Source code for openmdao.core.component

"""Define the Component class."""

import sys
import types
from types import LambdaType
from collections import defaultdict
from collections.abc import Iterable
from itertools import product

from numbers import Integral
import numpy as np
from numpy import ndarray, isscalar, ndim, atleast_1d, atleast_2d, promote_types
from scipy.sparse import issparse, coo_matrix

from openmdao.core.system import System, _supported_methods, _DEFAULT_COLORING_META, \
    global_meta_names, collect_errors
from openmdao.core.constants import INT_DTYPE
from openmdao.jacobians.dictionary_jacobian import DictionaryJacobian
from openmdao.utils.array_utils import shape_to_len
from openmdao.utils.class_util import overrides_method
from openmdao.utils.units import simplify_unit
from openmdao.utils.name_maps import abs_key_iter, 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, make_set, inconsistent_across_procs
from openmdao.utils.indexer import Indexer, indexer
import openmdao.utils.coloring as coloring_mod
from openmdao.utils.om_warnings import issue_warning, MPIWarning, DistributedComponentWarning, \
    DerivativesWarning, warn_deprecation
from openmdao.utils.code_utils import is_lambda, LambdaPickleWrapper


_forbidden_chars = {'.', '*', '?', '!', '[', ']'}
_whitespace = {' ', '\t', '\r', '\n'}
_allowed_types = (list, tuple, ndarray, Iterable)


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
    if _forbidden_chars.intersection(name):
        return False
    return name is name.strip()


[docs]class Component(System): """ Base Component class; not to be directly instantiated. Parameters ---------- **kwargs : dict of keyword arguments Available here and in all descendants of this system. Attributes ---------- _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_patterns : dict Dictionary of declared partials patterns. Each key is a tuple of the form (of, wrt) where of and wrt may be glob patterns. _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. _has_distrib_outputs : bool If True, this component has at least one distributed output. """
[docs] def __init__(self, **kwargs): """ Initialize all attributes. """ 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_patterns = {} self._declared_partial_checks = [] self._no_check_partials = False self._has_distrib_outputs = 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='If True, set all variables in this component as distributed ' 'across multiple processes') self.options.declare('run_root_only', types=bool, default=False, desc='If True, call compute, compute_partials, linearize, ' 'apply_linear, apply_nonlinear, and compute_jacvec_product ' 'only on rank 0 and broadcast the results to the other ranks.') self.options.declare('always_opt', types=bool, default=False, desc='If True, force nonlinear operations on this component to be ' 'included in the optimization loop even if this component is not ' 'relevant to the design variables and responses.') def _check_matfree_deprecation(self): # check for mixed distributed variables has_dist_ins = has_nd_ins = has_dist_outs = has_nd_outs = False for name in self._var_rel_names['input']: meta = self._var_rel2meta[name] if meta['distributed']: has_dist_ins = True else: has_nd_ins = True for name in self._var_rel_names['output']: meta = self._var_rel2meta[name] if meta['distributed']: has_dist_outs = True else: has_nd_outs = True if (has_nd_ins and has_dist_outs) or (has_dist_ins and has_nd_outs): warn_deprecation(f"{self.msginfo}: It appears this component mixes " "distributed/non-distributed inputs and outputs, so it may break " "starting with OpenMDAO 3.25, where the convention " "used when passing data between distributed and non-distributed " "inputs and outputs within a matrix free component will change. " "See https://github.com/OpenMDAO/POEMs/blob/master/POEM_075.md for " "details.")
[docs] def setup(self): """ Declare inputs and outputs. Available attributes: name pathname comm options """ pass
def _setup_procs(self, pathname, comm, 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. prob_meta : dict Problem level metadata. """ super()._setup_procs(pathname, comm, prob_meta) if self._num_par_fd > 1: if comm.size > 1: comm = self._setup_par_fd_procs(comm) elif not MPI: issue_warning(f"MPI is not active but num_par_fd = {self._num_par_fd}. No parallel " "finite difference will be performed.", prefix=self.msginfo, category=MPIWarning) self._num_par_fd = 1 self.comm = comm nprocs = comm.size # Clear out old variable information so that we can call setup on the component. self._var_rel_names = {'input': [], 'output': []} self._var_rel2meta = {} if comm.size == 1: self._has_distrib_vars = self._has_distrib_outputs = False for meta in self._static_var_rel2meta.values(): # 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__. if 'shape_by_conn' in meta and (meta['shape_by_conn'] or meta['compute_shape'] is not None): meta['shape'] = None if not isscalar(meta['val']): if meta['val'].size > 0: meta['val'] = meta['val'].flatten()[0] else: meta['val'] = 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._setup_check() self._set_vector_class() def _set_vector_class(self): if self._has_distrib_vars: dist_vec_class = self._problem_meta['distributed_vector_class'] if dist_vec_class is not None: self._vector_class = dist_vec_class else: issue_warning("Component contains distributed variables, " "but there is no distributed vector implementation (MPI/PETSc) " "available. The default non-distributed vectors will be used.", prefix=self.msginfo, category=DistributedComponentWarning) 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 meta in self._declared_partials_patterns.values(): if 'method' in meta and meta['method'] is not None: break else: method = self._coloring_info.method issue_warning("declare_coloring or use_fixed_coloring was called but no approx" " partials were declared. Declaring all partials as approximated " f"using default metadata and method='{method}'.", prefix=self.msginfo, category=DerivativesWarning) 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 + '.' for io in ['input', 'output']: abs2meta = self._var_abs2meta[io] allprocs_abs2meta = self._var_allprocs_abs2meta[io] is_input = io == 'input' for prom_name in 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 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['val'] 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 = () if self.comm.size > 1: # check that same variables are declared on all procs vnames = (list(self._var_rel_names['output']), list(self._var_rel_names['input'])) allnames = self.comm.gather(vnames, root=0) if self.comm.rank == 0: outset, inset = vnames msg = '' for oset, iset in allnames: if iset != inset or oset != outset: msg = self._missing_vars_error(allnames) break self.comm.bcast(msg, root=0) else: msg = self.comm.bcast(None, root=0) if msg: raise RuntimeError(msg) self._serial_idxs = None self._inconsistent_keys = set() def _missing_vars_error(self, allnames): msg = '' outset, inset = allnames[0] for rank, (olist, ilist) in enumerate(allnames): if rank != 0 and (olist != outset or ilist != inset): idiff = set(inset).symmetric_difference(ilist) odiff = set(outset).symmetric_difference(olist) if idiff or odiff: varnames = sorted(idiff | odiff) if len(varnames) == 1: varmsg = f"Variable '{varnames[0]}' exists on some ranks and not others." else: varmsg = f"Variables {varnames} exist on some ranks and not others." else: varmsg = "Variables have not been declared in the same order on all ranks." msg = (f"{self.msginfo}: {varmsg} A component must declare all variables in " "the same order on all ranks, even if the size of the variable is 0 on " "some ranks.") break return msg @collect_errors def _setup_var_sizes(self): """ Compute the arrays of variable sizes for all variables/procs on this system. """ iproc = self.comm.rank abs2idx = self._var_allprocs_abs2idx = {} for io in ('input', 'output'): sizes = self._var_sizes[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()): sz = metadata['size'] sizes[iproc, i] = 0 if sz is None else sz abs2idx[name] = i if self.comm.size > 1: my_sizes = sizes[iproc, :].copy() self.comm.Allgather(my_sizes, sizes) self._owned_sizes = self._var_sizes['output'] def _setup_partials(self): """ Process all partials and approximations that the user declared. """ self._subjacs_info = {} if not self.matrix_free: 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, pattern_meta in self._declared_partials_patterns.items(): of, wrt = key self._resolve_partials_patterns(of, wrt, pattern_meta)
[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 _setup_residuals(self): """ Process hook to call user-defined setup_residuals method if provided. """ pass def _declared_partials_iter(self): """ Iterate over all declared partials. Yields ------ key : tuple (of, wrt) Subjacobian key. """ yield from self._subjacs_info.keys() def _get_missing_partials(self, missing): """ Provide (of, wrt) tuples for which derivatives have not been declared in the component. Parameters ---------- missing : dict Dictionary containing set of missing derivatives keyed by system pathname. """ if ('*', '*') in self._declared_partials_patterns or \ (('*',), ('*',)) in self._declared_partials_patterns: return # keep old default behavior where matrix free components are assumed to have # 'dense' whole variable to whole variable partials if no partials are declared. if self.matrix_free and not self._declared_partials_patterns: return keyset = self._subjacs_info mset = set() for of in self._var_allprocs_abs2meta['output']: for wrt in self._var_allprocs_abs2meta['input']: if (of, wrt) not in keyset: mset.add((of, wrt)) if mset: missing[self.pathname] = mset @property def checking(self): """ Return True if check_partials or check_totals is executing. Returns ------- bool True if we're running within check_partials or check_totals. """ return self._problem_meta is not None and self._problem_meta['checking'] def _run_root_only(self): """ Return the value of the run_root_only option and check for possible errors. Returns ------- bool True if run_root_only is active. """ if self.options['run_root_only']: if self.comm.size > 1 or (self._full_comm is not None and self._full_comm.size > 1): if self._has_distrib_vars: raise RuntimeError(f"{self.msginfo}: Can't set 'run_root_only' option when " "a component has distributed variables.") if self._num_par_fd > 1: raise RuntimeError(f"{self.msginfo}: Can't set 'run_root_only' option when " "using parallel FD.") if self._problem_meta['has_par_deriv_color']: raise RuntimeError(f"{self.msginfo}: Can't set 'run_root_only' option when " "using parallel_deriv_color.") return True return False def _promoted_wrt_iter(self): yield from self._get_partials_wrts() 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 prefix = self.pathname + '.' for of, sub in sparsity.items(): of = prefix + of for wrt, tup in sub.items(): wrt = prefix + wrt abs_key = (of, wrt) 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, units=None, desc='', tags=None, shape_by_conn=False, copy_shape=None, compute_shape=None, distributed=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 val is not an array. Default is None. 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. compute_shape : function A function taking a dict arg containing names and shapes of this component's outputs and returning the shape of this input. distributed : bool If True, this variable is a distributed variable, so it can have different sizes/values across MPI processes. 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, _allowed_types): 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, (Integral, tuple, list)): raise TypeError("%s: The shape argument should be an int, tuple, or list but " "a '%s' was given" % (self.msginfo, type(shape))) if units is not None: if not isinstance(units, str): raise TypeError('%s: The units argument should be a str or None.' % self.msginfo) units = simplify_unit(units, msginfo=self.msginfo) if tags is not None and not isinstance(tags, (str, list, set)): raise TypeError('The tags argument should be a str, set, or list') if copy_shape and compute_shape: raise ValueError(f"{self.msginfo}: Only one of 'copy_shape' or 'compute_shape' can " "be specified.") if copy_shape and not isinstance(copy_shape, str): raise TypeError(f"{self.msginfo}: The copy_shape argument should be a str or None but " f"a '{type(copy_shape).__name__}' was given.") if compute_shape and not isinstance(compute_shape, types.FunctionType): raise TypeError(f"{self.msginfo}: The compute_shape argument should be a function but " f"a '{type(compute_shape).__name__}' was given.") if (shape_by_conn or copy_shape or compute_shape): if shape is not None or ndim(val) > 0: raise ValueError("%s: If shape is to be set dynamically using 'shape_by_conn', " "'copy_shape', or 'compute_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)) else: # value, shape: based on args, making sure they are compatible val, shape = ensure_compatible(name, val, shape) # until we get rid of component level distributed option, handle the case where # component distributed has been set to True but variable distributed has been set # to False by the caller. if distributed is not False: if distributed is None: distributed = False # using ._dict below to avoid tons of deprecation warnings distributed = distributed or ('distributed' in self.options and self.options._dict['distributed']['val']) if compute_shape is not None and is_lambda(compute_shape): compute_shape = LambdaPickleWrapper(compute_shape) metadata = { 'val': val, 'shape': shape, 'size': shape_to_len(shape), 'src_indices': None, 'flat_src_indices': None, 'units': units, 'desc': desc, 'distributed': distributed, 'tags': make_set(tags), 'shape_by_conn': shape_by_conn, 'compute_shape': compute_shape, 'copy_shape': copy_shape, } # this will get reset later if comm size is 1 self._has_distrib_vars |= metadata['distributed'] 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 = {} metadata.update({ 'val': 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=None, tags=None, shape_by_conn=False, copy_shape=None, compute_shape=None, distributed=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. compute_shape : function A function taking a dict arg containing names and shapes of this component's inputs and returning the shape of this output. distributed : bool If True, this variable is a distributed variable, so it can have different sizes/values across MPI processes. Returns ------- dict Metadata for added variable. """ global _allowed_types # First, type check all arguments if (shape_by_conn or copy_shape or compute_shape) and (shape is not None or ndim(val) > 0): raise ValueError("%s: If shape is to be set dynamically using 'shape_by_conn', " "'copy_shape', or 'compute_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 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: if not isinstance(res_units, str): msg = '%s: The res_units argument should be a str or None' % self.msginfo raise TypeError(msg) res_units = simplify_unit(res_units, msginfo=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) units = simplify_unit(units, msginfo=self.msginfo) 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 or compute_shape): if not isscalar(val) and not isinstance(val, _allowed_types): msg = '%s: The val argument should be a float, list, tuple, ndarray or Iterable' raise TypeError(msg % self.msginfo) # 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 item is not None and not isscalar(item): if not isinstance(item, _allowed_types): raise TypeError(f'{self.msginfo}: The {item_name} argument should be a ' 'float, list, tuple, ndarray or Iterable') it = atleast_1d(item) if it.shape != shape: raise ValueError(f"{self.msginfo}: When adding output '{name}', expected " f"shape {shape} but got shape {it.shape} for argument " f"'{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 self._has_output_adder |= ref0 != 0.0 else: self._has_output_scaling |= np.any(ref0) self._has_output_adder |= 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) # until we get rid of component level distributed option, handle the case where # component distributed has been set to True but variable distributed has been set # to False by the caller. if distributed is not False: if distributed is None: distributed = False # using ._dict below to avoid tons of deprecation warnings distributed = distributed or ('distributed' in self.options and self.options._dict['distributed']['val']) if copy_shape and compute_shape: raise ValueError(f"{self.msginfo}: Only one of 'copy_shape' or 'compute_shape' can " "be specified.") if copy_shape and not isinstance(copy_shape, str): raise TypeError(f"{self.msginfo}: The copy_shape argument should be a str or None but " f"a '{type(copy_shape).__name__}' was given.") if compute_shape and not isinstance(compute_shape, types.FunctionType): raise TypeError(f"{self.msginfo}: The compute_shape argument should be a function but " f"a '{type(compute_shape).__name__}' was given.") if compute_shape is not None and is_lambda(compute_shape): compute_shape = LambdaPickleWrapper(compute_shape) metadata = { 'val': val, 'shape': shape, 'size': shape_to_len(shape), 'units': units, 'res_units': res_units, 'desc': desc, 'distributed': 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, val_if_none=None), 'lower': lower, 'upper': upper, 'shape_by_conn': shape_by_conn, 'compute_shape': compute_shape, 'copy_shape': copy_shape, } # this will get reset later if comm size is 1 self._has_distrib_vars |= metadata['distributed'] self._has_distrib_outputs |= metadata['distributed'] # 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 = {} metadata.update({ 'val': 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 for any distributed 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 types to sizes of each variable in all procs. Returns ------- list Names of inputs where src_indices were added. """ iproc = self.comm.rank abs2meta_in = self._var_abs2meta['input'] all_abs2meta_in = all_abs2meta['input'] all_abs2meta_out = all_abs2meta['output'] abs_in2prom_info = self._problem_meta['abs_in2prom_info'] sizes_in = self._var_sizes['input'] sizes_out = all_sizes['output'] added_src_inds = [] # loop over continuous inputs for i, (iname, meta_in) in enumerate(abs2meta_in.items()): if meta_in['src_indices'] is None and iname not in abs_in2prom_info: src = abs_in2out[iname] dist_in = meta_in['distributed'] dist_out = all_abs2meta_out[src]['distributed'] if dist_in or dist_out: gsize_out = all_abs2meta_out[src]['global_size'] gsize_in = all_abs2meta_in[iname]['global_size'] vout_sizes = sizes_out[:, all_abs2idx[src]] offset = None if gsize_out == gsize_in or (not dist_out and np.sum(vout_sizes) == gsize_in): # This assumes one of: # 1) a distributed output with total size matching the total size of a # distributed input # 2) a non-distributed output with local size matching the total size of a # distributed input # 3) a non-distributed output with total size matching the total size of a # distributed input if dist_in: offset = np.sum(sizes_in[:iproc, i]) end = offset + sizes_in[iproc, i] # total sizes differ and output is distributed, so can't determine mapping if offset is None: self._collect_error(f"{self.msginfo}: Can't determine src_indices " f"automatically for input '{iname}'. They must be " "supplied manually.", ident=(self.pathname, iname)) continue if dist_in and not dist_out: src_shape = self._get_full_dist_shape(src, all_abs2meta_out[src]['shape']) else: src_shape = all_abs2meta_out[src]['global_shape'] if offset == end: idx = np.zeros(0, dtype=INT_DTYPE) else: idx = slice(offset, end) meta_in['src_indices'] = indexer(idx, flat_src=True, src_shape=src_shape) meta_in['flat_src_indices'] = True added_src_inds.append(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. """ self._has_approx = True info = self._subjacs_info for abs_key in self._matching_key_iter(of, wrt): 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, minimum_step=None): """ Declare information about this component's subjacobians. Parameters ---------- of : str or iter of str The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern. wrt : str or iter 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 : str 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 : str Step type for computing the size of the finite difference step. It can be 'abs' for absolute, 'rel_avg' for a size relative to the absolute value of the vector input, or 'rel_element' for a size relative to each value in the vector input. In addition, it can be 'rel_legacy' for a size relative to the norm of the vector. For backwards compatibilty, it can be 'rel', which is now equivalent to 'rel_avg'. Defaults to None, in which case the approximation method provides its default value. minimum_step : float Minimum step size allowed when using one of the relative step_calc options. 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 not isinstance(of, (str, Iterable)): raise ValueError(f"{self.msginfo}: in declare_partials, the 'of' arg must be a string " f"or an iter of strings, but got {of}.") if not isinstance(wrt, (str, Iterable)): raise ValueError(f"{self.msginfo}: in declare_partials, the 'wrt' arg must be a " f"string or an iter of strings, but got {wrt}.") of = of if isinstance(of, str) else tuple(of) wrt = wrt if isinstance(wrt, str) else tuple(wrt) key = (of, wrt) if key not in self._declared_partials_patterns: self._declared_partials_patterns[key] = {} meta = self._declared_partials_patterns[key] 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['val'] = val _val = val.data if issparse(val) else val if np.all(_val == 0): warn_deprecation(f'{self.msginfo}: d({of})/d({wrt}): Partial was declared to be ' f'exactly zero. This is inefficient and the declaration should ' f'be removed. In a future version of OpenMDAO this behavior ' f'will raise an error.') if rows is not None: rows = np.asarray(rows, dtype=INT_DTYPE) cols = np.asarray(cols, dtype=INT_DTYPE) # 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))) if rows.size > 0 and rows.min() < 0: msg = '{}: d({})/d({}): row indices must be non-negative' raise ValueError(msg.format(self.msginfo, of, wrt)) if cols.size > 0 and cols.min() < 0: msg = '{}: d({})/d({}): col indices must be non-negative' raise ValueError(msg.format(self.msginfo, of, wrt)) meta['rows'] = rows meta['cols'] = cols # Check for repeated rows/cols indices. size = len(rows) if size > 0: coo = coo_matrix((np.ones(size, dtype=np.short), (rows, cols))) dsize = coo.data.size csc = coo.tocsc() # csc adds values at duplicate indices together, so result will be that data # size is less if there are duplicates if csc.data.size < dsize: coo = csc.tocoo() del csc inds = np.where(coo.data > 1.) dups = list(zip(coo.row[inds], coo.col[inds])) 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 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 minimum_step is not None: if 'minimum_step' in default_opts: meta['minimum_step'] = minimum_step else: raise RuntimeError("{}: d({})/d({}): 'minimum_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, minimum_step=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 Step type for computing the size of the finite difference step. It can be 'abs' for absolute, 'rel_avg' for a size relative to the absolute value of the vector input, or 'rel_element' for a size relative to each value in the vector input. In addition, it can be 'rel_legacy' for a size relative to the norm of the vector. For backwards compatibilty, it can be 'rel', which is now equivalent to 'rel_avg'. Defaults to None, in which case the approximation method provides its default value.. minimum_step : float Minimum step size allowed when using one of the relative step_calc options. 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', 'rel_legacy', 'rel_avg', 'rel_element') 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, minimum_step, directional))
def _get_check_partial_options(self): """ Return dictionary of partial options with pattern matches processed. This is called by check_partials. Returns ------- dict(wrt: (options)) Dictionary keyed by name with tuples of options (method, form, step, step_calc, minimum_step, directional) """ if not self._declared_partial_checks: return {} opts = {} wrt = self._get_partials_wrts() invalid_wrt = [] matrix_free = self.matrix_free if matrix_free: n_directional = 0 for data_tup in self._declared_partial_checks: wrt_list, method, form, step, step_calc, minimum_step, directional = data_tup 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', 'minimum_step', 'directional'] for name, value in zip(keynames, [method, form, step, step_calc, minimum_step, directional]): if value is not None: opt[name] = value else: opts[match] = {'method': method, 'form': form, 'step': step, 'step_calc': step_calc, 'minimum_step': minimum_step, '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 _resolve_partials_patterns(self, of, wrt, pattern_meta): """ Store subjacobian metadata for specific of, wrt pairs after resolving glob patterns. 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. pattern_meta : dict Metadata dict specifying shape, and/or approx properties, keyed by (of, wrt) as described above. """ val = pattern_meta['val'] if 'val' in pattern_meta else None is_scalar = isscalar(val) dependent = pattern_meta['dependent'] matfree = self.matrix_free if dependent: if 'rows' in pattern_meta and pattern_meta['rows'] is not None: # sparse list format rows = pattern_meta['rows'] cols = pattern_meta['cols'] 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)) elif not matfree: val = np.zeros_like(rows, dtype=float) if rows.size > 0: 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 abs2meta_in = self._var_abs2meta['input'] abs2meta_out = self._var_abs2meta['output'] is_array = isinstance(val, ndarray) patmeta = dict(pattern_meta) patmeta_not_none = {k: v for k, v in pattern_meta.items() if v is not None} for abs_key in self._matching_key_iter(of, '*' if wrt is None else wrt): 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) dist_out = abs2meta_out[of]['distributed'] if wrt in abs2meta_in: dist_in = abs2meta_in[wrt]['distributed'] else: dist_in = abs2meta_out[wrt]['distributed'] if dist_in and not dist_out and not self.matrix_free: rel_key = abs_key2rel_key(self, abs_key) raise RuntimeError(f"{self.msginfo}: component has defined partial {rel_key} " "which is a non-distributed output wrt a distributed input." " This is only supported using the matrix free API.") if shape[0] == 0 or shape[1] == 0: msg = "{}: '{}' is an array of size 0" if shape[0] == 0: if dist_out: # distributed vars are allowed to have zero size inputs on some procs rows_max = -1 else: # non-distributed vars are not allowed to have zero size inputs raise ValueError(msg.format(self.msginfo, of)) if shape[1] == 0: if not dist_in: # non-distributed vars are not allowed to have zero size outputs raise ValueError(msg.format(self.msginfo, wrt)) else: # distributed vars are allowed to have zero size outputs on some procs cols_max = -1 if val is None and not matfree: # we can only get here if rows is None (we're not sparse list format) meta['val'] = np.zeros(shape) elif is_array: if rows is None and val.shape != shape and val.size == shape[0] * shape[1]: meta['val'] = val = val.copy().reshape(shape) else: meta['val'] = val.copy() elif is_scalar: meta['val'] = np.full(shape, val, dtype=float) else: meta['val'] = val if rows_max >= shape[0] or cols_max >= shape[1]: of, wrt = abs_key2rel_key(self, abs_key) raise ValueError(f"{self.msginfo}: d({of})/d({wrt}): Expected {shape[0]}x" f"{shape[1]} but declared at least {rows_max + 1}x" f"{cols_max + 1}") self._check_partials_meta(abs_key, meta['val'], shape if rows is None else (rows.shape[0], 1)) self._subjacs_info[abs_key] = meta def _get_partials_wrts(self): """ Get list of 'wrt' variables that form the partial jacobian. Returns ------- list List of 'wrt' relative variable names. """ # filter out any discrete inputs or outputs if self._discrete_inputs: return [n for n in self._var_rel_names['input'] if n not in self._discrete_inputs] return list(self._var_rel_names['input']) def _get_partials_ofs(self, use_resname=False): """ Get lists of 'of' variables that form the partial jacobian. Parameters ---------- use_resname : bool Ignored. Returns ------- list List of 'of' relative variable names. """ # filter out any discrete inputs or outputs if self._discrete_outputs: return [n for n in self._var_rel_names['output'] if n not in self._discrete_outputs] return list(self._var_rel_names['output']) def _matching_key_iter(self, of_patterns, wrt_patterns, use_resname=False): """ Iterate over all combinations of matching keys for the given patterns. Parameters ---------- of_patterns : list of str List of variable names and/or glob patterns for the 'of' variables. wrt_patterns : list of str List of variable names and/or glob patterns for the 'wrt' variables. use_resname : bool, optional If True, match of_patterns against residuals instead of outputs. Yields ------ tuple A tuple of matching keys, where the first element is the 'of' key and the second element is the 'wrt' key. Both are absolute names. """ of_bundles = self._find_of_matches(of_patterns, use_resname=use_resname) wrt_bundles = self._find_wrt_matches(wrt_patterns) for of_bundle, wrt_bundle in product(of_bundles, wrt_bundles): 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)) yield from abs_key_iter(self, of_matches, wrt_matches) def _find_of_matches(self, pattern, use_resname=False): """ Find all matches for the given 'of' pattern. Parameters ---------- pattern : str Glob pattern or relative variable name. use_resname : bool If True, match residual names instead of output names. Returns ------- list List of tuples of the form (abs_name, meta) where abs_name is the absolute name of the matching variable and meta is the metadata for that variable. """ of_list = [pattern] if isinstance(pattern, str) else pattern return [(pattern, find_matches(pattern, self._get_partials_ofs(use_resname=use_resname))) for pattern in of_list] def _find_wrt_matches(self, pattern): """ Find all matches for the given 'wrt' pattern. Parameters ---------- pattern : str Glob pattern or relative variable name. Returns ------- list List of tuples of the form (abs_name, meta) where abs_name is the absolute name of the matching variable and meta is the metadata for that variable. """ wrt_list = [pattern] if isinstance(pattern, str) else pattern return [(pattern, find_matches(pattern, self._get_partials_wrts())) for pattern in wrt_list] 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 wrtset = set() subjac_keys = self._get_approx_subjac_keys() # go through subjac keys in reverse and only add approx for the last of each wrt # (this prevents warnings that could confuse users) for i in range(len(subjac_keys) - 1, -1, -1): key = subjac_keys[i] if key[1] not in wrtset: wrtset.add(key[1]) 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: self._update_subjac_sparsity(coloring.get_subjac_sparsity()) if self._jacobian is not None: self._jacobian._restore_approx_sparsity() def _resolve_src_inds(self): abs2prom = self._var_abs2prom['input'] abs_in2prom_info = self._problem_meta['abs_in2prom_info'] all_abs2meta_in = self._var_allprocs_abs2meta['input'] abs2meta_in = self._var_abs2meta['input'] conns = self._problem_meta['model_ref']()._conn_global_abs_in2out all_abs2meta_out = self._problem_meta['model_ref']()._var_allprocs_abs2meta['output'] for tgt, meta in abs2meta_in.items(): if tgt in abs_in2prom_info: pinfo = abs_in2prom_info[tgt][-1] # component always last in the plist if pinfo is not None: inds, flat, shape = pinfo if inds is not None: all_abs2meta_in[tgt]['has_src_indices'] = True meta['src_shape'] = shape = all_abs2meta_out[conns[tgt]]['global_shape'] if inds._flat_src: meta['flat_src_indices'] = True elif meta['flat_src_indices'] is None: meta['flat_src_indices'] = flat try: if not isinstance(inds, Indexer): meta['src_indices'] = inds = indexer(inds, flat_src=flat, src_shape=shape) else: meta['src_indices'] = inds = inds.copy() inds.set_src_shape(shape) self._var_prom2inds[abs2prom[tgt]] = [shape, inds, flat] except Exception: type_exc, exc, tb = sys.exc_info() self._collect_error(f"When accessing '{conns[tgt]}' with src_shape " f"{shape} from '{pinfo.prom_path()}' using " f"src_indices {inds}: {exc}", exc_type=type_exc, tback=tb, ident=(conns[tgt], tgt)) def _check_consistent_serial_dinputs(self, nz_dist_outputs): """ Check consistency across ranks for serial d_inputs variables. This is used primarily to test that `compute_jacvec_product` and `apply_linear` methods follow the OpenMDAO convention that in reverse mode, the component should perform 'allreduce' sorts of operations only for derivatives of distributed outputs with-respect-to serial inputs. This should result in serial input derivatives being consistent across all ranks in the Component's communicator. Parameters ---------- nz_dist_outputs : set or list Set of distributed outputs with nonzero values for the most recent _apply_linear call. """ if not self.checking or not self._has_distrib_outputs or self.comm.size < 2: return if self._serial_idxs is None: ranges = defaultdict(list) output_len = 0 if self.is_explicit() else len(self._outputs) for _, offset, end, vec, slc, dist_sizes in self._jac_wrt_iter(): if dist_sizes is None: # not distributed if offset != end: if vec is self._outputs: ranges[vec].append(range(offset, end)) else: ranges[vec].append(range(offset - output_len, end - output_len)) self._serial_idxs = [] for vec, rlist in ranges.items(): if rlist: self._serial_idxs.append((vec, np.hstack(rlist))) for vec, inds in self._serial_idxs: # _jac_wrt_iter gives us _input and possibly _output vecs (for implicit comps), but we # want to check _dinputs and _doutputs v = self._dinputs if vec is self._inputs else self._doutputs result = inconsistent_across_procs(self.comm, v.asarray()[inds]) if self.comm.rank == 0 and np.any(result): bad_inds = np.arange(len(v), dtype=INT_DTYPE)[inds][result] bad_mask = np.zeros(len(v), dtype=bool) bad_mask[bad_inds] = True for inname, slc in v.get_slice_dict().items(): if np.any(bad_mask[slc]): for outname in nz_dist_outputs: key = (outname, inname) self._inconsistent_keys.add(key) def _get_dist_nz_dresids(self): """ Get names of distributed resids that are non-zero prior to computing derivatives. This should only be called when 'rev' mode is active. Returns ------- list of str List of names of distributed resids that have nonzero entries. """ nzresids = [] dresids = self._dresiduals.asarray() for of, start, end, _, dist_sizes in self._jac_of_iter(): if dist_sizes is not None: if np.any(dresids[start:end]): nzresids.append(of) full_nzresids = set() if self.comm.rank == 0: for nzoutlist in self.comm.gather(nzresids, root=0): full_nzresids.update(nzoutlist) return full_nzresids self.comm.gather(nzresids, root=0) return nzresids def _has_fast_rel_lookup(self): """ Return True if this System should have fast relative variable name lookup in vectors. Returns ------- bool True if this System should have fast relative variable name lookup in vectors. """ return True def _get_graph_node_meta(self): """ Return metadata to add to this system's graph node. Returns ------- dict Metadata for this system's graph node. """ meta = super()._get_graph_node_meta() meta['base'] = 'ExplicitComponent' if self.is_explicit() else 'ImplicitComponent' return meta
class _DictValues(object): """ A dict-like wrapper for a dict of metadata, where getitem returns 'val' from metadata. """ def __init__(self, dct): self._dict = dct def __getitem__(self, key): return self._dict[key]['val'] def __setitem__(self, key, value): self._dict[key]['val'] = 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]['val']) for key in self._dict]