Fixing errors related to default L2 grad in adjoints

firedrake/adjoint_utils/blocks/function.py

@@ -57,48 +57,38 @@ def evaluate_adj_component(self, inputs, adj_inputs, block_variable, idx,
                     # Catch the case where adj_inputs[0] is just a float
                     return adj_inputs[0]
             elif isconstant(block_variable.output):
-                R = block_variable.output._ad_function_space(
-                    prepared.function_space().mesh()
+                adj_output = self._adj_assign_constant(
+                    prepared, block_variable.output.function_space()
                 )
-                return self._adj_assign_constant(prepared, R)
             else:
                 adj_output = firedrake.Function(
-                    block_variable.output.function_space())
+                    block_variable.output.function_space()
+                )
                 adj_output.assign(prepared)
-                adj_output = adj_output.riesz_representation(riesz_map="l2")
-                return adj_output
+            return adj_output.riesz_representation(riesz_map="l2")
         else:
             # Linear combination
             expr, adj_input_func = prepared
-            adj_output = firedrake.Function(adj_input_func.function_space())
-            if not isconstant(block_variable.output):
-                diff_expr = ufl.algorithms.expand_derivatives(
-                    ufl.derivative(
-                        expr, block_variable.saved_output, adj_input_func
-                    )
+            if isconstant(block_variable.output):
+                R = block_variable.output._ad_function_space(
+                    adj_input_func.function_space().mesh()
                 )
-                # Firedrake does not support assignment of conjugate functions
-                adj_output.interpolate(ufl.conj(diff_expr))
-                adj_output = adj_output.riesz_representation(riesz_map="l2")
-            else:
-                mesh = adj_output.function_space().mesh()
                 diff_expr = ufl.algorithms.expand_derivatives(
-                    ufl.derivative(
-                        expr,
-                        block_variable.saved_output,
-                        firedrake.Constant(1., domain=mesh)
-                    )
+                    ufl.derivative(expr, block_variable.saved_output,
+                                   firedrake.Function(R, val=1.0))
                 )
-                adj_output.assign(diff_expr)
-                return adj_output.dat.inner(adj_input_func.dat)
-
-            if isconstant(block_variable.output):
-                R = block_variable.output._ad_function_space(
-                    adj_output.function_space().mesh()
+                diff_expr_assembled = firedrake.Function(adj_input_func.function_space())
+                diff_expr_assembled.interpolate(ufl.conj(diff_expr))
+                adj_output = firedrake.Function(
+                    R, val=firedrake.assemble(ufl.Action(diff_expr_assembled, adj_input_func))
                 )
-                return self._adj_assign_constant(adj_output, R)
             else:
-                return adj_output
+                adj_output = firedrake.Function(adj_input_func.function_space())
+                diff_expr = ufl.algorithms.expand_derivatives(
+                    ufl.derivative(expr, block_variable.saved_output, adj_input_func)
+                )
+                adj_output.interpolate(ufl.conj(diff_expr))
+            return adj_output.riesz_representation(riesz_map="l2")
 
     def _adj_assign_constant(self, adj_output, constant_fs):
         r = firedrake.Function(constant_fs)

firedrake/adjoint_utils/function.py

@@ -7,7 +7,6 @@
 import firedrake
 from .checkpointing import disk_checkpointing, CheckpointFunction, \
     CheckpointBase, checkpoint_init_data, DelegatedFunctionCheckpoint
-from numbers import Number
 
 
 class FunctionMixin(FloatingType):
@@ -224,26 +223,27 @@ def _ad_create_checkpoint(self):
     def _ad_convert_riesz(self, value, options=None):
         from firedrake import Function, Cofunction
 
-        options = {} if options is None else options
-        riesz_representation = options.get("riesz_representation", "l2")
-        solver_options = options.get("solver_options", {})
         V = options.get("function_space", self.function_space())
+        if value == 0.:
+            # In adjoint-based differentiation, value == 0. arises only when
+            # the functional is independent on the control variable.
+            # In this case, we do not apply the Riesz map and return a zero
+            # Cofunction.
+            return Cofunction(V.dual())
 
-        if riesz_representation != "l2" and not isinstance(value, Cofunction):
-            raise TypeError("Expected a Cofunction")
-        elif not isinstance(value, (Number, Cofunction, Function)):
-            raise TypeError("Expected a Cofunction, Function or a float")
+        options = {} if options is None else options
+        riesz_representation = options.get("riesz_representation", "L2")
+        solver_options = options.get("solver_options", {})
+        if not isinstance(value, (Cofunction, Function)):
+            raise TypeError("Expected a Cofunction or a Function")
 
         if riesz_representation == "l2":
-            if isinstance(value, (Cofunction, Function)):
-                return Function(V, val=value.dat)
-            else:
-                f = Function(V)
-                with stop_annotating():
-                    f.assign(value)
-                return f
+            return Function(V, val=value.dat)
 
         elif riesz_representation in ("L2", "H1"):
+            if not isinstance(value, Cofunction):
+                raise TypeError("Expected a Cofunction")
+
             ret = Function(V)
             a = self._define_riesz_map_form(riesz_representation, V)
             firedrake.solve(a == value, ret, **solver_options)
@@ -253,7 +253,7 @@ def _ad_convert_riesz(self, value, options=None):
             return riesz_representation(value)
 
         else:
-            raise NotImplementedError(
+            raise ValueError(
                 "Unknown Riesz representation %s" % riesz_representation)
 
     def _define_riesz_map_form(self, riesz_representation, V):
@@ -276,9 +276,9 @@ def _define_riesz_map_form(self, riesz_representation, V):
     @no_annotations
     def _ad_convert_type(self, value, options=None):
         # `_ad_convert_type` is not annotated, unlike `_ad_convert_riesz`
-        options = {} if options is None else options
-        riesz_representation = options.get("riesz_representation", "L2")
-        if riesz_representation is None:
+        options = {} if options is None else options.copy()
+        options.setdefault("riesz_representation", "L2")
+        if options["riesz_representation"] is None:
             return value
         else:
             return self._ad_convert_riesz(value, options=options)
@@ -317,7 +317,7 @@ def _ad_dot(self, other, options=None):
         from firedrake import assemble
 
         options = {} if options is None else options
-        riesz_representation = options.get("riesz_representation", "l2")
+        riesz_representation = options.get("riesz_representation", "L2")
         if riesz_representation == "l2":
             return self.dat.inner(other.dat)
         elif riesz_representation == "L2":

firedrake/ml/pytorch/fem_operator.py

@@ -83,7 +83,7 @@ def backward(ctx, grad_output):
             adj_input = float(adj_input)
 
         # Compute adjoint model of `F`: delegated to pyadjoint.ReducedFunctional
-        adj_output = F.derivative(adj_input=adj_input)
+        adj_output = F.derivative(adj_input=adj_input, options={"riesz_representation": "l2"})
 
         # Tuplify adjoint output
         adj_output = (adj_output,) if not isinstance(adj_output, collections.abc.Sequence) else adj_output

tests/regression/test_adjoint_operators.py

@@ -889,13 +889,43 @@ def test_cofunction_subfunctions_with_adjoint():
 
 
 @pytest.mark.skipcomplex  # Taping for complex-valued 0-forms not yet done
-def test_none_riesz_representation_to_derivative():
+def test_riesz_representation_for_adjoints():
+    # Check if the Riesz representation norms for adjoints are working as expected.
     mesh = UnitIntervalMesh(1)
     space = FunctionSpace(mesh, "Lagrange", 1)
-    u = Function(space).interpolate(SpatialCoordinate(mesh)[0])
-    J = assemble((u ** 2) * dx)
-    rf = ReducedFunctional(J, Control(u))
-    assert isinstance(rf.derivative(), Function)
-    assert isinstance(rf.derivative(options={"riesz_representation": "H1"}), Function)
-    assert isinstance(rf.derivative(options={"riesz_representation": "L2"}), Function)
-    assert isinstance(rf.derivative(options={"riesz_representation": None}), Cofunction)
+    f = Function(space).interpolate(SpatialCoordinate(mesh)[0])
+    J = assemble((f ** 2) * dx)
+    rf = ReducedFunctional(J, Control(f))
+    with stop_annotating():
+        v = TestFunction(space)
+        u = TrialFunction(space)
+        dJdu_cofunction = assemble(derivative((f ** 2) * dx, f, v))
+
+        # Riesz representation with l2
+        dJdu_function_l2 = Function(space, val=dJdu_cofunction.dat)
+
+        # Riesz representation with H1
+        a = u * v * dx + inner(grad(u), grad(v)) * dx
+        dJdu_function_H1 = Function(space)
+        solve(a == dJdu_cofunction, dJdu_function_H1)
+
+        # Riesz representation with L2
+        a = u*v*dx
+        dJdu_function_L2 = Function(space)
+        solve(a == dJdu_cofunction, dJdu_function_L2)
+
+    dJdu_none = rf.derivative(options={"riesz_representation": None})
+    dJdu_l2 = rf.derivative(options={"riesz_representation": "l2"})
+    dJdu_H1 = rf.derivative(options={"riesz_representation": "H1"})
+    dJdu_L2 = rf.derivative(options={"riesz_representation": "L2"})
+    dJdu_default_L2 = rf.derivative()
+    assert (
+        isinstance(dJdu_none, Cofunction) and isinstance(dJdu_function_l2, Function)
+        and isinstance(dJdu_H1, Function) and isinstance(dJdu_default_L2, Function)
+        and isinstance(dJdu_L2, Function)
+        and np.allclose(dJdu_none.dat.data, dJdu_cofunction.dat.data)
+        and np.allclose(dJdu_l2.dat.data, dJdu_function_l2.dat.data)
+        and np.allclose(dJdu_H1.dat.data, dJdu_function_H1.dat.data)
+        and np.allclose(dJdu_default_L2.dat.data, dJdu_function_L2.dat.data)
+        and np.allclose(dJdu_L2.dat.data, dJdu_function_L2.dat.data)
+    )