Source code for openmdao.core.component

"""Define the Component class."""

from collections import OrderedDict, Counter, defaultdict
from collections.abc import Iterable
from itertools import product

import numpy as np
from numpy import ndarray, isscalar, atleast_1d, atleast_2d, promote_types
from scipy.sparse import issparse

from openmdao.core.system import System, _supported_methods, _DEFAULT_COLORING_META, \
    global_meta_names
from openmdao.core.constants import _UNDEFINED, INT_DTYPE
from openmdao.jacobians.dictionary_jacobian import DictionaryJacobian
from openmdao.vectors.vector import _full_slice
from openmdao.utils.array_utils import shape_to_len
from openmdao.utils.units import valid_units
from openmdao.utils.name_maps import rel_key2abs_key, abs_key2rel_key, rel_name2abs_name
from openmdao.utils.mpi import MPI
from openmdao.utils.general_utils import format_as_float_or_array, ensure_compatible, \
    find_matches, simple_warning, make_set, _is_slicer_op
import openmdao.utils.coloring as coloring_mod


_forbidden_chars = ['.', '*', '?', '!', '[', ']']
_whitespace = set([' ', '\t', '\r', '\n'])


def _valid_var_name(name):
    """
    Determine if the proposed name is a valid variable name.

    Leading and trailing whitespace is illegal, and a specific list of characters
    are illegal anywhere in the string.

    Parameters
    ----------
    name : str
        Proposed name.

    Returns
    -------
    bool
        True if the proposed name is a valid variable name, else False.
    """
    global _forbidden_chars, _whitespace
    if not name:
        return False
    for char in _forbidden_chars:
        if char in name:
            return False
    return name[0] not in _whitespace and name[-1] not in _whitespace


[docs]class Component(System): """ Base Component class; not to be directly instantiated. Attributes ---------- _approx_schemes : OrderedDict A mapping of approximation types to the associated ApproximationScheme. _var_rel2meta : dict Dictionary mapping relative names to metadata. This is only needed while adding inputs and outputs. During setup, these are used to build the dictionaries of metadata. _static_var_rel2meta : dict Static version of above - stores data for variables added outside of setup. _var_rel_names : {'input': [str, ...], 'output': [str, ...]} List of relative names of owned variables existing on current proc. This is only needed while adding inputs and outputs. During setup, these are used to determine the list of absolute names. _static_var_rel_names : dict Static version of above - stores names of variables added outside of setup. _declared_partials : dict Cached storage of user-declared partials. _declared_partial_checks : list Cached storage of user-declared check partial options. _no_check_partials : bool If True, the check_partials function will ignore this component. """
[docs] def __init__(self, **kwargs): """ Initialize all attributes. Parameters ---------- **kwargs : dict of keyword arguments available here and in all descendants of this system. """ super().__init__(**kwargs) self._var_rel_names = {'input': [], 'output': []} self._var_rel2meta = {} self._static_var_rel_names = {'input': [], 'output': []} self._static_var_rel2meta = {} self._declared_partials = defaultdict(dict) self._declared_partial_checks = [] self._no_check_partials = False
def _declare_options(self): """ Declare options before kwargs are processed in the init method. """ super()._declare_options() self.options.declare('distributed', types=bool, default=False, desc='True if the component has variables that are distributed ' 'across multiple processes.')
[docs] def setup(self): """ Declare inputs and outputs. Available attributes: name pathname comm options """ pass
def _setup_procs(self, pathname, comm, mode, prob_meta): """ Execute first phase of the setup process. Distribute processors, assign pathnames, and call setup on the component. Parameters ---------- pathname : str Global name of the system, including the path. comm : MPI.Comm or <FakeComm> MPI communicator object. mode : str Derivatives calculation mode, 'fwd' for forward, and 'rev' for reverse (adjoint). Default is 'rev'. prob_meta : dict Problem level metadata. """ super()._setup_procs(pathname, comm, mode, prob_meta) orig_comm = comm if self._num_par_fd > 1: if comm.size > 1: comm = self._setup_par_fd_procs(comm) elif not MPI: msg = ("%s: MPI is not active but num_par_fd = %d. No parallel finite difference " "will be performed." % (self.msginfo, self._num_par_fd)) simple_warning(msg) self.comm = comm # Clear out old variable information so that we can call setup on the component. self._var_rel_names = {'input': [], 'output': []} self._var_rel2meta = {} # reset shape if any dynamic shape parameters are set in case this is a resetup # NOTE: this is necessary because we allow variables to be added in __init__. for meta in self._static_var_rel2meta.values(): if 'shape_by_conn' in meta and (meta['shape_by_conn'] or meta['copy_shape'] is not None): meta['shape'] = None if not np.isscalar(meta['value']): if meta['value'].size > 0: meta['value'] = meta['value'].flatten()[0] else: meta['value'] = 1.0 self._var_rel2meta.update(self._static_var_rel2meta) for io in ['input', 'output']: self._var_rel_names[io].extend(self._static_var_rel_names[io]) self.setup() self._set_vector_class() def _set_vector_class(self): if self.options['distributed']: dist_vec_class = self._problem_meta['distributed_vector_class'] if dist_vec_class is not None: self._vector_class = dist_vec_class else: simple_warning("The 'distributed' option is set to True for Component %s, " "but there is no distributed vector implementation (MPI/PETSc) " "available. The default non-distributed vectors will be used." % self.pathname) self._vector_class = self._problem_meta['local_vector_class'] else: self._vector_class = self._problem_meta['local_vector_class'] def _configure_check(self): """ Do any error checking on i/o configuration. """ # check here if declare_coloring was called during setup but declare_partials # wasn't. If declare partials wasn't called, call it with of='*' and wrt='*' so we'll # have something to color. if self._coloring_info['coloring'] is not None: for key, meta in self._declared_partials.items(): if 'method' in meta and meta['method'] is not None: break else: method = self._coloring_info['method'] simple_warning("%s: declare_coloring or use_fixed_coloring was called but no approx" " partials were declared. Declaring all partials as approximated " "using default metadata and method='%s'." % (self.msginfo, method)) self.declare_partials('*', '*', method=method) super()._configure_check() def _setup_var_data(self): """ Compute the list of abs var names, abs/prom name maps, and metadata dictionaries. """ global global_meta_names super()._setup_var_data() allprocs_prom2abs_list = self._var_allprocs_prom2abs_list abs2prom = self._var_allprocs_abs2prom = self._var_abs2prom # Compute the prefix for turning rel/prom names into abs names prefix = self.pathname + '.' if self.pathname else '' iproc = self.comm.rank for io in ['input', 'output']: abs2meta = self._var_abs2meta[io] allprocs_abs2meta = self._var_allprocs_abs2meta[io] is_input = io == 'input' for i, prom_name in enumerate(self._var_rel_names[io]): abs_name = prefix + prom_name abs2meta[abs_name] = metadata = self._var_rel2meta[prom_name] # Compute allprocs_prom2abs_list, abs2prom allprocs_prom2abs_list[io][prom_name] = [abs_name] abs2prom[io][abs_name] = prom_name allprocs_abs2meta[abs_name] = { meta_name: metadata[meta_name] for meta_name in global_meta_names[io] } if is_input and 'src_indices' in metadata: allprocs_abs2meta[abs_name]['has_src_indices'] = \ metadata['src_indices'] is not None # ensure that if src_indices is a slice we reset it to that instead of # the converted array value (in case this is a re-setup), so that we can # re-convert using potentially different sizing information. if metadata['src_slice'] is not None: metadata['src_indices'] = metadata['src_slice'] for prom_name, val in self._var_discrete[io].items(): abs_name = prefix + prom_name # Compute allprocs_prom2abs_list, abs2prom allprocs_prom2abs_list[io][prom_name] = [abs_name] abs2prom[io][abs_name] = prom_name # Compute allprocs_discrete (metadata for discrete vars) self._var_allprocs_discrete[io][abs_name] = v = val.copy() del v['value'] if self._var_discrete['input'] or self._var_discrete['output']: self._discrete_inputs = _DictValues(self._var_discrete['input']) self._discrete_outputs = _DictValues(self._var_discrete['output']) else: self._discrete_inputs = self._discrete_outputs = () def _setup_var_sizes(self): """ Compute the arrays of variable sizes for all variables/procs on this system. """ iproc = self.comm.rank for io in ('input', 'output'): sizes = self._var_sizes['nonlinear'][io] = np.zeros((self.comm.size, len(self._var_rel_names[io])), dtype=INT_DTYPE) for i, (name, metadata) in enumerate(self._var_allprocs_abs2meta[io].items()): sizes[iproc, i] = metadata['size'] if self.comm.size > 1: my_sizes = sizes[iproc, :].copy() self.comm.Allgather(my_sizes, sizes) # all names are relevant for the 'nonlinear' and 'linear' vectors. We # can then use them to compute the size arrays of for all other vectors # based on the nonlinear size array. nl_allprocs_relnames = self._var_allprocs_relevant_names['nonlinear'] nl_relnames = self._var_relevant_names['nonlinear'] for io in ('input', 'output'): nl_allprocs_relnames[io] = list(self._var_allprocs_abs2meta[io]) nl_relnames[io] = list(self._var_abs2meta[io]) self._setup_var_index_maps('nonlinear') self._owned_sizes = self._var_sizes['nonlinear']['output'] if self._use_derivatives: sizes = self._var_sizes nl_sizes = sizes['nonlinear'] nl_abs2idx = self._var_allprocs_abs2idx['nonlinear'] sizes['linear'] = nl_sizes self._var_allprocs_relevant_names['linear'] = nl_allprocs_relnames self._var_relevant_names['linear'] = nl_relnames self._var_allprocs_abs2idx['linear'] = nl_abs2idx # Initialize size arrays for other linear vecs besides 'linear' # (which is the same as 'nonlinear') for vec_name in self._lin_rel_vec_name_list[1:]: # at component level, _var_allprocs_* is the same as var_* since all vars exist in # all procs for a given component, so we don't have to mess with figuring out what # vars are local. relnames = self._var_allprocs_relevant_names[vec_name] sizes[vec_name] = {} for io in ('input', 'output'): sizes[vec_name][io] = sz = np.zeros((self.comm.size, len(relnames[io])), INT_DTYPE) # Variables for this vec_name are a subset of those for nonlinear, so just # take columns of the nonlinear sizes array for idx, abs_name in enumerate(relnames[io]): sz[:, idx] = nl_sizes[io][:, nl_abs2idx[abs_name]] self._setup_var_index_maps(vec_name) def _setup_partials(self): """ Process all partials and approximations that the user declared. """ self._subjacs_info = {} self._jacobian = DictionaryJacobian(system=self) self.setup_partials() # hook for component writers to specify sparsity patterns # check to make sure that if num_par_fd > 1 that this system is actually doing FD. # Unfortunately we have to do this check after system setup has been called because that's # when declare_partials generally happens, so we raise an exception here instead of just # resetting the value of num_par_fd (because the comm has already been split and possibly # used by the system setup). orig_comm = self._full_comm if self._full_comm is not None else self.comm if self._num_par_fd > 1 and orig_comm.size > 1 and not (self._owns_approx_jac or self._approx_schemes): raise RuntimeError("%s: num_par_fd is > 1 but no FD is active." % self.msginfo) for key, dct in self._declared_partials.items(): of, wrt = key self._declare_partials(of, wrt, dct)
[docs] def setup_partials(self): """ Declare partials. This is meant to be overridden by component classes. All partials should be declared here since this is called after all size/shape information is known for all variables. """ pass
def _update_wrt_matches(self, info): """ Determine the list of wrt variables that match the wildcard(s) given in declare_coloring. Parameters ---------- info : dict Coloring metadata dict. """ ofs, allwrt = self._get_partials_varlists() wrt_patterns = info['wrt_patterns'] matches_prom = set() for w in wrt_patterns: matches_prom.update(find_matches(w, allwrt)) # error if nothing matched if not matches_prom: raise ValueError("{}: Invalid 'wrt' variable(s) specified for colored approx partial " "options: {}.".format(self.msginfo, wrt_patterns)) info['wrt_matches_prom'] = matches_prom info['wrt_matches'] = [rel_name2abs_name(self, n) for n in matches_prom] def _update_subjac_sparsity(self, sparsity): """ Update subjac sparsity info based on the given coloring. The sparsity of the partial derivatives in this component will be used when computing the sparsity of the total jacobian for the entire model. Without this, all of this component's partials would be treated as dense, resulting in an overly conservative coloring of the total jacobian. Parameters ---------- sparsity : dict A nested dict of the form dct[of][wrt] = (rows, cols, shape) """ # sparsity uses relative names, so we need to convert to absolute pathname = self.pathname for of, sub in sparsity.items(): of_abs = '.'.join((pathname, of)) if pathname else of for wrt, tup in sub.items(): wrt_abs = '.'.join((pathname, wrt)) if pathname else wrt abs_key = (of_abs, wrt_abs) if abs_key in self._subjacs_info: # add sparsity info to existing partial info self._subjacs_info[abs_key]['sparsity'] = tup
[docs] def add_input(self, name, val=1.0, shape=None, src_indices=None, flat_src_indices=None, units=None, desc='', tags=None, shape_by_conn=False, copy_shape=None): """ Add an input variable to the component. Parameters ---------- name : str name of the variable in this component's namespace. val : float or list or tuple or ndarray or Iterable The initial value of the variable being added in user-defined units. Default is 1.0. shape : int or tuple or list or None Shape of this variable, only required if src_indices not provided and val is not an array. Default is None. src_indices : int or list of ints or tuple of ints or int ndarray or Iterable or None The global indices of the source variable to transfer data from. A value of None implies this input depends on all entries of source. Default is None. The shapes of the target and src_indices must match, and form of the entries within is determined by the value of 'flat_src_indices'. flat_src_indices : bool If True, each entry of src_indices is assumed to be an index into the flattened source. Otherwise each entry must be a tuple or list of size equal to the number of dimensions of the source. units : str or None Units in which this input variable will be provided to the component during execution. Default is None, which means it is unitless. desc : str description of the variable tags : str or list of strs User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs. shape_by_conn : bool If True, shape this input to match its connected output. copy_shape : str or None If a str, that str is the name of a variable. Shape this input to match that of the named variable. Returns ------- dict metadata for added variable """ # First, type check all arguments if not isinstance(name, str): raise TypeError('%s: The name argument should be a string.' % self.msginfo) if not _valid_var_name(name): raise NameError("%s: '%s' is not a valid input name." % (self.msginfo, name)) if not isscalar(val) and not isinstance(val, (list, tuple, ndarray, Iterable)): raise TypeError('%s: The val argument should be a float, list, tuple, ndarray or ' 'Iterable' % self.msginfo) if shape is not None and not isinstance(shape, (int, tuple, list, np.integer)): raise TypeError("%s: The shape argument should be an int, tuple, or list but " "a '%s' was given" % (self.msginfo, type(shape))) if src_indices is not None and not isinstance(src_indices, (int, list, tuple, ndarray, Iterable)): raise TypeError('%s: The src_indices argument should be an int, list, ' 'tuple, ndarray or Iterable' % self.msginfo) if units is not None: if not isinstance(units, str): raise TypeError('%s: The units argument should be a str or None.' % self.msginfo) if not valid_units(units): raise ValueError("%s: The units '%s' are invalid." % (self.msginfo, units)) if tags is not None and not isinstance(tags, (str, list)): raise TypeError('The tags argument should be a str or list') if (shape_by_conn or copy_shape): if shape is not None or not isscalar(val): raise ValueError("%s: If shape is to be set dynamically using 'shape_by_conn' or " "'copy_shape', 'shape' and 'val' should be a scalar, " "but shape of '%s' and val of '%s' was given for variable '%s'." % (self.msginfo, shape, val, name)) if src_indices is not None: raise ValueError("%s: Setting of 'src_indices' along with 'shape_by_conn' or " "'copy_shape' for variable '%s' is currently unsupported." % (self.msginfo, name)) src_slice = None if not (shape_by_conn or copy_shape): if src_indices is not None: if _is_slicer_op(src_indices): src_slice = src_indices if flat_src_indices is not None: simple_warning(f"{self.msginfo}: Input '{name}' was added with slice " "src_indices, so flat_src_indices is ignored.") flat_src_indices = True else: src_indices = np.asarray(src_indices, dtype=INT_DTYPE) # value, shape: based on args, making sure they are compatible val, shape, src_indices = ensure_compatible(name, val, shape, src_indices) metadata = { 'value': val, 'shape': shape, 'size': shape_to_len(shape), 'src_indices': src_indices, # these will ultimately be converted to a flat index array 'flat_src_indices': flat_src_indices, 'src_slice': src_slice, # store slice def here, if any. This is never overwritten 'units': units, 'desc': desc, 'distributed': self.options['distributed'], 'tags': make_set(tags), 'shape_by_conn': shape_by_conn, 'copy_shape': copy_shape, } if self._static_mode: var_rel2meta = self._static_var_rel2meta var_rel_names = self._static_var_rel_names else: var_rel2meta = self._var_rel2meta var_rel_names = self._var_rel_names # Disallow dupes if name in var_rel2meta: raise ValueError("{}: Variable name '{}' already exists.".format(self.msginfo, name)) var_rel2meta[name] = metadata var_rel_names['input'].append(name) self._var_added(name) return metadata
[docs] def add_discrete_input(self, name, val, desc='', tags=None): """ Add a discrete input variable to the component. Parameters ---------- name : str name of the variable in this component's namespace. val : a picklable object The initial value of the variable being added. desc : str description of the variable tags : str or list of strs User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs. Returns ------- dict metadata for added variable """ # First, type check all arguments if not isinstance(name, str): raise TypeError('%s: The name argument should be a string.' % self.msginfo) if not _valid_var_name(name): raise NameError("%s: '%s' is not a valid input name." % (self.msginfo, name)) if tags is not None and not isinstance(tags, (str, list)): raise TypeError('%s: The tags argument should be a str or list' % self.msginfo) metadata = { 'value': val, 'type': type(val), 'desc': desc, 'tags': make_set(tags), } if metadata['type'] == np.ndarray: metadata.update({'shape': val.shape}) if self._static_mode: var_rel2meta = self._static_var_rel2meta else: var_rel2meta = self._var_rel2meta # Disallow dupes if name in var_rel2meta: raise ValueError("{}: Variable name '{}' already exists.".format(self.msginfo, name)) var_rel2meta[name] = self._var_discrete['input'][name] = metadata self._var_added(name) return metadata
[docs] def add_output(self, name, val=1.0, shape=None, units=None, res_units=None, desc='', lower=None, upper=None, ref=1.0, ref0=0.0, res_ref=1.0, tags=None, shape_by_conn=False, copy_shape=None): """ Add an output variable to the component. Parameters ---------- name : str name of the variable in this component's namespace. val : float or list or tuple or ndarray The initial value of the variable being added in user-defined units. Default is 1.0. shape : int or tuple or list or None Shape of this variable, only required if val is not an array. Default is None. units : str or None Units in which the output variables will be provided to the component during execution. Default is None, which means it has no units. res_units : str or None Units in which the residuals of this output will be given to the user when requested. Default is None, which means it has no units. desc : str description of the variable. lower : float or list or tuple or ndarray or Iterable or None lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None. upper : float or list or tuple or ndarray or or Iterable None upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None. ref : float or ndarray Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1. ref0 : float or ndarray Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0. res_ref : float or ndarray Scaling parameter. The value in the user-defined res_units of this output's residual when the scaled value is 1. Default is 1. tags : str or list of strs or set of strs User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs. shape_by_conn : bool If True, shape this output to match its connected input(s). copy_shape : str or None If a str, that str is the name of a variable. Shape this output to match that of the named variable. Returns ------- dict metadata for added variable """ # First, type check all arguments if (shape_by_conn or copy_shape) and (shape is not None or not isscalar(val)): raise ValueError("%s: If shape is to be set dynamically using 'shape_by_conn' or " "'copy_shape', 'shape' and 'val' should be scalar, " "but shape of '%s' and val of '%s' was given for variable '%s'." % (self.msginfo, shape, val, name)) if not isinstance(name, str): raise TypeError('%s: The name argument should be a string.' % self.msginfo) if not _valid_var_name(name): raise NameError("%s: '%s' is not a valid output name." % (self.msginfo, name)) if not (copy_shape or shape_by_conn): if not isscalar(val) and not isinstance(val, (list, tuple, ndarray, Iterable)): msg = '%s: The val argument should be a float, list, tuple, ndarray or Iterable' raise TypeError(msg % self.msginfo) if not isscalar(ref) and not isinstance(val, (list, tuple, ndarray, Iterable)): msg = '%s: The ref argument should be a float, list, tuple, ndarray or Iterable' raise TypeError(msg % self.msginfo) if not isscalar(ref0) and not isinstance(val, (list, tuple, ndarray, Iterable)): msg = '%s: The ref0 argument should be a float, list, tuple, ndarray or Iterable' raise TypeError(msg % self.msginfo) if not isscalar(res_ref) and not isinstance(val, (list, tuple, ndarray, Iterable)): msg = '%s: The res_ref argument should be a float, list, tuple, ndarray or Iterable' raise TypeError(msg % self.msginfo) if shape is not None and not isinstance(shape, (int, tuple, list, np.integer)): raise TypeError("%s: The shape argument should be an int, tuple, or list but " "a '%s' was given" % (self.msginfo, type(shape))) if res_units is not None and not isinstance(res_units, str): raise TypeError('%s: The res_units argument should be a str or None' % self.msginfo) if units is not None: if not isinstance(units, str): raise TypeError('%s: The units argument should be a str or None' % self.msginfo) if not valid_units(units): raise ValueError("%s: The units '%s' are invalid" % (self.msginfo, units)) if tags is not None and not isinstance(tags, (str, set, list)): raise TypeError('The tags argument should be a str, set, or list') if not (copy_shape or shape_by_conn): # value, shape: based on args, making sure they are compatible val, shape, _ = ensure_compatible(name, val, shape) if lower is not None: lower = ensure_compatible(name, lower, shape)[0] self._has_bounds = True if upper is not None: upper = ensure_compatible(name, upper, shape)[0] self._has_bounds = True # All refs: check the shape if necessary for item, item_name in zip([ref, ref0, res_ref], ['ref', 'ref0', 'res_ref']): if not isscalar(item): it = atleast_1d(item) if it.shape != shape: raise ValueError("{}: When adding output '{}', expected shape {} but got " "shape {} for argument '{}'.".format(self.msginfo, name, shape, it.shape, item_name)) if isscalar(ref): self._has_output_scaling |= ref != 1.0 else: self._has_output_scaling |= np.any(ref != 1.0) if isscalar(ref0): self._has_output_scaling |= ref0 != 0.0 else: self._has_output_scaling |= np.any(ref0) if isscalar(res_ref): self._has_resid_scaling |= res_ref != 1.0 else: self._has_resid_scaling |= np.any(res_ref != 1.0) metadata = { 'value': val, 'shape': shape, 'size': shape_to_len(shape), 'units': units, 'res_units': res_units, 'desc': desc, 'distributed': self.options['distributed'], 'tags': make_set(tags), 'ref': format_as_float_or_array('ref', ref, flatten=True), 'ref0': format_as_float_or_array('ref0', ref0, flatten=True), 'res_ref': format_as_float_or_array('res_ref', res_ref, flatten=True), 'lower': lower, 'upper': upper, 'shape_by_conn': shape_by_conn, 'copy_shape': copy_shape } # We may not know the pathname yet, so we have to use name for now, instead of abs_name. if self._static_mode: var_rel2meta = self._static_var_rel2meta var_rel_names = self._static_var_rel_names else: var_rel2meta = self._var_rel2meta var_rel_names = self._var_rel_names # Disallow dupes if name in var_rel2meta: raise ValueError("{}: Variable name '{}' already exists.".format(self.msginfo, name)) var_rel2meta[name] = metadata var_rel_names['output'].append(name) self._var_added(name) return metadata
[docs] def add_discrete_output(self, name, val, desc='', tags=None): """ Add an output variable to the component. Parameters ---------- name : str name of the variable in this component's namespace. val : a picklable object The initial value of the variable being added. desc : str description of the variable. tags : str or list of strs or set of strs User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs. Returns ------- dict metadata for added variable """ if not isinstance(name, str): raise TypeError('%s: The name argument should be a string.' % self.msginfo) if not _valid_var_name(name): raise NameError("%s: '%s' is not a valid output name." % (self.msginfo, name)) if tags is not None and not isinstance(tags, (str, set, list)): raise TypeError('%s: The tags argument should be a str, set, or list' % self.msginfo) metadata = { 'value': val, 'type': type(val), 'desc': desc, 'tags': make_set(tags) } if metadata['type'] == np.ndarray: metadata.update({'shape': val.shape}) if self._static_mode: var_rel2meta = self._static_var_rel2meta else: var_rel2meta = self._var_rel2meta # Disallow dupes if name in var_rel2meta: raise ValueError("{}: Variable name '{}' already exists.".format(self.msginfo, name)) var_rel2meta[name] = self._var_discrete['output'][name] = metadata self._var_added(name) return metadata
def _var_added(self, name): """ Notify config that a variable has been added to this Component. Parameters ---------- name : str Name of the added variable. """ if self._problem_meta is not None and self._problem_meta['config_info'] is not None: self._problem_meta['config_info']._var_added(self.pathname, name) def _update_dist_src_indices(self, abs_in2out, all_abs2meta, all_abs2idx, all_sizes): """ Set default src_indices on distributed components for any inputs where they aren't set. Parameters ---------- abs_in2out : dict Mapping of connected inputs to their source. Names are absolute. all_abs2meta : dict Mapping of absolute names to metadata for all variables in the model. all_abs2idx : dict Dictionary mapping an absolute name to its allprocs variable index. all_sizes : dict Mapping of vec_names and types to sizes of each variable in all procs. Returns ------- set Names of inputs where src_indices were added. """ if not self.options['distributed'] or self.comm.size == 1: return set() iproc = self.comm.rank abs2meta_in = self._var_abs2meta['input'] all_abs2meta_in = all_abs2meta['input'] all_abs2meta_out = all_abs2meta['output'] sizes_in = self._var_sizes['nonlinear']['input'] sizes_out = all_sizes['nonlinear']['output'] added_src_inds = set() for i, iname in enumerate(self._var_allprocs_abs2meta['input']): if iname in abs2meta_in and abs2meta_in[iname]['src_indices'] is None: src = abs_in2out[iname] out_i = all_abs2idx[src] nzs = np.nonzero(sizes_out[:, out_i])[0] if (all_abs2meta_out[src]['global_size'] == all_abs2meta_in[iname]['global_size'] or nzs.size == self.comm.size): # This offset assumes a 'full' distributed output offset = np.sum(sizes_in[:iproc, i]) end = offset + sizes_in[iproc, i] else: # distributed output (may have some zero size entries) if nzs.size == 1: offset = 0 end = sizes_out[nzs[0], out_i] else: # total sizes differ and output is distributed, so can't determine mapping raise RuntimeError(f"{self.msginfo}: Can't determine src_indices " f"automatically for input '{iname}'. They must be " "supplied manually.") simple_warning(f"{self.msginfo}: Component is distributed but input '{iname}' was " "added without src_indices. Setting src_indices to " f"range({offset}, {end}).") abs2meta_in[iname]['src_indices'] = np.arange(offset, end, dtype=INT_DTYPE) all_abs2meta_in[iname]['has_src_indices'] = True added_src_inds.add(iname) return added_src_inds def _approx_partials(self, of, wrt, method='fd', **kwargs): """ Inform the framework that the specified derivatives are to be approximated. Parameters ---------- of : str or list of str The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern. wrt : str or list of str The name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern. method : str The type of approximation that should be used. Valid options include: - 'fd': Finite Difference **kwargs : dict Keyword arguments for controlling the behavior of the approximation. """ pattern_matches = self._find_partial_matches(of, wrt) self._has_approx = True for of_bundle, wrt_bundle in product(*pattern_matches): of_pattern, of_matches = of_bundle wrt_pattern, wrt_matches = wrt_bundle if not of_matches: raise ValueError('{}: No matches were found for of="{}"'.format(self.msginfo, of_pattern)) if not wrt_matches: raise ValueError('{}: No matches were found for wrt="{}"'.format(self.msginfo, wrt_pattern)) info = self._subjacs_info for rel_key in product(of_matches, wrt_matches): abs_key = rel_key2abs_key(self, rel_key) meta = info[abs_key] meta['method'] = method meta.update(kwargs) info[abs_key] = meta
[docs] def declare_partials(self, of, wrt, dependent=True, rows=None, cols=None, val=None, method='exact', step=None, form=None, step_calc=None): """ Declare information about this component's subjacobians. Parameters ---------- of : str or list of str The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern. wrt : str or list of str The name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern. dependent : bool(True) If False, specifies no dependence between the output(s) and the input(s). This is only necessary in the case of a sparse global jacobian, because if 'dependent=False' is not specified and declare_partials is not called for a given pair, then a dense matrix of zeros will be allocated in the sparse global jacobian for that pair. In the case of a dense global jacobian it doesn't matter because the space for a dense subjac will always be allocated for every pair. rows : ndarray of int or None Row indices for each nonzero entry. For sparse subjacobians only. cols : ndarray of int or None Column indices for each nonzero entry. For sparse subjacobians only. val : float or ndarray of float or scipy.sparse Value of subjacobian. If rows and cols are not None, this will contain the values found at each (row, col) location in the subjac. method : str The type of approximation that should be used. Valid options include: 'fd': Finite Difference, 'cs': Complex Step, 'exact': use the component defined analytic derivatives. Default is 'exact'. step : float Step size for approximation. Defaults to None, in which case the approximation method provides its default value. form : string Form for finite difference, can be 'forward', 'backward', or 'central'. Defaults to None, in which case the approximation method provides its default value. step_calc : string Step type for finite difference, can be 'abs' for absolute', or 'rel' for relative. Defaults to None, in which case the approximation method provides its default value. Returns ------- dict Metadata dict for the specified partial(s). """ try: method_func = _supported_methods[method] except KeyError: msg = '{}: d({})/d({}): method "{}" is not supported, method must be one of {}' raise ValueError(msg.format(self.msginfo, of, wrt, method, sorted(_supported_methods))) if isinstance(of, list): of = tuple(of) if isinstance(wrt, list): wrt = tuple(wrt) meta = self._declared_partials[of, wrt] meta['dependent'] = dependent # If only one of rows/cols is specified if (rows is None) ^ (cols is None): raise ValueError('{}: d({})/d({}): If one of rows/cols is specified, then ' 'both must be specified.'.format(self.msginfo, of, wrt)) if dependent: meta['value'] = val if rows is not None: meta['rows'] = rows meta['cols'] = cols # First, check the length of rows and cols to catch this easy mistake and give a # clear message. if len(cols) != len(rows): raise RuntimeError("{}: d({})/d({}): declare_partials has been called " "with rows and cols, which should be arrays of equal length," " but rows is length {} while cols is length " "{}.".format(self.msginfo, of, wrt, len(rows), len(cols))) # Check for repeated rows/cols indices. idxset = set(zip(rows, cols)) if len(rows) - len(idxset) > 0: dups = [n for n, val in Counter(zip(rows, cols)).items() if val > 1] raise RuntimeError("{}: d({})/d({}): declare_partials has been called " "with rows and cols that specify the following duplicate " "subjacobian entries: {}.".format(self.msginfo, of, wrt, sorted(dups))) if method_func is not None: # we're doing approximations self._has_approx = True meta['method'] = method self._get_approx_scheme(method) default_opts = method_func.DEFAULT_OPTIONS # If rows/cols is specified if rows is not None or cols is not None: raise ValueError("{}: d({})/d({}): Sparse FD specification not supported " "yet.".format(self.msginfo, of, wrt)) else: default_opts = () if step: if 'step' in default_opts: meta['step'] = step else: raise RuntimeError("{}: d({})/d({}): 'step' is not a valid option for " "'{}'".format(self.msginfo, of, wrt, method)) if form: if 'form' in default_opts: meta['form'] = form else: raise RuntimeError("{}: d({})/d({}): 'form' is not a valid option for " "'{}'".format(self.msginfo, of, wrt, method)) if step_calc: if 'step_calc' in default_opts: meta['step_calc'] = step_calc else: raise RuntimeError("{}: d({})/d({}): 'step_calc' is not a valid option " "for '{}'".format(self.msginfo, of, wrt, method)) return meta
[docs] def declare_coloring(self, wrt=_DEFAULT_COLORING_META['wrt_patterns'], method=_DEFAULT_COLORING_META['method'], form=None, step=None, per_instance=_DEFAULT_COLORING_META['per_instance'], num_full_jacs=_DEFAULT_COLORING_META['num_full_jacs'], tol=_DEFAULT_COLORING_META['tol'], orders=_DEFAULT_COLORING_META['orders'], perturb_size=_DEFAULT_COLORING_META['perturb_size'], min_improve_pct=_DEFAULT_COLORING_META['min_improve_pct'], show_summary=_DEFAULT_COLORING_META['show_summary'], show_sparsity=_DEFAULT_COLORING_META['show_sparsity']): """ Set options for deriv coloring of a set of wrt vars matching the given pattern(s). Parameters ---------- wrt : str or list of str The name or names of the variables that derivatives are taken with respect to. This can contain input names, output names, or glob patterns. method : str Method used to compute derivative: "fd" for finite difference, "cs" for complex step. form : str Finite difference form, can be "forward", "central", or "backward". Leave undeclared to keep unchanged from previous or default value. step : float Step size for finite difference. Leave undeclared to keep unchanged from previous or default value. per_instance : bool If True, a separate coloring will be generated for each instance of a given class. Otherwise, only one coloring for a given class will be generated and all instances of that class will use it. num_full_jacs : int Number of times to repeat partial jacobian computation when computing sparsity. tol : float Tolerance used to determine if an array entry is nonzero during sparsity determination. orders : int Number of orders above and below the tolerance to check during the tolerance sweep. perturb_size : float Size of input/output perturbation during generation of sparsity. min_improve_pct : float If coloring does not improve (decrease) the number of solves more than the given percentage, coloring will not be used. show_summary : bool If True, display summary information after generating coloring. show_sparsity : bool If True, display sparsity with coloring info after generating coloring. """ super().declare_coloring(wrt, method, form, step, per_instance, num_full_jacs, tol, orders, perturb_size, min_improve_pct, show_summary, show_sparsity) # create approx partials for all matches meta = self.declare_partials('*', wrt, method=method, step=step, form=form) meta['coloring'] = True
[docs] def set_check_partial_options(self, wrt, method='fd', form=None, step=None, step_calc=None, directional=False): """ Set options that will be used for checking partial derivatives. Parameters ---------- wrt : str or list of str The name or names of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern. method : str Method for check: "fd" for finite difference, "cs" for complex step. form : str Finite difference form for check, can be "forward", "central", or "backward". Leave undeclared to keep unchanged from previous or default value. step : float Step size for finite difference check. Leave undeclared to keep unchanged from previous or default value. step_calc : str Type of step calculation for check, can be "abs" for absolute (default) or "rel" for relative. Leave undeclared to keep unchanged from previous or default value. directional : bool Set to True to perform a single directional derivative for each vector variable in the pattern named in wrt. """ supported_methods = ('fd', 'cs') if method not in supported_methods: msg = "{}: Method '{}' is not supported, method must be one of {}" raise ValueError(msg.format(self.msginfo, method, supported_methods)) if step and not isinstance(step, (int, float)): msg = "{}: The value of 'step' must be numeric, but '{}' was specified." raise ValueError(msg.format(self.msginfo, step)) supported_step_calc = ('abs', 'rel') if step_calc and step_calc not in supported_step_calc: msg = "{}: The value of 'step_calc' must be one of {}, but '{}' was specified." raise ValueError(msg.format(self.msginfo, supported_step_calc, step_calc)) if not isinstance(wrt, (str, list, tuple)): msg = "{}: The value of 'wrt' must be a string or list of strings, but a type " \ "of '{}' was provided." raise ValueError(msg.format(self.msginfo, type(wrt).__name__)) if not isinstance(directional, bool): msg = "{}: The value of 'directional' must be True or False, but a type " \ "of '{}' was provided." raise ValueError(msg.format(self.msginfo, type(directional).__name__)) wrt_list = [wrt] if isinstance(wrt, str) else wrt self._declared_partial_checks.append((wrt_list, method, form, step, step_calc, directional))
def _get_check_partial_options(self, include_wrt_outputs=True): """ Return dictionary of partial options with pattern matches processed. This is called by check_partials. Parameters ---------- include_wrt_outputs : bool If True, include outputs in the wrt list. Returns ------- dict(wrt : (options)) Dictionary keyed by name with tuples of options (method, form, step, step_calc) """ opts = {} of, wrt = self._get_potential_partials_lists(include_wrt_outputs=include_wrt_outputs) invalid_wrt = [] matrix_free = self.matrix_free if matrix_free: n_directional = 0 for wrt_list, method, form, step, step_calc, directional in self._declared_partial_checks: for pattern in wrt_list: matches = find_matches(pattern, wrt) # if a non-wildcard var name was specified and not found, save for later Exception if len(matches) == 0 and _valid_var_name(pattern): invalid_wrt.append(pattern) for match in matches: if match in opts: opt = opts[match] # New assignments take precedence keynames = ['method', 'form', 'step', 'step_calc', 'directional'] for name, value in zip(keynames, [method, form, step, step_calc, directional]): if value is not None: opt[name] = value else: opts[match] = {'method': method, 'form': form, 'step': step, 'step_calc': step_calc, 'directional': directional} if matrix_free and directional: n_directional += 1 if invalid_wrt: msg = "{}: Invalid 'wrt' variables specified for check_partial options: {}." raise ValueError(msg.format(self.msginfo, invalid_wrt)) if matrix_free: if n_directional > 0 and n_directional < len(wrt): msg = "{}: For matrix free components, directional should be set to True for " + \ "all inputs." raise ValueError(msg.format(self.msginfo)) return opts def _declare_partials(self, of, wrt, dct, quick_declare=False): """ Store subjacobian metadata for later use. Parameters ---------- of : tuple of str The names of the residuals that derivatives are being computed for. May also contain glob patterns. wrt : tuple of str The names of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain glob patterns. dct : dict Metadata dict specifying shape, and/or approx properties. quick_declare : bool This is set to True when declaring the jacobian diagonal terms for explicit components. The checks and conversions are all skipped to improve performance for cases with large numbers of explicit components or indepvarcomps. """ if quick_declare: abs_key = rel_key2abs_key(self, (of, wrt)) meta = {} meta['rows'] = np.array(dct['rows'], dtype=INT_DTYPE, copy=False) meta['cols'] = np.array(dct['cols'], dtype=INT_DTYPE, copy=False) meta['shape'] = (len(dct['rows']), len(dct['cols'])) meta['value'] = dct['value'] self._subjacs_info[abs_key] = meta return val = dct['value'] if 'value' in dct else None is_scalar = isscalar(val) dependent = dct['dependent'] if dependent: if 'rows' in dct and dct['rows'] is not None: # sparse list format rows = dct['rows'] cols = dct['cols'] rows = np.array(rows, dtype=INT_DTYPE, copy=False) cols = np.array(cols, dtype=INT_DTYPE, copy=False) if rows.shape != cols.shape: raise ValueError('{}: d({})/d({}): rows and cols must have the same shape,' ' rows: {}, cols: {}'.format(self.msginfo, of, wrt, rows.shape, cols.shape)) if is_scalar: val = np.full(rows.size, val, dtype=float) is_scalar = False elif val is not None: # np.promote_types will choose the smallest dtype that can contain # both arguments val = atleast_1d(val) safe_dtype = promote_types(val.dtype, float) val = val.astype(safe_dtype, copy=False) if rows.shape != val.shape: raise ValueError('{}: d({})/d({}): If rows and cols are specified, val ' 'must be a scalar or have the same shape, val: {}, ' 'rows/cols: {}'.format(self.msginfo, of, wrt, val.shape, rows.shape)) else: val = np.zeros_like(rows, dtype=float) if rows.size > 0: if rows.min() < 0: msg = '{}: d({})/d({}): row indices must be non-negative' raise ValueError(msg.format(self.msginfo, of, wrt)) if cols.min() < 0: msg = '{}: d({})/d({}): col indices must be non-negative' raise ValueError(msg.format(self.msginfo, of, wrt)) rows_max = rows.max() cols_max = cols.max() else: rows_max = cols_max = 0 else: if val is not None and not is_scalar and not issparse(val): val = atleast_2d(val) val = val.astype(promote_types(val.dtype, float), copy=False) rows_max = cols_max = 0 rows = None cols = None pattern_matches = self._find_partial_matches(of, wrt) abs2meta_in = self._var_abs2meta['input'] abs2meta_out = self._var_abs2meta['output'] is_array = isinstance(val, ndarray) patmeta = dict(dct) patmeta_not_none = {k: v for k, v in dct.items() if v is not None} for of_bundle, wrt_bundle in product(*pattern_matches): of_pattern, of_matches = of_bundle wrt_pattern, wrt_matches = wrt_bundle if not of_matches: raise ValueError('{}: No matches were found for of="{}"'.format(self.msginfo, of_pattern)) if not wrt_matches: raise ValueError('{}: No matches were found for wrt="{}"'.format(self.msginfo, wrt_pattern)) for rel_key in product(of_matches, wrt_matches): abs_key = rel_key2abs_key(self, rel_key) if not dependent: if abs_key in self._subjacs_info: del self._subjacs_info[abs_key] continue if abs_key in self._subjacs_info: meta = self._subjacs_info[abs_key] meta.update(patmeta_not_none) else: meta = patmeta.copy() of, wrt = abs_key meta['rows'] = rows meta['cols'] = cols csz = abs2meta_in[wrt]['size'] if wrt in abs2meta_in else abs2meta_out[wrt]['size'] meta['shape'] = shape = (abs2meta_out[of]['size'], csz) if shape[0] == 0 or shape[1] == 0: msg = "{}: '{}' is an array of size 0" if shape[0] == 0: if not abs2meta_out[of]['distributed']: # non-distributed components are not allowed to have zero size inputs raise ValueError(msg.format(self.msginfo, of)) else: # distributed comp are allowed to have zero size inputs on some procs rows_max = -1 if shape[1] == 0: if wrt in abs2meta_in: distrib = abs2meta_in[wrt]['distributed'] else: distrib = abs2meta_out[wrt]['distributed'] if not distrib: # non-distributed components are not allowed to have zero size outputs raise ValueError(msg.format(self.msginfo, wrt)) else: # distributed comp are allowed to have zero size outputs on some procs cols_max = -1 if val is None: # we can only get here if rows is None (we're not sparse list format) meta['value'] = np.zeros(shape) elif is_array: if rows is None and val.shape != shape and val.size == shape[0] * shape[1]: meta['value'] = val = val.copy().reshape(shape) else: meta['value'] = val.copy() elif is_scalar: meta['value'] = np.full(shape, val, dtype=float) else: meta['value'] = val if rows_max >= shape[0] or cols_max >= shape[1]: of, wrt = rel_key msg = '{}: d({})/d({}): Expected {}x{} but declared at least {}x{}' raise ValueError(msg.format(self.msginfo, of, wrt, shape[0], shape[1], rows_max + 1, cols_max + 1)) self._check_partials_meta(abs_key, meta['value'], shape if rows is None else (rows.shape[0], 1)) self._subjacs_info[abs_key] = meta def _find_partial_matches(self, of, wrt): """ Find all partial derivative matches from of and wrt. Parameters ---------- of : str or list of str The relative name of the residual(s) that derivatives are being computed for. May also contain a glob pattern. wrt : str or list of str The relative name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern. Returns ------- tuple(list, list) Pair of lists containing pattern matches (if any). Returns (of_matches, wrt_matches) where of_matches is a list of tuples (pattern, matches) and wrt_matches is a list of tuples (pattern, output_matches, input_matches). """ of_list = [of] if isinstance(of, str) else of wrt_list = [wrt] if isinstance(wrt, str) else wrt of, wrt = self._get_potential_partials_lists() of_pattern_matches = [(pattern, find_matches(pattern, of)) for pattern in of_list] wrt_pattern_matches = [(pattern, find_matches(pattern, wrt)) for pattern in wrt_list] return of_pattern_matches, wrt_pattern_matches def _check_partials_meta(self, abs_key, val, shape): """ Check a given partial derivative and metadata for the correct shapes. Parameters ---------- abs_key : tuple(str, str) The of/wrt pair (given absolute names) defining the partial derivative. val : ndarray Subjac value. shape : tuple Expected shape of val. """ out_size, in_size = shape if in_size == 0 and self.comm.rank != 0: # 'inactive' component return if val is not None: val_shape = val.shape if len(val_shape) == 1: val_out, val_in = val_shape[0], 1 else: val_out, val_in = val.shape if val_out > out_size or val_in > in_size: of, wrt = abs_key2rel_key(self, abs_key) msg = '{}: d({})/d({}): Expected {}x{} but val is {}x{}' raise ValueError(msg.format(self.msginfo, of, wrt, out_size, in_size, val_out, val_in)) def _set_approx_partials_meta(self): """ Add approximations for those partials registered with method=fd or method=cs. """ self._get_static_wrt_matches() subjacs = self._subjacs_info for key in self._approx_subjac_keys_iter(): meta = subjacs[key] self._approx_schemes[meta['method']].add_approximation(key, self, meta) def _guess_nonlinear(self): """ Provide initial guess for states. Does nothing on any non-implicit component. """ pass def _clear_iprint(self): """ Clear out the iprint stack from the solvers. Components don't have nested solvers, so do nothing to prevent errors. """ pass def _check_first_linearize(self): if self._first_call_to_linearize: self._first_call_to_linearize = False # only do this once if coloring_mod._use_partial_sparsity: coloring = self._get_coloring() if coloring is not None: if not self._coloring_info['dynamic']: coloring._check_config_partial(self) self._update_subjac_sparsity(coloring.get_subjac_sparsity())
class _DictValues(object): """ A dict-like wrapper for a dict of metadata, where getitem returns 'value' from metadata. """ def __init__(self, dct): self._dict = dct def __getitem__(self, key): return self._dict[key]['value'] def __setitem__(self, key, value): self._dict[key]['value'] = value def __contains__(self, key): return key in self._dict def __len__(self): return len(self._dict) def items(self): return [(key, self._dict[key]['value']) for key in self._dict] def iteritems(self): for key, val in self._dict.iteritems(): yield key, val['value']