def build(self, cache, size):
for (e, pool) in self.wrapped_builder.build(cache, size):
try:
orig = e
# print(hasattr(orig, "_tag"), file=sys.stderr)
e = fixup_binders(e, self.binders_to_use)
for a in ATTRS_TO_PRESERVE:
if hasattr(orig, a):
setattr(e, a, getattr(orig, a))
except Exception:
_on_exp(e, "unable to rename binders")
continue
print("WARNING: skipping built expression {}".format(pprint(e)), file=sys.stderr)
if reject_symmetric_binops.value and size > 1 and isinstance(e, EBinOp) and e.op in COMMUTATIVE_OPERATORS and e.e2 < e.e1:
_on_exp(e, "rejecting symmetric use of commutative operator")
continue
try:
# Acceleration rules can produce arbitrary expressions, so we
# need to recursively check them. The regular grammar only
# produces expressions "one level deep"---all subexpressions
# have already been checked.
if hasattr(e, "_accel"):
exp_wf(e, self.state_vars, self.args, pool, assumptions=self.assumptions)
else:
exp_wf_nonrecursive(e, self.state_vars, self.args, pool, assumptions=self.assumptions)
except ExpIsNotWf as exc:
_on_exp(e, exc.reason, exc.offending_subexpression)
continue
yield (e, pool)
def check_wf(e, ctx, pool):
with task("checking well-formedness", size=e.size()):
try:
exp_wf(e,
pool=pool,
context=ctx,
assumptions=self.assumptions,
solver=self.wf_solver)
except ExpIsNotWf as exc:
return No("at {}: {}".format(
pprint(exc.offending_subexpression), exc.reason))
for (sub, sub_ctx, sub_pool) in shred(e, ctx, pool):
res = good_idea(self.wf_solver,
sub,
sub_ctx,
sub_pool,
assumptions=self.assumptions)
if not res:
return res
if pool == RUNTIME_POOL and self.cost_model.compare(
e, self.targets[0], ctx, pool) == Order.GT:
# from cozy.cost_model import debug_comparison
# debug_comparison(self.cost_model, e, self.target, ctx)
return No("too expensive")
# if isinstance(e.type, TBag):
# c = self.cost_model.cardinality(e)
# if all(cc < c for cc in cards):
# # print("too big: {}".format(pprint(e)))
# return No("too big")
return True
def test_repair_regression01(self):
e = EBinOp(EMapKeys(EMakeMap2(EVar('conns').with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), ELambda(EVar('_var17561').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), EBool(True).with_type(TBool()))).with_type(TMap(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))), TBool()))).with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), '-', EMapKeys(ELet(EUnaryOp('the', EMap(EFilter(EVar('conns').with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), ELambda(EVar('c').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), EBinOp(EGetField(EVar('c').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), 'conn_iface').with_type(TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), '==', EVar('i').with_type(TNative('mongo::executor::ConnectionPool::ConnectionInterface*'))).with_type(TBool()))).with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), ELambda(EVar('c').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), EVar('c').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))))).with_type(TList(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))))).with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), ELambda(EVar('c').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), EMakeMap2(EBinOp(EBinOp(EVar('conns').with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), '-', ESingleton(EVar('c').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))).with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))))).with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), '+', ESingleton(EMakeRecord((('conn_state', EEnumEntry('READY').with_type(TEnum(('READY', 'PROCESSING', 'CHECKED_OUT')))), ('conn_host', EGetField(EVar('c').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), 'conn_host').with_type(TNative('mongo::HostAndPort'))), ('conn_iface', EGetField(EVar('c').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), 'conn_iface').with_type(TNative('mongo::executor::ConnectionPool::ConnectionInterface*'))), ('conn_next_refresh', ECall('after', (EVar('lastUsed').with_type(TNative('mongo::Date_t')), EVar('refreshRequirement').with_type(TNative('mongo::Milliseconds')))).with_type(TNative('mongo::Date_t'))), ('conn_returned', EVar('now').with_type(TNative('mongo::Date_t'))), ('conn_last_used', EVar('retId').with_type(TInt())), ('conn_dropped', EGetField(EVar('c').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), 'conn_dropped').with_type(TBool())))).with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))).with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))))).with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), ELambda(EVar('_var17561').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), EBool(True).with_type(TBool()))).with_type(TMap(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))), TBool())))).with_type(TMap(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))), TBool()))).with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))))).with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))))
context = RootCtx(state_vars=OrderedSet([EVar('minConnections').with_type(TInt()), EVar('maxConnections').with_type(TInt()), EVar('maxConnecting').with_type(TInt()), EVar('refreshTimeout').with_type(TNative('mongo::Milliseconds')), EVar('refreshRequirement').with_type(TNative('mongo::Milliseconds')), EVar('hostTimeout').with_type(TNative('mongo::Milliseconds')), EVar('conns').with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), EVar('reqs').with_type(TBag(TRecord((('rq_callback', TNative('mongo::executor::ConnectionPool::GetConnectionCallback*')), ('rq_expiration', TNative('mongo::Date_t')), ('rq_host', TNative('mongo::HostAndPort')))))), EVar('_idleHosts').with_type(TBag(TRecord((('host_id', TNative('mongo::HostAndPort')), ('host_timeout', TNative('mongo::Date_t')))))), EVar('retId').with_type(TInt())]), args=OrderedSet([EVar('i').with_type(TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), EVar('lastUsed').with_type(TNative('mongo::Date_t')), EVar('now').with_type(TNative('mongo::Date_t'))]), funcs=OrderedDict([('eternity', TFunc((), TNative('mongo::Date_t'))), ('after', TFunc((TNative('mongo::Date_t'), TNative('mongo::Milliseconds')), TNative('mongo::Date_t'))), ('nullConn', TFunc((), TNative('mongo::executor::ConnectionPool::ConnectionInterface*'))), ('nullReq', TFunc((), TNative('mongo::executor::ConnectionPool::GetConnectionCallback*')))]))
assert not exp_wf(e, context=context, pool=RUNTIME_POOL)
extra_state = [EVar('reqs').with_type(TBag(TRecord((('rq_callback', TNative('mongo::executor::ConnectionPool::GetConnectionCallback*')), ('rq_expiration', TNative('mongo::Date_t')), ('rq_host', TNative('mongo::HostAndPort')))))), EVar('conns').with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), EVar('hostTimeout').with_type(TNative('mongo::Milliseconds')), EVar('refreshRequirement').with_type(TNative('mongo::Milliseconds')), EVar('retId').with_type(TInt()), EMakeMap2(EVar('conns').with_type(TBag(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))))), ELambda(EVar('_var17561').with_type(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool())))), EBool(True).with_type(TBool()))).with_type(TMap(TRecord((('conn_state', TEnum(('READY', 'PROCESSING', 'CHECKED_OUT'))), ('conn_host', TNative('mongo::HostAndPort')), ('conn_iface', TNative('mongo::executor::ConnectionPool::ConnectionInterface*')), ('conn_next_refresh', TNative('mongo::Date_t')), ('conn_returned', TNative('mongo::Date_t')), ('conn_last_used', TInt()), ('conn_dropped', TBool()))), TBool()))]
e_prime = repair_well_formedness(e, context, extra_state)
assert exp_wf(e_prime, context=context, pool=RUNTIME_POOL)
def test_heap_wf(self):
e = EHeapPeek2(EStateVar(EMakeMinHeap(EVar('xs'), ELambda(EVar('_var21501'), EVar('_var21501')))))
assert retypecheck(e, env={
"xs": INT_BAG,
"_var21501": INT})
state_vars = OrderedSet([EVar('xs').with_type(TBag(TInt()))])
args = OrderedSet([EVar('x').with_type(TInt())])
pool = RUNTIME_POOL
assert exp_wf(e, context=RootCtx(args=args, state_vars=state_vars), pool=pool)
def check_wf(e, ctx, pool):
with task("pruning", size=e.size()):
is_wf = exp_wf(e, pool=pool, context=ctx, solver=wf_solver)
if not is_wf:
return is_wf
res = possibly_useful(wf_solver, e, ctx, pool, ops=ops)
if not res:
return res
if cost_pruning.value and pool == RUNTIME_POOL and cost_model.compare(e, targets[0], ctx, pool) == Order.GT:
return No("too expensive")
return True
def check_wf(e, ctx, pool):
with task("pruning", size=e.size()):
is_wf = exp_wf(e, pool=pool, context=ctx, solver=wf_solver)
if not is_wf:
return is_wf
res = possibly_useful(wf_solver, e, ctx, pool, ops=ops)
if not res:
return res
if cost_pruning.value and pool == RUNTIME_POOL and cost_model.compare(
e, targets[0], ctx, pool) == Order.GT:
return No("too expensive")
return True
def pre_optimize(self, e, pool):
"""
Optimize `e` by replacing its subexpressions with the best cached
versions available (or leaving them untouched if they are new).
"""
if not hasattr(e, "_accel"):
return e
top_level = e
class V(BottomUpRewriter):
def visit_EStateVar(_, e):
return EStateVar(self.pre_optimize(e.e, STATE_POOL)).with_type(e.type)
def visit_ELambda(_, e):
if e.arg not in self.binders and e.arg in free_vars(e.body):
# Derp! Someone made an expression that uses an illegal
# binder. There is no way to compute a fingerprint for the
# body, unfortunately, so we just stop here.
return e
return ELambda(e.arg, super().visit_ADT(e.body)) # optimize children
def visit_Exp(_, e): # do not shadow `self`
if e is top_level:
return super().visit_ADT(e) # optimize children
fp = self._fingerprint(e)
prev = self.seen.find_one(pool, fp)
if prev is None:
return super().visit_ADT(e) # optimize children
prev_exp, prev_size, prev_cost = prev
if prev_exp == e:
return prev_exp
cost = self.cost_model.cost(e, pool)
ordering = self.compare_costs(cost, prev_cost)
if ordering == Cost.BETTER:
return super().visit_ADT(e) # optimize children
else:
# NOTE: no need to optimize children; if it is cached, then
# it is presumably already the best possible.
# if not alpha_equivalent(e, prev_exp):
# print("*** rewriting {} to {}".format(pprint(e), pprint(prev_exp)), file=sys.stderr)
return prev_exp
res = None
try:
res = V().visit(e)
assert exp_wf(res, state_vars=self.state_vars, args=self.args, pool=pool, assumptions=self.assumptions)
if hasattr(e, "_tag"):
res._tag = e._tag
return res
except:
traceback.print_exc(file=sys.stdout)
print("FAILED TO PREOPTIMIZE {} ---> {}".format(pprint(e), pprint(res)))
print(repr(e))
return e
def check_wf(e, ctx, pool):
with task("checking well-formedness", size=e.size()):
is_wf = exp_wf(e,
pool=pool,
context=ctx,
solver=self.wf_solver)
if not is_wf:
return is_wf
res = good_idea_recursive(self.wf_solver,
e,
ctx,
pool,
ops=self.ops)
if not res:
return res
if pool == RUNTIME_POOL and self.cost_model.compare(
e, self.targets[0], ctx, pool) == Order.GT:
return No("too expensive")
return True
def improve(target: Exp,
context: Context,
assumptions: Exp = T,
stop_callback=never_stop,
hints: [Exp] = (),
examples: [{
str: object
}] = (),
cost_model: CostModel = None):
"""
Improve the target expression using enumerative synthesis.
This function is a generator that yields increasingly better and better
versions of the input expression `target`.
Notes on internals of this algorithm follow.
Key differences from "regular" enumerative synthesis:
- Expressions are either "state" expressions or "runtime" expressions,
allowing this algorithm to choose what things to store on the data
structure and what things to compute at query execution time. (The
cost model is ultimately responsible for this choice.)
- If a better version of *any subexpression* for the target is found,
it is immediately substituted in and the overall expression is
returned. This "smooths out" the search space a little, and lets us
find kinda-good solutions very quickly, even if the best possible
solution is out of reach.
"""
print("call to improve:")
print("""improve(
target={target!r},
context={context!r},
assumptions={assumptions!r},
stop_callback={stop_callback!r},
hints={hints!r},
examples={examples!r},
cost_model={cost_model!r})""".format(target=target,
context=context,
assumptions=assumptions,
stop_callback=stop_callback,
hints=hints,
examples=examples,
cost_model=cost_model))
target = freshen_binders(target, context)
assumptions = freshen_binders(assumptions, context)
print()
print("improving: {}".format(pprint(target)))
print("subject to: {}".format(pprint(assumptions)))
print()
try:
assert exp_wf(target, context=context, assumptions=assumptions)
except ExpIsNotWf as ex:
print(
"WARNING: initial target is not well-formed [{}]; this might go poorly..."
.format(str(ex)))
print(pprint(ex.offending_subexpression))
print(pprint(ex.offending_subexpression.type))
# raise
state_vars = [v for (v, p) in context.vars() if p == STATE_POOL]
if eliminate_vars.value and can_elim_vars(target, assumptions, state_vars):
print("This job does not depend on state_vars.")
# TODO: what can we do about it?
hints = ([freshen_binders(h, context) for h in hints] + [
freshen_binders(wrap_naked_statevars(a, state_vars), context)
for a in break_conj(assumptions)
] + [target])
print("{} hints".format(len(hints)))
for h in hints:
print(" - {}".format(pprint(h)))
vars = list(v for (v, p) in context.vars())
funcs = context.funcs()
solver = None
if incremental.value:
solver = IncrementalSolver(vars=vars, funcs=funcs)
solver.add_assumption(assumptions)
_sat = solver.satisfy
else:
_sat = lambda e: satisfy(e, vars=vars, funcs=funcs)
if _sat(assumptions) is None:
print("assumptions are unsat; this query will never be called")
yield construct_value(target.type)
return
examples = list(examples)
if cost_model is None:
cost_model = CostModel(funcs=funcs, assumptions=assumptions)
watched_targets = [target]
learner = Learner(watched_targets, assumptions, context, examples,
cost_model, stop_callback, hints)
try:
while True:
# 1. find any potential improvement to any sub-exp of target
for new_target in learner.next():
print("Found candidate improvement: {}".format(
pprint(new_target)))
# 2. check
with task("verifying candidate"):
if incremental.value:
solver.push()
solver.add_assumption(
ENot(
EBinOp(target, "==",
new_target).with_type(BOOL)))
counterexample = _sat(T)
else:
formula = EAll([
assumptions,
ENot(
EBinOp(target, "==",
new_target).with_type(BOOL))
])
counterexample = _sat(formula)
if counterexample is not None:
if counterexample in examples:
print("assumptions = {!r}".format(assumptions))
print("duplicate example: {!r}".format(counterexample))
print("old target = {!r}".format(target))
print("new target = {!r}".format(new_target))
raise Exception("got a duplicate example")
# a. if incorrect: add example, reset the learner
examples.append(counterexample)
event("new example: {!r}".format(counterexample))
print("wrong; restarting with {} examples".format(
len(examples)))
learner.reset(examples)
break
else:
# b. if correct: yield it, watch the new target, goto 1
print("The candidate is valid!")
print(repr(new_target))
print("Determining whether to yield it...")
with task("updating frontier"):
to_evict = []
keep = True
old_better = None
for old_target in watched_targets:
evc = eviction_policy(new_target, context,
old_target, context,
RUNTIME_POOL, cost_model)
if old_target not in evc:
to_evict.append(old_target)
if new_target not in evc:
old_better = old_target
keep = False
break
for t in to_evict:
watched_targets.remove(t)
if not keep:
print(
"Whoops! Looks like we already found something better."
)
print(" --> {}".format(pprint(old_better)))
continue
if target in to_evict:
print("Yep, it's an improvement!")
yield new_target
if heuristic_done(new_target):
print("target now matches doneness heuristic")
raise NoMoreImprovements()
target = new_target
else:
print("Nope, it isn't substantially better!")
watched_targets.append(new_target)
print("Now watching {} targets".format(
len(watched_targets)))
learner.watch(watched_targets)
break
if incremental.value:
solver.pop()
except NoMoreImprovements:
return
except KeyboardInterrupt:
raise
def improve(
target : Exp,
assumptions : Exp,
binders : [EVar],
state_vars : [EVar],
args : [EVar],
cost_model : CostModel,
builder : ExpBuilder,
stop_callback = never_stop,
hints : [Exp] = None,
examples = None):
"""
Improve the target expression using enumerative synthesis.
This function is a generator that yields increasingly better and better
versions of the input expression `target`.
Notes on internals of this algorithm follow.
Key differences from "regular" enumerative synthesis:
- Expressions may be built using a set of "binders"---extra free
variables thrown into the mix at the beginning.
- Expressions are either "state" expressions or "runtime" expressions,
allowing this algorithm to choose what things to store on the data
structure and what things to compute at query execution time. (The
cost model is ultimately responsible for this choice.)
Other features of this algorithm:
- If a better version of *any subexpression* for the target is found,
it is immediately substituted in and the overall expression is
returned. This "smooths out" the search space a little, and lets us
find kinda-good solutions very quickly, even if the best possible
solution is out of reach.
"""
print("call to improve:")
print("""improve(
target={target!r},
assumptions={assumptions!r},
binders={binders!r},
state_vars={state_vars!r},
args={args!r},
cost_model={cost_model!r},
builder={builder!r},
stop_callback={stop_callback!r},
hints={hints!r},
examples={examples!r})""".format(
target=target,
assumptions=assumptions,
binders=binders,
state_vars=state_vars,
args=args,
cost_model=cost_model,
builder=builder,
stop_callback=stop_callback,
hints=hints,
examples=examples))
print()
print("improving: {}".format(pprint(target)))
print("subject to: {}".format(pprint(assumptions)))
print()
assert exp_wf(
target,
state_vars=set(state_vars),
args=set(args),
assumptions=assumptions)
binders = list(binders)
target = fixup_binders(target, binders, allow_add=False)
hints = [fixup_binders(h, binders, allow_add=False) for h in (hints or ())]
assumptions = fixup_binders(assumptions, binders, allow_add=False)
builder = FixedBuilder(builder, state_vars, args, binders, assumptions)
target_cost = cost_model.cost(target, RUNTIME_POOL)
if eliminate_vars.value and can_elim_vars(target, assumptions, state_vars):
print("This job does not depend on state_vars.")
builder = StateElimBuilder(builder)
vars = list(free_vars(target) | free_vars(assumptions))
funcs = free_funcs(EAll([target, assumptions]))
solver = None
if incremental.value:
solver = IncrementalSolver(vars=vars, funcs=funcs, collection_depth=check_depth.value)
solver.add_assumption(assumptions)
_sat = solver.satisfy
else:
_sat = lambda e: satisfy(e, vars=vars, funcs=funcs, collection_depth=check_depth.value)
if _sat(T) is None:
print("assumptions are unsat; this query will never be called")
yield construct_value(target.type)
return
if examples is None:
examples = []
learner = Learner(target, assumptions, binders, state_vars, args, vars + binders, examples, cost_model, builder, stop_callback, hints, solver=solver)
try:
while True:
# 1. find any potential improvement to any sub-exp of target
try:
old_e, new_e, local_assumptions, repl = learner.next()
except NoMoreImprovements:
break
# 2. substitute-in the improvement
print("Found candidate replacement [{}] for [{}] in".format(pprint(new_e), pprint(old_e)))
print(pprint(repl(EVar("@___"))))
new_target = repl(new_e)
# 3. check
if incremental.value:
solver.push()
solver.add_assumption(ENot(EBinOp(target, "==", new_target).with_type(BOOL)))
counterexample = _sat(T)
else:
formula = EAll([assumptions, ENot(EBinOp(target, "==", new_target).with_type(BOOL))])
counterexample = _sat(formula)
if counterexample is not None:
# Ok they aren't equal. Now we need an example that
# differentiates BOTH target/new_target AND old_e/new_e.
if incremental.value:
counterexample = _sat(EAll([
EAll(local_assumptions),
ENot(EBinOp(old_e, "===", new_e).with_type(BOOL))]))
else:
counterexample = _sat(EAll([
assumptions,
EAll(local_assumptions),
ENot(EBinOp(target, "==", new_target).with_type(BOOL)),
ENot(EBinOp(old_e, "===", new_e).with_type(BOOL))]))
if counterexample is None:
print("!!! unable to satisfy top- and sub-expressions")
print("assumptions = {!r}".format(assumptions))
print("local_assumptions = {!r}".format(EAll(local_assumptions)))
print("old_e = {!r}".format(old_e))
print("target = {!r}".format(target))
print("new_e = {!r}".format(new_e))
print("new_target = {!r}".format(new_target))
raise Exception("unable to find an example that differentiates both the toplevel- and sub-expressions")
if counterexample in examples:
print("assumptions = {!r}".format(assumptions))
print("duplicate example: {!r}".format(counterexample))
print("old target = {!r}".format(target))
print("new target = {!r}".format(new_target))
print("old fp = {}".format(learner._fingerprint(old_e)))
print("new fp = {}".format(learner._fingerprint(new_e)))
print("old target fp = {}".format(learner._fingerprint(target)))
print("new target fp = {}".format(learner._fingerprint(new_target)))
raise Exception("got a duplicate example")
# a. if incorrect: add example, reset the learner
examples.append(counterexample)
print("new example: {}".format(truncate(repr(counterexample))))
print("restarting with {} examples".format(len(examples)))
learner.reset(examples)
else:
# b. if correct: yield it, watch the new target, goto 1
if CHECK_FINAL_COST:
new_cost = cost_model.cost(new_target, RUNTIME_POOL)
print("cost: {} -----> {}".format(target_cost, new_cost))
if incremental.value:
ordering = new_cost.compare_to(target_cost, solver=solver)
else:
ordering = new_cost.compare_to(target_cost, assumptions=assumptions)
if ordering == Cost.WORSE:
if CHECK_SUBST_COST:
print("WHOOPS! COST GOT WORSE!")
if save_testcases.value:
with open(save_testcases.value, "a") as f:
f.write("def testcase():\n")
f.write(" costmodel = {}\n".format(repr(cost_model)))
f.write(" old_e = {}\n".format(repr(old_e)))
f.write(" new_e = {}\n".format(repr(new_e)))
f.write(" target = {}\n".format(repr(target)))
f.write(" new_target = {}\n".format(repr(new_target)))
f.write(" if costmodel.cost(new_e, RUNTIME_POOL) <= costmodel.cost(old_e, RUNTIME_POOL) and costmodel.cost(new_target, RUNTIME_POOL) > costmodel.cost(target, RUNTIME_POOL):\n")
f.write(' for name, x in zip(["old_e", "new_e", "target", "new_target"], [old_e, new_e, target, new_target]):\n')
f.write(' print("{}: {}".format(name, pprint(x)))\n')
f.write(' print(" cost = {}".format(costmodel.cost(x, RUNTIME_POOL)))\n')
f.write(" assert False\n")
# raise Exception("detected nonmonotonicity")
else:
print("*** cost is worse")
# print(repr(target))
# print(repr(new_target))
continue
elif ordering == Cost.UNORDERED:
print("*** cost is unchanged")
# print(repr(target))
# print(repr(new_target))
continue
target_cost = new_cost
print("found improvement: {} -----> {}".format(pprint(old_e), pprint(new_e)))
# print(repr(target))
# print(repr(new_target))
# binders are not allowed to "leak" out
to_yield = new_target
if any(v in binders for v in free_vars(new_target)):
print("WARNING: stripping binders in {}".format(pprint(new_target)), file=sys.stderr)
to_yield = subst(new_target, { b.id : construct_value(b.type) for b in binders })
yield to_yield
if reset_on_success.value and (not CHECK_FINAL_COST or ordering != Cost.UNORDERED):
learner.reset(examples)
learner.watch(new_target)
target = new_target
if heuristic_done(new_target, args):
print("target now matches doneness heuristic")
break
if incremental.value:
solver.pop()
except KeyboardInterrupt:
for e in learner.cache.random_sample(50):
print(pprint(e))
raise
def check(self, e, pool):
if enforce_exprs_wf.value:
assert exp_wf(e, state_vars=self.state_vars, args=self.args, pool=pool)
return (e, pool)
def is_legal_in_pool(self, e, pool):
try:
return exp_wf(e, state_vars=self.state_vars, args=self.args, pool=pool, assumptions=self.assumptions)
except ExpIsNotWf as exc:
return False
def improve(target: Exp,
context: Context,
assumptions: Exp = ETRUE,
stop_callback: Callable[[], bool] = never_stop,
hints: [Exp] = (),
examples: [{
str: object
}] = (),
cost_model: CostModel = None,
ops: [Op] = (),
improve_count: Value = None):
"""Improve the target expression using enumerative synthesis.
This function is a generator that yields increasingly better and better
versions of the input expression `target` in the given `context`. The
`cost_model` defines "better".
It periodically calls `stop_callback` and exits gracefully when
`stop_callback` returns True.
Other parameters:
- assumptions: a precondition. The yielded improvements will only be
correct when the assumptions are true.
- hints: expressions that might be useful. These will be explored
first when looking for improvements.
- examples: inputs that will be used internally to differentiate
semantically distinct expressions. This procedure discovers more
examples as it runs, so there usually isn't a reason to provide any.
- ops: update operations. This function may make different choices
about what expressions are state expressions based on what changes
can happen to that state.
Key differences from "regular" enumerative synthesis:
- Expressions are either "state" expressions or "runtime" expressions,
allowing this algorithm to choose what things to store on the data
structure and what things to compute at query execution time. (The
cost model is ultimately responsible for this choice.)
- If a better version of *any subexpression* for the target is found,
it is immediately substituted in and the overall expression is
returned. This "smooths out" the search space a little, allowing us
find kinda-good solutions very quickly, even if the best possible
solution is out of reach. This is more desireable than running for
an indeterminate amount of time doing nothing.
"""
print("call to improve:")
print("""improve(
target={target!r},
context={context!r},
assumptions={assumptions!r},
stop_callback={stop_callback!r},
hints={hints!r},
examples={examples!r},
cost_model={cost_model!r},
ops={ops!r})""".format(target=target,
context=context,
assumptions=assumptions,
stop_callback=stop_callback,
hints=hints,
examples=examples,
cost_model=cost_model,
ops=ops))
target = inline_lets(target)
target = freshen_binders(target, context)
assumptions = freshen_binders(assumptions, context)
if heuristic_done(target):
print("The target already looks great!")
return
print()
print("improving: {}".format(pprint(target)))
print("subject to: {}".format(pprint(assumptions)))
print()
is_wf = exp_wf(target, context=context, assumptions=assumptions)
assert is_wf, "initial target is not well-formed: {}".format(is_wf)
state_vars = [v for (v, p) in context.vars() if p == STATE_POOL]
if eliminate_vars.value and can_elim_vars(target, assumptions, state_vars):
print("This job does not depend on state_vars.")
# TODO: what can we do about it?
hints = ([freshen_binders(h, context) for h in hints] + [
freshen_binders(wrap_naked_statevars(a, state_vars), context)
for a in break_conj(assumptions)
] + [target])
print("{} hints".format(len(hints)))
for h in hints:
print(" - {}".format(pprint(h)))
vars = list(v for (v, p) in context.vars())
funcs = context.funcs()
solver = solver_for_context(context, assumptions=assumptions)
if not solver.satisfiable(ETRUE):
print("assumptions are unsat; this query will never be called")
yield construct_value(target.type)
return
is_good = possibly_useful(solver, target, context)
assert is_good, "WARNING: this target is already a bad idea\n is_good = {}, target = {}".format(
is_good, target)
examples = list(examples)
if cost_model is None:
cost_model = CostModel(funcs=funcs, assumptions=assumptions)
watched_targets = [target]
blacklist = {}
while True:
# 1. find any potential improvement to any sub-exp of target
for new_target in search_for_improvements(targets=watched_targets,
wf_solver=solver,
context=context,
examples=examples,
cost_model=cost_model,
stop_callback=stop_callback,
hints=hints,
ops=ops,
blacklist=blacklist):
print("Found candidate improvement: {}".format(pprint(new_target)))
# 2. check
with task("verifying candidate"):
counterexample = solver.satisfy(ENot(EEq(target, new_target)))
if counterexample is not None:
if counterexample in examples:
print("assumptions = {!r}".format(assumptions))
print("duplicate example: {!r}".format(counterexample))
print("old target = {!r}".format(target))
print("new target = {!r}".format(new_target))
raise Exception("got a duplicate example")
# a. if incorrect: add example, restart
examples.append(counterexample)
print("new example: {!r}".format(counterexample))
print("wrong; restarting with {} examples".format(
len(examples)))
break
else:
# b. if correct: yield it, watch the new target, goto 1
print("The candidate is valid!")
print(repr(new_target))
print("Determining whether to yield it...")
with task("updating frontier"):
to_evict = []
keep = True
old_better = None
for old_target in watched_targets:
evc = retention_policy(new_target, context, old_target,
context, RUNTIME_POOL,
cost_model)
if old_target not in evc:
to_evict.append(old_target)
if new_target not in evc:
old_better = old_target
keep = False
break
for t in to_evict:
watched_targets.remove(t)
if not keep:
print(
"Whoops! Looks like we already found something better."
)
print(" --> {}".format(pprint(old_better)))
continue
if target in to_evict:
print("Yep, it's an improvement!")
yield new_target
if heuristic_done(new_target):
print("target now matches doneness heuristic")
return
target = new_target
else:
print("Nope, it isn't substantially better!")
watched_targets.append(new_target)
print("Now watching {} targets".format(len(watched_targets)))
break
if improve_count is not None:
with improve_count.get_lock():
improve_count.value += 1
def improve(
target : Exp,
context : Context,
assumptions : Exp = ETRUE,
stop_callback : Callable[[], bool] = never_stop,
hints : [Exp] = (),
examples : [{str:object}] = (),
cost_model : CostModel = None,
ops : [Op] = (),
improve_count : Value = None):
"""Improve the target expression using enumerative synthesis.
This function is a generator that yields increasingly better and better
versions of the input expression `target` in the given `context`. The
`cost_model` defines "better".
It periodically calls `stop_callback` and exits gracefully when
`stop_callback` returns True.
Other parameters:
- assumptions: a precondition. The yielded improvements will only be
correct when the assumptions are true.
- hints: expressions that might be useful. These will be explored
first when looking for improvements.
- examples: inputs that will be used internally to differentiate
semantically distinct expressions. This procedure discovers more
examples as it runs, so there usually isn't a reason to provide any.
- ops: update operations. This function may make different choices
about what expressions are state expressions based on what changes
can happen to that state.
Key differences from "regular" enumerative synthesis:
- Expressions are either "state" expressions or "runtime" expressions,
allowing this algorithm to choose what things to store on the data
structure and what things to compute at query execution time. (The
cost model is ultimately responsible for this choice.)
- If a better version of *any subexpression* for the target is found,
it is immediately substituted in and the overall expression is
returned. This "smooths out" the search space a little, allowing us
find kinda-good solutions very quickly, even if the best possible
solution is out of reach. This is more desireable than running for
an indeterminate amount of time doing nothing.
"""
print("call to improve:")
print("""improve(
target={target!r},
context={context!r},
assumptions={assumptions!r},
stop_callback={stop_callback!r},
hints={hints!r},
examples={examples!r},
cost_model={cost_model!r},
ops={ops!r})""".format(
target=target,
context=context,
assumptions=assumptions,
stop_callback=stop_callback,
hints=hints,
examples=examples,
cost_model=cost_model,
ops=ops))
target = inline_lets(target)
target = freshen_binders(target, context)
assumptions = freshen_binders(assumptions, context)
if heuristic_done(target):
print("The target already looks great!")
return
print()
print("improving: {}".format(pprint(target)))
print("subject to: {}".format(pprint(assumptions)))
print()
is_wf = exp_wf(target, context=context, assumptions=assumptions)
assert is_wf, "initial target is not well-formed: {}".format(is_wf)
state_vars = [v for (v, p) in context.vars() if p == STATE_POOL]
if eliminate_vars.value and can_elim_vars(target, assumptions, state_vars):
print("This job does not depend on state_vars.")
# TODO: what can we do about it?
hints = ([freshen_binders(h, context) for h in hints]
+ [freshen_binders(wrap_naked_statevars(a, state_vars), context) for a in break_conj(assumptions)]
+ [target])
print("{} hints".format(len(hints)))
for h in hints:
print(" - {}".format(pprint(h)))
vars = list(v for (v, p) in context.vars())
funcs = context.funcs()
solver = solver_for_context(context, assumptions=assumptions)
if not solver.satisfiable(ETRUE):
print("assumptions are unsat; this query will never be called")
yield construct_value(target.type)
return
is_good = possibly_useful(solver, target, context)
assert is_good, "WARNING: this target is already a bad idea\n is_good = {}, target = {}".format(is_good, target)
examples = list(examples)
if cost_model is None:
cost_model = CostModel(funcs=funcs, assumptions=assumptions)
watched_targets = [target]
blacklist = {}
while True:
# 1. find any potential improvement to any sub-exp of target
for new_target in search_for_improvements(
targets=watched_targets,
wf_solver=solver,
context=context,
examples=examples,
cost_model=cost_model,
stop_callback=stop_callback,
hints=hints,
ops=ops,
blacklist=blacklist):
print("Found candidate improvement: {}".format(pprint(new_target)))
# 2. check
with task("verifying candidate"):
counterexample = solver.satisfy(ENot(EEq(target, new_target)))
if counterexample is not None:
if counterexample in examples:
print("assumptions = {!r}".format(assumptions))
print("duplicate example: {!r}".format(counterexample))
print("old target = {!r}".format(target))
print("new target = {!r}".format(new_target))
raise Exception("got a duplicate example")
# a. if incorrect: add example, restart
examples.append(counterexample)
print("new example: {!r}".format(counterexample))
print("wrong; restarting with {} examples".format(len(examples)))
break
else:
# b. if correct: yield it, watch the new target, goto 1
print("The candidate is valid!")
print(repr(new_target))
print("Determining whether to yield it...")
with task("updating frontier"):
to_evict = []
keep = True
old_better = None
for old_target in watched_targets:
evc = retention_policy(new_target, context, old_target, context, RUNTIME_POOL, cost_model)
if old_target not in evc:
to_evict.append(old_target)
if new_target not in evc:
old_better = old_target
keep = False
break
for t in to_evict:
watched_targets.remove(t)
if not keep:
print("Whoops! Looks like we already found something better.")
print(" --> {}".format(pprint(old_better)))
continue
if target in to_evict:
print("Yep, it's an improvement!")
yield new_target
if heuristic_done(new_target):
print("target now matches doneness heuristic")
return
target = new_target
else:
print("Nope, it isn't substantially better!")
watched_targets.append(new_target)
print("Now watching {} targets".format(len(watched_targets)))
break
if improve_count is not None:
with improve_count.get_lock():
improve_count.value += 1
