def __new__(cls, *args, **kwargs):
if len(args) == 1 and isinstance(args[0], LoweredEq):
# origin: LoweredEq(devito.LoweredEq, **kwargs)
input_expr = args[0]
expr = sympy.Eq.__new__(cls, *input_expr.args, evaluate=False)
for i in cls._state:
setattr(expr, '_%s' % i, kwargs.get(i) or getattr(input_expr, i))
return expr
elif len(args) == 1 and isinstance(args[0], Eq):
# origin: LoweredEq(devito.Eq)
input_expr = expr = args[0]
elif len(args) == 2:
expr = sympy.Eq.__new__(cls, *args, evaluate=False)
for i in cls._state:
setattr(expr, '_%s' % i, kwargs.pop(i))
return expr
else:
raise ValueError("Cannot construct LoweredEq from args=%s "
"and kwargs=%s" % (str(args), str(kwargs)))
# Well-defined dimension ordering
ordering = dimension_sort(expr)
# Analyze the expression
mapper = detect_accesses(expr)
oobs = detect_oobs(mapper)
conditionals = [i for i in ordering if i.is_Conditional]
# The iteration space is constructed so that information always flows
# from an iteration to another (i.e., no anti-dependences are created)
directions, _ = force_directions(detect_flow_directions(expr), lambda i: Any)
iterators = build_iterators(mapper)
intervals = build_intervals(Stencil.union(*mapper.values()))
intervals = IntervalGroup(intervals, relations=ordering.relations)
ispace = IterationSpace(intervals.zero(), iterators, directions)
# The data space is relative to the computational domain. Note that we
# are deliberately dropping the intervals ordering (by turning `intervals`
# into a list), as this is irrelevant (even more: dangerous) for data spaces
intervals = [i if i.dim in oobs else i.zero() for i in intervals]
intervals += [Interval(i, 0, 0) for i in ordering
if i not in ispace.dimensions + conditionals]
parts = {k: IntervalGroup(build_intervals(v)) for k, v in mapper.items() if k}
dspace = DataSpace(intervals, parts)
# Finally create the LoweredEq with all metadata attached
expr = super(LoweredEq, cls).__new__(cls, expr.lhs, expr.rhs, evaluate=False)
expr._dspace = dspace
expr._ispace = ispace
expr._conditionals = tuple(conditionals)
expr._reads, expr._writes = detect_io(expr)
expr._is_Increment = input_expr.is_Increment
expr._implicit_dims = input_expr.implicit_dims
return expr
def __new__(cls, *args, **kwargs):
if len(args) == 1 and isinstance(args[0], LoweredEq):
# origin: LoweredEq(devito.LoweredEq, **kwargs)
input_expr = args[0]
expr = Eq.__new__(cls, *input_expr.args, evaluate=False)
for i in cls._state:
setattr(expr, '_%s' % i, kwargs.get(i) or getattr(input_expr, i))
return expr
elif len(args) == 1 and isinstance(args[0], Eq):
# origin: LoweredEq(sympy.Eq)
input_expr = expr = args[0]
elif len(args) == 2:
expr = Eq.__new__(cls, *args, evaluate=False)
for i in cls._state:
setattr(expr, '_%s' % i, kwargs.pop(i))
return expr
else:
raise ValueError("Cannot construct LoweredEq from args=%s "
"and kwargs=%s" % (str(args), str(kwargs)))
# Well-defined dimension ordering
ordering = dimension_sort(expr)
# Analyze the expression
mapper = detect_accesses(expr)
oobs = detect_oobs(mapper)
conditionals = [i for i in ordering if i.is_Conditional]
# The iteration space is constructed so that information always flows
# from an iteration to another (i.e., no anti-dependences are created)
directions, _ = force_directions(detect_flow_directions(expr), lambda i: Any)
iterators = build_iterators(mapper)
intervals = build_intervals(Stencil.union(*mapper.values()))
intervals = IntervalGroup(intervals, relations=ordering.relations)
ispace = IterationSpace(intervals.zero(), iterators, directions)
# The data space is relative to the computational domain. Note that we
# are deliberately dropping the intervals ordering (by turning `intervals`
# into a list), as this is irrelevant (even more: dangerous) for data spaces
intervals = [i if i.dim in oobs else i.zero() for i in intervals]
intervals += [Interval(i, 0, 0) for i in ordering
if i not in ispace.dimensions + conditionals]
parts = {k: IntervalGroup(build_intervals(v)) for k, v in mapper.items() if k}
dspace = DataSpace(intervals, parts)
# Finally create the LoweredEq with all metadata attached
expr = super(LoweredEq, cls).__new__(cls, expr.lhs, expr.rhs, evaluate=False)
expr._is_Increment = getattr(input_expr, 'is_Increment', False)
expr._dspace = dspace
expr._ispace = ispace
expr._conditionals = tuple(conditionals)
expr._reads, expr._writes = detect_io(expr)
return expr
def dspace(self):
"""
Derive the DataSpace of the Cluster from its expressions, IterationSpace,
and Guards.
"""
accesses = detect_accesses(self.exprs)
# Construct the `parts` of the DataSpace, that is a projection of the data
# space for each Function appearing in `self.exprs`
parts = {}
for f, v in accesses.items():
if f is None:
continue
intervals = [
Interval(d, min(offs), max(offs)) for d, offs in v.items()
]
intervals = IntervalGroup(intervals)
# Factor in the IterationSpace -- if the min/max points aren't zero,
# then the data intervals need to shrink/expand accordingly
intervals = intervals.promote(lambda d: d.is_Block)
shift = self.ispace.intervals.promote(lambda d: d.is_Block)
intervals = intervals.add(shift)
# Map SubIterators to the corresponding data space Dimension
# E.g., `xs -> x -> x0_blk0 -> x` or `t0 -> t -> time`
intervals = intervals.promote(lambda d: d.is_SubIterator)
# If the bound of a Dimension is explicitly guarded, then we should
# shrink the `parts` accordingly
for d, v in self.guards.items():
ret = v.find(BaseGuardBoundNext)
assert len(ret) <= 1
if len(ret) != 1:
continue
if ret.pop().direction is Forward:
intervals = intervals.translate(d, v1=-1)
else:
intervals = intervals.translate(d, 1)
# Special case: if the factor of a ConditionalDimension has value 1,
# then we can safely resort to the parent's Interval
intervals = intervals.promote(
lambda d: d.is_Conditional and d.factor == 1)
parts[f] = intervals
# Determine the Dimensions requiring shifted min/max points to avoid
# OOB accesses
oobs = set()
for f, v in parts.items():
for i in v:
if i.dim.is_Sub:
d = i.dim.parent
else:
d = i.dim
try:
if i.lower < 0 or \
i.upper > f._size_nodomain[d].left + f._size_halo[d].right:
# It'd mean trying to access a point before the
# left halo (test0) or after the right halo (test1)
oobs.update(d._defines)
except (KeyError, TypeError):
# Unable to detect presence of OOB accesses (e.g., `d` not in
# `f._size_halo`, that is typical of indirect accesses `A[B[i]]`)
pass
# Construct the `intervals` of the DataSpace, that is a global,
# Dimension-centric view of the data space
intervals = IntervalGroup.generate('union', *parts.values())
# E.g., `db0 -> time`, but `xi NOT-> x`
intervals = intervals.promote(lambda d: not d.is_Sub)
intervals = intervals.zero(set(intervals.dimensions) - oobs)
return DataSpace(intervals, parts)
