misc: various Dimension internal fixes

devito/builtins/initializers.py

@@ -77,7 +77,12 @@ def assign(f, rhs=0, options=None, name='assign', assign_halo=False, **kwargs):
                                          symbolic_max=d.symbolic_max + h.right)
         eqs = [eq.xreplace(subs) for eq in eqs]
 
-    dv.Operator(eqs, name=name, **kwargs)()
+    op = dv.Operator(eqs, name=name, **kwargs)
+    try:
+        op()
+    except ValueError:
+        # Corner case such as assign(u, v) with v a Buffered TimeFunction
+        op(time_M=f._time_size)
 
 
 def smooth(f, g, axis=None):

devito/core/autotuning.py

@@ -209,8 +209,9 @@ def init_time_bounds(stepper, at_args, args):
     else:
         at_args[dim.max_name] = at_args[dim.min_name] + options['squeezer']
         if dim.size_name in args:
-            # May need to shrink to avoid OOB accesses
-            at_args[dim.max_name] = min(at_args[dim.max_name], args[dim.max_name])
+            if not isinstance(args[dim.size_name], range):
+                # May need to shrink to avoid OOB accesses
+                at_args[dim.max_name] = min(at_args[dim.max_name], args[dim.max_name])
         if at_args[dim.min_name] > at_args[dim.max_name]:
             warning("too few time iterations; skipping")
             return False

devito/ir/stree/algorithms.py

@@ -147,6 +147,12 @@ def preprocess(clusters, options=None, **kwargs):
             found = []
             for c1 in list(queue):
                 distributed_aindices = c1.halo_scheme.distributed_aindices
+                h_indices = set().union(*[d._defines for d in c1.halo_scheme.loc_indices])
+
+                # Skip if the halo exchange would end up outside
+                # its iteration space
+                if h_indices and not h_indices & dims:
+                    continue
 
                 diff = dims - distributed_aindices
                 intersection = dims & distributed_aindices

devito/mpi/halo_scheme.py

@@ -361,6 +361,10 @@ def distributed(self):
     def distributed_aindices(self):
         return set().union(*[i.dims for i in self.fmapper.values()])
 
+    @cached_property
+    def loc_indices(self):
+        return set().union(*[i.loc_indices.keys() for i in self.fmapper.values()])
+
     @cached_property
     def arguments(self):
         return self.dimensions | set(flatten(self.honored.values()))

devito/operations/interpolators.py

@@ -155,13 +155,31 @@ def _rdim(self):
                                 -self.r+1, self.r, 2*self.r, parent)
                 for d in self._gdims]
 
-        return DimensionTuple(*dims, getters=self._gdims)
+        # Make radius dimension conditional to avoid OOB
+        rdims = []
+        pos = self.sfunction._position_map.values()
+
+        for (d, rd, p) in zip(self._gdims, dims, 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)
+            rdims.append(ConditionalDimension(rd.name, rd, condition=cond,
+                                              indirect=True))
+
+        return DimensionTuple(*rdims, getters=self._gdims)
+
+    def _augment_implicit_dims(self, implicit_dims, extras=None):
+        if extras is not None:
+            extra = set([i for v in extras for i in v.dimensions]) - set(self._gdims)
+            extra = tuple(extra)
+        else:
+            extra = tuple()
 
-    def _augment_implicit_dims(self, implicit_dims):
         if self.sfunction._sparse_position == -1:
-            return self.sfunction.dimensions + as_tuple(implicit_dims)
+            return self.sfunction.dimensions + as_tuple(implicit_dims) + extra
         else:
-            return as_tuple(implicit_dims) + self.sfunction.dimensions
+            return as_tuple(implicit_dims) + self.sfunction.dimensions + extra
 
     def _coeff_temps(self, implicit_dims):
         return []
@@ -177,24 +195,17 @@ def _interp_idx(self, variables, implicit_dims=None):
         mapper = {}
         pos = self.sfunction._position_map.values()
 
-        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
+        mapper = self._rdim._getters
         idx_subs = {v: v.subs({k: c + 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
 
@@ -247,8 +258,6 @@ def _interpolate(self, expr, increment=False, self_subs={}, implicit_dims=None):
             interpolation expression, but that should be honored when constructing
             the operator.
         """
-        implicit_dims = self._augment_implicit_dims(implicit_dims)
-
         # Derivatives must be evaluated before the introduction of indirect accesses
         try:
             _expr = expr.evaluate
@@ -258,6 +267,9 @@ def _interpolate(self, expr, increment=False, self_subs={}, implicit_dims=None):
 
         variables = list(retrieve_function_carriers(_expr))
 
+        # Implicit dimensions
+        implicit_dims = self._augment_implicit_dims(implicit_dims)
+
         # List of indirection indices for all adjacent grid points
         idx_subs, temps = self._interp_idx(variables, implicit_dims=implicit_dims)
 
@@ -290,11 +302,10 @@ def _inject(self, field, expr, implicit_dims=None):
             injection expression, but that should be honored when constructing
             the operator.
         """
-        implicit_dims = self._augment_implicit_dims(implicit_dims) + self._rdim
-
         # Make iterable to support inject((u, v), expr=expr)
         # or inject((u, v), expr=(expr1, expr2))
         fields, exprs = as_tuple(field), as_tuple(expr)
+
         # Provide either one expr per field or on expr for all fields
         if len(fields) > 1:
             if len(exprs) == 1:
@@ -310,6 +321,14 @@ def _inject(self, field, expr, implicit_dims=None):
             _exprs = exprs
 
         variables = list(v for e in _exprs for v in retrieve_function_carriers(e))
+
+        # Implicit dimensions
+        implicit_dims = self._augment_implicit_dims(implicit_dims, variables)
+        # Move all temporaries inside inner loop to improve parallelism
+        # Can only be done for inject as interpolation need a temporary
+        # summing temp that wouldn't allow collapsing
+        implicit_dims = implicit_dims + tuple(r.parent for r in self._rdim)
+
         variables = variables + list(fields)
 
         # List of indirection indices for all adjacent grid points
@@ -380,5 +399,7 @@ def interpolation_coeffs(self):
     @property
     def _weights(self):
         ddim, cdim = self.interpolation_coeffs.dimensions[1:]
-        return Mul(*[self.interpolation_coeffs.subs({ddim: ri, cdim: rd-rd.symbolic_min})
-                     for (ri, rd) in enumerate(self._rdim)])
+        mappers = [{ddim: ri, cdim: rd-rd.parent.symbolic_min}
+                   for (ri, rd) in enumerate(self._rdim)]
+        return Mul(*[self.interpolation_coeffs.subs(mapper)
+                     for mapper in mappers])

devito/operator/operator.py

@@ -24,7 +24,7 @@
 from devito.symbolics import estimate_cost
 from devito.tools import (DAG, OrderedSet, Signer, ReducerMap, as_tuple, flatten,
                           filter_sorted, frozendict, is_integer, split, timed_pass,
-                          timed_region)
+                          timed_region, contains_val)
 from devito.types import Grid, Evaluable
 
 __all__ = ['Operator']
@@ -526,6 +526,7 @@ def _prepare_arguments(self, autotune=None, **kwargs):
         edges = [(i, i.parent) for i in self.dimensions
                  if i.is_Derived and i.parent in set(nodes)]
         toposort = DAG(nodes, edges).topological_sort()
+
         futures = {}
         for d in reversed(toposort):
             if set(d._arg_names).intersection(kwargs):
@@ -560,18 +561,24 @@ def _prepare_arguments(self, autotune=None, **kwargs):
                     # a TimeFunction `usave(t_sub, x, y)`, an override for `fact` is
                     # supplied w/o overriding `usave`; that's legal
                     pass
-                elif is_integer(args[k]) and args[k] not in as_tuple(v):
+                elif is_integer(args[k]) and not contains_val(args[k], v):
                     raise ValueError("Default `%s` is incompatible with other args as "
                                      "`%s=%s`, while `%s=%s` is expected. Perhaps you "
                                      "forgot to override `%s`?" %
                                      (p, k, v, k, args[k], p))
+
         args = kwargs['args'] = args.reduce_all()
 
         # DiscreteFunctions may be created from CartesianDiscretizations, which in
         # turn could be Grids or SubDomains. Both may provide arguments
         discretizations = {getattr(kwargs[p.name], 'grid', None) for p in overrides}
         discretizations.update({getattr(p, 'grid', None) for p in defaults})
         discretizations.discard(None)
+        # Remove subgrids if multiple grids
+        if len(discretizations) > 1:
+            discretizations = {g for g in discretizations
+                               if not any(d.is_Derived for d in g.dimensions)}
+
         for i in discretizations:
             args.update(i._arg_values(**kwargs))
 

devito/passes/iet/langbase.py

@@ -214,6 +214,9 @@ def DeviceIteration(self):
     def Prodder(self):
         return self.lang.Prodder
 
+    def _device_pointers(self, *args, **kwargs):
+        return {}
+
 
 class DeviceAwareMixin(object):
 
@@ -325,6 +328,11 @@ def _(iet):
 
         return _initialize(iet)
 
+    def _device_pointers(self, iet):
+        functions = FindSymbols().visit(iet)
+        devfuncs = [f for f in functions if f.is_Array and f._mem_local]
+        return set(devfuncs)
+
     def _is_offloadable(self, iet):
         """
         True if the IET computation is offloadable to device, False otherwise.

devito/passes/iet/parpragma.py

@@ -295,15 +295,13 @@ def _select_candidates(self, candidates):
                     except TypeError:
                         pass
 
-                # At least one inner loop (nested) or
-                # we do not collapse most inner loop if it is an atomic reduction
-                if not i.is_ParallelAtomic or nested:
-                    collapsable.append(i)
+                collapsable.append(i)
 
             # Give a score to this candidate, based on the number of fully-parallel
             # Iterations and their position (i.e. outermost to innermost) in the nest
             score = (
                 int(root.is_ParallelNoAtomic),
+                len(self._device_pointers(root)),  # Outermost offloadable
                 int(len([i for i in collapsable if i.is_ParallelNoAtomic]) >= 1),
                 int(len([i for i in collapsable if i.is_ParallelRelaxed]) >= 1),
                 -(n0 + 1)  # The outermost, the better
@@ -377,9 +375,14 @@ def _make_partree(self, candidates, nthreads=None):
                                           ncollapsed=ncollapsed, nthreads=nthreads,
                                           **root.args)
             prefix = []
+        elif nthreads is not None:
+            body = self.HostIteration(schedule='static',
+                                      parallel=nthreads is not self.nthreads_nested,
+                                      ncollapsed=ncollapsed, nthreads=nthreads,
+                                      **root.args)
+            prefix = []
         else:
             # pragma ... for ... schedule(..., expr)
-            assert nthreads is None
             nthreads = self.nthreads_nonaffine
             chunk_size = Symbol(name='chunk_size')
             body = self.HostIteration(ncollapsed=ncollapsed, chunk_size=chunk_size,
@@ -428,11 +431,6 @@ def _make_nested_partree(self, partree):
         if self.nhyperthreads <= self.nested:
             return partree
 
-        # Loop nest with atomic reductions are more likely to have less latency
-        # keep outer loop parallel
-        if partree.root.is_ParallelAtomic:
-            return partree
-
         # Note: there might be multiple sub-trees amenable to nested parallelism,
         # hence we loop over all of them
         #

devito/tools/data_structures.py

@@ -110,6 +110,7 @@ def unique(self, key):
             Key for which to retrieve a unique value.
         """
         candidates = self.getall(key)
+        candidates = [c for c in candidates if c is not None]
 
         def compare_to_first(v):
             first = candidates[0]
@@ -122,12 +123,23 @@ def compare_to_first(v):
                     return first in v
             elif isinstance(first, Set):
                 return v in first
+            elif isinstance(v, range):
+                if isinstance(first, range):
+                    return first.stop > v.start or v.stop > first.start
+                else:
+                    return first >= v.start and first < v.stop
+            elif isinstance(first, range):
+                return v >= first.start and v < first.stop
             else:
                 return first == v
 
         if len(candidates) == 1:
             return candidates[0]
         elif all(map(compare_to_first, candidates)):
+            # return first non-range
+            for c in candidates:
+                if not isinstance(c, range):
+                    return c
             return candidates[0]
         else:
             raise ValueError("Unable to find unique value for key %s, candidates: %s"

devito/tools/utils.py

@@ -12,7 +12,7 @@
            'roundm', 'powerset', 'invert', 'flatten', 'single_or', 'filter_ordered',
            'as_mapper', 'filter_sorted', 'pprint', 'sweep', 'all_equal', 'as_list',
            'indices_to_slices', 'indices_to_sections', 'transitive_closure',
-           'humanbytes']
+           'humanbytes', 'contains_val']
 
 
 def prod(iterable, initial=1):
@@ -75,6 +75,13 @@ def is_integer(value):
     return isinstance(value, (int, np.integer, sympy.Integer))
 
 
+def contains_val(val, items):
+    try:
+        return val in items
+    except TypeError:
+        return val == items
+
+
 def generator():
     """
     Return a function ``f`` that generates integer numbers starting at 0

devito/types/basic.py

@@ -838,13 +838,15 @@ def __new__(cls, *args, **kwargs):
         newobj._dimensions = dimensions
         newobj._shape = cls.__shape_setup__(**kwargs)
         newobj._dtype = cls.__dtype_setup__(**kwargs)
-        newobj.__init_finalize__(*args, **kwargs)
 
         # All objects created off an existing AbstractFunction `f` (e.g.,
         # via .func, or .subs, such as `f(x + 1)`) keep a reference to `f`
         # through the `function` field
         newobj.function = function or newobj
 
+        # Initialization
+        newobj.__init_finalize__(*args, **kwargs)
+
         return newobj
 
     def __init__(self, *args, **kwargs):

devito/types/dense.py

@@ -94,7 +94,7 @@ def __init_finalize__(self, *args, function=None, **kwargs):
             # a reference to the user-provided buffer
             self._initializer = None
             if len(initializer) > 0:
-                self.data_with_halo[:] = initializer
+                self.data_with_halo[:] = initializer[:]
             else:
                 # This is a corner case -- we might get here, for example, when
                 # running with MPI and some processes get 0-size arrays after

devito/types/dimension.py

@@ -298,13 +298,19 @@ def _arg_values(self, interval, grid=None, args=None, **kwargs):
         # may represent sets of legal values. If that's the case, here we just
         # pick one. Note that we sort for determinism
         try:
-            loc_minv = sorted(loc_minv).pop(0)
-        except TypeError:
-            pass
+            loc_minv = loc_minv.stop
+        except AttributeError:
+            try:
+                loc_minv = sorted(loc_minv).pop(0)
+            except TypeError:
+                pass
         try:
-            loc_maxv = sorted(loc_maxv).pop(0)
-        except TypeError:
-            pass
+            loc_maxv = loc_maxv.stop
+        except AttributeError:
+            try:
+                loc_maxv = sorted(loc_maxv).pop(0)
+            except TypeError:
+                pass
 
         return {self.min_name: loc_minv, self.max_name: loc_maxv}
 
@@ -853,8 +859,8 @@ def _arg_defaults(self, _min=None, size=None, alias=None):
             factor = defaults[dim._factor.name] = dim._factor.data
         except AttributeError:
             factor = dim._factor
-        defaults[dim.parent.max_name] = \
-            frozenset(range(factor*(size - 1), factor*(size)))
+
+        defaults[dim.parent.max_name] = range(1, factor*size - 1)
 
         return defaults