compiler: fix dimension sort determinism

devito/ir/equations/algorithms.py

@@ -74,10 +74,18 @@ def handle_indexed(indexed):
     # obtain the desired ordering `(time, x, xi)`. W/o `(time, x)`, the ordering
     # `(x, time, xi)` might be returned instead, which would be non-sense
     for i in relations:
-        dims = [di for d in i for di in (d.index, d)]
+        dims = []
+        for d in i:
+            # Only add index if a different Dimension name to avoid dropping conditionals
+            # with the same name as the parent
+            if d.index.name == d.name:
+                dims.append(d)
+            else:
+                dims.extend([d.index, d])
+
         implicit_relations.update({tuple(filter_ordered(dims))})
 
-    ordering = PartialOrderTuple(extra, relations=(relations | implicit_relations))
+    ordering = PartialOrderTuple(extra, relations=implicit_relations)
 
     return ordering
 

devito/operations/interpolators.py

@@ -176,22 +176,25 @@ def _interp_idx(self, variables, implicit_dims=None):
         """
         mapper = {}
         pos = self.sfunction._position_map.values()
-        # Temporaries for the position
-        temps = self._positions(implicit_dims)
 
-        # Coefficient symbol expression
-        temps.extend(self._coeff_temps(implicit_dims))
         for ((di, d), rd, p) in zip(enumerate(self._gdims), self._rdim, pos):
             # Add conditional to avoid OOB
             lb = sympy.And(rd + p >= d.symbolic_min - self.r, evaluate=False)
             ub = sympy.And(rd + p <= d.symbolic_max + self.r, evaluate=False)
             cond = sympy.And(lb, ub, evaluate=False)
             mapper[d] = ConditionalDimension(rd.name, rd, condition=cond, indirect=True)
 
+        # Temporaries for the position
+        temps = self._positions(implicit_dims)
+
+        # Coefficient symbol expression
+        temps.extend(self._coeff_temps(implicit_dims))
+
         # Substitution mapper for variables
         idx_subs = {v: v.subs({k: c - v.origin.get(k, 0) + p
                     for ((k, c), p) in zip(mapper.items(), pos)})
                     for v in variables}
+        idx_subs.update(dict(zip(self._rdim, mapper.values())))
 
         return idx_subs, temps
 

devito/symbolics/printer.py

@@ -105,10 +105,6 @@ def _print_Mod(self, expr):
         args = ['(%s)' % self._print(a) for a in expr.args]
         return '%'.join(args)
 
-    def _print_Mul(self, expr):
-        term = super()._print_Mul(expr)
-        return term.replace("(-1)*", "-")
-
     def _print_Min(self, expr):
         if has_integer_args(*expr.args) and len(expr.args) == 2:
             return "MIN(%s)" % self._print(expr.args)[1:-1]

devito/types/sparse.py

@@ -84,7 +84,7 @@ 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 shaoe issue in the SubFunction with mpi
+        # 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)})
@@ -724,7 +724,7 @@ class SparseFunction(AbstractSparseFunction):
         Discretisation order for space derivatives. Defaults to 0.
     shape : tuple of ints, optional
         Shape of the object. Defaults to ``(npoint,)``.
-    Dimensions : tuple of Dimension, optional
+    dimensions : tuple of Dimension, optional
         Dimensions associated with the object. Only necessary if the SparseFunction
         defines a multi-dimensional tensor.
     dtype : data-type, optional
@@ -845,7 +845,7 @@ class SparseTimeFunction(AbstractSparseTimeFunction, SparseFunction):
         Discretisation order for time derivatives. Defaults to 1.
     shape : tuple of ints, optional
         Shape of the object. Defaults to ``(nt, npoint)``.
-    Dimensions : tuple of Dimension, optional
+    dimensions : tuple of Dimension, optional
         Dimensions associated with the object. Only necessary if the SparseFunction
         defines a multi-dimensional tensor.
     dtype : data-type, optional
@@ -992,7 +992,7 @@ class PrecomputedSparseFunction(AbstractSparseFunction):
         Discretisation order for space derivatives. Defaults to 0.
     shape : tuple of ints, optional
         Shape of the object. Defaults to `(npoint,)`.
-    Dimensions : tuple of Dimension, optional
+    dimensions : tuple of Dimension, optional
         Dimensions associated with the object. Only necessary if the SparseFunction
         defines a multi-dimensional tensor.
     dtype : data-type, optional
@@ -1156,7 +1156,7 @@ class PrecomputedSparseTimeFunction(AbstractSparseTimeFunction,
         Discretisation order for time derivatives. Default to 1.
     shape : tuple of ints, optional
         Shape of the object. Defaults to `(npoint,)`.
-    Dimensions : tuple of Dimension, optional
+    dimensions : tuple of Dimension, optional
         Dimensions associated with the object. Only necessary if the SparseFunction
         defines a multi-dimensional tensor.
     dtype : data-type, optional