Test ReducedFunctional.tlm

.github/workflows/core.yml

@@ -207,7 +207,7 @@ jobs:
             --extra-index-url https://download.pytorch.org/whl/cpu \
             "$(echo ./firedrake-repo/dist/firedrake-*.tar.gz)[ci,docs]"
           : # TODO: Remove before merge
-          pip install --verbose --editable git+https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional#egg=pyadjoint-ad
+          pip install --verbose --editable git+https://github.com/dolfin-adjoint/pyadjoint.git@JHopeCollins/tlm#egg=pyadjoint-ad
 
           firedrake-clean
           pip list

firedrake/adjoint/ensemble_reduced_functional.py

@@ -209,7 +209,38 @@ def derivative(self, adj_input=1.0, apply_riesz=False):
             return dJdm_local.delist(dJdm_total)
         return dJdm_local
 
-    def hessian(self, m_dot, apply_riesz=False):
+    def tlm(self, m_dot):
+        """Return the action of the tangent linear model of the functional.
+
+        The tangent linear model is evaluated w.r.t. the control on a vector
+        m_dot, around the last supplied value of the control.
+
+        Parameters
+        ----------
+        m_dot : pyadjoint.OverloadedType
+            The direction in which to compute the action of the tangent linear model.
+
+        Returns
+        -------
+            pyadjoint.OverloadedType: The action of the tangent linear model in the
+            direction m_dot.  Should be an instance of the same type as the functional.
+        """
+        local_tlm = self.local_reduced_functional.tlm(m_dot)
+        ensemble_comm = self.ensemble.ensemble_comm
+        if self.gather_functional:
+            mdot_g = self._allgather_J(local_tlm)
+            total_tlm = self.gather_functional.tlm(mdot_g)
+        # if gather_functional is None then we do a sum
+        elif isinstance(local_tlm, float):
+            total_tlm = ensemble_comm.allreduce(sendobj=local_tlm, op=MPI.SUM)
+        elif isinstance(local_tlm, Function):
+            total_tlm = type(local_tlm)(local_tlm.function_space())
+            total_tlm = self.ensemble.allreduce(local_tlm, total_tlm)
+        else:
+            raise NotImplementedError("This type of functional is not supported.")
+        return total_tlm
+
+    def hessian(self, m_dot, hessian_input=None, evaluate_tlm=True, apply_riesz=False):
         """The Hessian is not yet implemented for ensemble reduced functional.
 
         Raises:

tests/firedrake/adjoint/test_tlm.py

@@ -54,9 +54,7 @@ def test_tlm_assemble():
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
     g = f.copy(deepcopy=True)
-    f.block_variable.tlm_value = h
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, g, h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, g, h, dJdm=Jhat.tlm(h)) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -80,12 +78,7 @@ def test_tlm_bc():
     J = assemble(c ** 2 * u * dx)
     Jhat = ReducedFunctional(J, Control(c))
 
-    # Need to specify the domain for the constant as `ufl.action`, which requires `ufl.Constant`
-    # to have a function space, will be applied on the tlm value.
-    c.block_variable.tlm_value = Function(R, val=1)
-    tape.evaluate_tlm()
-
-    assert (taylor_test(Jhat, c, Function(R, val=1), dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, c, Function(R, val=1), dJdm=Jhat.tlm(Function(R, val=1))) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -113,10 +106,8 @@ def test_tlm_func():
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
     g = c.copy(deepcopy=True)
-    c.block_variable.tlm_value = h
-    tape.evaluate_tlm()
 
-    assert (taylor_test(Jhat, g, h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, g, h, dJdm=Jhat.tlm(h)) > 1.9)
 
 
 @pytest.mark.parametrize("solve_type",
@@ -170,9 +161,7 @@ def test_time_dependent(solve_type):
     Jhat = ReducedFunctional(J, control)
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
-    u_1.tlm_value = h
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, control.tape_value(), h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, control.tape_value(), h, dJdm=Jhat.tlm(h)) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -224,9 +213,7 @@ def Dt(u, u_, timestep):
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
     g = ic.copy(deepcopy=True)
-    ic.block_variable.tlm_value = h
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, g, h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, g, h, dJdm=Jhat.tlm(h)) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -255,9 +242,7 @@ def test_projection():
     J = assemble(u_**2*dx)
     Jhat = ReducedFunctional(J, Control(k))
 
-    k.block_variable.tlm_value = Constant(1)
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, k, Function(R, val=1), dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, k, Function(R, val=1), dJdm=Jhat.tlm(Constant(1))) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -268,7 +253,6 @@ def test_projection_function():
     V = FunctionSpace(mesh, "CG", 1)
 
     bc = DirichletBC(V, Constant(1), "on_boundary")
-    # g = Function(V)
     x, y = SpatialCoordinate(mesh)
     g = project(sin(x)*sin(y), V, annotate=False)
     expr = sin(g*x)
@@ -289,6 +273,4 @@ def test_projection_function():
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
     m = g.copy(deepcopy=True)
-    g.block_variable.tlm_value = h
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, m, h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, m, h, dJdm=Jhat.tlm(h)) > 1.9)