Dham/abstract reduced functional

demos/full_waveform_inversion/full_waveform_inversion.py.rst

@@ -109,10 +109,12 @@ built over the ``my_ensemble.comm`` (spatial) communicator.
         dt = 0.03  # time step in seconds
         final_time = 0.6  # final time in seconds
         nx, ny = 15, 15
+        ftol = 0.9  # optimisation tolerance
     else:
         dt = 0.002  # time step in seconds
         final_time = 1.0  # final time in seconds
         nx, ny = 80, 80
+        ftol = 1e-2  # optimisation tolerance
 
     mesh = UnitSquareMesh(nx, ny, comm=my_ensemble.comm)
 
@@ -278,21 +280,28 @@ To have the step 4, we need first to tape the forward problem. That is done by c
 
 We now instantiate :class:`~.EnsembleReducedFunctional`::
 
-    J_hat = EnsembleReducedFunctional(J_val, Control(c_guess), my_ensemble)
+    J_hat = EnsembleReducedFunctional(J_val,
+                                      Control(c_guess, riesz_map="l2"),
+                                      my_ensemble)
 
 which enables us to recompute :math:`J` and its gradient :math:`\nabla_{\mathtt{c\_guess}} J`,
 where the :math:`J_s` and its gradients :math:`\nabla_{\mathtt{c\_guess}} J_s` are computed in parallel
 based on the ``my_ensemble`` configuration.
 
 
 **Steps 4-6**: The instance of the :class:`~.EnsembleReducedFunctional`, named ``J_hat``,
-is then passed as an argument to the ``minimize`` function::
+is then passed as an argument to the ``minimize`` function. The default ``minimize`` function
+uses ``scipy.minimize``, and wraps the ``ReducedFunctional`` in a ``ReducedFunctionalNumPy``
+that handles transferring data between Firedrake and numpy data structures. However, because
+we have a custom ``ReducedFunctional``, we need to do this ourselves::
 
-    c_optimised = minimize(J_hat, method="L-BFGS-B", options={"disp": True, "maxiter": 1},
-                            bounds=(1.5, 2.0), derivative_options={"riesz_representation": 'l2'}
-                            )
+    from pyadjoint.reduced_functional_numpy import ReducedFunctionalNumPy
+    Jnumpy = ReducedFunctionalNumPy(J_hat)
 
-The ``minimize`` function executes the optimisation algorithm until the stopping criterion (``maxiter``) is met.
+    c_optimised = minimize(Jnumpy, method="L-BFGS-B", options={"disp": True, "ftol": ftol},
+                           bounds=(1.5, 2.0))
+
+The ``minimize`` function executes the optimisation algorithm until the stopping criterion (``ftol``) is met.
 For 20 iterations, the predicted velocity model is shown in the following figure.
 
 .. image:: c_predicted.png
@@ -303,9 +312,7 @@ For 20 iterations, the predicted velocity model is shown in the following figure
 .. warning::
 
     The ``minimize`` function uses the SciPy library for optimisation. However, for scenarios that require higher
-    levels of spatial parallelism, you should assess whether SciPy is the most suitable option for your problem.
-    SciPy's optimisation algorithm is not inner-product-aware. Therefore, we configure the options with
-    ``derivative_options={"riesz_representation": 'l2'}`` to account for this requirement.
+    levels of spatial parallelism, you should assess whether SciPy is the most suitable option for your problem such as the pyadjoint's TAOSolver.
 
 .. note::
 

demos/shape_optimization/shape_optimization.py.rst

@@ -70,9 +70,10 @@ and evaluate the objective function::
 
 We now turn the objective function into a reduced function so that pyadjoint
 (and UFL shape differentiation capability) can automatically compute shape
-gradients, that is, directions of steepest ascent::
+gradients, that is, directions of steepest ascent. We also set the relevant
+Riesz map for this problem::
 
-  Jred = ReducedFunctional(J, Control(dT))
+  Jred = ReducedFunctional(J, Control(dT, riesz_map="H1"))
   stop_annotating()
 
 We now have all the ingredients to implement a basic steepest descent shape
@@ -84,8 +85,7 @@ optimization algorithm with fixed step size.::
     File.write(mesh.coordinates)
 
     # compute the gradient (steepest ascent)
-    opts = {"riesz_representation": "H1"}
-    gradJ = Jred.derivative(options=opts)
+    gradJ = Jred.derivative(apply_riesz=True)
 
     # update domain
     dT -= 0.2*gradJ

firedrake/adjoint/__init__.py

@@ -29,7 +29,7 @@
 from firedrake.adjoint_utils import get_solve_blocks  # noqa F401
 
 from pyadjoint.verification import taylor_test, taylor_to_dict  # noqa F401
-from pyadjoint.drivers import compute_gradient, compute_hessian  # noqa F401
+from pyadjoint.drivers import compute_gradient, compute_derivative, compute_hessian  # noqa F401
 from pyadjoint.adjfloat import AdjFloat  # noqa F401
 from pyadjoint.control import Control  # noqa F401
 from pyadjoint import IPOPTSolver, ROLSolver, MinimizationProblem, \

firedrake/adjoint/ensemble_reduced_functional.py

@@ -1,11 +1,12 @@
-from pyadjoint import ReducedFunctional
+from pyadjoint.reduced_functional import AbstractReducedFunctional, ReducedFunctional
 from pyadjoint.enlisting import Enlist
 from pyop2.mpi import MPI
 
-import firedrake
+from firedrake.function import Function
+from firedrake.cofunction import Cofunction
 
 
-class EnsembleReducedFunctional(ReducedFunctional):
+class EnsembleReducedFunctional(AbstractReducedFunctional):
     """Enable solving simultaneously reduced functionals in parallel.
 
     Consider a functional :math:`J` and its gradient :math:`\\dfrac{dJ}{dm}`,
@@ -34,7 +35,7 @@ class EnsembleReducedFunctional(ReducedFunctional):
 
     Parameters
     ----------
-    J : pyadjoint.OverloadedType
+    functional : pyadjoint.OverloadedType
         An instance of an OverloadedType, usually :class:`pyadjoint.AdjFloat`.
         This should be the functional that we want to reduce.
     control : pyadjoint.Control or list of pyadjoint.Control
@@ -86,28 +87,40 @@ class EnsembleReducedFunctional(ReducedFunctional):
     works, please refer to the `Firedrake manual
     <https://www.firedrakeproject.org/parallelism.html#ensemble-parallelism>`_.
     """
-    def __init__(self, J, control, ensemble, scatter_control=True,
-                 gather_functional=None, derivative_components=None,
-                 scale=1.0, tape=None, eval_cb_pre=lambda *args: None,
+    def __init__(self, functional, control, ensemble, scatter_control=True,
+                 gather_functional=None,
+                 derivative_components=None,
+                 scale=1.0, tape=None,
+                 eval_cb_pre=lambda *args: None,
                  eval_cb_post=lambda *args: None,
                  derivative_cb_pre=lambda controls: controls,
                  derivative_cb_post=lambda checkpoint, derivative_components, controls: derivative_components,
-                 hessian_cb_pre=lambda *args: None, hessian_cb_post=lambda *args: None):
-        super(EnsembleReducedFunctional, self).__init__(
-            J, control, derivative_components=derivative_components,
-            scale=scale, tape=tape, eval_cb_pre=eval_cb_pre,
-            eval_cb_post=eval_cb_post, derivative_cb_pre=derivative_cb_pre,
+                 hessian_cb_pre=lambda *args: None,
+                 hessian_cb_post=lambda *args: None):
+        self.local_reduced_functional = ReducedFunctional(
+            functional, control,
+            derivative_components=derivative_components,
+            scale=scale, tape=tape,
+            eval_cb_pre=eval_cb_pre,
+            eval_cb_post=eval_cb_post,
+            derivative_cb_pre=derivative_cb_pre,
             derivative_cb_post=derivative_cb_post,
-            hessian_cb_pre=hessian_cb_pre, hessian_cb_post=hessian_cb_post)
+            hessian_cb_pre=hessian_cb_pre,
+            hessian_cb_post=hessian_cb_post
+        )
 
         self.ensemble = ensemble
         self.scatter_control = scatter_control
         self.gather_functional = gather_functional
 
+    @property
+    def controls(self):
+        return self.local_reduced_functional.controls
+
     def _allgather_J(self, J):
         if isinstance(J, float):
             vals = self.ensemble.ensemble_comm.allgather(J)
-        elif isinstance(J, firedrake.Function):
+        elif isinstance(J, Function):
             #  allgather not implemented in ensemble.py
             vals = []
             for i in range(self.ensemble.ensemble_comm.size):
@@ -134,30 +147,31 @@ def __call__(self, values):
             The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`.
 
         """
-        local_functional = super(EnsembleReducedFunctional, self).__call__(values)
+        local_functional = self.local_reduced_functional(values)
         ensemble_comm = self.ensemble.ensemble_comm
         if self.gather_functional:
             controls_g = self._allgather_J(local_functional)
             total_functional = self.gather_functional(controls_g)
         # if gather_functional is None then we do a sum
         elif isinstance(local_functional, float):
             total_functional = ensemble_comm.allreduce(sendobj=local_functional, op=MPI.SUM)
-        elif isinstance(local_functional, firedrake.Function):
+        elif isinstance(local_functional, Function):
             total_functional = type(local_functional)(local_functional.function_space())
             total_functional = self.ensemble.allreduce(local_functional, total_functional)
         else:
             raise NotImplementedError("This type of functional is not supported.")
         return total_functional
 
-    def derivative(self, adj_input=1.0, options=None):
+    def derivative(self, adj_input=1.0, apply_riesz=False):
         """Compute derivatives of a functional with respect to the control parameters.
 
         Parameters
         ----------
         adj_input : float
             The adjoint input.
-        options : dict
-            Additional options for the derivative computation.
+        apply_riesz: bool
+            If True, apply the Riesz map of each control in order to return
+            a primal gradient rather than a derivative in the dual space.
 
         Returns
         -------
@@ -171,29 +185,62 @@ def derivative(self, adj_input=1.0, options=None):
 
         if self.gather_functional:
             dJg_dmg = self.gather_functional.derivative(adj_input=adj_input,
-                                                        options=options)
+                                                        apply_riesz=False)
             i = self.ensemble.ensemble_comm.rank
             adj_input = dJg_dmg[i]
 
-        dJdm_local = super(EnsembleReducedFunctional, self).derivative(adj_input=adj_input, options=options)
+        dJdm_local = self.local_reduced_functional.derivative(adj_input=adj_input,
+                                                              apply_riesz=apply_riesz)
 
         if self.scatter_control:
             dJdm_local = Enlist(dJdm_local)
             dJdm_total = []
 
             for dJdm in dJdm_local:
-                if not isinstance(dJdm, (firedrake.Function, float)):
-                    raise NotImplementedError("This type of gradient is not supported.")
+                if not isinstance(dJdm, (Cofunction, Function, float)):
+                    raise NotImplementedError(
+                        f"Gradients of type {type(dJdm).__name__} are not supported.")
 
                 dJdm_total.append(
                     self.ensemble.allreduce(dJdm, type(dJdm)(dJdm.function_space()))
-                    if isinstance(dJdm, firedrake.Function)
+                    if isinstance(dJdm, (Cofunction, Function))
                     else self.ensemble.ensemble_comm.allreduce(sendobj=dJdm, op=MPI.SUM)
                 )
             return dJdm_local.delist(dJdm_total)
         return dJdm_local
 
-    def hessian(self, m_dot, options=None):
+    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:

firedrake/adjoint_utils/blocks/solving.py

@@ -86,8 +86,15 @@ def _init_solver_parameters(self, args, kwargs):
         self.assemble_kwargs = {}
 
     def __str__(self):
-        return "solve({} = {})".format(ufl2unicode(self.lhs),
-                                       ufl2unicode(self.rhs))
+        try:
+            lhs_string = ufl2unicode(self.lhs)
+        except AttributeError:
+            lhs_string = str(self.lhs)
+        try:
+            rhs_string = ufl2unicode(self.rhs)
+        except AttributeError:
+            rhs_string = str(self.rhs)
+        return "solve({} = {})".format(lhs_string, rhs_string)
 
     def _create_F_form(self):
         # Process the equation forms, replacing values with checkpoints,
@@ -742,7 +749,7 @@ def evaluate_adj_component(self, inputs, adj_inputs, block_variable, idx,
         c = block_variable.output
         c_rep = block_variable.saved_output
 
-        if isinstance(c, firedrake.Function):
+        if isinstance(c, (firedrake.Function, firedrake.Cofunction)):
             trial_function = firedrake.TrialFunction(c.function_space())
         elif isinstance(c, firedrake.Constant):
             mesh = F_form.ufl_domain()
@@ -779,7 +786,12 @@ def evaluate_adj_component(self, inputs, adj_inputs, block_variable, idx,
         replace_map[self.func] = self.get_outputs()[0].saved_output
         dFdm = replace(dFdm, replace_map)
 
-        dFdm = dFdm * adj_sol
+        if isinstance(dFdm, firedrake.Argument):
+            #  Corner case. Should be fixed more permanently upstream in UFL.
+            #  See: https://github.com/FEniCS/ufl/issues/395
+            dFdm = ufl.Action(dFdm, adj_sol)
+        else:
+            dFdm = dFdm * adj_sol
         dFdm = firedrake.assemble(dFdm, **self.assemble_kwargs)
 
         return dFdm

firedrake/adjoint_utils/constant.py

@@ -2,7 +2,6 @@
 from pyadjoint.adjfloat import AdjFloat
 from pyadjoint.tape import get_working_tape, annotate_tape
 from pyadjoint.overloaded_type import OverloadedType, create_overloaded_object
-from pyadjoint.reduced_functional_numpy import gather
 
 from firedrake.functionspace import FunctionSpace
 from firedrake.adjoint_utils.blocks import ConstantAssignBlock
@@ -58,15 +57,8 @@ def wrapper(self, *args, **kwargs):
 
         return wrapper
 
-    def get_derivative(self, options={}):
-        return self._ad_convert_type(self.adj_value, options=options)
-
-    def _ad_convert_type(self, value, options={}):
-        if value is None:
-            # TODO: Should the default be 0 constant here or return just None?
-            return type(self)(numpy.zeros(self.ufl_shape))
-        value = gather(value)
-        return self._constant_from_values(value)
+    def _ad_init_zero(self, dual=False):
+        return type(self)(numpy.zeros(self.ufl_shape))
 
     def _ad_function_space(self, mesh):
         element = self.ufl_element()

firedrake/adjoint_utils/ensemble_function.py

@@ -55,6 +55,13 @@ def _ad_dot(self, other, options=None):
     def _ad_convert_riesz(self, value, options=None):
         raise NotImplementedError
 
+    def _ad_init_zero(self, dual=False):
+        from firedrake import EnsembleFunction, EnsembleCofunction
+        if dual:
+            return EnsembleCofunction(self.function_space().dual())
+        else:
+            return EnsembleFunction(self.function_space())
+
     def _ad_create_checkpoint(self):
         if disk_checkpointing():
             raise NotImplementedError(