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Update BCS for forward recomputation #4408

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Update BCS for forward recomputation #4408

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e664dcb
Update bcs with checkpoints for adjs
Ig-dolci Jun 26, 2025
bbec24b
testing
Ig-dolci Jun 26, 2025
2db7c97
Right test / cleaner code
Ig-dolci Jun 26, 2025
5ead717
Remove unecessary test
Ig-dolci Jun 26, 2025
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9 changes: 7 additions & 2 deletions firedrake/bcs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -450,10 +450,15 @@ def apply(self, r, u=None):
r = r.sub(idx)
if u:
u = u.sub(idx)

bc_checkpointed = self.block_variable.checkpoint
bc = bc_checkpointed.checkpoint \
if bc_checkpointed is not None else self.function_arg
Comment on lines +454 to +456
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Can this logic be put into a new property that gets overloaded in the corresponding adjoint subclass of DirichletBC?

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Yes, it must be. I am still checking to see if this works. It looks like it requires more thought. Sia just wrote that this is leading some Gadopt tests to fail.


if u:
r.assign(u - self.function_arg, subset=self.node_set)
r.assign(u - bc, subset=self.node_set)
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It is still missing a test for this case.

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You will get this case by passing pre_apply_bcs=False to the NLVS

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Thank you.

else:
r.assign(self.function_arg, subset=self.node_set)
r.assign(bc, subset=self.node_set)

def integrals(self):
return []
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22 changes: 22 additions & 0 deletions tests/firedrake/regression/test_adjoint_operators.py
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Expand Up @@ -1050,3 +1050,25 @@ def u_analytical(x, a, b):
# tape._blocks[1] is the DirichletBC block for the right boundary
assert isinstance(tape._blocks[1], DirichletBCBlock) and \
tape._blocks[1]._outputs[0].checkpoint.checkpoint is not bc_right._original_arg


@pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done
def test_bdy_update():
# Fix the issue https://github.com/firedrakeproject/firedrake/issues/4387
mesh = UnitSquareMesh(5, 5)
V = FunctionSpace(mesh, "CG", 1)
R = FunctionSpace(mesh, "R", 0)

bc_func = Function(R, val=2.0)
bc = DirichletBC(V, bc_func, 1)
u_ = Function(V)

v = TestFunction(V)
problem = NonlinearVariationalProblem(
inner(grad(u_), grad(v)) * dx - v * dx, u_, bcs=bc)
solver = NonlinearVariationalSolver(problem)
solver.solve()
J = assemble(u_**2 * dx)
c = Control(bc_func)
Jhat = ReducedFunctional(J, c)
assert taylor_test(Jhat, bc_func, Function(R, val=0.1)) > 1.9
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