Source code for firedrake.adjoint.variational_solver

import copy
from firedrake.constant import Constant
from functools import wraps
from pyadjoint.tape import get_working_tape, stop_annotating, annotate_tape, no_annotations
from firedrake.adjoint.blocks import NonlinearVariationalSolveBlock
from ufl import replace

[docs]class NonlinearVariationalProblemMixin: @staticmethod def _ad_annotate_init(init): @no_annotations @wraps(init) def wrapper(self, *args, **kwargs): init(self, *args, **kwargs) self._ad_F = self.F self._ad_u = self.u self._ad_bcs = self.bcs self._ad_J = self.J self._ad_kwargs = {'Jp': self.Jp, 'form_compiler_parameters': self.form_compiler_parameters, 'is_linear': self.is_linear} self._ad_count_map = {} return wrapper def _ad_count_map_update(self, updated_ad_count_map): self._ad_count_map = updated_ad_count_map
[docs]class NonlinearVariationalSolverMixin: @staticmethod def _ad_annotate_init(init): @no_annotations @wraps(init) def wrapper(self, problem, *args, **kwargs): init(self, problem, *args, **kwargs) self._ad_problem = problem self._ad_args = args self._ad_kwargs = kwargs self._ad_nlvs = None return wrapper @staticmethod def _ad_annotate_solve(solve): @wraps(solve) def wrapper(self, **kwargs): """To disable the annotation, just pass :py:data:`annotate=False` to this routine, and it acts exactly like the Firedrake solve call. This is useful in cases where the solve is known to be irrelevant or diagnostic for the purposes of the adjoint computation (such as projecting fields to other function spaces for the purposes of visualisation).""" annotate = annotate_tape(kwargs) if annotate: tape = get_working_tape() problem = self._ad_problem sb_kwargs = NonlinearVariationalSolveBlock.pop_kwargs(kwargs) sb_kwargs.update(kwargs) block = NonlinearVariationalSolveBlock(problem._ad_F == 0, problem._ad_u, problem._ad_bcs, problem_J=problem._ad_J, solver_params=self.parameters, solver_kwargs=self._ad_kwargs, **sb_kwargs) if not self._ad_nlvs: from firedrake import NonlinearVariationalSolver self._ad_nlvs = NonlinearVariationalSolver(self._ad_problem_clone(self._ad_problem, block.get_dependencies()), **self._ad_kwargs) block._ad_nlvs = self._ad_nlvs tape.add_block(block) with stop_annotating(): out = solve(self, **kwargs) if annotate: block.add_output(self._ad_problem._ad_u.create_block_variable()) return out return wrapper @no_annotations def _ad_problem_clone(self, problem, dependencies): """Replaces every coefficient in the residual and jacobian with a deepcopy to return a clone of the original NonlinearVariationalProblem instance. We'll be modifying the numerical values of the coefficients in the residual and jacobian, so in order not to affect the user-defined self._ad_problem.F, self._ad_problem.J and self._ad_problem.u expressions, we'll instead create clones of them. """ from firedrake import NonlinearVariationalProblem F_replace_map = {} J_replace_map = {} F_coefficients = problem.F.coefficients() J_coefficients = problem.J.coefficients() _ad_count_map = {} for block_variable in dependencies: coeff = block_variable.output if coeff in F_coefficients and coeff not in F_replace_map: if isinstance(coeff, Constant): F_replace_map[coeff] = copy.deepcopy(coeff) else: F_replace_map[coeff] = coeff.copy(deepcopy=True) _ad_count_map[F_replace_map[coeff]] = coeff.count() if coeff in J_coefficients and coeff not in J_replace_map: if coeff in F_replace_map: J_replace_map[coeff] = F_replace_map[coeff] elif isinstance(coeff, Constant): J_replace_map[coeff] = copy.deepcopy(coeff) else: J_replace_map[coeff] = coeff.copy() _ad_count_map[J_replace_map[coeff]] = coeff.count() nlvp = NonlinearVariationalProblem(replace(problem.F, F_replace_map), F_replace_map[problem.u], bcs=problem.bcs, J=replace(problem.J, J_replace_map)) nlvp._ad_count_map_update(_ad_count_map) return nlvp