def memo(funcdesc, arglist_s):
from rpython.annotator.model import SomePBC, SomeImpossibleValue, SomeBool
from rpython.annotator.model import unionof
# call the function now, and collect possible results
argvalues = []
for s in arglist_s:
if s.is_constant():
values = [s.const]
elif isinstance(s, SomePBC):
values = []
assert not s.can_be_None, "memo call: cannot mix None and PBCs"
for desc in s.descriptions:
if desc.pyobj is None:
raise annmodel.AnnotatorError(
"memo call with a class or PBC that has no "
"corresponding Python object (%r)" % (desc,))
values.append(desc.pyobj)
elif isinstance(s, SomeImpossibleValue):
return s # we will probably get more possible args later
elif isinstance(s, SomeBool):
values = [False, True]
else:
raise annmodel.AnnotatorError("memo call: argument must be a class "
"or a frozen PBC, got %r" % (s,))
argvalues.append(values)
# the list of all possible tuples of arguments to give to the memo function
possiblevalues = cartesian_product(argvalues)
# a MemoTable factory -- one MemoTable per family of arguments that can
# be called together, merged via a UnionFind.
bookkeeper = funcdesc.bookkeeper
try:
memotables = bookkeeper.all_specializations[funcdesc]
except KeyError:
func = funcdesc.pyobj
if func is None:
raise annmodel.AnnotatorError("memo call: no Python function object"
"to call (%r)" % (funcdesc,))
def compute_one_result(args):
value = func(*args)
memotable = MemoTable(funcdesc, args, value)
memotable.register_finish()
return memotable
memotables = UnionFind(compute_one_result)
bookkeeper.all_specializations[funcdesc] = memotables
# merge the MemoTables for the individual argument combinations
firstvalues = possiblevalues.next()
_, _, memotable = memotables.find(firstvalues)
for values in possiblevalues:
_, _, memotable = memotables.union(firstvalues, values)
if memotable.graph is not None:
return memotable.graph # if already computed
else:
# otherwise, for now, return the union of each possible result
return unionof(*[bookkeeper.immutablevalue(v)
for v in memotable.table.values()])
def test_cleanup():
state = []
class ReferencedByExternalState(object):
def __init__(self, obj):
state.append(self)
self.obj = obj
def absorb(self, other):
state.remove(other)
uf = UnionFind(ReferencedByExternalState)
uf.find(1)
for i in xrange(1, 10, 2):
uf.union(i, 1)
uf.find(2)
for i in xrange(2, 20, 2):
uf.union(i, 2)
assert len(state) == 2 # we have exactly 2 partitions
def test_cleanup():
state = []
class ReferencedByExternalState(object):
def __init__(self, obj):
state.append(self)
self.obj = obj
def absorb(self, other):
state.remove(other)
uf = UnionFind(ReferencedByExternalState)
uf.find(1)
for i in xrange(1, 10, 2):
uf.union(i, 1)
uf.find(2)
for i in xrange(2, 20, 2):
uf.union(i, 2)
assert len(state) == 2 # we have exactly 2 partitions
def memo(funcdesc, args_s):
# call the function now, and collect possible results
# the list of all possible tuples of arguments to give to the memo function
possiblevalues = cartesian_product([all_values(s_arg) for s_arg in args_s])
# a MemoTable factory -- one MemoTable per family of arguments that can
# be called together, merged via a UnionFind.
bookkeeper = funcdesc.bookkeeper
try:
memotables = bookkeeper.all_specializations[funcdesc]
except KeyError:
func = funcdesc.pyobj
if func is None:
raise annmodel.AnnotatorError(
"memo call: no Python function object"
"to call (%r)" % (funcdesc, ))
def compute_one_result(args):
value = func(*args)
memotable = MemoTable(funcdesc, args, value)
memotable.register_finish()
return memotable
memotables = UnionFind(compute_one_result)
bookkeeper.all_specializations[funcdesc] = memotables
# merge the MemoTables for the individual argument combinations
firstvalues = possiblevalues.next()
_, _, memotable = memotables.find(firstvalues)
for values in possiblevalues:
_, _, memotable = memotables.union(firstvalues, values)
if memotable.graph is not None:
return memotable.graph # if already computed
else:
# otherwise, for now, return the union of each possible result
return unionof(
*[bookkeeper.immutablevalue(v) for v in memotable.table.values()])
def memo(funcdesc, args_s):
# call the function now, and collect possible results
# the list of all possible tuples of arguments to give to the memo function
possiblevalues = cartesian_product([all_values(s_arg) for s_arg in args_s])
# a MemoTable factory -- one MemoTable per family of arguments that can
# be called together, merged via a UnionFind.
bookkeeper = funcdesc.bookkeeper
try:
memotables = bookkeeper.all_specializations[funcdesc]
except KeyError:
func = funcdesc.pyobj
if func is None:
raise annmodel.AnnotatorError("memo call: no Python function object"
"to call (%r)" % (funcdesc,))
def compute_one_result(args):
value = func(*args)
memotable = MemoTable(funcdesc, args, value)
memotable.register_finish()
return memotable
memotables = UnionFind(compute_one_result)
bookkeeper.all_specializations[funcdesc] = memotables
# merge the MemoTables for the individual argument combinations
firstvalues = possiblevalues.next()
_, _, memotable = memotables.find(firstvalues)
for values in possiblevalues:
_, _, memotable = memotables.union(firstvalues, values)
if memotable.graph is not None:
return memotable.graph # if already computed
else:
# otherwise, for now, return the union of each possible result
return unionof(*[bookkeeper.immutablevalue(v)
for v in memotable.table.values()])
def memo(funcdesc, arglist_s):
from rpython.annotator.model import SomePBC, SomeImpossibleValue, SomeBool
from rpython.annotator.model import unionof
# call the function now, and collect possible results
argvalues = []
for s in arglist_s:
if s.is_constant():
values = [s.const]
elif isinstance(s, SomePBC):
values = []
assert not s.can_be_None, "memo call: cannot mix None and PBCs"
for desc in s.descriptions:
if desc.pyobj is None:
raise annmodel.AnnotatorError(
"memo call with a class or PBC that has no "
"corresponding Python object (%r)" % (desc, ))
values.append(desc.pyobj)
elif isinstance(s, SomeImpossibleValue):
return s # we will probably get more possible args later
elif isinstance(s, SomeBool):
values = [False, True]
else:
raise annmodel.AnnotatorError(
"memo call: argument must be a class "
"or a frozen PBC, got %r" % (s, ))
argvalues.append(values)
# the list of all possible tuples of arguments to give to the memo function
possiblevalues = cartesian_product(argvalues)
# a MemoTable factory -- one MemoTable per family of arguments that can
# be called together, merged via a UnionFind.
bookkeeper = funcdesc.bookkeeper
try:
memotables = bookkeeper.all_specializations[funcdesc]
except KeyError:
func = funcdesc.pyobj
if func is None:
raise annmodel.AnnotatorError(
"memo call: no Python function object"
"to call (%r)" % (funcdesc, ))
def compute_one_result(args):
value = func(*args)
memotable = MemoTable(funcdesc, args, value)
memotable.register_finish()
return memotable
memotables = UnionFind(compute_one_result)
bookkeeper.all_specializations[funcdesc] = memotables
# merge the MemoTables for the individual argument combinations
firstvalues = possiblevalues.next()
_, _, memotable = memotables.find(firstvalues)
for values in possiblevalues:
_, _, memotable = memotables.union(firstvalues, values)
if memotable.graph is not None:
return memotable.graph # if already computed
else:
# otherwise, for now, return the union of each possible result
return unionof(
*[bookkeeper.immutablevalue(v) for v in memotable.table.values()])
def compute_lifetimes(self, graph):
"""Compute the static data flow of the graph: returns a list of LifeTime
instances, each of which corresponds to a set of Variables from the graph.
The variables are grouped in the same LifeTime if a value can pass from
one to the other by following the links. Each LifeTime also records all
places where a Variable in the set is used (read) or build (created).
"""
lifetimes = UnionFind(LifeTime)
def set_creation_point(block, var, *cp):
_, _, info = lifetimes.find((block, var))
info.creationpoints.add(cp)
def set_use_point(block, var, *usepoint):
_, _, info = lifetimes.find((block, var))
info.usepoints.add(usepoint)
def union(block1, var1, block2, var2):
if isinstance(var1, Variable):
lifetimes.union((block1, var1), (block2, var2))
elif isinstance(var1, Constant):
set_creation_point(block2, var2, "constant", var1)
else:
raise TypeError(var1)
for var in graph.startblock.inputargs:
set_creation_point(graph.startblock, var, "inputargs")
set_use_point(graph.returnblock, graph.returnblock.inputargs[0],
"return")
set_use_point(graph.exceptblock, graph.exceptblock.inputargs[0],
"except")
set_use_point(graph.exceptblock, graph.exceptblock.inputargs[1],
"except")
for node in graph.iterblocks():
for op in node.operations:
if op.opname in self.IDENTITY_OPS:
# special-case these operations to identify their input
# and output variables
union(node, op.args[0], node, op.result)
continue
if op.opname in self.SUBSTRUCT_OPS:
if self.visit_substruct_op(node, union, op):
continue
for i in range(len(op.args)):
if isinstance(op.args[i], Variable):
set_use_point(node, op.args[i], "op", node, op, i)
set_creation_point(node, op.result, "op", node, op)
if isinstance(node.exitswitch, Variable):
set_use_point(node, node.exitswitch, "exitswitch", node)
for node in graph.iterlinks():
if isinstance(node.last_exception, Variable):
set_creation_point(node.prevblock, node.last_exception,
"last_exception")
if isinstance(node.last_exc_value, Variable):
set_creation_point(node.prevblock, node.last_exc_value,
"last_exc_value")
duplicate_info = set()
for i, arg in enumerate(node.args):
union(node.prevblock, arg, node.target,
node.target.inputargs[i])
if isinstance(arg, Variable):
_, _, info = lifetimes.find((node.prevblock, arg))
if info in duplicate_info and len(info.creationpoints) > 1:
# same variable (up to renaming via same_as or
# cast_pointer) present several times in link.args, and
# the variable is created in two different places:
# consider it as a 'use' of the variable, which will
# disable malloc optimization (aliasing problems)
set_use_point(node.prevblock, arg, "dup", node, i)
else:
duplicate_info.add(info)
return lifetimes.infos()