Fix assembly of Real matrices

firedrake/assemble.py

@@ -29,6 +29,7 @@
 from firedrake.utils import ScalarType, assert_empty, tuplify
 from pyop2 import op2
 from pyop2.exceptions import MapValueError, SparsityFormatError
+from pyop2.types.mat import _GlobalMatPayload, _DatMatPayload
 from pyop2.utils import cached_property
 
 
@@ -965,22 +966,24 @@ def assemble(self, tensor=None):
             Result of assembly: `float` for 0-forms, `firedrake.cofunction.Cofunction` or `firedrake.function.Function` for 1-forms, and `matrix.MatrixBase` for 2-forms.
 
         """
-        self._check_tensor(tensor)
-        if tensor is None:
-            tensor = self.allocate()
-            needs_zeroing = False
-        else:
-            needs_zeroing = self._needs_zeroing
         if annotate_tape():
             raise NotImplementedError(
                 "Taping with explicit FormAssembler objects is not supported yet. "
                 "Use assemble instead."
             )
-        if needs_zeroing:
-            type(self)._as_pyop2_type(tensor).zero()
+
+        if tensor is None:
+            tensor = self.allocate()
+        else:
+            self._check_tensor(tensor)
+            if self._needs_zeroing:
+                self._as_pyop2_type(tensor).zero()
+
         self.execute_parloops(tensor)
+
         for bc in self._bcs:
             self._apply_bc(tensor, bc)
+
         return self.result(tensor)
 
     @abc.abstractmethod
@@ -992,9 +995,9 @@ def _check_tensor(self, tensor):
         """Check input tensor."""
 
     @staticmethod
-    def _as_pyop2_type(tensor):
-        """Return tensor as pyop2 type."""
-        raise NotImplementedError
+    @abc.abstractmethod
+    def _as_pyop2_type(tensor, indices=None):
+        """Cast a Firedrake tensor into a PyOP2 data structure, optionally indexing it."""
 
     def execute_parloops(self, tensor):
         for parloop in self.parloops(tensor):
@@ -1003,29 +1006,27 @@ def execute_parloops(self, tensor):
     def parloops(self, tensor):
         if hasattr(self, "_parloops"):
             for (lknl, _), parloop in zip(self.local_kernels, self._parloops):
-                data = _FormHandler.index_tensor(tensor, self._form, lknl.indices, self.diagonal)
+                data = self._as_pyop2_type(tensor, lknl.indices)
                 parloop.arguments[0].data = data
+
         else:
             # Make parloops for one concrete output tensor and cache them.
-            # TODO: Make parloops only with some symbolic information of the output tensor.
-            self._parloops = tuple(parloop_builder.build(tensor) for parloop_builder in self.parloop_builders)
-        return self._parloops
-
-    @cached_property
-    def parloop_builders(self):
-        out = []
-        for local_kernel, subdomain_id in self.local_kernels:
-            out.append(
-                ParloopBuilder(
+            parloops_ = []
+            for local_kernel, subdomain_id in self.local_kernels:
+                parloop_builder = ParloopBuilder(
                     self._form,
                     self._bcs,
                     local_kernel,
                     subdomain_id,
                     self.all_integer_subdomain_ids,
                     diagonal=self.diagonal,
                 )
-            )
-        return tuple(out)
+                pyop2_tensor = self._as_pyop2_type(tensor, local_kernel.indices)
+                parloop = parloop_builder.build(pyop2_tensor)
+                parloops_.append(parloop)
+            self._parloops = tuple(parloops_)
+
+        return self._parloops
 
     @cached_property
     def local_kernels(self):
@@ -1120,10 +1121,11 @@ def _apply_bc(self, tensor, bc):
         pass
 
     def _check_tensor(self, tensor):
-        assert tensor is None
+        pass
 
     @staticmethod
-    def _as_pyop2_type(tensor):
+    def _as_pyop2_type(tensor, indices=None):
+        assert not indices
         return tensor
 
     def result(self, tensor):
@@ -1198,15 +1200,16 @@ def _apply_dirichlet_bc(self, tensor, bc):
             bc.zero(tensor)
 
     def _check_tensor(self, tensor):
-        rank = len(self._form.arguments())
-        if rank == 1:
-            test, = self._form.arguments()
-            if tensor is not None and test.function_space() != tensor.function_space():
-                raise ValueError("Form's argument does not match provided result tensor")
+        if tensor.function_space() != self._form.arguments()[0].function_space():
+            raise ValueError("Form's argument does not match provided result tensor")
 
     @staticmethod
-    def _as_pyop2_type(tensor):
-        return tensor.dat
+    def _as_pyop2_type(tensor, indices=None):
+        if indices is not None and any(index is not None for index in indices):
+            i, = indices
+            return tensor.dat[i]
+        else:
+            return tensor.dat
 
     def execute_parloops(self, tensor):
         # We are repeatedly incrementing into the same Dat so intermediate halo exchanges
@@ -1454,12 +1457,26 @@ def _apply_bcs_mat_real_block(op2tensor, i, j, component, node_set):
         dat.zero(subset=node_set)
 
     def _check_tensor(self, tensor):
-        if tensor is not None and tensor.a.arguments() != self._form.arguments():
+        if tensor.a.arguments() != self._form.arguments():
             raise ValueError("Form's arguments do not match provided result tensor")
 
     @staticmethod
-    def _as_pyop2_type(tensor):
-        return tensor.M
+    def _as_pyop2_type(tensor, indices=None):
+        if indices is not None and any(index is not None for index in indices):
+            i, j = indices
+            mat = tensor.M[i, j]
+        else:
+            mat = tensor.M
+
+        if mat.handle.getType() == "python":
+            mat_context = mat.handle.getPythonContext()
+            if isinstance(mat_context, _GlobalMatPayload):
+                mat = mat_context.global_
+            else:
+                assert isinstance(mat_context, _DatMatPayload)
+                mat = mat_context.dat
+
+        return mat
 
     def result(self, tensor):
         tensor.M.assemble()
@@ -1471,7 +1488,7 @@ class MatrixFreeAssembler(FormAssembler):
 
     Parameters
     ----------
-    form : ufl.Form or slate.TensorBasehe
+    form : ufl.Form or slate.TensorBase
         2-form.
 
     Notes
@@ -1498,14 +1515,15 @@ def allocate(self):
                                      appctx=self._appctx or {})
 
     def assemble(self, tensor=None):
-        self._check_tensor(tensor)
         if tensor is None:
             tensor = self.allocate()
+        else:
+            self._check_tensor(tensor)
         tensor.assemble()
         return tensor
 
     def _check_tensor(self, tensor):
-        if tensor is not None and tensor.a.arguments() != self._form.arguments():
+        if tensor.a.arguments() != self._form.arguments():
             raise ValueError("Form's arguments do not match provided result tensor")
 
 
@@ -1820,12 +1838,12 @@ def __init__(self, form, bcs, local_knl, subdomain_id,
         self._active_coefficients = _FormHandler.iter_active_coefficients(form, local_knl.kinfo)
         self._constants = _FormHandler.iter_constants(form, local_knl.kinfo)
 
-    def build(self, tensor):
+    def build(self, tensor: op2.Global | op2.Dat | op2.Mat) -> op2.Parloop:
         """Construct the parloop.
 
         Parameters
         ----------
-        tensor : op2.Global or firedrake.cofunction.Cofunction or matrix.MatrixBase
+        tensor :
             The output tensor.
 
         """
@@ -1909,17 +1927,28 @@ def collect_lgmaps(self):
         :param local_knl: A :class:`tsfc_interface.SplitKernel`.
         :param bcs: Iterable of boundary conditions.
         """
+
         if len(self._form.arguments()) == 2 and not self._diagonal:
             if not self._bcs:
                 return None
-            lgmaps = []
-            for i, j in self.get_indicess():
+
+            if any(i is not None for i in self._local_knl.indices):
+                i, j = self._local_knl.indices
                 row_bcs, col_bcs = self._filter_bcs(i, j)
-                rlgmap, clgmap = self._tensor.M[i, j].local_to_global_maps
+                # the tensor is already indexed
+                rlgmap, clgmap = self._tensor.local_to_global_maps
                 rlgmap = self.test_function_space[i].local_to_global_map(row_bcs, rlgmap)
                 clgmap = self.trial_function_space[j].local_to_global_map(col_bcs, clgmap)
-                lgmaps.append((rlgmap, clgmap))
-            return tuple(lgmaps)
+                return ((rlgmap, clgmap),)
+            else:
+                lgmaps = []
+                for i, j in self.get_indicess():
+                    row_bcs, col_bcs = self._filter_bcs(i, j)
+                    rlgmap, clgmap = self._tensor[i, j].local_to_global_maps
+                    rlgmap = self.test_function_space[i].local_to_global_map(row_bcs, rlgmap)
+                    clgmap = self.trial_function_space[j].local_to_global_map(col_bcs, clgmap)
+                    lgmaps.append((rlgmap, clgmap))
+                return tuple(lgmaps)
         else:
             return None
 
@@ -1939,10 +1968,6 @@ def _integral_type(self):
     def _indexed_function_spaces(self):
         return _FormHandler.index_function_spaces(self._form, self._indices)
 
-    @property
-    def _indexed_tensor(self):
-        return _FormHandler.index_tensor(self._tensor, self._form, self._indices, self._diagonal)
-
     @cached_property
     def _mesh(self):
         return self._form.ufl_domains()[self._kinfo.domain_number]
@@ -1990,28 +2015,27 @@ def _as_parloop_arg(tsfc_arg, self):
 @_as_parloop_arg.register(kernel_args.OutputKernelArg)
 def _as_parloop_arg_output(_, self):
     rank = len(self._form.arguments())
-    tensor = self._indexed_tensor
     Vs = self._indexed_function_spaces
 
     if rank == 0:
-        return op2.GlobalParloopArg(tensor)
+        return op2.GlobalParloopArg(self._tensor)
     elif rank == 1 or rank == 2 and self._diagonal:
         V, = Vs
         if V.ufl_element().family() == "Real":
-            return op2.GlobalParloopArg(tensor)
+            return op2.GlobalParloopArg(self._tensor)
         else:
-            return op2.DatParloopArg(tensor, self._get_map(V))
+            return op2.DatParloopArg(self._tensor, self._get_map(V))
     elif rank == 2:
         rmap, cmap = [self._get_map(V) for V in Vs]
 
         if all(V.ufl_element().family() == "Real" for V in Vs):
             assert rmap is None and cmap is None
-            return op2.GlobalParloopArg(tensor.handle.getPythonContext().global_)
+            return op2.GlobalParloopArg(self._tensor)
         elif any(V.ufl_element().family() == "Real" for V in Vs):
             m = rmap or cmap
-            return op2.DatParloopArg(tensor.handle.getPythonContext().dat, m)
+            return op2.DatParloopArg(self._tensor, m)
         else:
-            return op2.MatParloopArg(tensor, (rmap, cmap), lgmaps=self.collect_lgmaps())
+            return op2.MatParloopArg(self._tensor, (rmap, cmap), lgmaps=self.collect_lgmaps())
     else:
         raise AssertionError
 
@@ -2122,22 +2146,3 @@ def index_function_spaces(form, indices):
             return tuple(a.ufl_function_space()[i] for i, a in zip(indices, form.arguments()))
         else:
             raise AssertionError
-
-    @staticmethod
-    def index_tensor(tensor, form, indices, diagonal):
-        """Return the PyOP2 data structure tied to ``tensor``, indexed
-        if necessary.
-        """
-        rank = len(form.arguments())
-        is_indexed = any(i is not None for i in indices)
-
-        if rank == 0:
-            return tensor
-        elif rank == 1 or rank == 2 and diagonal:
-            i, = indices
-            return tensor.dat[i] if is_indexed else tensor.dat
-        elif rank == 2:
-            i, j = indices
-            return tensor.M[i, j] if is_indexed else tensor.M
-        else:
-            raise AssertionError

tests/regression/test_assemble.py

@@ -1,6 +1,7 @@
 import pytest
 import numpy as np
 from firedrake import *
+from firedrake.assemble import TwoFormAssembler
 from firedrake.utils import ScalarType, IntType
 
 
@@ -125,6 +126,23 @@ def test_assemble_mat_with_tensor(mesh):
     assert np.allclose(M.M.values, 2*assemble(a).M.values, rtol=1e-14)
 
 
+@pytest.mark.skipcomplex
+def test_mat_nest_real_block_assembler_correctly_reuses_tensor(mesh):
+    V = FunctionSpace(mesh, "CG", 1)
+    R = FunctionSpace(mesh, "R", 0)
+    W = V * R
+
+    u = TrialFunction(W)
+    v = TestFunction(W)
+    a = inner(v, u) * dx
+
+    assembler = TwoFormAssembler(a, mat_type="nest")
+    A1 = assembler.assemble()
+    A2 = assembler.assemble(tensor=A1)
+
+    assert A2.M is A1.M
+
+
 def test_assemble_diagonal(mesh):
     V = FunctionSpace(mesh, "P", 3)
     u = TrialFunction(V)