def __init__(self):
self.name = ''
self.pathname = ''
self._params_dict = OrderedDict()
self._unknowns_dict = OrderedDict()
# specify which variables are promoted up to the parent. Wildcards
# are allowed.
self._promotes = ()
self.comm = None
# create placeholders for all of the vectors
self.unknowns = PlaceholderVecWrapper('unknowns')
self.resids = PlaceholderVecWrapper('resids')
self.params = PlaceholderVecWrapper('params')
self.dunknowns = PlaceholderVecWrapper('dunknowns')
self.dresids = PlaceholderVecWrapper('dresids')
self.dparams = PlaceholderVecWrapper('dparams')
# dicts of vectors used for parallel solution of multiple RHS
self.dumat = {}
self.dpmat = {}
self.drmat = {}
opt = self.fd_options = OptionsDictionary()
opt.add_option('force_fd', False,
desc="Set to True to finite difference this system.")
opt.add_option('form', 'forward',
values=['forward', 'backward', 'central', 'complex_step'],
desc="Finite difference mode. (forward, backward, central) "
"You can also set to 'complex_step' to peform the complex "
"step method if your components support it.")
opt.add_option("step_size", 1.0e-6,
desc = "Default finite difference stepsize")
opt.add_option("step_type", 'absolute',
values=['absolute', 'relative'],
desc='Set to absolute, relative')
self._relevance = None
self._impl_factory = None
class System(object):
""" Base class for systems in OpenMDAO. When building models, user should
inherit from `Group` or `Component`"""
def __init__(self):
self.name = ''
self.pathname = ''
self._params_dict = OrderedDict()
self._unknowns_dict = OrderedDict()
# specify which variables are promoted up to the parent. Wildcards
# are allowed.
self._promotes = ()
self.comm = None
# create placeholders for all of the vectors
self.unknowns = PlaceholderVecWrapper('unknowns')
self.resids = PlaceholderVecWrapper('resids')
self.params = PlaceholderVecWrapper('params')
self.dunknowns = PlaceholderVecWrapper('dunknowns')
self.dresids = PlaceholderVecWrapper('dresids')
self.dparams = PlaceholderVecWrapper('dparams')
# dicts of vectors used for parallel solution of multiple RHS
self.dumat = {}
self.dpmat = {}
self.drmat = {}
opt = self.fd_options = OptionsDictionary()
opt.add_option('force_fd', False,
desc="Set to True to finite difference this system.")
opt.add_option('form', 'forward',
values=['forward', 'backward', 'central', 'complex_step'],
desc="Finite difference mode. (forward, backward, central) "
"You can also set to 'complex_step' to peform the complex "
"step method if your components support it.")
opt.add_option("step_size", 1.0e-6,
desc = "Default finite difference stepsize")
opt.add_option("step_type", 'absolute',
values=['absolute', 'relative'],
desc='Set to absolute, relative')
self._relevance = None
self._impl_factory = None
def __getitem__(self, name):
"""
Return the variable of the given name from this system.
Args
----
name : str
The name of the variable.
Returns
-------
value
The unflattened value of the given variable.
"""
msg = "Variable '%s' must be accessed from a containing Group"
raise RuntimeError(msg % name)
def promoted(self, name):
"""Determine if the given variable name is being promoted from this
`System`.
Args
----
name : str
The name of a variable, relative to this `System`.
Returns
-------
bool
True if the named variable is being promoted from this `System`.
Raises
------
TypeError
if the promoted variable specifications are not in a valid format
"""
if isinstance(self._promotes, string_types):
raise TypeError("'%s' promotes must be specified as a list, "
"tuple or other iterator of strings, but '%s' was specified" %
(self.name, self._promotes))
for prom in self._promotes:
if fnmatch(name, prom):
for meta in chain(self._params_dict.values(),
self._unknowns_dict.values()):
if name == meta.get('promoted_name'):
return True
return False
def _check_setup(self, out_stream=sys.stdout):
pass
def _check_promotes(self):
"""Check that the `System`s promotes are valid. Raise an Exception if there
are any promotes that do not match at least one variable in the `System`.
Raises
------
TypeError
if the promoted variable specifications are not in a valid format
RuntimeError
if a promoted variable specification does not match any variables
"""
if isinstance(self._promotes, string_types):
raise TypeError("'%s' promotes must be specified as a list, "
"tuple or other iterator of strings, but '%s' was specified" %
(self.name, self._promotes))
for prom in self._promotes:
found = False
for name, meta in chain(self._params_dict.items(), self._unknowns_dict.items()):
if fnmatch(meta.get('promoted_name', name), prom):
found = True
if not found:
msg = "'%s' promotes '%s' but has no variables matching that specification"
raise RuntimeError(msg % (self.name, prom))
def subsystems(self, local=False, recurse=False, include_self=False):
""" Returns an iterator over subsystems. For `System`, this is an empty list.
Args
----
local : bool, optional
If True, only return those `Components` that are local. Default is False.
recurse : bool, optional
If True, return all `Components` in the system tree, subject to
the value of the local arg. Default is False.
typ : type, optional
If a class is specified here, only those subsystems that are instances
of that type will be returned. Default type is `System`.
include_self : bool, optional
If True, yield self before iterating over subsystems, assuming type
of self is appropriate. Default is False.
Returns
-------
iterator
Iterator over subsystems.
"""
if include_self:
yield self
def _setup_paths(self, parent_path):
"""Set the absolute pathname of each `System` in the tree.
Parameter
---------
parent_path : str
The pathname of the parent `System`, which is to be prepended to the
name of this child `System`.
"""
if parent_path:
self.pathname = '.'.join((parent_path, self.name))
else:
self.pathname = self.name
def clear_dparams(self):
""" Zeros out the dparams (dp) vector."""
for parallel_set in self._relevance.vars_of_interest():
for name in parallel_set:
if name in self.dpmat:
self.dpmat[name].vec[:] = 0.0
self.dpmat[None].vec[:] = 0.0
# Recurse to clear all dparams vectors.
for system in self.subsystems(local=True):
system.clear_dparams()
def solve_linear(self, dumat, drmat, vois, mode=None):
"""
Single linear solution applied to whatever input is sitting in
the rhs vector.
Args
----
dumat : dict of `VecWrappers`
In forward mode, each `VecWrapper` contains the incoming vector
for the states. There is one vector per quantity of interest for
this problem. In reverse mode, it contains the outgoing vector for
the states. (du)
drmat : `dict of VecWrappers`
`VecWrapper` containing either the outgoing result in forward mode
or the incoming vector in reverse mode. There is one vector per
quantity of interest for this problem. (dr)
vois : list of strings
List of all quantities of interest to key into the mats.
mode : string
Derivative mode, can be 'fwd' or 'rev', but generally should be
called without mode so that the user can set the mode in this
system's ln_solver.options.
"""
pass
def is_active(self):
"""
Returns
-------
bool
If running under MPI, returns True if this `System` has a valid
communicator. Always returns True if not running under MPI.
"""
return MPI is None or self.comm != MPI.COMM_NULL
def get_req_procs(self):
"""
Returns
-------
tuple
A tuple of the form (min_procs, max_procs), indicating the min and max
processors usable by this `System`.
"""
return (1, 1)
def _setup_communicators(self, comm):
"""
Assign communicator to this `System` and all of its subsystems.
Args
----
comm : an MPI communicator (real or fake)
The communicator being offered by the parent system.
"""
self.comm = comm
def _set_vars_as_remote(self):
"""
Set 'remote' attribute in metadata of all variables for this subsystem.
"""
for meta in self._params_dict.values():
meta['remote'] = True
for meta in self._unknowns_dict.values():
meta['remote'] = True
def fd_jacobian(self, params, unknowns, resids, step_size=None, form=None,
step_type=None, total_derivs=False):
"""Finite difference across all unknowns in this system w.r.t. all
incoming params.
Args
----
params : `VecWrapper`
`VecWrapper` containing parameters. (p)
unknowns : `VecWrapper`
`VecWrapper` containing outputs and states. (u)
resids : `VecWrapper`
`VecWrapper` containing residuals. (r)
step_size : float, optional
Override all other specifications of finite difference step size.
form : float, optional
Override all other specifications of form. Can be forward,
backward, or central.
step_type : float, optional
Override all other specifications of step_type. Can be absolute
or relative.
total_derivs : bool, optional
Set to true to calculate total derivatives. Otherwise, partial
derivatives are returned.
Returns
-------
dict
Dictionary whose keys are tuples of the form ('unknown', 'param')
and whose values are ndarrays containing the derivative for that
tuple pair.
"""
# Params and Unknowns that we provide at this level.
fd_params = self._get_fd_params()
fd_unknowns = self._get_fd_unknowns()
# Function call arguments have precedence over the system dict.
step_size = self.fd_options.get('step_size', step_size)
form = self.fd_options.get('form', form)
step_type = self.fd_options.get('step_type', step_type)
jac = {}
cache2 = None
# Prepare for calculating partial derivatives or total derivatives
if total_derivs == False:
run_model = self.apply_nonlinear
cache1 = resids.vec.copy()
resultvec = resids
states = [name for name, meta in self.unknowns.items() if meta.get('state')]
else:
run_model = self.solve_nonlinear
cache1 = unknowns.vec.copy()
resultvec = unknowns
states = []
# Compute gradient for this param or state.
for p_name in chain(fd_params, states):
if p_name in states:
inputs = unknowns
else:
inputs = params
target_input = inputs.flat[p_name]
# If our input is connected to a Paramcomp, then we need to twiddle
# the unknowns vector instead of the params vector.
param_src = self.connections.get(p_name)
if param_src is not None:
# Have to convert to promoted name to key into unknowns
if param_src not in self.unknowns:
param_src = self.unknowns.get_promoted_varname(param_src)
target_input = unknowns.flat[param_src]
mydict = {}
for val in self._params_dict.values():
if val['promoted_name'] == p_name:
mydict = val
break
# Local settings for this var trump all
fdstep = mydict.get('fd_step_size', step_size)
fdtype = mydict.get('fd_step_type', step_type)
fdform = mydict.get('fd_form', form)
# Size our Inputs
p_size = np.size(inputs[p_name])
# Size our Outputs
for u_name in fd_unknowns:
u_size = np.size(unknowns[u_name])
jac[u_name, p_name] = np.ones((u_size, p_size))
# Finite Difference each index in array
for idx in range(p_size):
# Relative or Absolute step size
if fdtype == 'relative':
step = inputs.flat[p_name][idx] * fdstep
if step < fdstep:
step = fdstep
else:
step = fdstep
if fdform == 'forward':
target_input[idx] += step
run_model(params, unknowns, resids)
target_input[idx] -= step
# delta resid is delta unknown
resultvec.vec[:] -= cache1
resultvec.vec[:] *= (1.0/step)
elif fdform == 'backward':
target_input[idx] -= step
run_model(params, unknowns, resids)
target_input[idx] += step
# delta resid is delta unknown
resultvec.vec[:] -= cache1
resultvec.vec[:] *= (-1.0/step)
elif fdform == 'central':
target_input[idx] += step
run_model(params, unknowns, resids)
cache2 = resultvec.vec.copy()
target_input[idx] -= step
resultvec.vec[:] = cache1
target_input[idx] -= step
run_model(params, unknowns, resids)
# central difference formula
resultvec.vec[:] -= cache2
resultvec.vec[:] *= (-0.5/step)
target_input[idx] += step
for u_name in fd_unknowns:
jac[u_name, p_name][:, idx] = resultvec.flat[u_name]
# Restore old residual
resultvec.vec[:] = cache1
return jac
def _apply_linear_jac(self, params, unknowns, dparams, dunknowns, dresids, mode):
""" See apply_linear. This method allows the framework to override
any derivative specification in any `Component` or `Group` to perform
finite difference."""
if not self._jacobian_cache:
msg = ("No derivatives defined for Component '{name}'")
msg = msg.format(name=self.name)
raise ValueError(msg)
for key, J in iteritems(self._jacobian_cache):
unknown, param = key
# States are never in dparams.
if param in dparams:
arg_vec = dparams
elif param in dunknowns:
arg_vec = dunknowns
else:
continue
if unknown not in dresids:
continue
result = dresids[unknown]
# Vectors are flipped during adjoint
if mode == 'fwd':
dresids[unknown] += J.dot(arg_vec[param].flat).reshape(result.shape)
else:
arg_vec[param] += J.T.dot(result.flat).reshape(arg_vec[param].shape)
def _create_vecs(self, my_params, relevance, var_of_interest, impl):
""" This creates our vecs and mats."""
comm = self.comm
sys_pathname = self.pathname
params_dict = self._params_dict
unknowns_dict = self._unknowns_dict
self.comm = comm
# create implementation specific VecWrappers
if var_of_interest is None:
self.unknowns = impl.create_src_vecwrapper(sys_pathname, comm)
self.resids = impl.create_src_vecwrapper(sys_pathname, comm)
self.params = impl.create_tgt_vecwrapper(sys_pathname, comm)
# populate the VecWrappers with data
self.unknowns.setup(unknowns_dict, store_byobjs=True)
self.resids.setup(unknowns_dict)
self.params.setup(None, params_dict, self.unknowns,
my_params, self.connections, store_byobjs=True)
dunknowns = impl.create_src_vecwrapper(sys_pathname, comm)
dresids = impl.create_src_vecwrapper(sys_pathname, comm)
dparams = impl.create_tgt_vecwrapper(sys_pathname, comm)
dunknowns.setup(unknowns_dict, relevant_vars=relevance[var_of_interest])
dresids.setup(unknowns_dict, relevant_vars=relevance[var_of_interest])
dparams.setup(None, params_dict, self.unknowns, my_params,
self.connections,
relevant_vars=relevance[var_of_interest])
self.dumat[var_of_interest] = dunknowns
self.drmat[var_of_interest] = dresids
self.dpmat[var_of_interest] = dparams
def _create_views(self, top_unknowns, parent, my_params, relevance,
var_of_interest=None):
"""
A manager of the data transfer of a possibly distributed collection of
variables. The variables are based on views into an existing
`VecWrapper`.
Args
----
top_unknowns : `VecWrapper`
The `Problem` level unknowns `VecWrapper`.
parent : `System`
The `System` which provides the `VecWrapper` on which to create views.
my_params : list
List of pathnames for parameters that this `Group` is
responsible for propagating.
relevance : `Relevance`
Object containing relevance info for each variable of interest.
var_of_interest : str
The name of a variable of interest.
Returns
-------
`VecTuple`
A namedtuple of six (6) `VecWrappers`:
unknowns, dunknowns, resids, dresids, params, dparams.
"""
comm = self.comm
unknowns_dict = self._unknowns_dict
params_dict = self._params_dict
voi = var_of_interest
# map promoted name in parent to corresponding promoted name in this view
umap = _get_relname_map(parent.unknowns, unknowns_dict, self.pathname)
if voi is None:
self.unknowns = parent.unknowns.get_view(self.pathname, comm, umap, relevance,
voi)
self.resids = parent.resids.get_view(self.pathname, comm, umap, relevance,
voi)
self.params = parent._impl_factory.create_tgt_vecwrapper(self.pathname, comm)
self.params.setup(parent.params, params_dict, top_unknowns,
my_params, self.connections, store_byobjs=True)
self.dumat[voi] = parent.dumat[voi].get_view(self.pathname, comm, umap,
relevance, voi)
self.drmat[voi] = parent.drmat[voi].get_view(self.pathname, comm, umap,
relevance, voi)
self.dpmat[voi] = parent._impl_factory.create_tgt_vecwrapper(self.pathname, comm)
self.dpmat[voi].setup(parent.dpmat[voi], params_dict, top_unknowns,
my_params, self.connections,
relevant_vars=relevance[voi])
#def get_combined_jac(self, J):
#"""
#Take a J dict that's distributed, i.e., has different values across
#different MPI processes, and return a dict that contains all of the
#values from all of the processes. If values are duplicated, use the
#value from the lowest rank process. Note that J has a nested dict
#structure.
#Args
#----
#J : `dict`
#Distributed Jacobian
#Returns
#-------
#`dict`
#Local gathered Jacobian
#"""
#comm = self.comm
#if not self.is_active():
#return J
#myrank = comm.rank
#tups = []
## Gather a list of local tuples for J.
#for output, dct in J.items():
#for param, value in dct.items():
## Params are already only on this process. We need to add
## only outputs of components that are on this process.
#sub = getattr(self, output.partition('.')[0])
#if sub.is_active() and value is not None and value.size > 0:
#tups.append((output, param))
#dist_tups = comm.gather(tups, root=0)
#tupdict = {}
#if myrank == 0:
#for rank, tups in enumerate(dist_tups):
#for tup in tups:
#if not tup in tupdict:
#tupdict[tup] = rank
##get rid of tups from the root proc before bcast
#for tup, rank in tupdict.items():
#if rank == 0:
#del tupdict[tup]
#tupdict = comm.bcast(tupdict, root=0)
#if myrank == 0:
#for (param, output), rank in tupdict.items():
#J[param][output] = comm.recv(source=rank, tag=0)
#else:
#for (param, output), rank in tupdict.items():
#if rank == myrank:
#comm.send(J[param][output], dest=0, tag=0)
## FIXME: rework some of this using knowledge of local_var_sizes in order
## to avoid any unnecessary data passing
## return the combined dict
#return comm.bcast(J, root=0)
def _get_var_pathname(self, name):
if self.pathname:
return '.'.join((self.pathname, name))
return name