def __init__(self, annotator):
self.annotator = annotator
self.policy = annotator.policy
self.descs = {} # map Python objects to their XxxDesc wrappers
self.methoddescs = {} # map (funcdesc, classdef) to the MethodDesc
self.classdefs = [] # list of all ClassDefs
self.pbctypes = {}
self.seen_mutable = {}
self.listdefs = {} # map position_keys to ListDefs
self.dictdefs = {} # map position_keys to DictDefs
self.immutable_cache = {}
self.classpbc_attr_families = {
} # {'attr': UnionFind(ClassAttrFamily)}
self.frozenpbc_attr_families = UnionFind(description.FrozenAttrFamily)
self.pbc_maximal_call_families = UnionFind(description.CallFamily)
self.emulated_pbc_calls = {}
self.all_specializations = {} # {FuncDesc: specialization-info}
self.pending_specializations = [] # list of callbacks
self.external_class_cache = {} # cache of ExternalType classes
self.needs_generic_instantiate = {}
self.stats = Stats(self)
# used in SomeObject.__new__ for keeping debugging info
self._isomeobject_coming_from = identity_dict()
delayed_imports()
def memo(funcdesc, arglist_s):
from pypy.annotation.model import SomePBC, SomeImpossibleValue, SomeBool
from pypy.annotation.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 Exception(
"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 Exception("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 Exception("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 pypy.annotation.model import SomePBC, SomeImpossibleValue, SomeBool
from pypy.annotation.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 Exception("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 Exception("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 Exception("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 __init__(self, hannotator):
self.pending_specializations = []
self.originflags = {}
self.virtual_containers = {}
self.descs = {}
self.tsgraph_maximal_call_families = UnionFind(TsGraphCallFamily)
self.annotator = hannotator
self.tsgraphsigs = {}
self.nonstuboriggraphcount = 0
self.stuboriggraphcount = 0
if hannotator is not None: # for tests
t = hannotator.base_translator
self.impurity_analyzer = ImpurityAnalyzer(t)
# circular imports hack
global hintmodel
from pypy.jit.hintannotator import model as hintmodel
def coalesce_variables(self):
self._unionfind = UnionFind()
pendingblocks = list(self.graph.iterblocks())
while pendingblocks:
block = pendingblocks.pop()
# Aggressively try to coalesce each source variable with its
# target. We start from the end of the graph instead of
# from the beginning. This is a bit arbitrary, but the idea
# is that the end of the graph runs typically more often
# than the start, given that we resume execution from the
# middle during blackholing.
for link in block.exits:
if link.last_exception is not None:
self._depgraph.add_node(link.last_exception)
if link.last_exc_value is not None:
self._depgraph.add_node(link.last_exc_value)
for i, v in enumerate(link.args):
self._try_coalesce(v, link.target.inputargs[i])
def get_classpbc_attr_families(self, attrname):
"""Return the UnionFind for the ClassAttrFamilies corresponding to
attributes of the given name.
"""
map = self.classpbc_attr_families
try:
access_sets = map[attrname]
except KeyError:
access_sets = map[attrname] = UnionFind(description.ClassAttrFamily)
return access_sets
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 coalesce_variables(self):
self._unionfind = UnionFind()
pendingblocks = list(self.graph.iterblocks())
while pendingblocks:
block = pendingblocks.pop()
# Aggressively try to coalesce each source variable with its
# target. We start from the end of the graph instead of
# from the beginning. This is a bit arbitrary, but the idea
# is that the end of the graph runs typically more often
# than the start, given that we resume execution from the
# middle during blackholing.
for link in block.exits:
if link.last_exception is not None:
self._depgraph.add_node(link.last_exception)
if link.last_exc_value is not None:
self._depgraph.add_node(link.last_exc_value)
for i, v in enumerate(link.args):
self._try_coalesce(v, link.target.inputargs[i])
def __init__(self, graph):
# Build a list of "unification opportunities": for each block and each
# 'n', an "opportunity" groups the block's nth input variable with
# the nth output variable from each of the incoming links, in a list:
# [Block, blockvar, linkvar, linkvar, linkvar...]
opportunities = []
opportunities_with_const = []
for block, links in mkinsideentrymap(graph).items():
assert links
for n, inputvar in enumerate(block.inputargs):
vars = [block, inputvar]
put_in = opportunities
for link in links:
var = link.args[n]
if not isinstance(var, Variable):
put_in = opportunities_with_const
vars.append(var)
# if any link provides a Constant, record this in
# the opportunities_with_const list instead
put_in.append(vars)
self.opportunities = opportunities
self.opportunities_with_const = opportunities_with_const
self.variable_families = UnionFind()
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[cp] = True
def set_use_point(block, var, *up):
_, _, info = lifetimes.find((block, var))
info.usepoints[up] = True
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")
def visit(node):
if isinstance(node, Block):
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)
if isinstance(node, Link):
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")
d = {}
for i, arg in enumerate(node.args):
union(node.prevblock, arg,
node.target, node.target.inputargs[i])
if isinstance(arg, Variable):
if arg in d:
# same variable present several times in link.args
# 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:
d[arg] = True
traverse(visit, graph)
return lifetimes.infos()
class RegAllocator(object):
DEBUG_REGALLOC = False
def __init__(self, graph, kind):
self.graph = graph
self.kind = kind
def make_dependencies(self):
dg = DependencyGraph()
for block in self.graph.iterblocks():
# Compute die_at = {Variable: index_of_operation_with_last_usage}
die_at = dict.fromkeys(block.inputargs, 0)
for i, op in enumerate(block.operations):
for v in op.args:
if isinstance(v, Variable):
die_at[v] = i
elif isinstance(v, ListOfKind):
for v1 in v:
if isinstance(v1, Variable):
die_at[v1] = i
if op.result is not None:
die_at[op.result] = i + 1
if isinstance(block.exitswitch, tuple):
for x in block.exitswitch:
die_at.pop(x, None)
else:
die_at.pop(block.exitswitch, None)
for link in block.exits:
for v in link.args:
die_at.pop(v, None)
die_at = [(value, key) for (key, value) in die_at.items()]
die_at.sort()
die_at.append((sys.maxint,))
# Done. XXX the code above this line runs 3 times
# (for kind in KINDS) to produce the same result...
livevars = [v for v in block.inputargs
if getkind(v.concretetype) == self.kind]
# Add the variables of this block to the dependency graph
for i, v in enumerate(livevars):
dg.add_node(v)
for j in range(i):
dg.add_edge(livevars[j], v)
livevars = set(livevars)
die_index = 0
for i, op in enumerate(block.operations):
while die_at[die_index][0] == i:
try:
livevars.remove(die_at[die_index][1])
except KeyError:
pass
die_index += 1
if (op.result is not None and
getkind(op.result.concretetype) == self.kind):
dg.add_node(op.result)
for v in livevars:
if getkind(v.concretetype) == self.kind:
dg.add_edge(v, op.result)
livevars.add(op.result)
self._depgraph = dg
def coalesce_variables(self):
self._unionfind = UnionFind()
pendingblocks = list(self.graph.iterblocks())
while pendingblocks:
block = pendingblocks.pop()
# Aggressively try to coalesce each source variable with its
# target. We start from the end of the graph instead of
# from the beginning. This is a bit arbitrary, but the idea
# is that the end of the graph runs typically more often
# than the start, given that we resume execution from the
# middle during blackholing.
for link in block.exits:
if link.last_exception is not None:
self._depgraph.add_node(link.last_exception)
if link.last_exc_value is not None:
self._depgraph.add_node(link.last_exc_value)
for i, v in enumerate(link.args):
self._try_coalesce(v, link.target.inputargs[i])
def _try_coalesce(self, v, w):
if isinstance(v, Variable) and getkind(v.concretetype) == self.kind:
dg = self._depgraph
uf = self._unionfind
v0 = uf.find_rep(v)
w0 = uf.find_rep(w)
if v0 is not w0 and v0 not in dg.neighbours[w0]:
_, rep, _ = uf.union(v0, w0)
assert uf.find_rep(v0) is uf.find_rep(w0) is rep
if rep is v0:
dg.coalesce(w0, v0)
else:
assert rep is w0
dg.coalesce(v0, w0)
def find_node_coloring(self):
self._coloring = self._depgraph.find_node_coloring()
if self.DEBUG_REGALLOC:
for block in self.graph.iterblocks():
print block
for v in block.getvariables():
print '\t', v, '\t', self.getcolor(v)
def getcolor(self, v):
return self._coloring[self._unionfind.find_rep(v)]
def swapcolors(self, col1, col2):
for key, value in self._coloring.items():
if value == col1:
self._coloring[key] = col2
elif value == col2:
self._coloring[key] = col1
class HintBookkeeper(object):
def __init__(self, hannotator):
self.pending_specializations = []
self.originflags = {}
self.virtual_containers = {}
self.descs = {}
self.tsgraph_maximal_call_families = UnionFind(TsGraphCallFamily)
self.annotator = hannotator
self.tsgraphsigs = {}
self.nonstuboriggraphcount = 0
self.stuboriggraphcount = 0
if hannotator is not None: # for tests
t = hannotator.base_translator
self.impurity_analyzer = ImpurityAnalyzer(t)
# circular imports hack
global hintmodel
from pypy.jit.hintannotator import model as hintmodel
def getdesc(self, graph):
try:
return self.descs[graph]
except KeyError:
self.descs[graph] = desc = GraphDesc(self, graph)
return desc
def enter(self, position_key):
"""Start of an operation.
The operation is uniquely identified by the given key."""
res = getattr(self, 'position_key', None)
self.position_key = position_key
TLS.bookkeeper = self
return res
def leave(self, old=None):
"""End of an operation."""
if old is None:
del TLS.bookkeeper
del self.position_key
else:
self.position_key = old
def myinputargorigin(self, graph, i):
try:
origin = self.originflags[graph, i]
except KeyError:
origin = hintmodel.InputArgOriginFlags(self, graph, i)
self.originflags[graph, i] = origin
return origin
def myorigin(self):
try:
origin = self.originflags[self.position_key]
except KeyError:
assert len(self.position_key) == 3
graph, block, i = self.position_key
spaceop = block.operations[i]
spaceop = SpaceOperation(spaceop.opname,
list(spaceop.args),
spaceop.result)
origin = hintmodel.OriginFlags(self, spaceop)
self.originflags[self.position_key] = origin
return origin
def compute_at_fixpoint(self):
binding = self.annotator.binding
# for the entry point, we need to remove the 'myorigin' of
# the input arguments (otherwise they will always be green,
# as there is no call to the entry point to make them red)
tsgraph = self.annotator.translator.graphs[0]
for v in tsgraph.getargs():
hs_arg = binding(v)
if isinstance(hs_arg, hintmodel.SomeLLAbstractConstant):
hs_arg.myorigin = None
# for convenience, force the return var to be red too, as
# the timeshifter doesn't support anything else
if self.annotator.policy.entrypoint_returns_red:
v = tsgraph.getreturnvar()
hs_red = hintmodel.variableoftype(v.concretetype)
self.annotator.setbinding(v, hs_red)
# propagate the green/red constraints
log.event("Computing maximal green set...")
greenorigindependencies = {}
callreturndependencies = {}
for origin in self.originflags.values():
origin.greenargs = True
origin.record_dependencies(greenorigindependencies,
callreturndependencies)
while True:
progress = False
# check all calls to see if they are green calls or not
for origin, graphs in callreturndependencies.items():
if self.is_green_call(origin.spaceop):
pass # green call => don't force spaceop.result to red
else:
# non-green calls: replace the dependency with a regular
# dependency from graph.getreturnvar() to spaceop.result
del callreturndependencies[origin]
retdeps = greenorigindependencies.setdefault(origin, [])
for graph in graphs:
retdeps.append(graph.getreturnvar())
# propagate normal dependencies
for origin, deps in greenorigindependencies.items():
for v in deps:
if not binding(v).is_green():
# not green => force the origin to be red too
origin.greenargs = False
del greenorigindependencies[origin]
progress = True
break
if not progress:
break
for callfamily in self.tsgraph_maximal_call_families.infos():
if len(callfamily.tsgraphs) > 1:
# if at least one graph in the family returns a red,
# we force a red as the return of all of them
returns_red = False
for graph in callfamily.tsgraphs:
if not binding(graph.getreturnvar()).is_green():
returns_red = True
if returns_red:
for graph in callfamily.tsgraphs:
v = graph.getreturnvar()
hs_red = hintmodel.variableoftype(v.concretetype)
self.annotator.setbinding(v, hs_red)
# compute and cache the signature of the graphs before they are
# modified by further code
ha = self.annotator
for tsgraph in ha.translator.graphs:
sig_hs = ([ha.binding(v) for v in tsgraph.getargs()],
ha.binding(tsgraph.getreturnvar()))
self.tsgraphsigs[tsgraph] = sig_hs
def is_pure_graph(self, graph):
impure = self.impurity_analyzer.analyze_direct_call(graph)
return not impure
def is_green_call(self, callop):
"Is the given call operation completely computable at compile-time?"
for v in callop.args:
hs_arg = self.annotator.binding(v)
if not hs_arg.is_green():
return False
# all-green arguments. Note that we can return True even if the
# result appears to be red; it's not a real red result then.
impure = self.impurity_analyzer.analyze(callop)
return not impure
def immutableconstant(self, const):
res = hintmodel.SomeLLAbstractConstant(const.concretetype, {})
res.const = const.value
# we want null pointers to be deepfrozen!
if isinstance(const.concretetype, (lltype.Ptr,
ootype.Instance,
ootype.BuiltinType,
ootype.StaticMethod)):
if not const.value:
res.deepfrozen = True
return res
def immutablevalue(self, value):
return self.immutableconstant(Constant(value, lltype.typeOf(value)))
def current_op_concretetype(self):
_, block, i = self.position_key
op = block.operations[i]
return op.result.concretetype
def current_op_binding(self):
_, block, i = self.position_key
op = block.operations[i]
hs_res = self.annotator.binding(op.result, annmodel.s_ImpossibleValue)
return hs_res
def getvirtualcontainerdef(self, TYPE, constructor=None):
try:
res = self.virtual_containers[self.position_key]
assert res.T == TYPE
except KeyError:
if constructor is None:
from pypy.jit.hintannotator.container import virtualcontainerdef
constructor = virtualcontainerdef
res = constructor(self, TYPE)
self.virtual_containers[self.position_key] = res
return res
def warning(self, msg):
return self.annotator.warning(msg)
def specialization_key(self, fixed, args_hs):
if fixed:
return 'fixed'
else:
key = []
specialize = False
for i, arg_hs in enumerate(args_hs):
if isinstance(arg_hs, hintmodel.SomeLLAbstractVariable):
key.append('v')
specialize = True
continue
if (isinstance(arg_hs, hintmodel.SomeLLAbstractConstant)
and arg_hs.eager_concrete):
key.append('E')
specialize = True
else:
key.append('x')
if (isinstance(arg_hs, hintmodel.SomeLLAbstractConstant)
and arg_hs.deepfrozen):
key.append('D')
specialize = True
else:
key.append('x')
if specialize:
return ''.join(key)
else:
return None
def get_graph_by_key(self, graph, specialization_key):
desc = self.getdesc(graph)
return desc._cache[specialization_key]
def get_graph_for_call(self, graph, fixed, args_hs):
# this can modify args_hs in-place!
key = self.specialization_key(fixed, args_hs)
if key is None:
alt_name = None
else:
alt_name = graph.name + '_H'+key
desc = self.getdesc(graph)
graph = desc.specialize(args_hs, key=key, alt_name=alt_name)
return graph
def graph_call(self, graph, fixed, args_hs,
tsgraph_accum=None, hs_callable=None):
input_args_hs = list(args_hs)
graph = self.get_graph_for_call(graph, fixed, input_args_hs)
if tsgraph_accum is not None:
tsgraph_accum.append(graph) # save this if the caller cares
# propagate fixing of arguments in the function to the caller
for inp_arg_hs, arg_hs in zip(input_args_hs, args_hs):
if isinstance(arg_hs, hintmodel.SomeLLAbstractConstant):
assert len(inp_arg_hs.origins) == 1
[o] = inp_arg_hs.origins.keys()
if o.read_fixed():
for o in arg_hs.origins:
o.set_fixed()
hs_res = self.annotator.recursivecall(graph,
self.position_key,
input_args_hs)
# look on which input args the hs_res result depends on
if isinstance(hs_res, hintmodel.SomeLLAbstractConstant):
if (hs_callable is not None and
not isinstance(hs_callable, hintmodel.SomeLLAbstractConstant)):
hs_res = hintmodel.variableoftype(hs_res.concretetype,
hs_res.deepfrozen)
else:
deps_hs = []
for hs_inputarg, hs_arg in zip(input_args_hs, args_hs):
if isinstance(hs_inputarg,
hintmodel.SomeLLAbstractConstant):
assert len(hs_inputarg.origins) == 1
[o] = hs_inputarg.origins.keys()
if o in hs_res.origins:
deps_hs.append(hs_arg)
if fixed:
deps_hs.append(hs_res)
hs_res = hintmodel.reorigin(hs_res, hs_callable, *deps_hs)
return hs_res
def graph_family_call(self, graph_list, fixed, args_hs,
tsgraphs_accum=None, hs_callable=None):
if tsgraphs_accum is None:
tsgraphs = []
else:
tsgraphs = tsgraphs_accum
results_hs = []
for graph in graph_list:
results_hs.append(self.graph_call(graph, fixed, args_hs,
tsgraphs, hs_callable))
# put the tsgraphs in the same call family
call_families = self.tsgraph_maximal_call_families
_, rep, callfamily = call_families.find(tsgraphs[0])
for tsgraph in tsgraphs[1:]:
_, rep, callfamily = call_families.union(rep, tsgraph)
return annmodel.unionof(*results_hs)
class RegAllocator(object):
DEBUG_REGALLOC = False
def __init__(self, graph, consider_var, ListOfKind):
self.graph = graph
self.consider_var = consider_var
self.ListOfKind = ListOfKind
def make_dependencies(self):
dg = DependencyGraph()
for block in self.graph.iterblocks():
# Compute die_at = {Variable: index_of_operation_with_last_usage}
die_at = dict.fromkeys(block.inputargs, 0)
for i, op in enumerate(block.operations):
for v in op.args:
if isinstance(v, Variable):
die_at[v] = i
elif isinstance(v, self.ListOfKind):
for v1 in v:
if isinstance(v1, Variable):
die_at[v1] = i
if op.result is not None:
die_at[op.result] = i + 1
if isinstance(block.exitswitch, tuple):
for x in block.exitswitch:
die_at.pop(x, None)
else:
die_at.pop(block.exitswitch, None)
for link in block.exits:
for v in link.args:
die_at.pop(v, None)
die_at = [(value, key) for (key, value) in die_at.items()]
die_at.sort()
die_at.append((sys.maxint, ))
# Done. XXX the code above this line runs 3 times
# (for kind in KINDS) to produce the same result...
livevars = [v for v in block.inputargs if self.consider_var(v)]
# Add the variables of this block to the dependency graph
for i, v in enumerate(livevars):
dg.add_node(v)
for j in range(i):
dg.add_edge(livevars[j], v)
livevars = set(livevars)
die_index = 0
for i, op in enumerate(block.operations):
while die_at[die_index][0] == i:
try:
livevars.remove(die_at[die_index][1])
except KeyError:
pass
die_index += 1
if (op.result is not None and self.consider_var(op.result)):
dg.add_node(op.result)
for v in livevars:
if self.consider_var(v):
dg.add_edge(v, op.result)
livevars.add(op.result)
self._depgraph = dg
def coalesce_variables(self):
self._unionfind = UnionFind()
pendingblocks = list(self.graph.iterblocks())
while pendingblocks:
block = pendingblocks.pop()
# Aggressively try to coalesce each source variable with its
# target. We start from the end of the graph instead of
# from the beginning. This is a bit arbitrary, but the idea
# is that the end of the graph runs typically more often
# than the start, given that we resume execution from the
# middle during blackholing.
for link in block.exits:
if link.last_exception is not None:
self._depgraph.add_node(link.last_exception)
if link.last_exc_value is not None:
self._depgraph.add_node(link.last_exc_value)
for i, v in enumerate(link.args):
self._try_coalesce(v, link.target.inputargs[i])
def _try_coalesce(self, v, w):
if isinstance(v, Variable) and self.consider_var(v):
assert self.consider_var(w)
dg = self._depgraph
uf = self._unionfind
v0 = uf.find_rep(v)
w0 = uf.find_rep(w)
if v0 is not w0 and v0 not in dg.neighbours[w0]:
_, rep, _ = uf.union(v0, w0)
assert uf.find_rep(v0) is uf.find_rep(w0) is rep
if rep is v0:
dg.coalesce(w0, v0)
else:
assert rep is w0
dg.coalesce(v0, w0)
def find_node_coloring(self):
self._coloring = self._depgraph.find_node_coloring()
if self.DEBUG_REGALLOC:
for block in self.graph.iterblocks():
print block
for v in block.getvariables():
print '\t', v, '\t', self.getcolor(v)
def getcolor(self, v):
return self._coloring[self._unionfind.find_rep(v)]
def swapcolors(self, col1, col2):
for key, value in self._coloring.items():
if value == col1:
self._coloring[key] = col2
elif value == col2:
self._coloring[key] = col1
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[cp] = True
def set_use_point(block, var, *up):
_, _, info = lifetimes.find((block, var))
info.usepoints[up] = True
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")
def visit(node):
if isinstance(node, Block):
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)
if isinstance(node, Link):
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")
d = {}
for i, arg in enumerate(node.args):
union(node.prevblock, arg, node.target,
node.target.inputargs[i])
if isinstance(arg, Variable):
if arg in d:
# same variable present several times in link.args
# 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:
d[arg] = True
traverse(visit, graph)
return lifetimes.infos()
