# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Contributors:
# INITIAL AUTHORS - API and implementation and/or documentation
# :author: Charlie Vanaret
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""An advanced MDA splitting algorithm based on graphs."""
from __future__ import division, unicode_literals
import logging
from itertools import repeat
from multiprocessing import cpu_count
from os.path import join, split
from typing import Any, Iterable, List, Mapping, Optional, Sequence, Tuple, Union
from gemseo.api import create_mda
from gemseo.core.chain import MDOChain
from gemseo.core.coupling_structure import MDOCouplingStructure
from gemseo.core.discipline import MDODiscipline
from gemseo.core.execution_sequence import SerialExecSequence
from gemseo.mda.mda import MDA
LOGGER = logging.getLogger(__name__)
N_CPUS = cpu_count()
[docs]class MDAChain(MDA):
"""A chain of sub-MDAs.
The execution sequence is provided by the :class:`.DependencyGraph`.
"""
_ATTR_TO_SERIALIZE = MDA._ATTR_TO_SERIALIZE + (
"mdo_chain",
"_chain_linearize",
"lin_cache_tol_fact",
"assembly",
"coupling_structure",
"linear_solver",
"linear_solver_options",
"linear_solver_tolerance",
"matrix_type",
"use_lu_fact",
"all_couplings",
)
def __init__(
self,
disciplines, # type: Sequence[MDODiscipline]
sub_mda_class="MDAJacobi", # type: str
max_mda_iter=20, # type: int
name=None, # type: Optional[str]
n_processes=N_CPUS, # type: int
chain_linearize=False, # type: bool
tolerance=1e-6, # type: float
linear_solver_tolerance=1e-12, # type: float
use_lu_fact=False, # type: bool
grammar_type=MDODiscipline.JSON_GRAMMAR_TYPE, # type: str
coupling_structure=None, # type: Optional[MDOCouplingStructure]
sub_coupling_structures=None, # type: Optional[Iterable[MDOCouplingStructure]]
log_convergence=False, # type: bool
linear_solver="DEFAULT", # type: str
linear_solver_options=None, # type: Mapping[str,Any]
**sub_mda_options # type: Optional[Union[float, int, bool, str]]
):
"""
Args:
sub_mda_class: The class name of the sub-MDA.
n_processes: The number of processes.
chain_linearize: Whether to linearize the chain of execution.
Otherwise, linearize the overall MDA with base class method.
This last option is preferred to minimize computations in adjoint mode,
while in direct mode, linearizing the chain may be cheaper.
sub_coupling_structures: The coupling structures to be used by the sub-MDAs.
If None, they are created from the sub-disciplines.
**sub_mda_options: The options to be passed to the sub-MDAs.
"""
self.n_processes = n_processes
self.mdo_chain = None
self._chain_linearize = chain_linearize
self.sub_mda_list = []
# compute execution sequence of the disciplines
super(MDAChain, self).__init__(
disciplines,
max_mda_iter=max_mda_iter,
name=name,
tolerance=tolerance,
linear_solver_tolerance=linear_solver_tolerance,
use_lu_fact=use_lu_fact,
grammar_type=grammar_type,
coupling_structure=coupling_structure,
linear_solver=linear_solver,
linear_solver_options=linear_solver_options,
)
if (
not self.coupling_structure.get_all_couplings()
and not self._chain_linearize
):
LOGGER.warning("No coupling in MDA, switching chain_linearize to True")
self._chain_linearize = True
self._create_mdo_chain(
disciplines,
sub_mda_class=sub_mda_class,
sub_coupling_structures=sub_coupling_structures,
**sub_mda_options
)
self.log_convergence = log_convergence
self._initialize_grammars()
self._check_consistency()
self._set_default_inputs()
self._compute_input_couplings()
# cascade the tolerance
for sub_mda in self.sub_mda_list:
sub_mda.tolerance = self.tolerance
@MDA.log_convergence.setter
def log_convergence(
self,
value, # type: bool
): # type: (...) -> None
self._log_convergence = value
for mda in self.sub_mda_list:
mda.log_convergence = value
def _create_mdo_chain(
self,
disciplines, # type: Sequence[MDODiscipline]
sub_mda_class="MDAJacobi", # type: str
sub_coupling_structures=None, # type: Optional[Iterable[MDOCouplingStructure]]
**sub_mda_options # type: Optional[Union[float,int,bool,str]]
):
"""Create an MDO chain from the execution sequence of the disciplines.
Args:
sub_mda_class: The name of the class of the sub-MDAs.
disciplines: The disciplines.
sub_coupling_structures: The coupling structures to be used by the sub-MDAs.
If None, they are created from the sub-disciplines.
**sub_mda_options: The options to be used to initialize the sub-MDAs.
"""
chained_disciplines = []
self.sub_mda_list = []
if sub_coupling_structures is None:
sub_coupling_structures = repeat(None)
sub_coupling_structures_iterator = iter(sub_coupling_structures)
for parallel_tasks in self.coupling_structure.sequence:
# to parallelize, check if 1 < len(parallel_tasks)
# for now, parallel tasks are run sequentially
for coupled_disciplines in parallel_tasks:
first_disc = coupled_disciplines[0]
if len(coupled_disciplines) > 1 or (
len(coupled_disciplines) == 1
and self.coupling_structure.is_self_coupled(first_disc)
):
# several disciplines coupled
# order the MDA disciplines the same way as the
# original disciplines
sub_mda_disciplines = []
for disc in disciplines:
if disc in coupled_disciplines:
sub_mda_disciplines.append(disc)
# create a sub-MDA
sub_mda = create_mda(
sub_mda_class,
sub_mda_disciplines,
max_mda_iter=self.max_mda_iter,
tolerance=self.tolerance,
linear_solver_tolerance=self.linear_solver_tolerance,
grammar_type=self.grammar_type,
use_lu_fact=self.use_lu_fact,
linear_solver=self.linear_solver,
linear_solver_options=self.linear_solver_options,
coupling_structure=next(sub_coupling_structures_iterator),
**sub_mda_options
)
sub_mda.n_processes = self.n_processes
chained_disciplines.append(sub_mda)
self.sub_mda_list.append(sub_mda)
else:
# single discipline
chained_disciplines.append(first_disc)
# create the MDO chain that sequentially evaluates the sub-MDAs and the
# single disciplines
self.mdo_chain = MDOChain(
chained_disciplines, name="MDA chain", grammar_type=self.grammar_type
)
def _initialize_grammars(self): # type: (...) -> None
"""Define all inputs and outputs of the chain."""
if self.mdo_chain is None: # First call by super class must be ignored.
return
self.input_grammar.update_from(self.mdo_chain.input_grammar)
self.output_grammar.update_from(self.mdo_chain.output_grammar)
def _check_consistency(self):
"""Check if there is no more than 1 equation per variable.
For instance if a strong coupling is not also a self coupling.
"""
if self.mdo_chain is None: # First call by super class must be ignored.
return
super(MDAChain, self)._check_consistency()
def _run(self): # type -> None
if self.warm_start:
self._couplings_warm_start()
self.local_data = self.mdo_chain.execute(self.local_data)
return self.local_data
def _compute_jacobian(
self,
inputs=None, # type: Optional[Sequence[str]]
outputs=None, # type: Optional[Sequence[str]]
): # type: (...) -> None
if self._chain_linearize:
self.mdo_chain.add_differentiated_inputs(inputs)
self.mdo_chain.add_differentiated_outputs(outputs)
# the Jacobian of the MDA chain is the Jacobian of the MDO chain
last_cached = self.cache.get_last_cached_inputs()
self.mdo_chain.linearize(last_cached)
self.jac = self.mdo_chain.jac
else:
super(MDAChain, self)._compute_jacobian(inputs, outputs)
[docs] def add_differentiated_outputs(
self,
outputs=None, # type: Optional[Iterable[str]]
): # type: (...) -> None
MDA.add_differentiated_outputs(self, outputs=outputs)
if self._chain_linearize:
self.mdo_chain.add_differentiated_outputs(outputs)
@property
def normed_residual(self): # type: (...) -> float
"""The normed_residuals, computed from the sub-MDAs residuals."""
return sum((mda.normed_residual ** 2 for mda in self.sub_mda_list)) ** 0.5
@normed_residual.setter
def normed_residual(
self,
normed_residual, # type: float
): # type: (...) ->None
"""Set the normed_residual.
Has no effect,
since the normed residuals are defined by sub-MDAs residuals
(see associated property).
Here for compatibility with mother class.
"""
pass
[docs] def get_expected_dataflow(
self,
): # type: (...) -> List[Tuple[MDODiscipline,MDODiscipline,List[str]]]
return self.mdo_chain.get_expected_dataflow()
[docs] def get_expected_workflow(self): # type: (...) ->SerialExecSequence
exec_s = SerialExecSequence(self)
workflow = self.mdo_chain.get_expected_workflow()
exec_s.extend(workflow)
return exec_s
[docs] def reset_statuses_for_run(self): # type: (...) -> None
super(MDAChain, self).reset_statuses_for_run()
self.mdo_chain.reset_statuses_for_run()
[docs] def plot_residual_history(
self,
show=False, # type: bool
save=True, # type: bool
n_iterations=None, # type: Optional[int]
logscale=None, # type: Optional[Tuple[int,int]]
filename=None, # type: Optional[str]
figsize=(50, 10), # type: Tuple[int,int]
): # type: (...) -> None
for sub_mda in self.sub_mda_list:
if filename is not None:
s_filename = split(filename)
filename = join(
s_filename[0],
"{}_{}".format(sub_mda.__class__.__name__, s_filename[1]),
)
sub_mda.plot_residual_history(
show, save, n_iterations, logscale, filename, figsize
)
