyopt gives us to an ImportError, give it a readable message, # but raise with the original traceback. msg = "Optimizer %s is not available in this installation." % optimizer raise ImportError(msg) # Process any default optimizer-specific settings. if optimizer in DEFAULT_OPT_SETTINGS: for name, value in DEFAULT_OPT_SETTINGS[optimizer].items(): if name not in self.opt_settings: self.opt_settings[name] = value # Set optimization options for option, value in self.opt_settings.items(): opt.setOption(option, value) self._exc_info = None try: # Execute the optimization problem if self.options['gradient method'] == 'pyopt_fd': # Use pyOpt's internal finite difference # TODO: Need to get this from OpenMDAO # fd_step = problem.model.deriv_options['step_size'] fd_step = 1e-6 sol = opt(opt_prob, sens='FD', sensStep=fd_step, storeHistory=self.hist_file, hotStart=self.hotstart_file) elif self.options['gradient method'] == 'snopt_fd': if self.options['optimizer'] == 'SNOPT': # Use SNOPT's internal finite difference # TODO: Need to get this from OpenMDAO # fd_step = problem.model.deriv_options['step_size'] fd_step = 1e-6 sol = opt(opt_prob, sens=None, sensStep=fd_step, storeHistory=self.hist_file, hotStart=self.hotstart_file) else: msg = "SNOPT's internal finite difference can only be used with SNOPT" self._exc_info = Exception(msg) else: # Use OpenMDAO's differentiator for the gradient sol = opt(opt_prob, sens=WeakMethodWrapper(self, '_gradfunc'), storeHistory=self.hist_file, hotStart=self.hotstart_file) except Exception as _: if not self._exc_info: raise() if self._exc_info: raise self._exc_info # Print results if self.options['print_results']: print(sol) # Pull optimal parameters back into framework and re-run, so that # framework is left in the right final state dv_dict = sol.getDVs() for name in indep_list: self.set_design_var(name, dv_dict[name]) with RecordingDebugging(self._get_name(), self.iter_count, self) as rec: try: model.run_solve_nonlinear() except AnalysisError: model._clear_iprint() rec.abs = 0.0 rec.rel = 0.0 self.iter_count += 1 # Save the most recent solution. self.pyopt_solution = sol try: exit_status = sol.optInform['value'] self.fail = False # These are various failed statuses. if optimizer == 'IPOPT': if exit_status not in {0, 1}: self.fail = True elif exit_status > 2: self.fail = True except KeyError: # optimizers other than pySNOPT may not populate this dict pass # revert signal handler to cached version sigusr = self.options['user_terminate_signal'] if sigusr is not None: signal.signal(sigusr, self._signal_cache) self._signal_cache = None # to prevent memory leak test from failing return self.fail
def _objfunc(self, dv_dict): """ Compute the objective function and constraints. This function is passed to pyOpt's Optimization object and is called from its optimizers. Parameters ---------- dv_dict : dict Dictionary of design variable values. Returns ------- func_dict : dict Dictionary of all functional variables evaluated at design point. fail : int 0 for successful function evaluation 1 for unsuccessful function evaluation """ model = self._problem().model fail = 0 # Note: we place our handler as late as possible so that codes that run in the # workflow can place their own handlers. sigusr = self.options['user_terminate_signal'] if sigusr is not None and self._signal_cache is None: self._signal_cache = signal.getsignal(sigusr) signal.signal(sigusr, self._signal_handler) try: for name in self._indep_list: self.set_design_var(name, dv_dict[name]) # print("Setting DV") # print(dv_dict) # Check if we caught a termination signal while SNOPT was running. if self._user_termination_flag: func_dict = self.get_objective_values() func_dict.update(self.get_constraint_values(lintype='nonlinear')) return func_dict, 2 # Execute the model with RecordingDebugging(self._get_name(), self.iter_count, self) as rec: self.iter_count += 1 try: self._in_user_function = True model.run_solve_nonlinear() # Let the optimizer try to handle the error except AnalysisError: model._clear_iprint() fail = 1 # User requested termination except UserRequestedException: model._clear_iprint() fail = 2 func_dict = self.get_objective_values() func_dict.update(self.get_constraint_values(lintype='nonlinear')) # Record after getting obj and constraint to assure they have # been gathered in MPI. rec.abs = 0.0 rec.rel = 0.0 except Exception as raised: self._exc_info = raised fail = 1 func_dict = {} # print("Functions calculated") # print(dv_dict) # print(func_dict, flush=True) self._in_user_function = False return func_dict, fail def _gradfunc(self, dv_dict, func_dict): """ Compute the gradient of the objective function and constraints. This function is passed to pyOpt's Optimization object and is called from its optimizers. Parameters ---------- dv_dict : dict Dictionary of design variable values. func_dict : dict Dictionary of all functional variables evaluated at design point. Returns ------- sens_dict : dict Dictionary of dictionaries for gradient of each dv/func pair fail : int 0 for successful function evaluation 1 for unsuccessful function evaluation """ prob = self._problem() fail = 0 try: # Check if we caught a termination signal while SNOPT was running. if self._user_termination_flag: return {}, 2 try: self._in_user_function = True sens_dict = self._compute_totals(of=self._quantities, wrt=self._indep_list, return_format='dict') # First time through, check for zero row/col. if self._check_jac: raise_error = self.options['singular_jac_behavior'] == 'error' self._total_jac.check_total_jac(raise_error=raise_error, tol=self.options['singular_jac_tol']) self._check_jac = False # Let the optimizer try to handle the error except AnalysisError: prob.model._clear_iprint() fail = 1 # User requested termination except UserRequestedException: prob.model._clear_iprint() fail = 2 else: # if we don't convert to 'coo' here, pyoptsparse will do a # conversion of our dense array into a fully dense 'coo', which is bad. # TODO: look into getting rid of all of these conversions! new_sens = OrderedDict() res_jacs = self._res_jacs for okey in func_dict: new_sens[okey] = newdv = OrderedDict() okey_src = self._responses[okey]['ivc_source'] for ikey in dv_dict: ikey_src = self._designvars[ikey]['ivc_source'] if okey_src in res_jacs and ikey_src in res_jacs[okey_src]: arr = sens_dict[okey][ikey] coo = res_jacs[okey_src][ikey_src] row, col, data = coo['coo'] coo['coo'][2] = arr[row, col].flatten() newdv[ikey] = coo elif okey in sens_dict: newdv[ikey] = sens_dict[okey][ikey] sens_dict = new_sens if fail > 0: # We need to cobble together a sens_dict of the correct size. # Best we can do is return zeros. sens_dict = OrderedDict() for okey, oval in func_dict.items(): sens_dict[okey] = OrderedDict() osize = len(oval) for ikey, ival in dv_dict.items(): isize = len(ival) sens_dict[okey][ikey] = np.zeros((osize, isize)) except Exception as raised: self._exc_info = raised fail = 1 sens_dict = {} # print("Derivatives calculated") # print(dv_dict) # print(sens_dict, flush=True) self._in_user_function = False return sens_dict, fail def _get_name(self): """ Get name of current optimizer. Returns ------- str The name of the current optimizer. """ return "pyOptSparse_" + self.options['optimizer'] 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. """ nl_order = list(self._objs) neq_order = [] for n, meta in self._cons.items(): if 'linear' not in meta or not meta['linear']: if meta['equals'] is not None: nl_order.append(n) else: neq_order.append(n) nl_order.extend(neq_order) return nl_order def _setup_tot_jac_sparsity(self, coloring=None): """ Set up total jacobian subjac sparsity. Parameters ---------- coloring : Coloring or None Current coloring. """ total_sparsity = None self._res_jacs = {} coloring = coloring if coloring is not None else self._get_static_coloring() if coloring is not None: total_sparsity = coloring.get_subjac_sparsity() if self._total_jac_sparsity is not None: raise RuntimeError("Total jac sparsity was set in both _total_coloring" " and _total_jac_sparsity.") elif self._total_jac_sparsity is not None: if isinstance(self._total_jac_sparsity, str): with open(self._total_jac_sparsity, 'r') as f: self._total_jac_sparsity = json.load(f) total_sparsity = self._total_jac_sparsity if total_sparsity is None: return for res, resdict in total_sparsity.items(): if res in self._objs: # skip objectives continue self._res_jacs[res] = {} for dv, (rows, cols, shape) in resdict.items(): rows = np.array(rows, dtype=INT_DTYPE) cols = np.array(cols, dtype=INT_DTYPE) self._res_jacs[res][dv] = { 'coo': [rows, cols, np.zeros(rows.size)], 'shape': shape, } def _signal_handler(self, signum, frame): # Subsystems (particularly external codes) may declare their own signal handling, so # execute the cached handler first. if self._signal_cache is not signal.Handlers.SIG_DFL: self._signal_cache(signum, frame) self._user_termination_flag = True if self._in_user_function: raise UserRequestedException('User requested termination.')