-
Notifications
You must be signed in to change notification settings - Fork 247
/
pyoptsparse_driver.py
970 lines (812 loc) · 38 KB
/
pyoptsparse_driver.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
"""
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.
"""
import sys
import json
import signal
from packaging.version import Version
import numpy as np
from scipy.sparse import coo_matrix
try:
import pyoptsparse
Optimization = pyoptsparse.Optimization
except ImportError:
pyoptsparse = None
except Exception as err:
pyoptsparse = err
from openmdao.core.constants import _DEFAULT_REPORTS_DIR, _ReprClass
from openmdao.core.analysis_error import AnalysisError
from openmdao.core.driver import Driver, RecordingDebugging, filter_by_meta
from openmdao.core.group import Group
from openmdao.utils.class_util import WeakMethodWrapper
from openmdao.utils.mpi import FakeComm, MPI
from openmdao.utils.om_warnings import issue_warning, warn_deprecation
# what version of pyoptspare are we working with
if pyoptsparse and hasattr(pyoptsparse, '__version__'):
pyoptsparse_version = Version(pyoptsparse.__version__)
else:
pyoptsparse_version = None
# All optimizers in pyoptsparse
optlist = {'ALPSO', 'CONMIN', 'IPOPT', 'NLPQLP', 'NSGA2', 'ParOpt', 'PSQP', 'SLSQP', 'SNOPT'}
if pyoptsparse_version is None or pyoptsparse_version < Version('2.6.0'):
optlist.add('NOMAD')
if pyoptsparse_version is None or pyoptsparse_version < Version('2.1.2'):
optlist.update({'FSQP', 'NLPY_AUGLAG'})
# names of optimizers that use gradients
grad_drivers = optlist.intersection({'CONMIN', 'FSQP', 'IPOPT', 'NLPQLP', 'PSQP',
'SLSQP', 'SNOPT', 'NLPY_AUGLAG', 'ParOpt'})
# names of optimizers that allow multiple objectives
multi_obj_drivers = {'NSGA2'}
# All optimizers that require an initial run
run_required = {'NSGA2'}
if pyoptsparse_version is None or pyoptsparse_version < Version('2.9.4'):
run_required.add('ALPSO') # ALPSO bug fixed in v2.9.4
# The pyoptsparse API provides for an optional 'fail' flag in the return value of
# objective and gradient functions, but this flag is only used by a subset of the
# available optimizers. If the flag is not respected by the optimizer, we have to
# return NaN values to indicate a bad evaluation.
respects_fail_flag = {
# Currently supported optimizers (v2.9.0)
'ALPSO': False,
'CONMIN': False,
'IPOPT': False,
'NLPQLP': False,
'NSGA2': False,
'PSQP': False,
'ParOpt': True,
'SLSQP': False,
'SNOPT': True, # as of v2.0.0, requires SNOPT 7.7
'FSQP': False, # no longer supported as of v2.1.2
'NLPY_AUGLAG': False, # no longer supported as of v2.1.2
'NOMAD': False # no longer supported as of v2.6.0
}
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
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
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.
opt_settings : dict
Dictionary for setting optimizer-specific options.
pyopt_solution : Solution
Pyopt_sparse solution object.
_fill_NANs : bool
Used internally to control when to return NANs for a bad evaluation.
_check_jac : bool
Used internally to control when to perform singular checks on computed total derivs.
_exc_info : 3 item tuple
Storage for exception and traceback information for 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.
_nl_responses : 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.
_total_jac_sparsity : dict, str, or None
Specifies sparsity of sub-jacobians of the total jacobian.
_user_termination_flag : bool
This is set to True when the user sends a signal to terminate the job.
_model_ran : bool
This is set to True after the full model has been run at least once.
"""
def __init__(self, **kwargs):
"""
Initialize pyopt.
"""
if pyoptsparse is None:
# pyoptsparse is not installed
raise RuntimeError('pyOptSparseDriver is not available, pyOptsparse is not installed.')
if isinstance(pyoptsparse, Exception):
# there is some other issue with the pyoptsparse installation
raise pyoptsparse
super().__init__(**kwargs)
# What we support
self.supports['optimization'] = True
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['linear_only_designvars'] = 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 = {}
# We save the pyopt_solution so that it can be queried later on.
self.pyopt_solution = None
# we have to return NANs in order for some optimizers that don't respect
# the fail flag (e.g. IPOPT) to recognize a bad point and respond accordingly
self._fill_NANs = False
self._nl_responses = []
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._total_jac_format = 'dict'
self._total_jac_sparsity = None
self._model_ran = False
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_opt_prob', types=bool, default=False,
desc='Print the opt problem summary before running the optimization')
self.options.declare('print_results', types=(bool, str), 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.')
self.options.declare('hist_file', types=str, default=None, allow_none=True,
desc='File location for saving pyopt_sparse optimization history. '
'Default is None for no output.')
self.options.declare('hotstart_file', types=str, default=None, allow_none=True,
desc='File location of a pyopt_sparse optimization history to use '
'to hot start the optimization. Default is None.')
self.options.declare('output_dir', types=(str, _ReprClass), default=_DEFAULT_REPORTS_DIR,
allow_none=True,
desc='Directory location of pyopt_sparse output files.'
'Default is {prob_name}_out/reports.')
@property
def hist_file(self):
"""
Get the 'hist_file' option for this driver.
"""
warn_deprecation("The 'hist_file' attribute is deprecated. "
"Use the 'hist_file' option instead.")
return self.options['hist_file']
@hist_file.setter
def hist_file(self, file_name):
"""
Set the 'hist_file' option for this driver.
"""
warn_deprecation("The 'hist_file' attribute is deprecated. "
"Use the 'hist_file' option instead.")
self.options['hist_file'] = file_name
@property
def hotstart_file(self):
"""
Get the 'hotstart_file' option for this driver.
"""
warn_deprecation("The 'hotstart_file' attribute is deprecated. "
"Use the 'hotstart_file' option instead.")
return self.options['hotstart_file']
@hotstart_file.setter
def hotstart_file(self, file_name):
"""
Set the 'hotstart_file' option for this driver.
"""
warn_deprecation("The 'hotstart_file' attribute is deprecated. "
"Use the 'hotstart_file' option instead.")
self.options['hotstart_file'] = file_name
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._model_ran = False
self._setup_tot_jac_sparsity()
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.
"""
self.result.reset()
problem = self._problem()
model = problem.model
relevance = model._relevance
self.pyopt_solution = None
self._total_jac = None
self._total_jac_linear = None
self.iter_count = 0
self._nl_responses = []
optimizer = self.options['optimizer']
self._fill_NANs = not respects_fail_flag[self.options['optimizer']]
self._check_for_missing_objective()
self._check_for_invalid_desvar_values()
self._check_jac = self.options['singular_jac_behavior'] in ['error', 'warn']
linear_constraints = [key for key, con in self._cons.items() if con['linear']]
# Only need initial run if we have linear constraints or if we are using an optimizer that
# doesn't perform one initially.
model_ran = False
if optimizer in run_required or linear_constraints:
with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:
self._run_solve_nonlinear()
rec.abs = 0.0
rec.rel = 0.0
model_ran = True
self.iter_count += 1
self._model_ran = model_ran
self._coloring_info.run_model = not model_ran
comm = None if isinstance(problem.comm, FakeComm) else problem.comm
opt_prob = Optimization(self.options['title'], WeakMethodWrapper(self, '_objfunc'),
comm=comm)
input_vals = self.get_design_var_values()
for name, meta in self._designvars.items():
# translate absolute var names to promoted names for pyoptsparse
size = meta['global_size'] if meta['distributed'] else meta['size']
if pyoptsparse_version is None or pyoptsparse_version < Version('2.6.1'):
opt_prob.addVarGroup(name, size, type='c',
value=input_vals[name],
lower=meta['lower'], upper=meta['upper'])
else:
opt_prob.addVarGroup(name, size, varType='c',
value=input_vals[name],
lower=meta['lower'], upper=meta['upper'])
if pyoptsparse_version is None or pyoptsparse_version < Version('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(model._get_prom_name(name))
self._nl_responses.append(name)
lin_dvs = self._get_lin_dvs()
nl_dvs = self._get_nl_dvs()
# Calculate and save derivatives for any linear constraints.
if linear_constraints:
_lin_jacs = self._compute_totals(of=linear_constraints, wrt=list(lin_dvs),
return_format=self._total_jac_format)
_con_vals = self.get_constraint_values(lintype='linear')
# 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!
_y_intercepts = {}
for name, jacdct in _lin_jacs.items():
_y_intercepts[name] = _con_vals[name]
for n, subjac in jacdct.items():
if isinstance(subjac, np.ndarray):
_y_intercepts[name] -= subjac.dot(input_vals[n])
# 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}
# # compute dynamic simul deriv coloring
problem.get_total_coloring(self._coloring_info, run_model=not model_ran)
bad_resps = [n for n in relevance._no_dv_responses if n in self._cons]
bad_cons = [n for n, m in self._cons.items() if m['source'] in bad_resps]
if bad_cons:
issue_warning(f"Equality constraint(s) {sorted(bad_cons)} do not depend on any design "
"variables and were not added to the optimization.")
for name in bad_cons:
del self._cons[name]
del self._responses[name]
eqcons = {n: m for n, m in self._cons.items() if m['equals'] is not None}
if eqcons:
# Add all equality constraints
for name, meta in eqcons.items():
size = meta['global_size'] if meta['distributed'] else meta['size']
lower = upper = meta['equals']
# set equality constraints as reverse seeds to see what dvs are relevant
with relevance.seeds_active(rev_seeds=meta['source']):
if meta['linear']:
wrts = [v for v in lin_dvs
if relevance.is_relevant(lin_dvs[v]['source'])]
jac = {w: _lin_jacs[name][w] for w in wrts}
opt_prob.addConGroup(name, size,
lower=lower - _y_intercepts[name],
upper=upper - _y_intercepts[name],
linear=True, wrt=wrts, jac=jac)
else:
wrts = [v for v in nl_dvs
if relevance.is_relevant(nl_dvs[v]['source'])]
if name in self._con_subjacs:
resjac = self._con_subjacs[name]
jac = {n: resjac[n] for n in wrts}
else:
jac = None
opt_prob.addConGroup(name, size, lower=lower, upper=upper, wrt=wrts,
jac=jac)
self._nl_responses.append(name)
ineqcons = {n: m for n, m in self._cons.items() if m['equals'] is None}
if ineqcons:
# Add all inequality constraints
for name, meta in ineqcons.items():
size = meta['global_size'] if meta['distributed'] else meta['size']
# Bounds - double sided is supported
lower = meta['lower']
upper = meta['upper']
# set inequality constraints as reverse seeds to see what dvs are relevant
with relevance.seeds_active(rev_seeds=(meta['source'],)):
if meta['linear']:
wrts = [n for n, meta in lin_dvs.items()
if relevance.is_relevant(meta['source'])]
jac = {w: _lin_jacs[name][w] for w in wrts}
opt_prob.addConGroup(name, size,
upper=upper - _y_intercepts[name],
lower=lower - _y_intercepts[name],
linear=True, wrt=wrts, jac=jac)
else:
wrts = [n for n, meta in nl_dvs.items()
if relevance.is_relevant(meta['source'])]
if name in self._con_subjacs:
resjac = self._con_subjacs[name]
jac = {n: resjac[n] for n in wrts}
else:
jac = None
opt_prob.addConGroup(name, size, upper=upper, lower=lower,
wrt=wrts, jac=jac)
self._nl_responses.append(name)
# Instantiate the requested optimizer
try:
_tmp = __import__('pyoptsparse', globals(), locals(), [optimizer], 0)
opt = getattr(_tmp, optimizer)()
except Exception:
# 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)
# Need to tell optimizer where to put its .out files
if self.options['output_dir'] in (None, _DEFAULT_REPORTS_DIR):
output_dir = str(self._problem().get_outputs_dir())
else:
output_dir = str(self.options['output_dir'])
optimizers_and_output_files = {
# ALPSO uses a single option `filename` to determine name of both output files
'ALPSO': [('filename', 'ALPSO.out')],
'CONMIN': [('IFILE', 'CONMIN.out')],
'IPOPT': [('output_file', 'IPOPT.out')],
'PSQP': [('IFILE', 'PSQP.out')],
'SLSQP': [('IFILE', 'SLSQP.out')],
'SNOPT': [('Print file', 'SNOPT_print.out'), ('Summary file', 'SNOPT_summary.out')]
}
if optimizer in optimizers_and_output_files:
for opt_setting_name, output_file_name in optimizers_and_output_files[optimizer]:
self.opt_settings[opt_setting_name] = f'{output_dir}/{output_file_name}'
# 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)
# Print the pyoptsparse optimization problem summary before running the optimization.
# This allows users to confirm their optimization setup.
if self.options['print_opt_prob']:
if not MPI or model.comm.rank == 0:
print(opt_prob)
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.options['hist_file'],
hotStart=self.options['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.options['hist_file'],
hotStart=self.options['hotstart_file'])
else:
msg = "SNOPT's internal finite difference can only be used with SNOPT"
self._exc_info = (Exception, Exception(msg), None)
else:
# Use OpenMDAO's differentiator for the gradient
sol = opt(opt_prob, sens=WeakMethodWrapper(self, '_gradfunc'),
storeHistory=self.options['hist_file'],
hotStart=self.options['hotstart_file'])
except Exception:
if self._exc_info is None:
raise
if self._exc_info is not None:
exc_info = self._exc_info
self._exc_info = None
if exc_info[2] is None:
raise exc_info[1]
raise exc_info[1].with_traceback(exc_info[2])
# Print results
if self.options['print_results']:
if not MPI or model.comm.rank == 0:
if self.options['print_results'] == 'minimal':
if hasattr(sol, 'summary_str'):
print(sol.summary_str(minimal_print=True))
else:
print('minimal_print is not available for this solution')
print(sol)
else:
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 self._designvars:
self.set_design_var(name, dv_dict[model._get_prom_name(name)])
with RecordingDebugging(self._get_name(), self.iter_count, self) as rec:
try:
self._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
else:
# exit status may be the empty string for optimizers that don't support it
if exit_status and 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._designvars:
self.set_design_var(name, dv_dict[model._get_prom_name(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'))
# convert func_dict to use promoted names
func_dict = model._prom_names_dict(func_dict)
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
# deactivate the relevance if we haven't run the full model yet, so that
# the full model will run at least once.
with model._relevance.nonlinear_active('iter', active=self._model_ran):
self._run_solve_nonlinear()
self._model_ran = True
# 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
# Record after getting obj and constraint to assure they have
# been gathered in MPI.
rec.abs = 0.0
rec.rel = 0.0
except Exception:
if self._exc_info is None: # avoid overwriting an earlier exception
self._exc_info = sys.exc_info()
fail = 1
func_dict = self.get_objective_values()
func_dict.update(self.get_constraint_values(lintype='nonlinear'))
if fail > 0 and self._fill_NANs:
for name in func_dict:
func_dict[name].fill(np.nan)
# convert func_dict to use promoted names
func_dict = model._prom_names_dict(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. Keys are user facing names.
func_dict : dict
Dictionary of all functional variables evaluated at design point. Keys are
sources and aliases.
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()
model = prob.model
fail = 0
sens_dict = {}
nl_dvs = self._get_nl_dvs()
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._nl_responses, wrt=nl_dvs,
return_format=self._total_jac_format)
# First time through, check for zero row/col.
if self._check_jac and self._total_jac is not None:
for subsys in model.system_iter(include_self=True, recurse=True, typ=Group):
if subsys._has_approx:
break
else:
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 = {}
con_subjacs = self._con_subjacs
for okey in self._nl_responses:
new_sens[okey] = newdv = {}
if okey in con_subjacs:
con_outs = con_subjacs[okey]
for ikey in nl_dvs:
if ikey in con_outs:
arr = sens_dict[okey][ikey]
coo = con_outs[ikey]
row, col, _ = coo['coo']
coo['coo'][2] = arr[row, col].flatten()
newdv[ikey] = coo
elif okey in sens_dict:
for ikey in nl_dvs:
newdv[ikey] = sens_dict[okey][ikey]
sens_dict = new_sens
except Exception:
if self._exc_info is None: # avoid overwriting an earlier exception
self._exc_info = sys.exc_info()
fail = 1
if fail > 0:
# We need to cobble together a sens_dict of the correct size.
# Best we can do is return zeros or NaNs.
for okey in self._nl_responses:
if okey not in sens_dict:
sens_dict[okey] = {}
oval = func_dict[model._get_prom_name(okey)]
osize = len(oval)
for ikey in nl_dvs.keys():
ival = dv_dict[model._get_prom_name(ikey)]
isize = len(ival)
if ikey not in sens_dict[okey] or self._fill_NANs:
sens_dict[okey][ikey] = np.zeros((osize, isize))
if self._fill_NANs:
sens_dict[okey][ikey].fill(np.nan)
# convert sens_dict to use promoted names
sens_dict = model._prom_names_jac(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._con_subjacs = {}
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 _setup_tot_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
use_approx = self._problem().model._owns_approx_of is not None
nl_dvs = self._get_nl_dvs()
# exclude linear cons and dvs that only impact linear cons
for con, conmeta in filter_by_meta(self._cons.items(), 'linear', exclude=True):
self._con_subjacs[con] = {}
consrc = conmeta['source']
for dv, dvmeta in nl_dvs.items():
if use_approx:
dvsrc = dvmeta['source']
rows, cols, shape = total_sparsity[consrc][dvsrc]
else:
rows, cols, shape = total_sparsity[con][dv]
self._con_subjacs[con][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.')