"""Define the Problem class and a FakeComm class for non-MPI users."""
from __future__ import division, print_function
import sys
import pprint
import os
import logging
from collections import defaultdict, namedtuple
from fnmatch import fnmatchcase
from itertools import product
from six import iteritems, iterkeys, itervalues
from six.moves import range, cStringIO
import numpy as np
import scipy.sparse as sparse
from openmdao.core.component import Component
from openmdao.core.driver import Driver
from openmdao.core.explicitcomponent import ExplicitComponent
from openmdao.core.group import Group
from openmdao.core.group import System
from openmdao.core.indepvarcomp import IndepVarComp
from openmdao.core.total_jac import _TotalJacInfo
from openmdao.approximation_schemes.complex_step import ComplexStep
from openmdao.approximation_schemes.finite_difference import FiniteDifference
from openmdao.solvers.solver import SolverInfo
from openmdao.error_checking.check_config import _default_checks, _all_checks
from openmdao.recorders.recording_iteration_stack import _RecIteration
from openmdao.recorders.recording_manager import RecordingManager, record_viewer_data
from openmdao.utils.record_util import create_local_meta
from openmdao.utils.general_utils import warn_deprecation, ContainsAll, pad_name, simple_warning
from openmdao.utils.mpi import FakeComm
from openmdao.utils.mpi import MPI
from openmdao.utils.name_maps import prom_name2abs_name
from openmdao.utils.options_dictionary import OptionsDictionary
from openmdao.utils.units import get_conversion
from openmdao.utils import coloring as coloring_mod
from openmdao.utils.name_maps import abs_key2rel_key
from openmdao.vectors.default_vector import DefaultVector
from openmdao.utils.logger_utils import get_logger, TestLogger
import openmdao.utils.coloring as coloring_mod
try:
from openmdao.vectors.petsc_vector import PETScVector
except ImportError:
PETScVector = None
from openmdao.utils.name_maps import rel_key2abs_key, rel_name2abs_name
# Use this as a special value to be able to tell if the caller set a value for the optional
# out_stream argument. We run into problems running testflo if we use a default of sys.stdout.
_DEFAULT_OUT_STREAM = object()
ErrorTuple = namedtuple('ErrorTuple', ['forward', 'reverse', 'forward_reverse'])
MagnitudeTuple = namedtuple('MagnitudeTuple', ['forward', 'reverse', 'fd'])
_contains_all = ContainsAll()
_undefined = object()
CITATION = """@article{openmdao_2019,
Author={Justin S. Gray and John T. Hwang and Joaquim R. R. A.
Martins and Kenneth T. Moore and Bret A. Naylor},
Title="{OpenMDAO: An Open-Source Framework for Multidisciplinary
Design, Analysis, and Optimization}",
Journal="{Structural and Multidisciplinary Optimization}",
Year={2019},
Publisher={Springer},
pdf={http://openmdao.org/pubs/openmdao_overview_2019.pdf},
note= {In Press}
}"""
[docs]class Problem(object):
"""
Top-level container for the systems and drivers.
Attributes
----------
model : <System>
Pointer to the top-level <System> object (root node in the tree).
comm : MPI.Comm or <FakeComm>
The global communicator.
driver : <Driver>
Slot for the driver. The default driver is `Driver`, which just runs
the model once.
_mode : 'fwd' or 'rev'
Derivatives calculation mode, 'fwd' for forward, and 'rev' for
reverse (adjoint).
_orig_mode : 'fwd', 'rev', or 'auto'
Derivatives calculation mode assigned by the user. If set to 'auto', _mode will be
automatically assigned to 'fwd' or 'rev' based on relative sizes of design variables vs.
responses.
_solver_print_cache : list
Allows solver iprints to be set to requested values after setup calls.
_initial_condition_cache : dict
Any initial conditions that are set at the problem level via setitem are cached here
until they can be processed.
_setup_status : int
Current status of the setup in _model.
0 -- Newly initialized problem or newly added model.
1 -- The `setup` method has been called, but vectors not initialized.
2 -- The `final_setup` has been run, everything ready to run.
cite : str
Listing of relevant citations that should be referenced when
publishing work that uses this class.
options : <OptionsDictionary>
Dictionary with general options for the problem.
recording_options : <OptionsDictionary>
Dictionary with problem recording options.
_rec_mgr : <RecordingManager>
Object that manages all recorders added to this problem.
_remote_var_set : set
Set of variables (absolute names) that require remote data transfer to reach all procs.
_check : bool
If True, call check_config at the end of final_setup.
_recording_iter : _RecIteration
Manages recording of iterations.
_filtered_vars_to_record : dict
Dictionary of lists of design vars, constraints, etc. to record.
_logger : object or None
Object for logging config checks if _check is True.
_force_alloc_complex : bool
Force allocation of imaginary part in nonlinear vectors. OpenMDAO can generally
detect when you need to do this, but in some cases (e.g., complex step is used
after a reconfiguration) you may need to set this to True.
_color_dir_hash : str
Hash used to detect collisions of coloring files.
__get_remote : bool
Flag used to determine when __getitem__ will retrieve remote variables.
"""
_post_setup_func = None
[docs] def __init__(self, model=None, driver=None, comm=None, root=None, **options):
"""
Initialize attributes.
Parameters
----------
model : <System> or None
The top-level <System>. If not specified, an empty <Group> will be created.
driver : <Driver> or None
The driver for the problem. If not specified, a simple "Run Once" driver will be used.
comm : MPI.Comm or <FakeComm> or None
The global communicator.
root : <System> or None
Deprecated kwarg for `model`.
**options : named args
All remaining named args are converted to options.
"""
# used by the get_val function when get_remote is True
self.__get_remote = False
self.cite = CITATION
if comm is None:
try:
from mpi4py import MPI
comm = MPI.COMM_WORLD
except ImportError:
comm = FakeComm()
if root is not None:
if model is not None:
raise ValueError("Cannot specify both 'root' and 'model'. "
"'root' has been deprecated, please use 'model'.")
warn_deprecation("The 'root' argument provides backwards compatibility "
"with OpenMDAO <= 1.x ; use 'model' instead.")
model = root
if model is None:
self.model = Group()
elif isinstance(model, System):
self.model = model
else:
raise TypeError("The value provided for 'model' is not a valid System.")
if driver is None:
self.driver = Driver()
elif isinstance(driver, Driver):
self.driver = driver
else:
raise TypeError("The value provided for 'driver' is not a valid Driver.")
self.comm = comm
self._solver_print_cache = []
self._mode = None # mode is assigned in setup()
self._initial_condition_cache = {}
# Status of the setup of _model.
# 0 -- Newly initialized problem or newly added model.
# 1 -- The `setup` method has been called, but vectors not initialized.
# 2 -- The `final_setup` has been run, everything ready to run.
self._setup_status = 0
self._rec_mgr = RecordingManager()
# General options
self.options = OptionsDictionary(parent_name=type(self).__name__)
self.options.declare('coloring_dir', types=str,
default=os.path.join(os.getcwd(), 'coloring_files'),
desc='Directory containing coloring files (if any) for this Problem.')
self.options.update(options)
# Case recording options
self.recording_options = OptionsDictionary(parent_name=type(self).__name__)
self.recording_options.declare('record_desvars', types=bool, default=True,
desc='Set to True to record design variables at the '
'problem level')
self.recording_options.declare('record_objectives', types=bool, default=True,
desc='Set to True to record objectives at the problem level')
self.recording_options.declare('record_constraints', types=bool, default=True,
desc='Set to True to record constraints at the '
'problem level')
self.recording_options.declare('includes', types=list, default=['*'],
desc='Patterns for variables to include in recording')
self.recording_options.declare('excludes', types=list, default=[],
desc='Patterns for vars to exclude in recording '
'(processed post-includes)')
def _get_var_abs_name(self, name):
if name in self.model._var_allprocs_abs2meta:
return name
elif name in self.model._var_allprocs_prom2abs_list['output']:
return self.model._var_allprocs_prom2abs_list['output'][name][0]
elif name in self.model._var_allprocs_prom2abs_list['input']:
abs_names = self.model._var_allprocs_prom2abs_list['input'][name]
if len(abs_names) == 1:
return abs_names[0]
else:
raise KeyError("Using promoted name `{}' is ambiguous and matches unconnected "
"inputs %s. Use absolute name to disambiguate.".format(name,
abs_names))
raise KeyError("Variable '{}' not found.".format(name))
[docs] def is_local(self, name):
"""
Return True if the named variable or system is local to the current process.
Parameters
----------
name : str
Name of a variable or system.
Returns
-------
bool
True if the named system or variable is local to this process.
"""
if self._setup_status < 1:
raise RuntimeError("is_local('{}') was called before setup() completed.".format(name))
try:
abs_name = self._get_var_abs_name(name)
except KeyError:
sub = self.model._get_subsystem(name)
if sub is None: # either the sub is remote or there is no sub by that name
# TODO: raise exception if sub does not exist
return False
else:
# if system has been set up, _var_sizes will be initialized
return sub._var_sizes is not None
# variable exists, but may be remote
return abs_name in self.model._var_abs2meta
[docs] def __getitem__(self, name):
"""
Get an output/input variable.
Parameters
----------
name : str
Promoted or relative variable name in the root system's namespace.
Returns
-------
float or ndarray or any python object
the requested output/input variable.
"""
# Caching only needed if vectors aren't allocated yet.
proms = self.model._var_allprocs_prom2abs_list
meta = self.model._var_abs2meta
val = _undefined
abs_name = None
if self._setup_status == 1:
# We have set and cached already
if name in self._initial_condition_cache:
return self._initial_condition_cache[name]
# Vector not setup, so we need to pull values from saved metadata request.
else:
if name in meta:
if isinstance(self.model, Group) and name in self.model._conn_abs_in2out:
src_name = self.model._conn_abs_in2out[name]
val = meta[src_name]['value']
else:
val = meta[name]['value']
elif name in proms['output']:
abs_name = prom_name2abs_name(self.model, name, 'output')
if abs_name in meta:
val = meta[abs_name]['value']
elif name in proms['input']:
abs_name = proms['input'][name][0]
conn = self.model._conn_abs_in2out
if abs_name in meta:
if isinstance(self.model, Group) and abs_name in conn:
src_name = self.model._conn_abs_in2out[abs_name]
# So, if the inputs and outputs are promoted to the same name, then we
# allow getitem, but if they aren't, then we raise an error due to non
# uniqueness.
if name not in proms['output']:
# This triggers a check for unconnected non-unique inputs, and
# raises the same error as vector access.
abs_name = prom_name2abs_name(self.model, name, 'input')
val = meta[src_name]['value']
else:
# This triggers a check for unconnected non-unique inputs, and
# raises the same error as vector access.
abs_name = prom_name2abs_name(self.model, name, 'input')
val = meta[abs_name]['value']
if val is not _undefined:
# Need to cache the "get" in case the user calls in-place numpy operations.
self._initial_condition_cache[name] = val
else:
if name in proms['output']:
name = proms['output'][name][0]
elif name in proms['input']:
name = prom_name2abs_name(self.model, name, 'input')
if name in meta: # local var
if name in self.model._outputs._views:
val = self.model._outputs[name]
else:
val = self.model._inputs[name]
elif name in self.model._discrete_outputs:
val = self.model._discrete_outputs[name]
elif name in self.model._discrete_inputs:
val = self.model._discrete_inputs[name]
if self.model.comm.size > 1:
allprocs_meta = self.model._var_allprocs_abs2meta
# check for remote var
if name in allprocs_meta:
abs_name = name
elif name in proms['output']:
abs_name = proms['output'][name][0]
elif name in proms['input']:
abs_name = proms['input'][name][0]
if abs_name in self._remote_var_set:
if self.__get_remote:
if abs_name in allprocs_meta and allprocs_meta[abs_name]['distributed']:
raise RuntimeError("Retrieval of the full distributed variable '%s' is not "
"supported." % abs_name)
loc_val = val
owner = self.model._owning_rank[abs_name]
if owner != self.model.comm.rank:
val = None
else:
owner = self.model._owning_rank[abs_name]
if val is _undefined:
val = None
new_val = self.model.comm.bcast(val, root=owner)
if loc_val is not None and loc_val is not _undefined:
val = loc_val
else:
val = new_val
elif val is _undefined:
raise RuntimeError(
"Variable '{}' is not local to rank {}. You can retrieve values from "
"other processes using "
"`problem.get_val(<name>, get_remote=True)`.".format(name, self.comm.rank))
if val is _undefined:
raise KeyError('Variable name "{}" not found.'.format(name))
return val
[docs] def get_val(self, name, units=None, indices=None, get_remote=False):
"""
Get an output/input variable.
Function is used if you want to specify display units.
Parameters
----------
name : str
Promoted or relative variable name in the root system's namespace.
units : str, optional
Units to convert to before upon return.
indices : int or list of ints or tuple of ints or int ndarray or Iterable or None, optional
Indices or slice to return.
get_remote : bool
If True, retrieve the value even if it is on a remote process. Note that if the
variable is remote on ANY process, this function must be called on EVERY process
in the Problem's MPI communicator.
Returns
-------
float or ndarray
The requested output/input variable.
"""
if get_remote:
self.__get_remote = True
try:
val = self[name]
finally:
self.__get_remote = False
else:
val = self[name]
if indices is not None:
val = val[indices]
if units is not None:
base_units = self._get_units(name)
if base_units is None:
msg = "Can't express variable '{}' with units of 'None' in units of '{}'."
raise TypeError(msg.format(name, units))
try:
scale, offset = get_conversion(base_units, units)
except TypeError:
msg = "Can't express variable '{}' with units of '{}' in units of '{}'."
raise TypeError(msg.format(name, base_units, units))
val = (val + offset) * scale
return val
def _get_units(self, name):
"""
Get the units for a variable name.
Parameters
----------
name : str
Promoted or relative variable name in the root system's namespace.
Returns
-------
str
Unit string.
"""
meta = self.model._var_allprocs_abs2meta
if name in meta:
return meta[name]['units']
proms = self.model._var_allprocs_prom2abs_list
if self._setup_status >= 1:
if name in proms['output']:
abs_name = prom_name2abs_name(self.model, name, 'output')
return meta[abs_name]['units']
elif name in proms['input']:
# This triggers a check for unconnected non-unique inputs, and
# raises the same error as vector access.
abs_name = prom_name2abs_name(self.model, name, 'input')
return meta[abs_name]['units']
raise KeyError('Variable name "{}" not found.'.format(name))
[docs] def __setitem__(self, name, value):
"""
Set an output/input variable.
Parameters
----------
name : str
Promoted or relative variable name in the root system's namespace.
value : float or ndarray or any python object
value to set this variable to.
"""
# Caching only needed if vectors aren't allocated yet.
if self._setup_status == 1:
self._initial_condition_cache[name] = value
else:
all_proms = self.model._var_allprocs_prom2abs_list
if name in all_proms['output']:
abs_name = all_proms['output'][name][0]
elif name in all_proms['input']:
abs_name = prom_name2abs_name(self.model, name, 'input')
else:
abs_name = name
if abs_name in self.model._outputs._views:
self.model._outputs[abs_name] = value
elif abs_name in self.model._inputs._views:
self.model._inputs[abs_name] = value
elif abs_name in self.model._discrete_outputs:
self.model._discrete_outputs[abs_name] = value
elif abs_name in self.model._discrete_inputs:
self.model._discrete_inputs[abs_name] = value
else:
# might be a remote var. If so, just do nothing on this proc
if abs_name in self.model._var_allprocs_abs2meta:
print("Variable '{}' is remote on rank {}. "
"Local assignment ignored.".format(name, self.comm.rank))
else:
raise KeyError('Variable name "{}" not found.'.format(name))
[docs] def set_val(self, name, value, units=None, indices=None):
"""
Set an output/input variable.
Function is used if you want to set a value using a different unit.
Parameters
----------
name : str
Promoted or relative variable name in the root system's namespace.
value : float or ndarray or list
Value to set this variable to.
units : str, optional
Units that value is defined in.
indices : int or list of ints or tuple of ints or int ndarray or Iterable or None, optional
Indices or slice to set to specified value.
"""
if units is not None:
base_units = self._get_units(name)
if base_units is None:
msg = "Can't set variable '{}' with units of 'None' to value with units of '{}'."
raise TypeError(msg.format(name, units))
try:
scale, offset = get_conversion(units, base_units)
except TypeError:
msg = "Can't set variable '{}' with units of '{}' to value with units of '{}'."
raise TypeError(msg.format(name, base_units, units))
value = (value + offset) * scale
if indices is not None:
self[name][indices] = value
else:
self[name] = value
def _set_initial_conditions(self):
"""
Set all initial conditions that have been saved in cache after setup.
"""
for name, value in iteritems(self._initial_condition_cache):
self[name] = value
# Clean up cache
self._initial_condition_cache = {}
@property
def root(self):
"""
Provide 'root' property for backwards compatibility.
Returns
-------
<Group>
reference to the 'model' property.
"""
warn_deprecation("The 'root' property provides backwards compatibility "
"with OpenMDAO <= 1.x ; use 'model' instead.")
return self.model
@root.setter
def root(self, model):
"""
Provide for setting the 'root' property for backwards compatibility.
Parameters
----------
model : <Group>
reference to a <Group> to be assigned to the 'model' property.
"""
warn_deprecation("The 'root' property provides backwards compatibility "
"with OpenMDAO <= 1.x ; use 'model' instead.")
self.model = model
[docs] def run_model(self, case_prefix=None, reset_iter_counts=True):
"""
Run the model by calling the root system's solve_nonlinear.
Parameters
----------
case_prefix : str or None
Prefix to prepend to coordinates when recording.
reset_iter_counts : bool
If True and model has been run previously, reset all iteration counters.
"""
if self._mode is None:
raise RuntimeError("The `setup` method must be called before `run_model`.")
if case_prefix:
if not isinstance(case_prefix, str):
raise TypeError("The 'case_prefix' argument should be a string.")
self._recording_iter.prefix = case_prefix
else:
self._recording_iter.prefix = None
if self.model.iter_count > 0 and reset_iter_counts:
self.driver.iter_count = 0
self.model._reset_iter_counts()
self.final_setup()
self.model._clear_iprint()
self.model.run_solve_nonlinear()
[docs] def run_driver(self, case_prefix=None, reset_iter_counts=True):
"""
Run the driver on the model.
Parameters
----------
case_prefix : str or None
Prefix to prepend to coordinates when recording.
reset_iter_counts : bool
If True and model has been run previously, reset all iteration counters.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
if self._mode is None:
raise RuntimeError("The `setup` method must be called before `run_driver`.")
if case_prefix:
if not isinstance(case_prefix, str):
raise TypeError("The 'case_prefix' argument should be a string.")
self._recording_iter.prefix = case_prefix
else:
self._recording_iter.prefix = None
if self.model.iter_count > 0 and reset_iter_counts:
self.driver.iter_count = 0
self.model._reset_iter_counts()
self.final_setup()
self.model._clear_iprint()
return self.driver.run()
[docs] def run_once(self):
"""
Backward compatible call for run_model.
"""
warn_deprecation("The 'run_once' method provides backwards compatibility with "
"OpenMDAO <= 1.x ; use 'run_model' instead.")
self.run_model()
[docs] def run(self):
"""
Backward compatible call for run_driver.
Returns
-------
boolean
Failure flag; True if failed to converge, False is successful.
"""
warn_deprecation("The 'run' method provides backwards compatibility with "
"OpenMDAO <= 1.x ; use 'run_driver' instead.")
return self.run_driver()
def _setup_recording(self):
"""
Set up case recording.
"""
self._filtered_vars_to_record = self.driver._get_vars_to_record(self.recording_options)
self._rec_mgr.startup(self)
[docs] def add_recorder(self, recorder):
"""
Add a recorder to the problem.
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()
# clean up driver and model resources
self.driver.cleanup()
for system in self.model.system_iter(include_self=True, recurse=True):
system.cleanup()
[docs] def record_iteration(self, case_name):
"""
Record the variables at the Problem level.
Parameters
----------
case_name : str
Name used to identify this Problem case.
"""
if not self._rec_mgr._recorders:
return
# Get the data to record (collective calls that get across all ranks)
opts = self.recording_options
filt = self._filtered_vars_to_record
model = self.model
driver = self.driver
if opts['record_desvars']:
des_vars = driver.get_design_var_values()
else:
des_vars = {}
if opts['record_objectives']:
obj_vars = driver.get_objective_values()
else:
obj_vars = {}
if opts['record_constraints']:
con_vars = driver.get_constraint_values()
else:
con_vars = {}
des_vars = {name: des_vars[name] for name in filt['des']}
obj_vars = {name: obj_vars[name] for name in filt['obj']}
con_vars = {name: con_vars[name] for name in filt['con']}
names = model._outputs._names
views = model._outputs._views
sys_vars = {name: views[name] for name in names if name in filt['sys']}
if MPI:
des_vars = driver._gather_vars(model, des_vars)
obj_vars = driver._gather_vars(model, obj_vars)
con_vars = driver._gather_vars(model, con_vars)
sys_vars = driver._gather_vars(model, sys_vars)
outs = {}
if not MPI or model.comm.rank == 0:
outs.update(des_vars)
outs.update(obj_vars)
outs.update(con_vars)
outs.update(sys_vars)
data = {
'out': outs,
}
metadata = create_local_meta(case_name)
self._rec_mgr.record_iteration(self, data, metadata)
[docs] def setup(self, vector_class=None, check=False, logger=None, mode='auto',
force_alloc_complex=False, distributed_vector_class=PETScVector,
local_vector_class=DefaultVector, derivatives=True):
"""
Set up the model hierarchy.
When `setup` is called, the model hierarchy is assembled, the processors are allocated
(for MPI), and variables and connections are all assigned. This method traverses down
the model hierarchy to call `setup` on each subsystem, and then traverses up the model
hierarchy to call `configure` on each subsystem.
Parameters
----------
vector_class : type
Reference to an actual <Vector> class; not an instance. This is deprecated. Use
distributed_vector_class instead.
check : boolean
whether to run config check after setup is complete.
logger : object
Object for logging config checks if check is True.
mode : string
Derivatives calculation mode, 'fwd' for forward, and 'rev' for
reverse (adjoint). Default is 'auto', which will pick 'fwd' or 'rev' based on
the direction resulting in the smallest number of linear solves required to
compute derivatives.
force_alloc_complex : bool
Force allocation of imaginary part in nonlinear vectors. OpenMDAO can generally
detect when you need to do this, but in some cases (e.g., complex step is used
after a reconfiguration) you may need to set this to True.
distributed_vector_class : type
Reference to the <Vector> class or factory function used to instantiate vectors
and associated transfers involved in interprocess communication.
local_vector_class : type
Reference to the <Vector> class or factory function used to instantiate vectors
and associated transfers involved in intraprocess communication.
derivatives : bool
If True, perform any memory allocations necessary for derivative computation.
Returns
-------
self : <Problem>
this enables the user to instantiate and setup in one line.
"""
model = self.model
model.force_alloc_complex = force_alloc_complex
comm = self.comm
if vector_class is not None:
warn_deprecation("'vector_class' has been deprecated. Use "
"'distributed_vector_class' and/or 'local_vector_class' instead.")
distributed_vector_class = vector_class
# PETScVector is required for MPI
if comm.size > 1:
if PETScVector is None:
raise ValueError("Attempting to run in parallel under MPI but PETScVector could not"
"be imported.")
elif distributed_vector_class is not PETScVector:
raise ValueError("The `distributed_vector_class` argument must be `PETScVector` "
"when running in parallel under MPI but '%s' was specified."
% distributed_vector_class.__name__)
if mode not in ['fwd', 'rev', 'auto']:
msg = "Unsupported mode: '%s'. Use either 'fwd' or 'rev'." % mode
raise ValueError(msg)
self._mode = self._orig_mode = mode
# this will be shared by all Solvers in the model
model._solver_info = SolverInfo()
self._recording_iter = _RecIteration()
model._recording_iter = self._recording_iter
model_comm = self.driver._setup_comm(comm)
model._setup(model_comm, 'full', mode, distributed_vector_class, local_vector_class,
derivatives, self.options)
# get set of all vars that we may need to bcast later
self._remote_var_set = remote_var_set = set()
if model_comm.size > 1:
for type_ in ('input', 'output'):
remote_discrete = set()
sizes = self.model._var_sizes['nonlinear'][type_]
for i, vname in enumerate(self.model._var_allprocs_abs_names[type_]):
if not np.all(sizes[:, i]):
remote_var_set.add(vname)
if self.model._var_allprocs_discrete[type_]:
local = list(self.model._var_discrete[type_])
byrank = self.comm.gather(local, root=0)
if model_comm.rank == 0:
full = set()
for names in byrank:
full.update(names)
for names in byrank:
diff = full.difference(names)
remote_discrete.update(diff)
model_comm.bcast(remote_discrete, root=0)
else:
remote_discrete = model_comm.bcast(None, root=0)
remote_var_set.update(remote_discrete)
# Cache all args for final setup.
self._check = check
self._logger = logger
self._force_alloc_complex = force_alloc_complex
self._setup_status = 1
return self
[docs] def final_setup(self):
"""
Perform final setup phase on problem in preparation for run.
This is the second phase of setup, and is done automatically at the start of `run_driver`
and `run_model`. At the beginning of final_setup, we have a model hierarchy with defined
variables, solvers, case_recorders, and derivative settings. During this phase, the vectors
are created and populated, the drivers and solvers are initialized, and the recorders are
started, and the rest of the framework is prepared for execution.
"""
driver = self.driver
response_size, desvar_size = driver._update_voi_meta(self.model)
# update mode if it's been set to 'auto'
if self._orig_mode == 'auto':
mode = 'rev' if response_size < desvar_size else 'fwd'
self._mode = mode
else:
mode = self._orig_mode
if self._setup_status < 2:
self.model._final_setup(self.comm, 'full',
force_alloc_complex=self._force_alloc_complex)
driver._setup_driver(self)
coloring = driver._coloring_info['coloring']
if coloring is coloring_mod._STD_COLORING_FNAME:
coloring = driver._get_static_coloring()
if (coloring and coloring is not coloring_mod._DYN_COLORING and
coloring_mod._use_total_sparsity):
# if we're using simultaneous total derivatives then our effective size is less
# than the full size
if coloring._fwd and coloring._rev:
pass # we're doing both!
elif mode == 'fwd' and coloring._fwd:
desvar_size = coloring.total_solves()
elif mode == 'rev' and coloring._rev:
response_size = coloring.total_solves()
if ((mode == 'fwd' and desvar_size > response_size) or
(mode == 'rev' and response_size > desvar_size)):
simple_warning("Inefficient choice of derivative mode. You chose '%s' for a "
"problem with %d design variables and %d response variables "
"(objectives and nonlinear constraints)." %
(mode, desvar_size, response_size), RuntimeWarning)
# we only want to set up recording once, after problem setup
if self._setup_status == 1:
driver._setup_recording()
self._setup_recording()
record_viewer_data(self)
# Now that setup has been called, we can set the iprints.
for items in self._solver_print_cache:
self.set_solver_print(level=items[0], depth=items[1], type_=items[2])
self._solver_print_cache = []
if self._check:
if self._check is True:
checks = _default_checks
else:
checks = self._check
if self.comm.rank == 0:
logger = self._logger
else:
logger = TestLogger()
self.check_config(logger, checks=checks)
if self._setup_status < 2:
self._setup_status = 2
self._set_initial_conditions()
# check for post-setup hook
if Problem._post_setup_func is not None:
Problem._post_setup_func(self)
[docs] def check_partials(self, out_stream=_DEFAULT_OUT_STREAM, includes=None, excludes=None,
compact_print=False, abs_err_tol=1e-6, rel_err_tol=1e-6,
method='fd', step=None, form='forward', step_calc='abs',
force_dense=True, show_only_incorrect=False):
"""
Check partial derivatives comprehensively for all components in your model.
Parameters
----------
out_stream : file-like object
Where to send human readable output. By default it goes to stdout.
Set to None to suppress.
includes : None or list_like
List of glob patterns for pathnames to include in the check. Default is None, which
includes all components in the model.
excludes : None or list_like
List of glob patterns for pathnames to exclude from the check. Default is None, which
excludes nothing.
compact_print : bool
Set to True to just print the essentials, one line per unknown-param pair.
abs_err_tol : float
Threshold value for absolute error. Errors about this value will have a '*' displayed
next to them in output, making them easy to search for. Default is 1.0E-6.
rel_err_tol : float
Threshold value for relative error. Errors about this value will have a '*' displayed
next to them in output, making them easy to search for. Note at times there may be a
significant relative error due to a minor absolute error. Default is 1.0E-6.
method : str
Method, 'fd' for finite difference or 'cs' for complex step. Default is 'fd'.
step : float
Step size for approximation. Default is None, which means 1e-6 for 'fd' and 1e-40 for
'cs'.
form : string
Form for finite difference, can be 'forward', 'backward', or 'central'. Default
'forward'.
step_calc : string
Step type for finite difference, can be 'abs' for absolute', or 'rel' for relative.
Default is 'abs'.
force_dense : bool
If True, analytic derivatives will be coerced into arrays. Default is True.
show_only_incorrect : bool, optional
Set to True if output should print only the subjacs found to be incorrect.
Returns
-------
dict of dicts of dicts
First key:
is the component name;
Second key:
is the (output, input) tuple of strings;
Third key:
is one of ['rel error', 'abs error', 'magnitude', 'J_fd', 'J_fwd', 'J_rev'];
For 'rel error', 'abs error', 'magnitude' the value is: A tuple containing norms for
forward - fd, adjoint - fd, forward - adjoint.
For 'J_fd', 'J_fwd', 'J_rev' the value is: A numpy array representing the computed
Jacobian for the three different methods of computation.
"""
if self._setup_status < 2:
self.final_setup()
model = self.model
if not model._use_derivatives:
raise RuntimeError("Can't check partials. Derivative support has been turned off.")
# TODO: Once we're tracking iteration counts, run the model if it has not been run before.
includes = [includes] if isinstance(includes, str) else includes
excludes = [excludes] if isinstance(excludes, str) else excludes
comps = []
for comp in model.system_iter(typ=Component, include_self=True):
if isinstance(comp, IndepVarComp):
continue
name = comp.pathname
# Process includes
if includes is not None:
for pattern in includes:
if fnmatchcase(name, pattern):
break
else:
continue
# Process excludes
if excludes is not None:
match = False
for pattern in excludes:
if fnmatchcase(name, pattern):
match = True
break
if match:
continue
comps.append(comp)
self.set_solver_print(level=0)
# This is a defaultdict of (defaultdict of dicts).
partials_data = defaultdict(lambda: defaultdict(dict))
# Caching current point to restore after setups.
input_cache = model._inputs._clone()
output_cache = model._outputs._clone()
# Keep track of derivative keys that are declared dependent so that we don't print them
# unless they are in error.
indep_key = {}
# Analytic Jacobians
for mode in ('fwd', 'rev'):
model._inputs.set_vec(input_cache)
model._outputs.set_vec(output_cache)
# Make sure we're in a valid state
model.run_apply_nonlinear()
jac_key = 'J_' + mode
for comp in comps:
# Only really need to linearize once.
if mode == 'fwd':
comp.run_linearize()
explicit = isinstance(comp, ExplicitComponent)
matrix_free = comp.matrix_free
c_name = comp.pathname
indep_key[c_name] = set()
# TODO: Check deprecated deriv_options.
with comp._unscaled_context():
of_list, wrt_list = \
comp._get_potential_partials_lists(include_wrt_outputs=not explicit)
# Matrix-free components need to calculate their Jacobian by matrix-vector
# product.
if matrix_free:
dstate = comp._vectors['output']['linear']
if mode == 'fwd':
dinputs = comp._vectors['input']['linear']
doutputs = comp._vectors['residual']['linear']
in_list = wrt_list
out_list = of_list
else:
dinputs = comp._vectors['residual']['linear']
doutputs = comp._vectors['input']['linear']
in_list = of_list
out_list = wrt_list
for inp in in_list:
inp_abs = rel_name2abs_name(comp, inp)
try:
flat_view = dinputs._views_flat[inp_abs]
except KeyError:
# Implicit state
flat_view = dstate._views_flat[inp_abs]
n_in = len(flat_view)
for idx in range(n_in):
dinputs.set_const(0.0)
dstate.set_const(0.0)
# Dictionary access returns a scalar for 1d input, and we
# need a vector for clean code, so use _views_flat.
flat_view[idx] = 1.0
# Matrix Vector Product
comp._apply_linear(None, ['linear'], _contains_all, mode)
for out in out_list:
out_abs = rel_name2abs_name(comp, out)
try:
derivs = doutputs._views_flat[out_abs]
except KeyError:
# Implicit state
derivs = dstate._views_flat[out_abs]
if mode == 'fwd':
key = out, inp
deriv = partials_data[c_name][key]
# Allocate first time
if jac_key not in deriv:
shape = (len(derivs), n_in)
deriv[jac_key] = np.zeros(shape)
deriv[jac_key][:, idx] = derivs
else:
key = inp, out
deriv = partials_data[c_name][key]
# Allocate first time
if jac_key not in deriv:
shape = (n_in, len(derivs))
deriv[jac_key] = np.zeros(shape)
deriv[jac_key][idx, :] = derivs
# These components already have a Jacobian with calculated derivatives.
else:
subjacs = comp._jacobian._subjacs_info
for rel_key in product(of_list, wrt_list):
abs_key = rel_key2abs_key(comp, rel_key)
of, wrt = abs_key
# No need to calculate partials; they are already stored
try:
deriv_value = subjacs[abs_key]['value']
rows = subjacs[abs_key]['rows']
except KeyError:
deriv_value = rows = None
# Testing for pairs that are not dependent so that we suppress printing
# them unless the fd is non zero. Note: subjacs_info is empty for
# undeclared partials, which is the default behavior now.
try:
if not subjacs[abs_key]['dependent']:
indep_key[c_name].add(rel_key)
except KeyError:
indep_key[c_name].add(rel_key)
if deriv_value is None:
# Missing derivatives are assumed 0.
in_size = comp._var_abs2meta[wrt]['size']
out_size = comp._var_abs2meta[of]['size']
deriv_value = np.zeros((out_size, in_size))
if force_dense:
if rows is not None:
try:
in_size = comp._var_abs2meta[wrt]['size']
except KeyError:
in_size = comp._var_abs2meta[wrt]['size']
out_size = comp._var_abs2meta[of]['size']
tmp_value = np.zeros((out_size, in_size))
# if a scalar value is provided (in declare_partials),
# expand to the correct size array value for zipping
if deriv_value.size == 1:
deriv_value *= np.ones(rows.size)
for i, j, val in zip(rows, subjacs[abs_key]['cols'],
deriv_value):
tmp_value[i, j] += val
deriv_value = tmp_value
elif sparse.issparse(deriv_value):
deriv_value = deriv_value.todense()
partials_data[c_name][rel_key][jac_key] = deriv_value.copy()
model._inputs.set_vec(input_cache)
model._outputs.set_vec(output_cache)
model.run_apply_nonlinear()
# Finite Difference to calculate Jacobian
jac_key = 'J_fd'
alloc_complex = model._outputs._alloc_complex
all_fd_options = {}
comps_could_not_cs = set()
requested_method = method
for comp in comps:
c_name = comp.pathname
all_fd_options[c_name] = {}
explicit = isinstance(comp, ExplicitComponent)
approximations = {'fd': FiniteDifference(),
'cs': ComplexStep()}
of, wrt = comp._get_potential_partials_lists(include_wrt_outputs=not explicit)
# Load up approximation objects with the requested settings.
local_opts = comp._get_check_partial_options()
for rel_key in product(of, wrt):
abs_key = rel_key2abs_key(comp, rel_key)
local_wrt = rel_key[1]
# Determine if fd or cs.
method = requested_method
if local_wrt in local_opts:
local_method = local_opts[local_wrt]['method']
if local_method:
method = local_method
# We can't use CS if we haven't allocated a complex vector, so we fall back on fd.
if method == 'cs' and not alloc_complex:
comps_could_not_cs.add(c_name)
method = 'fd'
fd_options = {'order': None,
'method': method}
if method == 'cs':
defaults = ComplexStep.DEFAULT_OPTIONS
fd_options['form'] = None
fd_options['step_calc'] = None
elif method == 'fd':
defaults = FiniteDifference.DEFAULT_OPTIONS
fd_options['form'] = form
fd_options['step_calc'] = step_calc
if step and requested_method == method:
fd_options['step'] = step
else:
fd_options['step'] = defaults['step']
# Precedence: component options > global options > defaults
if local_wrt in local_opts:
for name in ['form', 'step', 'step_calc', 'directional']:
value = local_opts[local_wrt][name]
if value is not None:
fd_options[name] = value
all_fd_options[c_name][local_wrt] = fd_options
approximations[fd_options['method']].add_approximation(abs_key, self.model,
fd_options)
approx_jac = {}
for approximation in itervalues(approximations):
# Perform the FD here.
approximation.compute_approximations(comp, jac=approx_jac)
for abs_key, partial in iteritems(approx_jac):
rel_key = abs_key2rel_key(comp, abs_key)
partials_data[c_name][rel_key][jac_key] = partial
# If this is a directional derivative, convert the analytic to a directional one.
wrt = rel_key[1]
if wrt in local_opts and local_opts[wrt]['directional']:
deriv = partials_data[c_name][rel_key]
for key in ['J_fwd', 'J_rev']:
deriv[key] = np.atleast_2d(np.sum(deriv[key], axis=1)).T
# Conversion of defaultdict to dicts
partials_data = {comp_name: dict(outer) for comp_name, outer in iteritems(partials_data)}
if out_stream == _DEFAULT_OUT_STREAM:
out_stream = sys.stdout
if len(comps_could_not_cs) > 0:
msg = "The following components requested complex step, but force_alloc_complex " + \
"has not been set to True, so finite difference was used: "
msg += str(list(comps_could_not_cs))
msg += "\nTo enable complex step, specify 'force_alloc_complex=True' when calling " + \
"setup on the problem, e.g. 'problem.setup(force_alloc_complex=True)'"
simple_warning(msg)
_assemble_derivative_data(partials_data, rel_err_tol, abs_err_tol, out_stream,
compact_print, comps, all_fd_options, indep_key=indep_key,
all_comps_provide_jacs=not self.model.matrix_free,
show_only_incorrect=show_only_incorrect)
return partials_data
[docs] def check_totals(self, of=None, wrt=None, out_stream=_DEFAULT_OUT_STREAM, compact_print=False,
driver_scaling=False, abs_err_tol=1e-6, rel_err_tol=1e-6,
method='fd', step=None, form=None, step_calc='abs'):
"""
Check total derivatives for the model vs. finite difference.
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.
out_stream : file-like object
Where to send human readable output. By default it goes to stdout.
Set to None to suppress.
compact_print : bool
Set to True to just print the essentials, one line per unknown-param pair.
driver_scaling : bool
When True, return derivatives 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 False, which is unscaled.
abs_err_tol : float
Threshold value for absolute error. Errors about this value will have a '*' displayed
next to them in output, making them easy to search for. Default is 1.0E-6.
rel_err_tol : float
Threshold value for relative error. Errors about this value will have a '*' displayed
next to them in output, making them easy to search for. Note at times there may be a
significant relative error due to a minor absolute error. Default is 1.0E-6.
method : str
Method, 'fd' for finite difference or 'cs' for complex step. Default is 'fd'
step : float
Step size for approximation. Default is None, which means 1e-6 for 'fd' and 1e-40 for
'cs'.
form : string
Form for finite difference, can be 'forward', 'backward', or 'central'. Default
None, which defaults to 'forward' for FD.
step_calc : string
Step type for finite difference, can be 'abs' for absolute', or 'rel' for relative.
Default is 'abs'.
Returns
-------
Dict of Dicts of Tuples of Floats
First key:
is the (output, input) tuple of strings;
Second key:
is one of ['rel error', 'abs error', 'magnitude', 'fdstep'];
For 'rel error', 'abs error', 'magnitude' the value is: A tuple containing norms for
forward - fd, adjoint - fd, forward - adjoint.
"""
if self._setup_status < 2:
raise RuntimeError("run_model must be called before total derivatives can be checked.")
model = self.model
if method == 'cs' and not model._outputs._alloc_complex:
msg = "\nTo enable complex step, specify 'force_alloc_complex=True' when calling " + \
"setup on the problem, e.g. 'problem.setup(force_alloc_complex=True)'"
raise RuntimeError(msg)
# TODO: Once we're tracking iteration counts, run the model if it has not been run before.
# Calculate Total Derivatives
total_info = _TotalJacInfo(self, of, wrt, False, return_format='flat_dict',
driver_scaling=driver_scaling)
Jcalc = total_info.compute_totals()
if step is None:
if method == 'cs':
step = ComplexStep.DEFAULT_OPTIONS['step']
else:
step = FiniteDifference.DEFAULT_OPTIONS['step']
# Approximate FD
fd_args = {
'step': step,
'form': form,
'step_calc': step_calc,
}
approx = model._owns_approx_jac
old_jac = model._jacobian
old_subjacs = model._subjacs_info.copy()
model.approx_totals(method=method, step=step, form=form,
step_calc=step_calc if method is 'fd' else None)
total_info = _TotalJacInfo(self, of, wrt, False, return_format='flat_dict', approx=True,
driver_scaling=driver_scaling)
Jfd = total_info.compute_totals_approx(initialize=True)
# reset the _owns_approx_jac flag after approximation is complete.
if not approx:
model._jacobian = old_jac
model._owns_approx_jac = False
model._subjacs_info = old_subjacs
# Assemble and Return all metrics.
data = {}
data[''] = {}
for key, val in iteritems(Jcalc):
data[''][key] = {}
data[''][key]['J_fwd'] = val
data[''][key]['J_fd'] = Jfd[key]
fd_args['method'] = 'fd'
if out_stream == _DEFAULT_OUT_STREAM:
out_stream = sys.stdout
_assemble_derivative_data(data, rel_err_tol, abs_err_tol, out_stream, compact_print,
[model], {'': fd_args}, totals=True)
return data['']
[docs] def compute_totals(self, of=None, wrt=None, return_format='flat_dict', debug_print=False,
driver_scaling=False):
"""
Compute derivatives of desired quantities with respect to desired inputs.
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. Can be either 'dict' or 'flat_dict'.
Default is a 'flat_dict', which returns them in a dictionary whose keys are
tuples of form (of, wrt).
debug_print : bool
Set to True to print out some debug information during linear solve.
driver_scaling : bool
When True, return derivatives 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 False, which is unscaled.
Returns
-------
derivs : object
Derivatives in form requested by 'return_format'.
"""
if self._setup_status < 2:
self.final_setup()
if self.model._owns_approx_jac:
total_info = _TotalJacInfo(self, of, wrt, False, return_format,
approx=True, driver_scaling=driver_scaling)
return total_info.compute_totals_approx(initialize=True)
else:
total_info = _TotalJacInfo(self, of, wrt, False, return_format,
debug_print=debug_print, driver_scaling=driver_scaling)
return total_info.compute_totals()
[docs] def set_solver_print(self, level=2, depth=1e99, type_='all'):
"""
Control printing for solvers and subsolvers in the model.
Parameters
----------
level : int
iprint level. Set to 2 to print residuals each iteration; set to 1
to print just the iteration totals; set to 0 to disable all printing
except for failures, and set to -1 to disable all printing including failures.
depth : int
How deep to recurse. For example, you can set this to 0 if you only want
to print the top level linear and nonlinear solver messages. Default
prints everything.
type_ : str
Type of solver to set: 'LN' for linear, 'NL' for nonlinear, or 'all' for all.
"""
if (level, depth, type_) not in self._solver_print_cache:
self._solver_print_cache.append((level, depth, type_))
self.model._set_solver_print(level=level, depth=depth, type_=type_)
[docs] def list_problem_vars(self,
show_promoted_name=True,
print_arrays=False,
desvar_opts=[],
cons_opts=[],
objs_opts=[],
):
"""
Print all design variables and responses (objectives and constraints).
Parameters
----------
show_promoted_name : bool
If True, then show the promoted names of the variables.
print_arrays : bool, optional
When False, in the columnar display, just display norm of any ndarrays with size > 1.
The norm is surrounded by vertical bars to indicate that it is a norm.
When True, also display full values of the ndarray below the row. Format is affected
by the values set with numpy.set_printoptions
Default is False.
desvar_opts : list of str
List of optional columns to be displayed in the desvars table.
Allowed values are:
['lower', 'upper', 'ref', 'ref0', 'indices', 'adder', 'scaler', 'parallel_deriv_color',
'vectorize_derivs', 'cache_linear_solution']
cons_opts : list of str
List of optional columns to be displayed in the cons table.
Allowed values are:
['lower', 'upper', 'equals', 'ref', 'ref0', 'indices', 'index', 'adder', 'scaler',
'linear', 'parallel_deriv_color', 'vectorize_derivs',
'cache_linear_solution']
objs_opts : list of str
List of optional columns to be displayed in the objs table.
Allowed values are:
['ref', 'ref0', 'indices', 'adder', 'scaler',
'parallel_deriv_color', 'vectorize_derivs', 'cache_linear_solution']
"""
default_col_names = ['name', 'value', 'size']
# Design vars
desvars = self.model.get_design_vars()
header = "Design Variables"
col_names = default_col_names + desvar_opts
self._write_var_info_table(header, col_names, desvars,
show_promoted_name=show_promoted_name,
print_arrays=print_arrays,
col_spacing=2)
# Constraints
cons = self.model.get_constraints()
header = "Constraints"
col_names = default_col_names + cons_opts
self._write_var_info_table(header, col_names, cons, show_promoted_name=show_promoted_name,
print_arrays=print_arrays,
col_spacing=2)
objs = self.model.get_objectives()
header = "Objectives"
col_names = default_col_names + objs_opts
self._write_var_info_table(header, col_names, objs, show_promoted_name=show_promoted_name,
print_arrays=print_arrays,
col_spacing=2)
def _write_var_info_table(self, header, col_names, vars, print_arrays=False,
show_promoted_name=True, col_spacing=1):
"""
Write a table of information for the data in vars.
Parameters
----------
header : str
The header line for the table.
col_names : list of str
List of column labels.
vars : OrderedDict
Keys are variable names and values are metadata for the variables.
print_arrays : bool, optional
When False, in the columnar display, just display norm of any ndarrays with size > 1.
The norm is surrounded by vertical bars to indicate that it is a norm.
When True, also display full values of the ndarray below the row. Format is affected
by the values set with numpy.set_printoptions
Default is False.
show_promoted_name : bool
If True, then show the promoted names of the variables.
col_spacing : int
Number of spaces between columns in the table.
"""
abs2prom = self.model._var_abs2prom
# Get the values for all the elements in the tables
rows = []
for name, meta in iteritems(vars):
row = {}
for col_name in col_names:
if col_name == 'name':
if show_promoted_name:
row[col_name] = name
else:
if name in abs2prom['input']:
row[col_name] = abs2prom['input'][name]
else:
row[col_name] = abs2prom['output'][name]
elif col_name == 'value':
row[col_name] = self[name]
else:
row[col_name] = meta[col_name]
rows.append(row)
col_space = ' ' * col_spacing
print("-" * len(header))
print(header)
print("-" * len(header))
# loop through the rows finding the max widths
max_width = {}
for col_name in col_names:
max_width[col_name] = len(col_name)
for row in rows:
for col_name in col_names:
cell = row[col_name]
if isinstance(cell, np.ndarray) and cell.size > 1:
out = '|{}|'.format(str(np.linalg.norm(cell)))
else:
out = str(cell)
max_width[col_name] = max(len(out), max_width[col_name])
# print col headers
header_div = ''
header_col_names = ''
for col_name in col_names:
header_div += '-' * max_width[col_name] + col_space
header_col_names += pad_name(col_name, max_width[col_name], quotes=False) + col_space
print(header_col_names)
print(header_div[:-1])
# print rows with var info
for row in rows:
have_array_values = [] # keep track of which values are arrays
row_string = ''
for col_name in col_names:
cell = row[col_name]
if isinstance(cell, np.ndarray) and cell.size > 1:
out = '|{}|'.format(str(np.linalg.norm(cell)))
have_array_values.append(col_name)
else:
out = str(cell)
row_string += pad_name(out, max_width[col_name], quotes=False) + col_space
print(row_string)
if print_arrays:
left_column_width = max_width['name']
for col_name in have_array_values:
print("{}{}:".format((left_column_width + col_spacing) * ' ', col_name))
cell = row[col_name]
out_str = pprint.pformat(cell)
indented_lines = [(left_column_width + col_spacing) * ' ' +
s for s in out_str.splitlines()]
print('\n'.join(indented_lines) + '\n')
print()
[docs] def load_case(self, case):
"""
Pull all input and output variables from a case into the model.
Parameters
----------
case : Case object
A Case from a CaseRecorder file.
"""
inputs = case.inputs if case.inputs is not None else None
if inputs:
for name in inputs.absolute_names():
if name not in self.model._var_abs_names['input']:
raise KeyError("Input variable, '{}', recorded in the case is not "
"found in the model".format(name))
self[name] = inputs[name]
outputs = case.outputs if case.outputs is not None else None
if outputs:
for name in outputs.absolute_names():
if name not in self.model._var_abs_names['output']:
raise KeyError("Output variable, '{}', recorded in the case is not "
"found in the model".format(name))
self[name] = outputs[name]
return
[docs] def check_config(self, logger=None, checks=None, out_file='openmdao_checks.out'):
"""
Perform optional error checks on a Problem.
Parameters
----------
logger : object
Logging object.
checks : list of str or None
List of specific checks to be performed.
out_file : str or None
If not None, output will be written to this file in addition to stdout.
"""
if logger is None:
logger = get_logger('check_config', out_file=out_file, use_format=True)
if checks is None:
checks = sorted(_default_checks)
elif checks == 'all':
checks = sorted(_all_checks)
for c in checks:
if c not in _all_checks:
print("WARNING: '%s' is not a recognized check. Available checks are: %s" %
(c, sorted(_all_checks)))
continue
logger.info('checking %s' % c)
_all_checks[c](self, logger)
def _assemble_derivative_data(derivative_data, rel_error_tol, abs_error_tol, out_stream,
compact_print, system_list, global_options, totals=False,
indep_key=None, all_comps_provide_jacs=False,
show_only_incorrect=False):
"""
Compute the relative and absolute errors in the given derivatives and print to the out_stream.
Parameters
----------
derivative_data : dict
Dictionary containing derivative information keyed by system name.
rel_error_tol : float
Relative error tolerance.
abs_error_tol : float
Absolute error tolerance.
out_stream : file-like object
Where to send human readable output.
Set to None to suppress.
compact_print : bool
If results should be printed verbosely or in a table.
system_list : Iterable
The systems (in the proper order) that were checked.0
global_options : dict
Dictionary containing the options for the approximation.
totals : bool
Set to True if we are doing check_totals to skip a bunch of stuff.
indep_key : dict of sets, optional
Keyed by component name, contains the of/wrt keys that are declared not dependent.
all_comps_provide_jacs : bool, optional
Set to True if all components provide a Jacobian (are not matrix-free).
show_only_incorrect : bool, optional
Set to True if output should print only the subjacs found to be incorrect.
"""
nan = float('nan')
suppress_output = out_stream is None
if compact_print:
if totals:
deriv_line = "{0} wrt {1} | {2:.4e} | {3:.4e} | {4:.4e} | {5:.4e}"
else:
if not all_comps_provide_jacs:
deriv_line = "{0} wrt {1} | {2:.4e} | {3} | {4:.4e} | {5:.4e} | {6} | {7}" \
" | {8:.4e} | {9} | {10}"
else:
deriv_line = "{0} wrt {1} | {2:.4e} | {3:.4e} | {4:.4e} | {5:.4e}"
# Keep track of the worst subjac in terms of relative error for fwd and rev
if not suppress_output and compact_print and not totals:
worst_subjac_rel_err = 0.0
worst_subjac = None
if not suppress_output and not totals and show_only_incorrect:
out_stream.write('\n** Only writing information about components with '
'incorrect Jacobians **\n\n')
for system in system_list:
sys_name = system.pathname
sys_class_name = type(system).__name__
# Match header to appropriate type.
if isinstance(system, Component):
sys_type = 'Component'
elif isinstance(system, Group):
sys_type = 'Group'
else:
sys_type = type(system).__name__
if sys_name not in derivative_data:
msg = "No derivative data found for %s '%s'." % (sys_type, sys_name)
simple_warning(msg)
continue
derivatives = derivative_data[sys_name]
if totals:
sys_name = 'Full Model'
# Sorted keys ensures deterministic ordering
sorted_keys = sorted(iterkeys(derivatives))
if not suppress_output:
# Need to capture the output of a component's derivative
# info so that it can be used if that component is the
# worst subjac. That info is printed at the bottom of all the output
out_buffer = cStringIO()
num_bad_jacs = 0 # Keep track of number of bad derivative values for each component
if out_stream:
header_str = '-' * (len(sys_name) + len(sys_type) + len(sys_class_name) + 5) + '\n'
out_buffer.write(header_str)
out_buffer.write("{}: {} '{}'".format(sys_type, sys_class_name, sys_name) + '\n')
out_buffer.write(header_str)
if compact_print:
# Error Header
if totals:
header = "{0} wrt {1} | {2} | {3} | {4} | {5}"\
.format(
pad_name('<output>', 30, quotes=True),
pad_name('<variable>', 30, quotes=True),
pad_name('calc mag.'),
pad_name('check mag.'),
pad_name('a(cal-chk)'),
pad_name('r(cal-chk)'),
)
else:
max_width_of = len("'<output>'")
max_width_wrt = len("'<variable>'")
for of, wrt in sorted_keys:
max_width_of = max(max_width_of, len(of) + 2) # 2 to include quotes
max_width_wrt = max(max_width_wrt, len(wrt) + 2)
if not all_comps_provide_jacs:
header = \
"{0} wrt {1} | {2} | {3} | {4} | {5} | {6} | {7} | {8} | {9} | {10}" \
.format(
pad_name('<output>', max_width_of, quotes=True),
pad_name('<variable>', max_width_wrt, quotes=True),
pad_name('fwd mag.'),
pad_name('rev mag.'),
pad_name('check mag.'),
pad_name('a(fwd-chk)'),
pad_name('a(rev-chk)'),
pad_name('a(fwd-rev)'),
pad_name('r(fwd-chk)'),
pad_name('r(rev-chk)'),
pad_name('r(fwd-rev)')
)
else:
header = "{0} wrt {1} | {2} | {3} | {4} | {5}"\
.format(
pad_name('<output>', max_width_of, quotes=True),
pad_name('<variable>', max_width_wrt, quotes=True),
pad_name('fwd mag.'),
pad_name('check mag.'),
pad_name('a(fwd-chk)'),
pad_name('r(fwd-chk)'),
)
if out_stream:
out_buffer.write(header + '\n')
out_buffer.write('-' * len(header) + '\n' + '\n')
for of, wrt in sorted_keys:
if totals:
fd_opts = global_options['']
else:
fd_opts = global_options[sys_name][wrt]
derivative_info = derivatives[of, wrt]
forward = derivative_info['J_fwd']
if not totals:
reverse = derivative_info.get('J_rev')
fd = derivative_info['J_fd']
fwd_error = np.linalg.norm(forward - fd)
if totals:
rev_error = fwd_rev_error = None
else:
rev_error = np.linalg.norm(reverse - fd)
fwd_rev_error = np.linalg.norm(forward - reverse)
fwd_norm = np.linalg.norm(forward)
if totals:
rev_norm = None
else:
rev_norm = np.linalg.norm(reverse)
fd_norm = np.linalg.norm(fd)
derivative_info['abs error'] = abs_err = ErrorTuple(fwd_error, rev_error, fwd_rev_error)
derivative_info['magnitude'] = magnitude = MagnitudeTuple(fwd_norm, rev_norm, fd_norm)
if fd_norm == 0.:
if fwd_norm == 0.:
derivative_info['rel error'] = rel_err = ErrorTuple(nan, nan, nan)
else:
# If fd_norm is zero, let's use fwd_norm as the divisor for relative
# check. That way we don't accidentally squelch a legitimate problem.
if totals:
derivative_info['rel error'] = rel_err = ErrorTuple(fwd_error / fwd_norm,
nan,
nan)
else:
rel_err = ErrorTuple(fwd_error / fwd_norm,
rev_error / fwd_norm,
fwd_rev_error / fwd_norm)
derivative_info['rel error'] = rel_err
else:
if totals:
derivative_info['rel error'] = rel_err = ErrorTuple(fwd_error / fd_norm,
nan,
nan)
else:
derivative_info['rel error'] = rel_err = ErrorTuple(fwd_error / fd_norm,
rev_error / fd_norm,
fwd_rev_error / fd_norm)
# Skip printing the dependent keys if the derivatives are fine.
if not compact_print and indep_key is not None:
rel_key = (of, wrt)
if rel_key in indep_key[sys_name] and fd_norm < abs_error_tol:
del derivative_data[sys_name][rel_key]
continue
if not suppress_output:
directional = fd_opts.get('directional')
if compact_print:
if totals:
if out_stream:
out_stream.write(deriv_line.format(
pad_name(of, 30, quotes=True),
pad_name(wrt, 30, quotes=True),
magnitude.forward,
magnitude.fd,
abs_err.forward,
rel_err.forward,
) + '\n')
else:
error_string = ''
for error in abs_err:
if not np.isnan(error) and error >= abs_error_tol:
error_string += ' >ABS_TOL'
break
# See if this component has the greater
# error in the derivative computation
# compared to the other components so far
is_worst_subjac = False
for i, error in enumerate(rel_err):
if not np.isnan(error):
# only 1st and 2d errs
if i < 2 and error > worst_subjac_rel_err:
worst_subjac_rel_err = error
worst_subjac = (sys_type, sys_class_name, sys_name)
is_worst_subjac = True
if not np.isnan(error) and error >= rel_error_tol:
error_string += ' >REL_TOL'
break
if error_string: # Any error string indicates that at least one of the
# derivative calcs is greater than the rel tolerance
num_bad_jacs += 1
if out_stream:
if directional:
wrt = "(d)'%s'" % wrt
wrt_padded = pad_name(wrt, max_width_wrt, quotes=False)
else:
wrt_padded = pad_name(wrt, max_width_wrt, quotes=True)
if not all_comps_provide_jacs:
deriv_info_line = \
deriv_line.format(
pad_name(of, max_width_of, quotes=True),
wrt_padded,
magnitude.forward,
_format_if_not_matrix_free(
system.matrix_free, magnitude.reverse),
magnitude.fd,
abs_err.forward,
_format_if_not_matrix_free(system.matrix_free,
abs_err.reverse),
_format_if_not_matrix_free(
system.matrix_free, abs_err.forward_reverse),
rel_err.forward,
_format_if_not_matrix_free(system.matrix_free,
rel_err.reverse),
_format_if_not_matrix_free(
system.matrix_free, rel_err.forward_reverse),
)
else:
deriv_info_line = \
deriv_line.format(
pad_name(of, max_width_of, quotes=True),
wrt_padded,
magnitude.forward,
magnitude.fd,
abs_err.forward,
rel_err.forward,
)
if not show_only_incorrect or error_string:
out_buffer.write(deriv_info_line + error_string + '\n')
if is_worst_subjac:
worst_subjac_line = deriv_info_line
else: # not compact print
fd_desc = "{}:{}".format(fd_opts['method'], fd_opts['form'])
# Magnitudes
if out_stream:
if directional:
out_buffer.write(" {}: '{}' wrt (d)'{}'\n".format(sys_name, of, wrt))
else:
out_buffer.write(" {}: '{}' wrt '{}'\n".format(sys_name, of, wrt))
out_buffer.write(' Forward Magnitude : {:.6e}\n'.format(
magnitude.forward))
if not totals and system.matrix_free:
txt = ' Reverse Magnitude : {:.6e}'
if out_stream:
out_buffer.write(txt.format(magnitude.reverse) + '\n')
if out_stream:
out_buffer.write(' Fd Magnitude : {:.6e} ({})\n'.format(
magnitude.fd, fd_desc))
# Absolute Errors
if totals or not system.matrix_free:
error_descs = ('(Jfor - Jfd) ', )
else:
error_descs = ('(Jfor - Jfd) ', '(Jrev - Jfd) ', '(Jfor - Jrev)')
for error, desc in zip(abs_err, error_descs):
error_str = _format_error(error, abs_error_tol)
if error_str.endswith('*'):
num_bad_jacs += 1
if out_stream:
out_buffer.write(' Absolute Error {}: {}\n'.format(desc, error_str))
if out_stream:
out_buffer.write('\n')
# Relative Errors
for error, desc in zip(rel_err, error_descs):
error_str = _format_error(error, rel_error_tol)
if error_str.endswith('*'):
num_bad_jacs += 1
if out_stream:
out_buffer.write(' Relative Error {}: {}\n'.format(desc, error_str))
if out_stream:
if MPI and MPI.COMM_WORLD.size > 1:
out_buffer.write(' MPI Rank {}\n'.format(MPI.COMM_WORLD.rank))
out_buffer.write('\n')
# Raw Derivatives
if out_stream:
if directional:
out_buffer.write(' Directional Forward Derivative (Jfor)\n')
else:
out_buffer.write(' Raw Forward Derivative (Jfor)\n')
out_buffer.write(str(forward) + '\n')
out_buffer.write('\n')
if not totals and system.matrix_free:
if out_stream:
if directional:
out_buffer.write(' Directional Reverse Derivative (Jrev)\n')
else:
out_buffer.write(' Raw Reverse Derivative (Jrev)\n')
out_buffer.write(str(reverse) + '\n')
out_buffer.write('\n')
if out_stream:
if directional:
out_buffer.write(' Directional FD Derivative (Jfd)\n')
else:
out_buffer.write(' Raw FD Derivative (Jfd)\n')
out_buffer.write(str(fd) + '\n')
out_buffer.write('\n')
if out_stream:
out_buffer.write(' -' * 30 + '\n')
# End of if compact print if/else
# End of if not suppress_output
# End of for of, wrt in sorted_keys
if not show_only_incorrect or num_bad_jacs:
if out_stream and not suppress_output:
out_stream.write(out_buffer.getvalue())
# End of for system in system_list
if not suppress_output and compact_print and not totals:
if worst_subjac:
worst_subjac_header = \
"Sub Jacobian with Largest Relative Error: {1} '{2}'".format(*worst_subjac)
out_stream.write('\n' + '#' * len(worst_subjac_header) + '\n')
out_stream.write("{}\n".format(worst_subjac_header))
out_stream.write('#' * len(worst_subjac_header) + '\n')
out_stream.write(header + '\n')
out_stream.write('-' * len(header) + '\n')
out_stream.write(worst_subjac_line + '\n')
def _format_if_not_matrix_free(matrix_free, val):
"""
Return string to represent deriv check value in compact display.
Parameters
----------
matrix_free : bool
If True, then the associated Component is matrix-free.
val : float
The deriv check value.
Returns
-------
str
String which is the actual value if matrix-free, otherwise 'n/a'
"""
if matrix_free:
return '{0:.4e}'.format(val)
else:
return pad_name('n/a')
def _format_error(error, tol):
"""
Format the error, flagging if necessary.
Parameters
----------
error : float
The absolute or relative error.
tol : float
Tolerance above which errors are flagged
Returns
-------
str
Formatted and possibly flagged error.
"""
if np.isnan(error) or error < tol:
return '{:.6e}'.format(error)
return '{:.6e} *'.format(error)
