def export_state(self, targetop):
original_jump_args = targetop.getarglist()
jump_args = [
self.getvalue(a).get_key_box() for a in original_jump_args
]
assert self.optimizer.loop.resume_at_jump_descr
resume_at_jump_descr = self.optimizer.loop.resume_at_jump_descr.clone_if_mutable(
)
assert isinstance(resume_at_jump_descr, ResumeGuardDescr)
resume_at_jump_descr.rd_snapshot = self.fix_snapshot(
jump_args, resume_at_jump_descr.rd_snapshot)
modifier = VirtualStateAdder(self.optimizer)
virtual_state = modifier.get_virtual_state(jump_args)
values = [self.getvalue(arg) for arg in jump_args]
inputargs = virtual_state.make_inputargs(values, self.optimizer)
short_inputargs = virtual_state.make_inputargs(values,
self.optimizer,
keyboxes=True)
if self.boxes_created_this_iteration is not None:
for box in self.inputargs:
self.boxes_created_this_iteration[box] = True
short_boxes = ShortBoxes(self.optimizer, inputargs,
self.boxes_created_this_iteration)
self.optimizer.clear_newoperations()
for i in range(len(original_jump_args)):
if values[i].is_virtual():
values[i].force_box(self.optimizer)
if original_jump_args[i] is not jump_args[i]:
op = ResOperation(rop.SAME_AS, [jump_args[i]],
original_jump_args[i])
self.optimizer.emit_operation(op)
inputarg_setup_ops = self.optimizer.get_newoperations()
target_token = targetop.getdescr()
assert isinstance(target_token, TargetToken)
targetop.initarglist(inputargs)
target_token.virtual_state = virtual_state
target_token.short_preamble = [
ResOperation(rop.LABEL, short_inputargs, None)
]
target_token.resume_at_jump_descr = resume_at_jump_descr
exported_values = {}
for box in inputargs:
exported_values[box] = self.optimizer.getvalue(box)
for op in short_boxes.operations():
if op and op.result:
box = op.result
exported_values[box] = self.optimizer.getvalue(box)
target_token.exported_state = ExportedState(short_boxes,
inputarg_setup_ops,
exported_values)
def export_state(self, targetop):
original_jump_args = targetop.getarglist()
jump_args = [self.getvalue(a).get_key_box() for a in original_jump_args]
assert self.optimizer.loop.resume_at_jump_descr
resume_at_jump_descr = self.optimizer.loop.resume_at_jump_descr.clone_if_mutable()
assert isinstance(resume_at_jump_descr, ResumeGuardDescr)
resume_at_jump_descr.rd_snapshot = self.fix_snapshot(jump_args, resume_at_jump_descr.rd_snapshot)
modifier = VirtualStateAdder(self.optimizer)
virtual_state = modifier.get_virtual_state(jump_args)
values = [self.getvalue(arg) for arg in jump_args]
inputargs = virtual_state.make_inputargs(values, self.optimizer)
short_inputargs = virtual_state.make_inputargs(values, self.optimizer, keyboxes=True)
if self.boxes_created_this_iteration is not None:
for box in self.inputargs:
self.boxes_created_this_iteration[box] = True
short_boxes = ShortBoxes(self.optimizer, inputargs,
self.boxes_created_this_iteration)
self.optimizer.clear_newoperations()
for i in range(len(original_jump_args)):
if values[i].is_virtual():
values[i].force_box(self.optimizer)
if original_jump_args[i] is not jump_args[i]:
op = ResOperation(rop.SAME_AS, [jump_args[i]], original_jump_args[i])
self.optimizer.emit_operation(op)
inputarg_setup_ops = self.optimizer.get_newoperations()
target_token = targetop.getdescr()
assert isinstance(target_token, TargetToken)
targetop.initarglist(inputargs)
target_token.virtual_state = virtual_state
target_token.short_preamble = [ResOperation(rop.LABEL, short_inputargs, None)]
target_token.resume_at_jump_descr = resume_at_jump_descr
exported_values = {}
for box in inputargs:
exported_values[box] = self.optimizer.getvalue(box)
for op in short_boxes.operations():
if op and op.result:
box = op.result
exported_values[box] = self.optimizer.getvalue(box)
target_token.exported_state = ExportedState(short_boxes, inputarg_setup_ops,
exported_values)
def jump_to_already_compiled_trace(self, jumpop):
assert jumpop.getopnum() == rop.JUMP
cell_token = jumpop.getdescr()
assert isinstance(cell_token, JitCellToken)
if not cell_token.target_tokens:
return False
if not self.inline_short_preamble:
assert cell_token.target_tokens[0].virtual_state is None
jumpop.setdescr(cell_token.target_tokens[0])
self.optimizer.send_extra_operation(jumpop)
return True
args = jumpop.getarglist()
modifier = VirtualStateAdder(self.optimizer)
virtual_state = modifier.get_virtual_state(args)
debug_start('jit-log-virtualstate')
virtual_state.debug_print("Looking for ")
for target in cell_token.target_tokens:
if not target.virtual_state:
continue
ok = False
extra_guards = []
bad = {}
debugmsg = 'Did not match '
if target.virtual_state.generalization_of(virtual_state, bad):
ok = True
debugmsg = 'Matched '
else:
try:
cpu = self.optimizer.cpu
target.virtual_state.generate_guards(virtual_state,
args, cpu,
extra_guards)
ok = True
debugmsg = 'Guarded to match '
except InvalidLoop:
pass
target.virtual_state.debug_print(debugmsg, bad)
if ok:
debug_stop('jit-log-virtualstate')
values = [self.getvalue(arg)
for arg in jumpop.getarglist()]
args = target.virtual_state.make_inputargs(values, self.optimizer,
keyboxes=True)
short_inputargs = target.short_preamble[0].getarglist()
inliner = Inliner(short_inputargs, args)
for guard in extra_guards:
if guard.is_guard():
descr = target.resume_at_jump_descr.clone_if_mutable()
inliner.inline_descr_inplace(descr)
guard.setdescr(descr)
self.optimizer.send_extra_operation(guard)
try:
for shop in target.short_preamble[1:]:
newop = inliner.inline_op(shop)
self.optimizer.send_extra_operation(newop)
except InvalidLoop:
debug_print("Inlining failed unexpectedly",
"jumping to preamble instead")
assert cell_token.target_tokens[0].virtual_state is None
jumpop.setdescr(cell_token.target_tokens[0])
self.optimizer.send_extra_operation(jumpop)
return True
debug_stop('jit-log-virtualstate')
return False
def close_loop(self, start_label, jumpop):
virtual_state = self.initial_virtual_state
short_inputargs = self.short[0].getarglist()
inputargs = self.inputargs
short_jumpargs = inputargs[:]
# Construct jumpargs from the virtual state
original_jumpargs = jumpop.getarglist()[:]
values = [self.getvalue(arg) for arg in jumpop.getarglist()]
try:
jumpargs = virtual_state.make_inputargs(values, self.optimizer)
except BadVirtualState:
raise InvalidLoop
jumpop.initarglist(jumpargs)
# Inline the short preamble at the end of the loop
jmp_to_short_args = virtual_state.make_inputargs(values, self.optimizer, keyboxes=True)
assert len(short_inputargs) == len(jmp_to_short_args)
args = {}
for i in range(len(short_inputargs)):
if short_inputargs[i] in args:
if args[short_inputargs[i]] != jmp_to_short_args[i]:
raise InvalidLoop
args[short_inputargs[i]] = jmp_to_short_args[i]
self.short_inliner = Inliner(short_inputargs, jmp_to_short_args)
for op in self.short[1:]:
newop = self.short_inliner.inline_op(op)
self.optimizer.send_extra_operation(newop)
# Import boxes produced in the preamble but used in the loop
newoperations = self.optimizer.get_newoperations()
self.boxes_created_this_iteration = {}
i = j = 0
while i < len(newoperations) or j < len(jumpargs):
if i == len(newoperations):
while j < len(jumpargs):
a = jumpargs[j]
if self.optimizer.loop.logops:
debug_print('J: ' + self.optimizer.loop.logops.repr_of_arg(a))
self.import_box(a, inputargs, short_jumpargs, jumpargs)
j += 1
else:
op = newoperations[i]
self.boxes_created_this_iteration[op.result] = True
args = op.getarglist()
if op.is_guard():
args = args + op.getfailargs()
if self.optimizer.loop.logops:
debug_print('OP: ' + self.optimizer.loop.logops.repr_of_resop(op))
for a in args:
if self.optimizer.loop.logops:
debug_print('A: ' + self.optimizer.loop.logops.repr_of_arg(a))
self.import_box(a, inputargs, short_jumpargs, jumpargs)
i += 1
newoperations = self.optimizer.get_newoperations()
jumpop.initarglist(jumpargs)
self.optimizer.send_extra_operation(jumpop)
self.short.append(ResOperation(rop.JUMP, short_jumpargs, None, descr=jumpop.getdescr()))
# Verify that the virtual state at the end of the loop is one
# that is compatible with the virtual state at the start of the loop
modifier = VirtualStateAdder(self.optimizer)
final_virtual_state = modifier.get_virtual_state(original_jumpargs)
debug_start('jit-log-virtualstate')
virtual_state.debug_print('Closed loop with ')
bad = {}
if not virtual_state.generalization_of(final_virtual_state, bad):
# We ended up with a virtual state that is not compatible
# and we are thus unable to jump to the start of the loop
final_virtual_state.debug_print("Bad virtual state at end of loop, ",
bad)
debug_stop('jit-log-virtualstate')
raise InvalidLoop
debug_stop('jit-log-virtualstate')
maxguards = self.optimizer.metainterp_sd.warmrunnerdesc.memory_manager.max_retrace_guards
if self.optimizer.emitted_guards > maxguards:
target_token = jumpop.getdescr()
assert isinstance(target_token, TargetToken)
target_token.targeting_jitcell_token.retraced_count = sys.maxint
self.finilize_short_preamble(start_label)
def import_state(self, targetop):
if not targetop: # Trace did not start with a label
self.inputargs = self.optimizer.loop.inputargs
self.short = None
self.initial_virtual_state = None
return
self.inputargs = targetop.getarglist()
target_token = targetop.getdescr()
assert isinstance(target_token, TargetToken)
exported_state = target_token.exported_state
if not exported_state:
# No state exported, construct one without virtuals
self.short = None
modifier = VirtualStateAdder(self.optimizer)
virtual_state = modifier.get_virtual_state(self.inputargs)
self.initial_virtual_state = virtual_state
return
self.short = target_token.short_preamble[:]
self.short_seen = {}
self.short_boxes = exported_state.short_boxes
self.short_resume_at_jump_descr = target_token.resume_at_jump_descr
self.initial_virtual_state = target_token.virtual_state
seen = {}
for box in self.inputargs:
if box in seen:
continue
seen[box] = True
preamble_value = exported_state.exported_values[box]
value = self.optimizer.getvalue(box)
value.import_from(preamble_value, self.optimizer)
# Setup the state of the new optimizer by emiting the
# short operations and discarding the result
self.optimizer.emitting_dissabled = True
for op in exported_state.inputarg_setup_ops:
self.optimizer.send_extra_operation(op)
seen = {}
for op in self.short_boxes.operations():
self.ensure_short_op_emitted(op, self.optimizer, seen)
if op and op.result:
preamble_value = exported_state.exported_values[op.result]
value = self.optimizer.getvalue(op.result)
if not value.is_virtual():
imp = ValueImporter(self, preamble_value, op)
self.optimizer.importable_values[value] = imp
newvalue = self.optimizer.getvalue(op.result)
newresult = newvalue.get_key_box()
# note that emitting here SAME_AS should not happen, but
# in case it does, we would prefer to be suboptimal in asm
# to a fatal RPython exception.
if newresult is not op.result and not newvalue.is_constant():
op = ResOperation(rop.SAME_AS, [op.result], newresult)
self.optimizer._newoperations.append(op)
if self.optimizer.loop.logops:
debug_print(' Falling back to add extra: ' +
self.optimizer.loop.logops.repr_of_resop(op))
self.optimizer.flush()
self.optimizer.emitting_dissabled = False
def propagate_all_forward(self):
loop = self.optimizer.loop
jumpop = loop.operations[-1]
if jumpop.getopnum() == rop.JUMP:
loop.operations = loop.operations[:-1]
else:
loopop = None
self.optimizer.propagate_all_forward()
if jumpop:
assert jumpop.getdescr() is loop.token
jump_args = jumpop.getarglist()
jumpop.initarglist([])
self.optimizer.flush()
KillHugeIntBounds(self.optimizer).apply()
loop.preamble.operations = self.optimizer.newoperations
jump_args = [self.getvalue(a).get_key_box() for a in jump_args]
start_resumedescr = loop.preamble.start_resumedescr.clone_if_mutable()
self.start_resumedescr = start_resumedescr
assert isinstance(start_resumedescr, ResumeGuardDescr)
start_resumedescr.rd_snapshot = self.fix_snapshot(loop, jump_args, start_resumedescr.rd_snapshot)
modifier = VirtualStateAdder(self.optimizer)
virtual_state = modifier.get_virtual_state(jump_args)
values = [self.getvalue(arg) for arg in jump_args]
inputargs = virtual_state.make_inputargs(values)
short_inputargs = virtual_state.make_inputargs(values, keyboxes=True)
self.constant_inputargs = {}
for box in jump_args:
const = self.get_constant_box(box)
if const:
self.constant_inputargs[box] = const
sb = ShortBoxes(self.optimizer, inputargs + self.constant_inputargs.keys())
self.short_boxes = sb
preamble_optimizer = self.optimizer
loop.preamble.quasi_immutable_deps = self.optimizer.quasi_immutable_deps
self.optimizer = self.optimizer.new()
loop.quasi_immutable_deps = self.optimizer.quasi_immutable_deps
logops = self.optimizer.loop.logops
if logops:
args = ", ".join([logops.repr_of_arg(arg) for arg in inputargs])
debug_print("inputargs: " + args)
args = ", ".join([logops.repr_of_arg(arg) for arg in short_inputargs])
debug_print("short inputargs: " + args)
self.short_boxes.debug_print(logops)
# Force virtuals amoung the jump_args of the preamble to get the
# operations needed to setup the proper state of those virtuals
# in the peeled loop
inputarg_setup_ops = []
preamble_optimizer.newoperations = []
seen = {}
for box in inputargs:
if box in seen:
continue
seen[box] = True
value = preamble_optimizer.getvalue(box)
inputarg_setup_ops.extend(value.make_guards(box))
for box in short_inputargs:
if box in seen:
continue
seen[box] = True
value = preamble_optimizer.getvalue(box)
value.force_box()
preamble_optimizer.flush()
inputarg_setup_ops += preamble_optimizer.newoperations
# Setup the state of the new optimizer by emiting the
# short preamble operations and discarding the result
self.optimizer.emitting_dissabled = True
for op in inputarg_setup_ops:
self.optimizer.send_extra_operation(op)
seen = {}
for op in self.short_boxes.operations():
self.ensure_short_op_emitted(op, self.optimizer, seen)
if op and op.result:
# The order of these guards is not important as
# self.optimizer.emitting_dissabled is False
value = preamble_optimizer.getvalue(op.result)
for guard in value.make_guards(op.result):
self.optimizer.send_extra_operation(guard)
newresult = self.optimizer.getvalue(op.result).get_key_box()
if newresult is not op.result:
self.short_boxes.alias(newresult, op.result)
self.optimizer.flush()
self.optimizer.emitting_dissabled = False
# XXX Hack to prevent the arraylen/strlen/unicodelen ops generated
# by value.make_guards() from ending up in pure_operations
for key, op in self.optimizer.pure_operations.items():
if not self.short_boxes.has_producer(op.result):
del self.optimizer.pure_operations[key]
initial_inputargs_len = len(inputargs)
self.inliner = Inliner(loop.inputargs, jump_args)
short = self.inline(inputargs, self.cloned_operations, loop.inputargs, short_inputargs, virtual_state)
loop.inputargs = inputargs
args = [preamble_optimizer.getvalue(self.short_boxes.original(a)).force_box() for a in inputargs]
jmp = ResOperation(rop.JUMP, args, None)
jmp.setdescr(loop.token)
loop.preamble.operations.append(jmp)
loop.operations = self.optimizer.newoperations
maxguards = self.optimizer.metainterp_sd.warmrunnerdesc.memory_manager.max_retrace_guards
if self.optimizer.emitted_guards > maxguards:
loop.preamble.token.retraced_count = sys.maxint
if short:
assert short[-1].getopnum() == rop.JUMP
short[-1].setdescr(loop.token)
# Turn guards into conditional jumps to the preamble
for i in range(len(short)):
op = short[i]
if op.is_guard():
op = op.clone()
op.setfailargs(None)
descr = self.start_resumedescr.clone_if_mutable()
op.setdescr(descr)
short[i] = op
short_loop = TreeLoop("short preamble")
short_loop.inputargs = short_inputargs
short_loop.operations = short
# Clone ops and boxes to get private versions and
boxmap = {}
newargs = [None] * len(short_loop.inputargs)
for i in range(len(short_loop.inputargs)):
a = short_loop.inputargs[i]
if a in boxmap:
newargs[i] = boxmap[a]
else:
newargs[i] = a.clonebox()
boxmap[a] = newargs[i]
inliner = Inliner(short_loop.inputargs, newargs)
for box, const in self.constant_inputargs.items():
inliner.argmap[box] = const
short_loop.inputargs = newargs
ops = [inliner.inline_op(op) for op in short_loop.operations]
short_loop.operations = ops
descr = self.start_resumedescr.clone_if_mutable()
inliner.inline_descr_inplace(descr)
short_loop.start_resumedescr = descr
assert isinstance(loop.preamble.token, LoopToken)
if loop.preamble.token.short_preamble:
loop.preamble.token.short_preamble.append(short_loop)
else:
loop.preamble.token.short_preamble = [short_loop]
short_loop.virtual_state = virtual_state
# Forget the values to allow them to be freed
for box in short_loop.inputargs:
box.forget_value()
for op in short_loop.operations:
if op.result:
op.result.forget_value()
def propagate_forward(self, op):
if op.getopnum() == rop.JUMP:
loop_token = op.getdescr()
assert isinstance(loop_token, LoopToken)
# FIXME: Use a tree, similar to the tree formed by the full
# preamble and it's bridges, instead of a list to save time and
# memory. This should also allow better behaviour in
# situations that the is_emittable() chain currently cant
# handle and the inlining fails unexpectedly belwo.
short = loop_token.short_preamble
if short:
args = op.getarglist()
modifier = VirtualStateAdder(self.optimizer)
virtual_state = modifier.get_virtual_state(args)
debug_start("jit-log-virtualstate")
virtual_state.debug_print("Looking for ")
for sh in short:
ok = False
extra_guards = []
bad = {}
debugmsg = "Did not match "
if sh.virtual_state.generalization_of(virtual_state, bad):
ok = True
debugmsg = "Matched "
else:
try:
cpu = self.optimizer.cpu
sh.virtual_state.generate_guards(virtual_state, args, cpu, extra_guards)
ok = True
debugmsg = "Guarded to match "
except InvalidLoop:
pass
sh.virtual_state.debug_print(debugmsg, bad)
if ok:
debug_stop("jit-log-virtualstate")
values = [self.getvalue(arg) for arg in op.getarglist()]
args = sh.virtual_state.make_inputargs(values, keyboxes=True)
inliner = Inliner(sh.inputargs, args)
for guard in extra_guards:
if guard.is_guard():
descr = sh.start_resumedescr.clone_if_mutable()
inliner.inline_descr_inplace(descr)
guard.setdescr(descr)
self.emit_operation(guard)
try:
for shop in sh.operations:
newop = inliner.inline_op(shop)
self.emit_operation(newop)
except InvalidLoop:
debug_print("Inlining failed unexpectedly", "jumping to preamble instead")
self.emit_operation(op)
return
debug_stop("jit-log-virtualstate")
retraced_count = loop_token.retraced_count
limit = self.optimizer.metainterp_sd.warmrunnerdesc.memory_manager.retrace_limit
if not self.retraced and retraced_count < limit:
loop_token.retraced_count += 1
if not loop_token.failed_states:
debug_print("Retracing (%d of %d)" % (retraced_count, limit))
raise RetraceLoop
for failed in loop_token.failed_states:
if failed.generalization_of(virtual_state):
# Retracing once more will most likely fail again
break
else:
debug_print("Retracing (%d of %d)" % (retraced_count, limit))
raise RetraceLoop
else:
if not loop_token.failed_states:
loop_token.failed_states = [virtual_state]
else:
loop_token.failed_states.append(virtual_state)
self.emit_operation(op)
def inline(self, inputargs, loop_operations, loop_args, short_inputargs, virtual_state):
inliner = self.inliner
short_jumpargs = inputargs[:]
short = []
short_seen = {}
for box, const in self.constant_inputargs.items():
short_seen[box] = True
for op in self.short_boxes.operations():
if op is not None:
if len(self.getvalue(op.result).make_guards(op.result)) > 0:
self.add_op_to_short(op, short, short_seen, False, True)
# This loop is equivalent to the main optimization loop in
# Optimizer.propagate_all_forward
jumpop = None
for newop in loop_operations:
newop = inliner.inline_op(newop, clone=False)
if newop.getopnum() == rop.JUMP:
jumpop = newop
break
# self.optimizer.first_optimization.propagate_forward(newop)
self.optimizer.send_extra_operation(newop)
self.boxes_created_this_iteration = {}
assert jumpop
original_jumpargs = jumpop.getarglist()[:]
values = [self.getvalue(arg) for arg in jumpop.getarglist()]
jumpargs = virtual_state.make_inputargs(values)
jumpop.initarglist(jumpargs)
jmp_to_short_args = virtual_state.make_inputargs(values, keyboxes=True)
self.short_inliner = Inliner(short_inputargs, jmp_to_short_args)
for box, const in self.constant_inputargs.items():
self.short_inliner.argmap[box] = const
for op in short:
newop = self.short_inliner.inline_op(op)
self.optimizer.send_extra_operation(newop)
self.optimizer.flush()
i = j = 0
while i < len(self.optimizer.newoperations) or j < len(jumpargs):
if i == len(self.optimizer.newoperations):
while j < len(jumpargs):
a = jumpargs[j]
if self.optimizer.loop.logops:
debug_print("J: " + self.optimizer.loop.logops.repr_of_arg(a))
self.import_box(a, inputargs, short, short_jumpargs, jumpargs, short_seen)
j += 1
else:
op = self.optimizer.newoperations[i]
self.boxes_created_this_iteration[op.result] = True
args = op.getarglist()
if op.is_guard():
args = args + op.getfailargs()
if self.optimizer.loop.logops:
debug_print("OP: " + self.optimizer.loop.logops.repr_of_resop(op))
for a in args:
if self.optimizer.loop.logops:
debug_print("A: " + self.optimizer.loop.logops.repr_of_arg(a))
self.import_box(a, inputargs, short, short_jumpargs, jumpargs, short_seen)
i += 1
jumpop.initarglist(jumpargs)
self.optimizer.send_extra_operation(jumpop)
short.append(ResOperation(rop.JUMP, short_jumpargs, None))
modifier = VirtualStateAdder(self.optimizer)
final_virtual_state = modifier.get_virtual_state(original_jumpargs)
debug_start("jit-log-virtualstate")
virtual_state.debug_print("Closed loop with ")
bad = {}
if not virtual_state.generalization_of(final_virtual_state, bad):
# We ended up with a virtual state that is not compatible
# and we are thus unable to jump to the start of the loop
# XXX Is it possible to end up here? If so, consider:
# - Fallback on having the preamble jump to itself?
# - Would virtual_state.generate_guards make sense here?
final_virtual_state.debug_print("Bad virtual state at end of loop, ", bad)
debug_stop("jit-log-virtualstate")
raise InvalidLoop
debug_stop("jit-log-virtualstate")
return short
def propagate_forward(self, op):
if op.getopnum() == rop.JUMP:
loop_token = op.getdescr()
assert isinstance(loop_token, LoopToken)
short = loop_token.short_preamble
if short:
args = op.getarglist()
modifier = VirtualStateAdder(self.optimizer)
virtual_state = modifier.get_virtual_state(args)
debug_start('jit-log-virtualstate')
virtual_state.debug_print("Looking for ")
for sh in short:
ok = False
extra_guards = []
bad = {}
debugmsg = 'Did not match '
if sh.virtual_state.generalization_of(virtual_state, bad):
ok = True
debugmsg = 'Matched '
else:
try:
cpu = self.optimizer.cpu
sh.virtual_state.generate_guards(virtual_state,
args, cpu,
extra_guards)
ok = True
debugmsg = 'Guarded to match '
except InvalidLoop:
pass
sh.virtual_state.debug_print(debugmsg, bad)
if ok:
debug_stop('jit-log-virtualstate')
values = [self.getvalue(arg)
for arg in op.getarglist()]
args = sh.virtual_state.make_inputargs(values, self.optimizer,
keyboxes=True)
inliner = Inliner(sh.inputargs, args)
for guard in extra_guards:
if guard.is_guard():
descr = sh.start_resumedescr.clone_if_mutable()
inliner.inline_descr_inplace(descr)
guard.setdescr(descr)
self.emit_operation(guard)
try:
for shop in sh.operations:
newop = inliner.inline_op(shop)
self.emit_operation(newop)
except InvalidLoop:
debug_print("Inlining failed unexpectedly",
"jumping to preamble instead")
self.emit_operation(op)
return
debug_stop('jit-log-virtualstate')
retraced_count = loop_token.retraced_count
limit = self.optimizer.metainterp_sd.warmrunnerdesc.memory_manager.retrace_limit
if not self.retraced and retraced_count<limit:
loop_token.retraced_count += 1
if not loop_token.failed_states:
debug_print("Retracing (%d of %d)" % (retraced_count,
limit))
raise RetraceLoop
for failed in loop_token.failed_states:
if failed.generalization_of(virtual_state):
# Retracing once more will most likely fail again
break
else:
debug_print("Retracing (%d of %d)" % (retraced_count,
limit))
raise RetraceLoop
else:
if not loop_token.failed_states:
loop_token.failed_states=[virtual_state]
else:
loop_token.failed_states.append(virtual_state)
self.emit_operation(op)
