def derivative_outer_action(
dependencies, values, variable, contraction_vector, hermitian, input, coefficient, context
):
dolfin_variable = values[dependencies.index(variable)].data
dolfin_values = [val.data for val in values]
expressions.update_expressions(frozen_expressions)
constant.update_constants(frozen_constants)
current_form = backend.replace(eq_lhs, dict(zip(diag_coeffs, dolfin_values)))
deriv = backend.derivative(current_form, dolfin_variable)
args = ufl.algorithms.extract_arguments(deriv)
deriv = backend.replace(deriv, {args[2]: contraction_vector.data}) # contract over the outer index
# Assemble the G-matrix now, so that we can apply the Dirichlet BCs to it
if len(ufl.algorithms.extract_arguments(ufl.algorithms.expand_derivatives(coefficient * deriv))) == 0:
return adjlinalg.Vector(None)
G = coefficient * deriv
if hermitian:
output = backend.action(backend.adjoint(G), input.data)
else:
output = backend.action(G, input.data)
return adjlinalg.Vector(output)
def diag_assembly_cb(dependencies, values, hermitian, coefficient, context):
"""This callback must conform to the libadjoint Python block assembly
interface. It returns either the form or its transpose, depending on
the value of the logical hermitian."""
assert coefficient == 1
value_coeffs = [v.data for v in values]
expressions.update_expressions(frozen_expressions)
constant.update_constants(frozen_constants)
eq_l = backend.replace(eq_lhs, dict(zip(diag_coeffs, value_coeffs)))
kwargs = {"cache": eq_l in caching.assembled_fwd_forms} # should we cache our matrices on the way backwards?
if hermitian:
# Homogenise the adjoint boundary conditions. This creates the adjoint
# solution associated with the lifted discrete system that is actually solved.
adjoint_bcs = [utils.homogenize(bc) for bc in eq_bcs if isinstance(bc, backend.DirichletBC)] + [
bc for bc in eq_bcs if not isinstance(bc, backend.DirichletBC)
]
if len(adjoint_bcs) == 0:
adjoint_bcs = None
else:
adjoint_bcs = misc.uniq(adjoint_bcs)
kwargs["bcs"] = adjoint_bcs
kwargs["solver_parameters"] = solver_parameters
kwargs["adjoint"] = True
if initial_guess:
kwargs["initial_guess"] = value_coeffs[dependencies.index(initial_guess_var)]
if replace_map:
kwargs["replace_map"] = dict(zip(diag_coeffs, value_coeffs))
return (
matrix_class(
backend.adjoint(eq_l, reordered_arguments=ufl.algorithms.extract_arguments(eq_l)), **kwargs
),
adjlinalg.Vector(None, fn_space=u.function_space()),
)
else:
kwargs["bcs"] = misc.uniq(eq_bcs)
kwargs["solver_parameters"] = solver_parameters
kwargs["adjoint"] = False
if initial_guess:
kwargs["initial_guess"] = value_coeffs[dependencies.index(initial_guess_var)]
if replace_map:
kwargs["replace_map"] = dict(zip(diag_coeffs, value_coeffs))
return (matrix_class(eq_l, **kwargs), adjlinalg.Vector(None, fn_space=u.function_space()))
def diag_action_cb(dependencies, values, hermitian, coefficient, input, context):
value_coeffs = [v.data for v in values]
expressions.update_expressions(frozen_expressions)
constant.update_constants(frozen_constants)
eq_l = backend.replace(eq_lhs, dict(zip(diag_coeffs, value_coeffs)))
if hermitian:
eq_l = backend.adjoint(eq_l)
output = coefficient * backend.action(eq_l, input.data)
return adjlinalg.Vector(output)
def __call__(self, dependencies, values):
expressions.update_expressions(self.frozen_expressions)
constant.update_constants(self.frozen_constants)
coeffs = [x for x in ufl.algorithms.extract_coefficients(self.scheme.rhs_form()) if hasattr(x, 'function_space')]
for (coeff, value) in zip(coeffs, values):
coeff.assign(value.data)
self.scheme.t().assign(self.time)
self.solver.step(self.dt)
# FIXME: This form should actually be from before the solve.
out = adjlinalg.Vector(self.scheme.solution())
out.nonlinear_form = -self.dt*self.scheme.rhs_form()
out.nonlinear_u = self.scheme.solution()
out.nonlinear_bcs = []
out.nonlinear_J = ufl.derivative(out.nonlinear_form, out.nonlinear_u)
return out
def derivative_action(self, dependencies, values, variable, contraction_vector, hermitian):
expressions.update_expressions(self.frozen_expressions)
constant.update_constants(self.frozen_constants)
if not hermitian:
if self.solver not in caching.pis_fwd_to_tlm:
dolfin.info_blue("No TLM solver, creating ... ")
creation_timer = dolfin.Timer("to_adm")
if contraction_vector.data is not None:
tlm_scheme = self.scheme.to_tlm(contraction_vector.data)
else:
tlm_scheme = self.scheme.to_tlm(dolfin.Function(self.fn_space))
creation_time = creation_timer.stop()
dolfin.info_red("TLM creation time: %s" % creation_time)
tlm_solver = dolfin.PointIntegralSolver(tlm_scheme)
tlm_solver.parameters.update(self.solver.parameters)
caching.pis_fwd_to_tlm[self.solver] = tlm_solver
else:
tlm_solver = caching.pis_fwd_to_tlm[self.solver]
tlm_scheme = tlm_solver.scheme()
if contraction_vector.data is not None:
tlm_scheme.contraction.assign(contraction_vector.data)
else:
tlm_scheme.contraction.vector().zero()
coeffs = [
x
for x in ufl.algorithms.extract_coefficients(tlm_scheme.rhs_form())
if hasattr(x, "function_space")
]
for (coeff, value) in zip(coeffs, values):
coeff.assign(value.data)
tlm_scheme.t().assign(self.time)
tlm_solver.step(self.dt)
return adjlinalg.Vector(tlm_scheme.solution())
else:
if self.solver not in caching.pis_fwd_to_adj:
dolfin.info_blue("No ADM solver, creating ... ")
creation_timer = dolfin.Timer("to_adm")
if contraction_vector.data is not None:
adm_scheme = self.scheme.to_adm(contraction_vector.data)
else:
adm_scheme = self.scheme.to_adm(dolfin.Function(self.fn_space))
creation_time = creation_timer.stop()
dolfin.info_red("ADM creation time: %s" % creation_time)
adm_solver = dolfin.PointIntegralSolver(adm_scheme)
adm_solver.parameters.update(self.solver.parameters)
caching.pis_fwd_to_adj[self.solver] = adm_solver
else:
adm_solver = caching.pis_fwd_to_adj[self.solver]
adm_scheme = adm_solver.scheme()
if contraction_vector.data is not None:
adm_scheme.contraction.assign(contraction_vector.data)
else:
adm_scheme.contraction.vector().zero()
coeffs = [
x
for x in ufl.algorithms.extract_coefficients(adm_scheme.rhs_form())
if hasattr(x, "function_space")
]
for (coeff, value) in zip(coeffs, values):
coeff.assign(value.data)
adm_scheme.t().assign(self.time)
adm_solver.step(self.dt)
return adjlinalg.Vector(adm_scheme.solution())
