Merge branch 'devitocodes:master' into Andrew_add_tests_to_test_Opera…

…tor_2194

.github/workflows/asv.yml

@@ -58,7 +58,7 @@ jobs:
 
     - name: Run benchmarks
       run: |
-        asv run -v --strict --show-stderr --config benchmarks/regression/asv.conf.json --cpu-affinity 0-7 --machine i7-6700K
+        asv run -v --show-stderr --config benchmarks/regression/asv.conf.json --cpu-affinity 0-7 --machine i7-6700K
 
     - name: Checkout asv-results branch
       uses: actions/checkout@v3

devito/passes/clusters/factorization.py

@@ -142,13 +142,16 @@ def run(expr):
                     terms.append(i)
 
             # Collect common funcs
-            w_funcs = Add(*w_funcs, evaluate=False)
-            w_funcs = collect(w_funcs, funcs, evaluate=False)
-            try:
-                terms.extend([Mul(k, collect_const(v), evaluate=False)
-                              for k, v in w_funcs.items()])
-            except AttributeError:
-                assert w_funcs == 0
+            if len(w_funcs) > 1:
+                w_funcs = Add(*w_funcs, evaluate=False)
+                w_funcs = collect(w_funcs, funcs, evaluate=False)
+                try:
+                    terms.extend([Mul(k, collect_const(v), evaluate=False)
+                                  for k, v in w_funcs.items()])
+                except AttributeError:
+                    assert w_funcs == 0
+            else:
+                terms.extend(w_funcs)
 
             # Collect common pows
             w_pows = Add(*w_pows, evaluate=False)

devito/symbolics/queries.py

@@ -20,7 +20,8 @@
 # * Number
 # * Symbol
 # * Indexed
-extra_leaves = (FieldFromPointer, FieldFromComposite, IndexedBase, AbstractObject)
+extra_leaves = (FieldFromPointer, FieldFromComposite, IndexedBase, AbstractObject,
+                IndexedPointer)
 
 
 def q_symbol(expr):
@@ -31,7 +32,9 @@ def q_symbol(expr):
 
 
 def q_leaf(expr):
-    return expr.is_Atom or expr.is_Indexed or isinstance(expr, extra_leaves)
+    return (expr.is_Atom or
+            expr.is_Indexed or
+            isinstance(expr, extra_leaves))
 
 
 def q_indexed(expr):
@@ -51,7 +54,7 @@ def q_derivative(expr):
 def q_terminal(expr):
     return (expr.is_Symbol or
             expr.is_Indexed or
-            isinstance(expr, extra_leaves + (IndexedPointer,)))
+            isinstance(expr, extra_leaves))
 
 
 def q_routine(expr):

devito/types/dense.py

@@ -1040,6 +1040,7 @@ def __indices_setup__(cls, *args, **kwargs):
             dimensions = grid.dimensions
 
         if args:
+            assert len(args) == len(dimensions)
             return tuple(dimensions), tuple(args)
 
         # Staggered indices
@@ -1449,16 +1450,10 @@ class SubFunction(Function):
     """
     A Function bound to a "parent" DiscreteFunction.
 
-    A SubFunction hands control of argument binding and halo exchange to its
-    parent DiscreteFunction.
+    A SubFunction hands control of argument binding and halo exchange to the
+    DiscreteFunction it's bound to.
     """
 
-    __rkwargs__ = Function.__rkwargs__ + ('parent',)
-
-    def __init_finalize__(self, *args, **kwargs):
-        super(SubFunction, self).__init_finalize__(*args, **kwargs)
-        self._parent = kwargs['parent']
-
     def __padding_setup__(self, **kwargs):
         # SubFunctions aren't expected to be used in time-consuming loops
         return tuple((0, 0) for i in range(self.ndim))
@@ -1470,12 +1465,8 @@ def _arg_values(self, **kwargs):
         if self.name in kwargs:
             raise RuntimeError("`%s` is a SubFunction, so it can't be assigned "
                                "a value dynamically" % self.name)
-        else:
-            return self._parent._arg_defaults(alias=self._parent).reduce_all()
 
-    @property
-    def parent(self):
-        return self._parent
+        return self._arg_defaults(alias=self)
 
     @property
     def origin(self):

devito/types/sparse.py

@@ -61,11 +61,9 @@ def __indices_setup__(cls, *args, **kwargs):
             dimensions = (Dimension(name='p_%s' % kwargs["name"]),)
 
         if args:
-            indices = args
+            return tuple(dimensions), tuple(args)
         else:
-            indices = dimensions
-
-        return dimensions, indices
+            return dimensions, dimensions
 
     @classmethod
     def __shape_setup__(cls, **kwargs):
@@ -80,16 +78,6 @@ def __shape_setup__(cls, **kwargs):
             shape = (glb_npoint[grid.distributor.myrank],)
         return shape
 
-    def func(self, *args, **kwargs):
-        # Rebuild subfunctions first to avoid new data creation as we have to use `_data`
-        # as a reconstruction kwargs to avoid the circular dependency
-        # with the parent in SubFunction
-        # This is also necessary to avoid shape issue in the SubFunction with mpi
-        for s in self._sub_functions:
-            if getattr(self, s) is not None:
-                kwargs.update({s: getattr(self, s).func(*args, **kwargs)})
-        return super().func(*args, **kwargs)
-
     def __fd_setup__(self):
         """
         Dynamically add derivative short-cuts.
@@ -108,24 +96,39 @@ def __distributor_setup__(self, **kwargs):
         )
 
     def __subfunc_setup__(self, key, suffix, dtype=None):
+        # Shape and dimensions from args
+        name = '%s_%s' % (self.name, suffix)
+
+        if key is not None and not isinstance(key, SubFunction):
+            key = np.array(key)
+
+        if key is not None:
+            dimensions = (self._sparse_dim, Dimension(name='d'))
+            if key.ndim > 2:
+                dimensions = (self._sparse_dim, Dimension(name='d'),
+                              *mkdims("i", n=key.ndim-2))
+            else:
+                dimensions = (self._sparse_dim, Dimension(name='d'))
+            shape = (self.npoint, self.grid.dim, *key.shape[2:])
+        else:
+            dimensions = (self._sparse_dim, Dimension(name='d'))
+            shape = (self.npoint, self.grid.dim)
+
+        # Check if already a SubFunction
         if isinstance(key, SubFunction):
-            return key
+            # Need to rebuild so the dimensions match the parent SparseFunction
+            indices = (self.indices[self._sparse_position], *key.indices[1:])
+            return key._rebuild(*indices, name=name, shape=shape,
+                                alias=self.alias, halo=None)
         elif key is not None and not isinstance(key, Iterable):
             raise ValueError("`%s` must be either SubFunction "
                              "or iterable (e.g., list, np.ndarray)" % key)
 
-        name = '%s_%s' % (self.name, suffix)
-        dimensions = (self._sparse_dim, Dimension(name='d'))
-        shape = (self.npoint, self.grid.dim)
-
         if key is None:
             # Fallback to default behaviour
             dtype = dtype or self.dtype
         else:
-            if key is not None:
-                key = np.array(key)
-
-            if (shape != key.shape[:2] and key.shape != (shape[1],)) and \
+            if (shape != key.shape and key.shape != (shape[1],)) and \
                     self._distributor.nprocs == 1:
                 raise ValueError("Incompatible shape for %s, `%s`; expected `%s`" %
                                  (suffix, key.shape[:2], shape))
@@ -136,12 +139,8 @@ def __subfunc_setup__(self, key, suffix, dtype=None):
             else:
                 dtype = dtype or self.dtype
 
-        if key is not None and key.ndim > 2:
-            shape = (*shape, *key.shape[2:])
-            dimensions = (*dimensions, *mkdims("i", n=key.ndim-2))
-
         sf = SubFunction(
-            name=name, parent=self, dtype=dtype, dimensions=dimensions,
+            name=name, dtype=dtype, dimensions=dimensions,
             shape=shape, space_order=0, initializer=key, alias=self.alias,
             distributor=self._distributor
         )
@@ -657,20 +656,6 @@ def time_dim(self):
         """The time Dimension."""
         return self._time_dim
 
-    @classmethod
-    def __indices_setup__(cls, *args, **kwargs):
-        dimensions = as_tuple(kwargs.get('dimensions'))
-        if not dimensions:
-            dimensions = (kwargs['grid'].time_dim,
-                          Dimension(name='p_%s' % kwargs["name"]))
-
-        if args:
-            indices = args
-        else:
-            indices = dimensions
-
-        return dimensions, indices
-
     @classmethod
     def __shape_setup__(cls, **kwargs):
         shape = kwargs.get('shape')
@@ -686,6 +671,18 @@ def __shape_setup__(cls, **kwargs):
 
         return tuple(shape)
 
+    @classmethod
+    def __indices_setup__(cls, *args, **kwargs):
+        dimensions = as_tuple(kwargs.get('dimensions'))
+        if not dimensions:
+            dimensions = (kwargs['grid'].time_dim,
+                          Dimension(name='p_%s' % kwargs["name"]),)
+
+        if args:
+            return tuple(dimensions), tuple(args)
+        else:
+            return dimensions, dimensions
+
     @property
     def nt(self):
         return self.shape[self._time_position]
@@ -791,7 +788,7 @@ class SparseFunction(AbstractSparseFunction):
 
     _sub_functions = ('coordinates',)
 
-    __rkwargs__ = AbstractSparseFunction.__rkwargs__ + ('coordinates_data',)
+    __rkwargs__ = AbstractSparseFunction.__rkwargs__ + ('coordinates',)
 
     def __init_finalize__(self, *args, **kwargs):
         super().__init_finalize__(*args, **kwargs)
@@ -1014,8 +1011,8 @@ class PrecomputedSparseFunction(AbstractSparseFunction):
     _sub_functions = ('gridpoints', 'coordinates', 'interpolation_coeffs')
 
     __rkwargs__ = (AbstractSparseFunction.__rkwargs__ +
-                   ('r', 'gridpoints_data', 'coordinates_data',
-                    'interpolation_coeffs_data'))
+                   ('r', 'gridpoints', 'coordinates',
+                    'interpolation_coeffs'))
 
     def __init_finalize__(self, *args, **kwargs):
         super().__init_finalize__(*args, **kwargs)
@@ -1032,13 +1029,14 @@ def __init_finalize__(self, *args, **kwargs):
         if r <= 0:
             raise ValueError('`r` must be > 0')
         # Make sure radius matches the coefficients size
-        nr = interpolation_coeffs.shape[-1]
-        if nr // 2 != r:
-            if nr == r:
-                r = r // 2
-            else:
-                raise ValueError("Interpolation coefficients shape %d do "
-                                 "not match specified radius %d" % (r, nr))
+        if interpolation_coeffs is not None:
+            nr = interpolation_coeffs.shape[-1]
+            if nr // 2 != r:
+                if nr == r:
+                    r = r // 2
+                else:
+                    raise ValueError("Interpolation coefficients shape %d do "
+                                     "not match specified radius %d" % (r, nr))
         self._radius = r
 
         if coordinates is not None and gridpoints is not None:
@@ -1179,6 +1177,15 @@ class PrecomputedSparseTimeFunction(AbstractSparseTimeFunction,
                                        PrecomputedSparseFunction.__rkwargs__))
 
 
+# *** MatrixSparse*Function API
+# This is mostly legacy stuff which often escapes the devito's modus operandi
+
+class DynamicSubFunction(SubFunction):
+
+    def _arg_defaults(self, **kwargs):
+        return {}
+
+
 class MatrixSparseTimeFunction(AbstractSparseTimeFunction):
     """
     A specialised type of SparseTimeFunction where the interpolation is externally
@@ -1378,7 +1385,7 @@ def __init_finalize__(self, *args, **kwargs):
         else:
             nnz_size = 1
 
-        self._mrow = SubFunction(
+        self._mrow = DynamicSubFunction(
             name='mrow_%s' % self.name,
             dtype=np.int32,
             dimensions=(self.nnzdim,),
@@ -1387,7 +1394,7 @@ def __init_finalize__(self, *args, **kwargs):
             parent=self,
             allocator=self._allocator,
         )
-        self._mcol = SubFunction(
+        self._mcol = DynamicSubFunction(
             name='mcol_%s' % self.name,
             dtype=np.int32,
             dimensions=(self.nnzdim,),
@@ -1396,7 +1403,7 @@ def __init_finalize__(self, *args, **kwargs):
             parent=self,
             allocator=self._allocator,
         )
-        self._mval = SubFunction(
+        self._mval = DynamicSubFunction(
             name='mval_%s' % self.name,
             dtype=self.dtype,
             dimensions=(self.nnzdim,),
@@ -1413,8 +1420,8 @@ def __init_finalize__(self, *args, **kwargs):
         self.par_dim_to_nnz_dim = DynamicDimension('par_dim_to_nnz_%s' % self.name)
 
         # This map acts as an indirect sort of the sources according to their
-        # position along the parallelisation Dimension
-        self._par_dim_to_nnz_map = SubFunction(
+        # position along the parallelisation dimension
+        self._par_dim_to_nnz_map = DynamicSubFunction(
             name='par_dim_to_nnz_map_%s' % self.name,
             dtype=np.int32,
             dimensions=(self.par_dim_to_nnz_dim,),
@@ -1423,7 +1430,7 @@ def __init_finalize__(self, *args, **kwargs):
             space_order=0,
             parent=self,
         )
-        self._par_dim_to_nnz_m = SubFunction(
+        self._par_dim_to_nnz_m = DynamicSubFunction(
             name='par_dim_to_nnz_m_%s' % self.name,
             dtype=np.int32,
             dimensions=(self._par_dim,),
@@ -1432,7 +1439,7 @@ def __init_finalize__(self, *args, **kwargs):
             space_order=0,
             parent=self,
         )
-        self._par_dim_to_nnz_M = SubFunction(
+        self._par_dim_to_nnz_M = DynamicSubFunction(
             name='par_dim_to_nnz_M_%s' % self.name,
             dtype=np.int32,
             dimensions=(self._par_dim,),
@@ -1671,23 +1678,6 @@ def inject(self, field, expr, u_t=None, p_t=None):
 
         return out
 
-    @classmethod
-    def __indices_setup__(cls, *args, **kwargs):
-        """
-        Return the default Dimension indices for a given data shape.
-        """
-        dimensions = kwargs.get('dimensions')
-        if dimensions is None:
-            dimensions = (kwargs['grid'].time_dim, Dimension(
-                name='p_%s' % kwargs["name"]))
-
-        if args:
-            indices = args
-        else:
-            indices = dimensions
-
-        return dimensions, indices
-
     @classmethod
     def __shape_setup__(cls, **kwargs):
         # This happens before __init__, so we have to get 'npoint'