def eliminate_guards(self, loop):
self.renamer = Renamer()
for i, op in enumerate(loop.operations):
op = loop.operations[i]
if op.is_guard():
if i in self.guards:
# either a stronger guard has been saved
# or it should not be emitted
guard = self.guards[i]
# this guard is implied or marked as not emitted (= None)
self.strength_reduced += 1
if guard is None:
continue
guard.emit_operations(self)
continue
else:
self.emit_operation(op)
continue
if not op.returns_void():
index_var = self.index_vars.get(op, None)
if index_var:
if not index_var.is_identity():
var = index_var.emit_operations(self, op)
self.renamer.start_renaming(op, var)
continue
self.emit_operation(op)
self.renamer.rename(loop.jump)
#
loop.operations = self._newoperations[:]
def __init__(self, graph):
self.renamer = Renamer()
self.graph = graph
self.oplist = []
self.worklist = []
self.invariant_oplist = []
self.invariant_vector_vars = []
self.seen = {}
def __init__(self, cpu, graph):
self.cpu = cpu
self.renamer = Renamer()
self.graph = graph
self.oplist = []
self.worklist = []
self.invariant_oplist = []
self.invariant_vector_vars = []
self.seen = {}
self.delayed = []
def schedule(self,
loop,
packs,
vec_reg_size=16,
prepend_invariant=False,
overwrite_funcs=None):
cm = GenericCostModel(self.cpu, 0)
cm.profitable = lambda: True
pairs = []
for pack in packs:
for i in range(len(pack.operations) - 1):
o1 = pack.operations[i]
o2 = pack.operations[i + 1]
pair = Pair(o1, o2)
pairs.append(pair)
packset = FakePackSet(pairs)
state = VecScheduleState(loop.graph, packset, self.cpu, cm)
for name, overwrite in (overwrite_funcs or {}).items():
setattr(state, name, overwrite)
renamer = Renamer()
metainterp_sd = FakeMetaInterpStaticData(self.cpu)
jitdriver_sd = FakeJitDriverStaticData()
opt = VectorizingOptimizer(metainterp_sd, jitdriver_sd, 0)
opt.packset = packset
opt.combine_packset()
opt.schedule(state)
# works for now. might be the wrong class?
# wrap label + operations + jump it in tree loop otherwise
loop = state.graph.loop
if prepend_invariant:
loop.operations = loop.prefix + loop.operations
return loop
def eliminate_guards(self, loop):
self.renamer = Renamer()
for i,op in enumerate(loop.operations):
op = loop.operations[i]
if op.is_guard():
if i in self.guards:
# either a stronger guard has been saved
# or it should not be emitted
guard = self.guards[i]
# this guard is implied or marked as not emitted (= None)
self.strength_reduced += 1
if guard is None:
continue
guard.emit_operations(self)
continue
else:
self.emit_operation(op)
continue
if not op.returns_void():
index_var = self.index_vars.get(op, None)
if index_var:
if not index_var.is_identity():
var = index_var.emit_operations(self, op)
self.renamer.start_renaming(op, var)
continue
self.emit_operation(op)
self.renamer.rename(loop.jump)
#
loop.operations = self._newoperations[:]
def __init__(self, graph):
self.renamer = Renamer()
self.graph = graph
self.oplist = []
self.worklist = []
self.invariant_oplist = []
self.invariant_vector_vars = []
self.seen = {}
def unroll_loop_iterations(self, loop, unroll_count):
""" Unroll the loop X times. unroll_count + 1 = unroll_factor """
numops = len(loop.operations)
renamer = Renamer()
operations = loop.operations
unrolled = []
prohibit_opnums = (rop.GUARD_FUTURE_CONDITION,
rop.GUARD_NOT_INVALIDATED)
orig_jump_args = loop.jump.getarglist()[:]
# it is assumed that #label_args == #jump_args
label_arg_count = len(orig_jump_args)
for u in range(unroll_count):
# fill the map with the renaming boxes. keys are boxes from the label
for i in range(label_arg_count):
la = loop.label.getarg(i)
ja = loop.jump.getarg(i)
ja = renamer.rename_box(ja)
if la != ja:
renamer.start_renaming(la, ja)
#
for i, op in enumerate(operations):
if op.getopnum() in prohibit_opnums:
continue # do not unroll this operation twice
copied_op = copy_resop(op)
if not copied_op.returns_void():
# every result assigns a new box, thus creates an entry
# to the rename map.
renamer.start_renaming(op, copied_op)
#
args = copied_op.getarglist()
for a, arg in enumerate(args):
value = renamer.rename_box(arg)
copied_op.setarg(a, value)
# not only the arguments, but also the fail args need
# to be adjusted. rd_snapshot stores the live variables
# that are needed to resume.
if copied_op.is_guard():
self.copy_guard_descr(renamer, copied_op)
#
unrolled.append(copied_op)
# the jump arguments have been changed
# if label(iX) ... jump(i(X+1)) is called, at the next unrolled loop
# must look like this: label(i(X+1)) ... jump(i(X+2))
args = loop.jump.getarglist()
for i, arg in enumerate(args):
value = renamer.rename_box(arg)
loop.jump.setarg(i, value)
#
loop.operations = operations + unrolled
def __init__(self, cpu, graph):
self.cpu = cpu
self.renamer = Renamer()
self.graph = graph
self.oplist = []
self.worklist = []
self.invariant_oplist = []
self.invariant_vector_vars = []
self.seen = {}
self.delayed = []
class SchedulerState(object):
def __init__(self, graph):
self.renamer = Renamer()
self.graph = graph
self.oplist = []
self.worklist = []
self.invariant_oplist = []
self.invariant_vector_vars = []
self.seen = {}
def post_schedule(self):
loop = self.graph.loop
self.renamer.rename(loop.jump)
self.ensure_args_unpacked(loop.jump)
loop.operations = self.oplist
loop.prefix = self.invariant_oplist
if len(self.invariant_vector_vars) + len(self.invariant_oplist) > 0:
# label
args = loop.label.getarglist_copy() + self.invariant_vector_vars
opnum = loop.label.getopnum()
op = loop.label.copy_and_change(opnum, args)
self.renamer.rename(op)
loop.prefix_label = op
# jump
args = loop.jump.getarglist_copy() + self.invariant_vector_vars
opnum = loop.jump.getopnum()
op = loop.jump.copy_and_change(opnum, args)
self.renamer.rename(op)
loop.jump = op
def profitable(self):
return True
def prepare(self):
for node in self.graph.nodes:
if node.depends_count() == 0:
self.worklist.insert(0, node)
def emit(self, node, scheduler):
# implement me in subclass. e.g. as in VecScheduleState
return False
def delay(self, node):
return False
def has_more(self):
return len(self.worklist) > 0
def ensure_args_unpacked(self, op):
pass
def post_emit(self, node):
pass
def pre_emit(self, node):
pass
class SchedulerState(object):
def __init__(self, graph):
self.renamer = Renamer()
self.graph = graph
self.oplist = []
self.worklist = []
self.invariant_oplist = []
self.invariant_vector_vars = []
self.seen = {}
def post_schedule(self):
loop = self.graph.loop
self.renamer.rename(loop.jump)
self.ensure_args_unpacked(loop.jump)
loop.operations = self.oplist
loop.prefix = self.invariant_oplist
if len(self.invariant_vector_vars) + len(self.invariant_oplist) > 0:
# label
args = loop.label.getarglist_copy() + self.invariant_vector_vars
opnum = loop.label.getopnum()
op = loop.label.copy_and_change(opnum, args)
self.renamer.rename(op)
loop.prefix_label = op
# jump
args = loop.jump.getarglist_copy() + self.invariant_vector_vars
opnum = loop.jump.getopnum()
op = loop.jump.copy_and_change(opnum, args)
self.renamer.rename(op)
loop.jump = op
def profitable(self):
return True
def prepare(self):
for node in self.graph.nodes:
if node.depends_count() == 0:
self.worklist.insert(0, node)
def emit(self, node, scheduler):
# implement me in subclass. e.g. as in VecScheduleState
return False
def delay(self, node):
return False
def has_more(self):
return len(self.worklist) > 0
def ensure_args_unpacked(self, op):
pass
def post_emit(self, node):
pass
def pre_emit(self, node):
pass
class SchedulerState(object):
def __init__(self, cpu, graph):
self.cpu = cpu
self.renamer = Renamer()
self.graph = graph
self.oplist = []
self.worklist = []
self.invariant_oplist = []
self.invariant_vector_vars = []
self.seen = {}
self.delayed = []
def resolve_delayed(self, needs_resolving, delayed, op):
# recursive solving of all delayed objects
if not delayed:
return
args = op.getarglist()
if op.is_guard():
args = args[:] + op.getfailargs()
for arg in args:
if arg is None or arg.is_constant() or arg.is_inputarg():
continue
if arg not in self.seen:
box = self.renamer.rename_box(arg)
needs_resolving[box] = None
indexvars = self.graph.index_vars
i = len(delayed) - 1
while i >= 0:
node = delayed[i]
op = node.getoperation()
if op in needs_resolving:
# either it is a normal operation, or we know that there is a linear combination
del needs_resolving[op]
if op in indexvars:
opindexvar = indexvars[op]
# there might be a variable already, that
# calculated the index variable, thus just reuse it
for var, indexvar in indexvars.items():
if indexvar == opindexvar and var in self.seen:
self.renamer.start_renaming(op, var)
break
else:
if opindexvar.calculated_by(op):
# just append this operation
self.seen[op] = None
self.append_to_oplist(op)
else:
# here is an easier way to calculate just this operation
last = op
for operation in opindexvar.get_operations():
self.append_to_oplist(operation)
last = operation
indexvars[last] = opindexvar
self.renamer.start_renaming(op, last)
self.seen[op] = None
self.seen[last] = None
else:
self.resolve_delayed(needs_resolving, delayed, op)
self.append_to_oplist(op)
self.seen[op] = None
if len(delayed) > i:
del delayed[i]
i -= 1
# some times the recursive call can remove several items from delayed,
# thus we correct the index here
if len(delayed) <= i:
i = len(delayed) - 1
def append_to_oplist(self, op):
self.renamer.rename(op)
self.oplist.append(op)
def schedule(self):
self.prepare()
Scheduler().walk_and_emit(self)
self.post_schedule()
def post_schedule(self):
loop = self.graph.loop
jump = loop.jump
if self.delayed:
# some operations can be delayed until the jump instruction,
# handle them here
self.resolve_delayed({}, self.delayed, jump)
self.renamer.rename(jump)
loop.operations = self.oplist
def profitable(self):
return True
def prepare(self):
for node in self.graph.nodes:
if node.depends_count() == 0:
self.worklist.insert(0, node)
def try_emit_or_delay(self, node):
if not node.is_imaginary() and node.is_pure():
# this operation might never be emitted. only if it is really needed
self.delay_emit(node)
return
# emit a now!
self.pre_emit(node, True)
self.mark_emitted(node)
if not node.is_imaginary():
op = node.getoperation()
self.seen[op] = None
self.append_to_oplist(op)
def delay_emit(self, node):
""" it has been decided that the operation might be scheduled later """
delayed = node.delayed or []
if node not in delayed:
delayed.append(node)
node.delayed = None
provides = node.provides()
if len(provides) == 0:
for n in delayed:
self.delayed.append(n)
else:
for to in node.provides():
tnode = to.target_node()
self.delegate_delay(tnode, delayed[:])
self.mark_emitted(node)
def delegate_delay(self, node, delayed):
""" Chain up delays, this can reduce many more of the operations """
if node.delayed is None:
node.delayed = delayed
else:
delayedlist = node.delayed
for d in delayed:
if d not in delayedlist:
delayedlist.append(d)
def mark_emitted(state, node, unpack=True):
""" An operation has been emitted, adds new operations to the worklist
whenever their dependency count drops to zero.
Keeps worklist sorted (see priority) """
worklist = state.worklist
provides = node.provides()[:]
for dep in provides: # COPY
target = dep.to
node.remove_edge_to(target)
if not target.emitted and target.depends_count() == 0:
# sorts them by priority
i = len(worklist) - 1
while i >= 0:
cur = worklist[i]
c = (cur.priority - target.priority)
if c < 0: # meaning itnode.priority < target.priority:
worklist.insert(i + 1, target)
break
elif c == 0:
# if they have the same priority, sort them
# using the original position in the trace
if target.getindex() < cur.getindex():
worklist.insert(i + 1, target)
break
i -= 1
else:
worklist.insert(0, target)
node.clear_dependencies()
node.emitted = True
if not node.is_imaginary():
op = node.getoperation()
state.renamer.rename(op)
if unpack:
state.ensure_args_unpacked(op)
state.post_emit(node)
def delay(self, node):
return False
def has_more(self):
return len(self.worklist) > 0
def ensure_args_unpacked(self, op):
pass
def post_emit(self, node):
pass
def pre_emit(self, orignode, pack_first=True):
delayed = orignode.delayed
if delayed:
# there are some nodes that have been delayed just for this operation
if pack_first:
op = orignode.getoperation()
self.resolve_delayed({}, delayed, op)
for node in delayed:
op = node.getoperation()
if op in self.seen:
continue
if node is not None:
provides = node.provides()
if len(provides) == 0:
# add this node to the final delay list
# might be emitted before jump!
self.delayed.append(node)
else:
for to in node.provides():
tnode = to.target_node()
self.delegate_delay(tnode, [node])
orignode.delayed = None
def clone(self):
renamer = Renamer()
label = copy_resop(self.label)
prefix = []
for op in self.prefix:
newop = copy_resop(op)
renamer.rename(newop)
if not newop.returns_void():
renamer.start_renaming(op, newop)
prefix.append(newop)
prefix_label = None
if self.prefix_label:
prefix_label = copy_resop(self.prefix_label)
renamer.rename(prefix_label)
oplist = []
for op in self.operations:
newop = copy_resop(op)
renamer.rename(newop)
if not newop.returns_void():
renamer.start_renaming(op, newop)
oplist.append(newop)
jump = copy_resop(self.jump)
renamer.rename(jump)
loop = VectorLoop(copy_resop(self.label), oplist, jump)
loop.prefix = prefix
loop.prefix_label = prefix_label
return loop
def unroll_loop_iterations(self, loop, unroll_count, align_unroll_once=False):
""" Unroll the loop `unroll_count` times. There can be an additional unroll step
if alignment might benefit """
numops = len(loop.operations)
renamer = Renamer()
operations = loop.operations
orig_jump_args = loop.jump.getarglist()[:]
prohibit_opnums = (rop.GUARD_FUTURE_CONDITION,
rop.GUARD_NOT_INVALIDATED,
rop.DEBUG_MERGE_POINT)
unrolled = []
if align_unroll_once:
unroll_count += 1
# it is assumed that #label_args == #jump_args
label_arg_count = len(orig_jump_args)
label = loop.label
jump = loop.jump
new_label = loop.label
for u in range(unroll_count):
# fill the map with the renaming boxes. keys are boxes from the label
for i in range(label_arg_count):
la = label.getarg(i)
ja = jump.getarg(i)
ja = renamer.rename_box(ja)
if la != ja:
renamer.start_renaming(la, ja)
#
for i, op in enumerate(operations):
if op.getopnum() in prohibit_opnums:
continue # do not unroll this operation twice
copied_op = copy_resop(op)
if not copied_op.returns_void():
# every result assigns a new box, thus creates an entry
# to the rename map.
renamer.start_renaming(op, copied_op)
#
args = copied_op.getarglist()
for a, arg in enumerate(args):
value = renamer.rename_box(arg)
copied_op.setarg(a, value)
# not only the arguments, but also the fail args need
# to be adjusted. rd_snapshot stores the live variables
# that are needed to resume.
if copied_op.is_guard():
self.copy_guard_descr(renamer, copied_op)
#
unrolled.append(copied_op)
#
if align_unroll_once and u == 0:
descr = label.getdescr()
args = label.getarglist()[:]
new_label = ResOperation(rop.LABEL, args, descr)
renamer.rename(new_label)
#
# the jump arguments have been changed
# if label(iX) ... jump(i(X+1)) is called, at the next unrolled loop
# must look like this: label(i(X+1)) ... jump(i(X+2))
args = loop.jump.getarglist()
for i, arg in enumerate(args):
value = renamer.rename_box(arg)
loop.jump.setarg(i, value)
#
loop.label = new_label
if align_unroll_once:
loop.align_operations = operations
loop.operations = unrolled
else:
loop.operations = operations + unrolled
class GuardStrengthenOpt(object):
""" Note that this optimization is only used in the vector optimizer (yet) """
def __init__(self, index_vars):
self.index_vars = index_vars
self._newoperations = []
self.strength_reduced = 0 # how many guards could be removed?
self.strongest_guards = {}
self.guards = {}
self.delayed = {}
def collect_guard_information(self, loop):
operations = loop.operations
last_guard = None
for i,op in enumerate(operations):
op = operations[i]
if not op.is_guard():
continue
if op.getopnum() in (rop.GUARD_TRUE, rop.GUARD_FALSE):
guard = Guard.of(op.getarg(0), operations, i, self.index_vars)
if guard is None:
continue
self.record_guard(guard.getleftkey(), guard)
self.record_guard(guard.getrightkey(), guard)
def record_guard(self, key, guard):
if key is None:
return
# the operations are processed from 1..n (forward),
# thus if the key is not present (1), the guard is saved
# (2) guard(s) with this key is/are already present,
# thus each of is seen as possible candidate to strengthen
# or imply the current. in both cases the current guard is
# not emitted and the original is replaced with the current
others = self.strongest_guards.setdefault(key, [])
if len(others) > 0: # (2)
replaced = False
for i,other in enumerate(others):
assert guard is not other
if guard.implies(other, self):
# strengthend
others[i] = guard
self.guards[guard.index] = None # mark as 'do not emit'
guard.inhert_attributes(other)
self.guards[other.index] = guard
replaced = True
continue
elif other.implies(guard, self):
# implied
self.guards[guard.index] = None # mark as 'do not emit'
replaced = True
continue
if not replaced:
others.append(guard)
else: # (2)
others.append(guard)
def eliminate_guards(self, loop):
self.renamer = Renamer()
for i,op in enumerate(loop.operations):
op = loop.operations[i]
if op.is_guard():
if i in self.guards:
# either a stronger guard has been saved
# or it should not be emitted
guard = self.guards[i]
# this guard is implied or marked as not emitted (= None)
self.strength_reduced += 1
if guard is None:
continue
guard.emit_operations(self)
continue
else:
self.emit_operation(op)
continue
if not op.returns_void():
index_var = self.index_vars.get(op, None)
if index_var:
if not index_var.is_identity():
var = index_var.emit_operations(self, op)
self.renamer.start_renaming(op, var)
continue
self.emit_operation(op)
self.renamer.rename(loop.jump)
#
loop.operations = self._newoperations[:]
def propagate_all_forward(self, info, loop, user_code=False):
""" strengthens the guards that protect an integral value """
# the guards are ordered. guards[i] is before guards[j] iff i < j
self.collect_guard_information(loop)
self.eliminate_guards(loop)
#
assert len(info.versions) == 1
version = info.versions[0]
for i,op in enumerate(loop.operations):
if not op.is_guard():
continue
descr = op.getdescr()
if descr and descr.loop_version():
assert isinstance(descr, AbstractFailDescr)
info.track(op, descr, version)
if user_code:
self.eliminate_array_bound_checks(info, loop)
def emit_operation(self, op):
self.renamer.rename(op)
self._newoperations.append(op)
def operation_position(self):
return len(self._newoperations)
def eliminate_array_bound_checks(self, info, loop):
info.mark()
version = None
self._newoperations = []
for key, guards in self.strongest_guards.items():
if len(guards) <= 1:
continue
# there is more than one guard for that key,
# that is why we could imply the guards 2..n
# iff we add invariant guards
one = guards[0]
for other in guards[1:]:
transitive_guard = one.transitive_imply(other, self, loop)
if transitive_guard:
if version is None:
version = info.snapshot(loop)
info.remove(other.op.getdescr())
other.set_to_none(info, loop)
descr = transitive_guard.getdescr()
assert isinstance(descr, AbstractFailDescr)
info.track(transitive_guard, descr, version)
info.clear()
loop.prefix = self._newoperations + loop.prefix
loop.operations = [op for op in loop.operations if op]
class GuardStrengthenOpt(object):
""" Note that this optimization is only used in the vector optimizer (yet) """
def __init__(self, index_vars):
self.index_vars = index_vars
self._newoperations = []
self.strength_reduced = 0 # how many guards could be removed?
self.strongest_guards = {}
self.guards = {}
self.delayed = {}
def collect_guard_information(self, loop):
operations = loop.operations
last_guard = None
for i, op in enumerate(operations):
op = operations[i]
if not op.is_guard():
continue
if op.getopnum() in (rop.GUARD_TRUE, rop.GUARD_FALSE):
guard = Guard.of(op.getarg(0), operations, i, self.index_vars)
if guard is None:
continue
self.record_guard(guard.getleftkey(), guard)
self.record_guard(guard.getrightkey(), guard)
def record_guard(self, key, guard):
if key is None:
return
# the operations are processed from 1..n (forward),
# thus if the key is not present (1), the guard is saved
# (2) guard(s) with this key is/are already present,
# thus each of is seen as possible candidate to strengthen
# or imply the current. in both cases the current guard is
# not emitted and the original is replaced with the current
others = self.strongest_guards.setdefault(key, [])
if len(others) > 0: # (2)
replaced = False
for i, other in enumerate(others):
assert guard is not other
if guard.implies(other, self):
# strengthend
others[i] = guard
self.guards[guard.index] = None # mark as 'do not emit'
guard.inhert_attributes(other)
self.guards[other.index] = guard
replaced = True
continue
elif other.implies(guard, self):
# implied
self.guards[guard.index] = None # mark as 'do not emit'
replaced = True
continue
if not replaced:
others.append(guard)
else: # (2)
others.append(guard)
def eliminate_guards(self, loop):
self.renamer = Renamer()
for i, op in enumerate(loop.operations):
op = loop.operations[i]
if op.is_guard():
if i in self.guards:
# either a stronger guard has been saved
# or it should not be emitted
guard = self.guards[i]
# this guard is implied or marked as not emitted (= None)
self.strength_reduced += 1
if guard is None:
continue
guard.emit_operations(self)
continue
else:
self.emit_operation(op)
continue
if not op.returns_void():
index_var = self.index_vars.get(op, None)
if index_var:
if not index_var.is_identity():
var = index_var.emit_operations(self, op)
self.renamer.start_renaming(op, var)
continue
self.emit_operation(op)
self.renamer.rename(loop.jump)
#
loop.operations = self._newoperations[:]
def propagate_all_forward(self, info, loop, user_code=False):
""" strengthens the guards that protect an integral value """
# the guards are ordered. guards[i] is before guards[j] iff i < j
self.collect_guard_information(loop)
self.eliminate_guards(loop)
#
assert len(info.versions) == 1
version = info.versions[0]
for i, op in enumerate(loop.operations):
if not op.is_guard():
continue
descr = op.getdescr()
if descr and descr.loop_version():
assert isinstance(descr, AbstractFailDescr)
info.track(op, descr, version)
if user_code:
self.eliminate_array_bound_checks(info, loop)
def emit_operation(self, op):
self.renamer.rename(op)
self._newoperations.append(op)
def operation_position(self):
return len(self._newoperations)
def eliminate_array_bound_checks(self, info, loop):
info.mark()
version = None
self._newoperations = []
for key, guards in self.strongest_guards.items():
if len(guards) <= 1:
continue
# there is more than one guard for that key,
# that is why we could imply the guards 2..n
# iff we add invariant guards
one = guards[0]
for other in guards[1:]:
transitive_guard = one.transitive_imply(other, self, loop)
if transitive_guard:
if version is None:
version = info.snapshot(loop)
info.remove(other.op.getdescr())
other.set_to_none(info, loop)
descr = transitive_guard.getdescr()
assert isinstance(descr, AbstractFailDescr)
info.track(transitive_guard, descr, version)
info.clear()
loop.prefix = self._newoperations + loop.prefix
loop.operations = [op for op in loop.operations if op]
class SchedulerState(object):
def __init__(self, cpu, graph):
self.cpu = cpu
self.renamer = Renamer()
self.graph = graph
self.oplist = []
self.worklist = []
self.invariant_oplist = []
self.invariant_vector_vars = []
self.seen = {}
self.delayed = []
def resolve_delayed(self, needs_resolving, delayed, op):
# recursive solving of all delayed objects
if not delayed:
return
args = op.getarglist()
if op.is_guard():
args = args[:] + op.getfailargs()
for arg in args:
if arg is None or arg.is_constant() or arg.is_inputarg():
continue
if arg not in self.seen:
box = self.renamer.rename_box(arg)
needs_resolving[box] = None
indexvars = self.graph.index_vars
i = len(delayed)-1
while i >= 0:
node = delayed[i]
op = node.getoperation()
if op in needs_resolving:
# either it is a normal operation, or we know that there is a linear combination
del needs_resolving[op]
if op in indexvars:
opindexvar = indexvars[op]
# there might be a variable already, that
# calculated the index variable, thus just reuse it
for var, indexvar in indexvars.items():
if indexvar == opindexvar and var in self.seen:
self.renamer.start_renaming(op, var)
break
else:
if opindexvar.calculated_by(op):
# just append this operation
self.seen[op] = None
self.append_to_oplist(op)
else:
# here is an easier way to calculate just this operation
last = op
for operation in opindexvar.get_operations():
self.append_to_oplist(operation)
last = operation
indexvars[last] = opindexvar
self.renamer.start_renaming(op, last)
self.seen[op] = None
self.seen[last] = None
else:
self.resolve_delayed(needs_resolving, delayed, op)
self.append_to_oplist(op)
self.seen[op] = None
if len(delayed) > i:
del delayed[i]
i -= 1
# some times the recursive call can remove several items from delayed,
# thus we correct the index here
if len(delayed) <= i:
i = len(delayed)-1
def append_to_oplist(self, op):
self.renamer.rename(op)
self.oplist.append(op)
def schedule(self):
self.prepare()
Scheduler().walk_and_emit(self)
self.post_schedule()
def post_schedule(self):
loop = self.graph.loop
jump = loop.jump
if self.delayed:
# some operations can be delayed until the jump instruction,
# handle them here
self.resolve_delayed({}, self.delayed, jump)
self.renamer.rename(jump)
loop.operations = self.oplist
def profitable(self):
return True
def prepare(self):
for node in self.graph.nodes:
if node.depends_count() == 0:
self.worklist.insert(0, node)
def try_emit_or_delay(self, node):
if not node.is_imaginary() and node.is_pure():
# this operation might never be emitted. only if it is really needed
self.delay_emit(node)
return
# emit a now!
self.pre_emit(node, True)
self.mark_emitted(node)
if not node.is_imaginary():
op = node.getoperation()
self.seen[op] = None
self.append_to_oplist(op)
def delay_emit(self, node):
""" it has been decided that the operation might be scheduled later """
delayed = node.delayed or []
if node not in delayed:
delayed.append(node)
node.delayed = None
provides = node.provides()
if len(provides) == 0:
for n in delayed:
self.delayed.append(n)
else:
for to in node.provides():
tnode = to.target_node()
self.delegate_delay(tnode, delayed[:])
self.mark_emitted(node)
def delegate_delay(self, node, delayed):
""" Chain up delays, this can reduce many more of the operations """
if node.delayed is None:
node.delayed = delayed
else:
delayedlist = node.delayed
for d in delayed:
if d not in delayedlist:
delayedlist.append(d)
def mark_emitted(state, node, unpack=True):
""" An operation has been emitted, adds new operations to the worklist
whenever their dependency count drops to zero.
Keeps worklist sorted (see priority) """
worklist = state.worklist
provides = node.provides()[:]
for dep in provides: # COPY
target = dep.to
node.remove_edge_to(target)
if not target.emitted and target.depends_count() == 0:
# sorts them by priority
i = len(worklist)-1
while i >= 0:
cur = worklist[i]
c = (cur.priority - target.priority)
if c < 0: # meaning itnode.priority < target.priority:
worklist.insert(i+1, target)
break
elif c == 0:
# if they have the same priority, sort them
# using the original position in the trace
if target.getindex() < cur.getindex():
worklist.insert(i+1, target)
break
i -= 1
else:
worklist.insert(0, target)
node.clear_dependencies()
node.emitted = True
if not node.is_imaginary():
op = node.getoperation()
state.renamer.rename(op)
if unpack:
state.ensure_args_unpacked(op)
state.post_emit(node)
def delay(self, node):
return False
def has_more(self):
return len(self.worklist) > 0
def ensure_args_unpacked(self, op):
pass
def post_emit(self, node):
pass
def pre_emit(self, orignode, pack_first=True):
delayed = orignode.delayed
if delayed:
# there are some nodes that have been delayed just for this operation
if pack_first:
op = orignode.getoperation()
self.resolve_delayed({}, delayed, op)
for node in delayed:
op = node.getoperation()
if op in self.seen:
continue
if node is not None:
provides = node.provides()
if len(provides) == 0:
# add this node to the final delay list
# might be emitted before jump!
self.delayed.append(node)
else:
for to in node.provides():
tnode = to.target_node()
self.delegate_delay(tnode, [node])
orignode.delayed = None
