def exp_wf_nonrecursive(solver, e : Exp, context : Context, pool = RUNTIME_POOL, assumptions : Exp = ETRUE):
"""Check the well-formedness of `e` but do not recurse into its children.
Returns True or an instance of No explaining why `e` is not well-formed.
See `exp_wf` for an explanation of well-formedness and the parameters that
this function requires.
"""
if hasattr(e, "_wf"):
return True
state_vars = OrderedSet(v for v, p in context.vars() if p == STATE_POOL)
args = OrderedSet(v for v, p in context.vars() if p == RUNTIME_POOL)
h = extension_handler(type(e))
if h is not None:
assumptions = EAll([assumptions, context.path_condition()])
msg = h.check_wf(e, state_vars=state_vars, args=args, pool=pool, assumptions=assumptions, is_valid=solver.valid)
if msg is not None:
return No(msg)
e._wf = True
return True
at_runtime = pool == RUNTIME_POOL
if isinstance(e, EStateVar) and not at_runtime:
return No("EStateVar in state pool position")
if isinstance(e, EVar):
if at_runtime and e in state_vars:
return No("state var at runtime")
elif not at_runtime and e in args:
return No("arg in state exp")
e._wf = True
return True
def exp_wf_nonrecursive(solver,
e: Exp,
context: Context,
pool=RUNTIME_POOL,
assumptions: Exp = T):
state_vars = OrderedSet(v for v, p in context.vars() if p == STATE_POOL)
args = OrderedSet(v for v, p in context.vars() if p == RUNTIME_POOL)
assumptions = EAll([assumptions, context.path_condition()])
h = extension_handler(type(e))
if h is not None:
msg = h.check_wf(e,
state_vars=state_vars,
args=args,
pool=pool,
assumptions=assumptions,
is_valid=solver.valid)
if msg is not None:
raise ExpIsNotWf(e, e, msg)
return
at_runtime = pool == RUNTIME_POOL
if isinstance(e, EStateVar) and not at_runtime:
raise ExpIsNotWf(e, e, "EStateVar in state pool position")
if isinstance(e, EVar):
if at_runtime and e in state_vars:
raise ExpIsNotWf(e, e, "state var at runtime")
elif not at_runtime and e in args:
raise ExpIsNotWf(e, e, "arg in state exp")
def enumerate_with_info(self, context: Context, size: int,
pool: Pool) -> [EnumeratedExp]:
"""Enumerate expressions (and fingerprints) of the given size.
The output of this function is cached, so subsequent calls are very
cheap.
Arguments:
context : a Context object describing the vars in scope
size : size of expressions to enumerate
pool : expression pool to visit
"""
canonical_context = self.canonical_context(context)
if canonical_context is not context:
print("adapting request: {} ---> {}".format(
context, canonical_context))
for info in self.enumerate_with_info(canonical_context, size,
pool):
yield info._replace(e=context.adapt(info.e, canonical_context))
return
k = (pool, size, context)
cache = self.cache
if k in self.complete:
yield from cache.find_expressions_of_size(context, pool, size)
else:
assert k not in self.in_progress, "recursive enumeration?? {}".format(
k)
self.in_progress.add(k)
yield from self._enumerate_with_info(context, size, pool)
self.in_progress.remove(k)
self.complete.add(k)
def exp_wf_nonrecursive(solver,
e: Exp,
context: Context,
pool=RUNTIME_POOL,
assumptions: Exp = ETRUE):
"""Check the well-formedness of `e` but do not recurse into its children.
Returns True or an instance of No explaining why `e` is not well-formed.
See `exp_wf` for an explanation of well-formedness and the parameters that
this function requires.
"""
if hasattr(e, "_wf"):
return True
state_vars = OrderedSet(v for v, p in context.vars() if p == STATE_POOL)
args = OrderedSet(v for v, p in context.vars() if p == RUNTIME_POOL)
h = extension_handler(type(e))
if h is not None:
assumptions = EAll([assumptions, context.path_condition()])
msg = h.check_wf(e,
state_vars=state_vars,
args=args,
pool=pool,
assumptions=assumptions,
is_valid=solver.valid)
if msg is not None:
return No(msg)
e._wf = True
return True
at_runtime = pool == RUNTIME_POOL
if isinstance(e, EStateVar) and not at_runtime:
return No("EStateVar in state pool position")
if isinstance(e, EVar):
if at_runtime and e in state_vars:
return No("state var at runtime")
elif not at_runtime and e in args:
return No("arg in state exp")
e._wf = True
return True
def repair_well_formedness(e: Exp,
context: Context,
extra_available_state: [Exp] = []) -> Exp:
"""Repair the EStateVar nodes in an expression that is not well-formed.
Parameters:
e - the expression to repair
context - the intended context for e
extra_available_state - extra state expressions that e can use
Assuming that all expressions in extra_available_state are well-formed
state expressions, the output will be a well-formed runtime expression that
behaves like `e`.
"""
with task("repairing"):
e = strip_EStateVar(e)
# state expressions in decreasing order of size
available_state = sorted(unique(
itertools.chain((v for v, p in context.vars() if p == STATE_POOL),
extra_available_state)),
key=lambda e: -e.size())
with task("making replacements", size=e.size()):
for s in available_state:
e = replace(e,
s,
EStateVar(s).with_type(s.type),
match=alpha_equivalent,
filter=lambda e: not isinstance(e, EStateVar))
with task("freshening binders"):
e = freshen_binders(e, context)
with task("checking correctness"):
res = exp_wf(e, context, RUNTIME_POOL)
assert res, str(res)
return e
def _compare(self, e1: Exp, e2: Exp, context: Context):
e1_constant = not free_vars(e1) and not free_funcs(e1)
e2_constant = not free_vars(e2) and not free_funcs(e2)
if e1_constant and e2_constant:
e1v = eval(e1, {})
e2v = eval(e2, {})
event("comparison obvious on constants: {} vs {}".format(e1v, e2v))
return order_objects(e1v, e2v)
if alpha_equivalent(e1, e2):
event("shortcutting comparison of identical terms")
return Order.EQUAL
path_condition = EAll(context.path_conditions())
always_le = self.solver.valid(EImplies(path_condition, ELe(e1, e2)))
always_ge = self.solver.valid(EImplies(path_condition, EGe(e1, e2)))
if always_le and always_ge:
return Order.EQUAL
if always_le:
return Order.LT
if always_ge:
return Order.GT
return Order.AMBIGUOUS
def repair_well_formedness(e : Exp, context : Context, extra_available_state : [Exp] = []) -> Exp:
"""Repair the EStateVar nodes in an expression that is not well-formed.
Parameters:
e - the expression to repair
context - the intended context for e
extra_available_state - extra state expressions that e can use
Assuming that all expressions in extra_available_state are well-formed
state expressions, the output will be a well-formed runtime expression that
behaves like `e`.
"""
with task("repairing"):
e = strip_EStateVar(e)
# state expressions in decreasing order of size
available_state = sorted(unique(itertools.chain(
(v for v, p in context.vars() if p == STATE_POOL),
extra_available_state)), key=lambda e: -e.size())
with task("making replacements", size=e.size()):
for s in available_state:
e = replace(e, s, EStateVar(s).with_type(s.type),
match=alpha_equivalent,
filter=lambda e: not isinstance(e, EStateVar))
with task("freshening binders"):
e = freshen_binders(e, context)
with task("checking correctness"):
res = exp_wf(e, context, RUNTIME_POOL)
assert res, str(res)
return e
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 exp_wf_nonrecursive(solver, e : Exp, context : Context, pool = RUNTIME_POOL, assumptions : Exp = T):
state_vars = OrderedSet(v for v, p in context.vars() if p == STATE_POOL)
args = OrderedSet(v for v, p in context.vars() if p == RUNTIME_POOL)
assumptions = EAll([assumptions, context.path_condition()])
h = extension_handler(type(e))
if h is not None:
msg = h.check_wf(e, state_vars=state_vars, args=args, pool=pool, assumptions=assumptions, is_valid=solver.valid)
if msg is not None:
raise ExpIsNotWf(e, e, msg)
return
at_runtime = pool == RUNTIME_POOL
if isinstance(e, EStateVar) and not at_runtime:
raise ExpIsNotWf(e, e, "EStateVar in state pool position")
if isinstance(e, EStateVar):
fvs = free_vars(e.e)
if not fvs:
raise ExpIsNotWf(e, e, "constant value in state position")
bad = [v for v in fvs if v not in state_vars]
if bad:
raise ExpIsNotWf(e, e, "free non-statevars in state position: {}".format(", ".join(v.id for v in bad)))
if (isinstance(e, EDropFront) or isinstance(e, EDropBack)) and not at_runtime:
raise ExpIsNotWf(e, e, "EDrop* in state position")
if isinstance(e, EFlatMap) and not at_runtime:
raise ExpIsNotWf(e, e, "EFlatMap in state position")
if not allow_int_arithmetic_state.value and not at_runtime and isinstance(e, EBinOp) and e.type == INT:
raise ExpIsNotWf(e, e, "integer arithmetic in state position")
# if isinstance(e, EUnaryOp) and e.op == UOp.Distinct and not at_runtime:
# raise ExpIsNotWf(e, e, "'distinct' in state position")
# if isinstance(e, EMapKeys) and not at_runtime:
# raise ExpIsNotWf(e, e, "'mapkeys' in state position")
if isinstance(e, EVar):
if at_runtime and e in state_vars:
raise ExpIsNotWf(e, e, "state var at runtime")
elif not at_runtime and e in args:
raise ExpIsNotWf(e, e, "arg in state exp")
# if is_collection(e.type) and is_collection(e.type.t):
# raise ExpIsNotWf(e, e, "collection of collection")
if is_collection(e.type) and not is_scalar(e.type.t):
raise ExpIsNotWf(e, e, "collection of nonscalar")
if isinstance(e.type, TMap) and not is_scalar(e.type.k):
raise ExpIsNotWf(e, e, "bad key type {}".format(pprint(e.type.k)))
if isinstance(e.type, TMap) and isinstance(e.type.v, TMap):
raise ExpIsNotWf(e, e, "map to map")
# This check is probably a bad idea: whether `the` is legal may depend on
# the contex that the expression is embedded within, so we can't skip it
# during synthesis just because it looks invalid now.
# if isinstance(e, EUnaryOp) and e.op == UOp.The:
# len = EUnaryOp(UOp.Length, e.e).with_type(INT)
# if not valid(EImplies(assumptions, EBinOp(len, "<=", ENum(1).with_type(INT)).with_type(BOOL))):
# raise ExpIsNotWf(e, e, "illegal application of 'the': could have >1 elems")
if not at_runtime and isinstance(e, EBinOp) and e.op == "-" and is_collection(e.type):
raise ExpIsNotWf(e, e, "collection subtraction in state position")
if not at_runtime and isinstance(e, ESingleton):
raise ExpIsNotWf(e, e, "singleton in state position")
# if not at_runtime and isinstance(e, ENum) and e.val != 0 and e.type == INT:
# raise ExpIsNotWf(e, e, "nonzero integer constant in state position")
if not allow_conditional_state.value and not at_runtime and isinstance(e, ECond):
raise ExpIsNotWf(e, e, "conditional in state position")
if isinstance(e, EMakeMap2) and isinstance(e.e, EEmptyList):
raise ExpIsNotWf(e, e, "trivially empty map")
if do_expensive_checks.value and not at_runtime and isinstance(e, EFilter):
# catch "peels": removal of zero or one elements
if solver.valid(EImplies(assumptions, ELe(ELen(EFilter(e.e, ELambda(e.p.arg, ENot(e.p.body))).with_type(e.type)), ONE))):
raise ExpIsNotWf(e, e, "filter is a peel")
if do_expensive_checks.value and not at_runtime and isinstance(e, EMakeMap2) and is_collection(e.type.v):
all_collections = [sv for sv in state_vars if is_collection(sv.type)]
total_size = ENum(0).with_type(INT)
for c in all_collections:
total_size = EBinOp(total_size, "+", EUnaryOp(UOp.Length, c).with_type(INT)).with_type(INT)
my_size = EUnaryOp(UOp.Length, EFlatMap(EUnaryOp(UOp.Distinct, e.e).with_type(e.e.type), e.value).with_type(e.type.v)).with_type(INT)
s = EImplies(
assumptions,
EBinOp(total_size, ">=", my_size).with_type(BOOL))
if not solver.valid(s):
# from cozy.evaluation import eval
# from cozy.solver import satisfy
# model = satisfy(EAll([assumptions, EBinOp(total_size, "<", my_size).with_type(BOOL)]), collection_depth=3, validate_model=True)
# assert model is not None
# raise ExpIsNotWf(e, e, "non-polynomial-sized map ({}); total_size={}, this_size={}".format(model, eval(total_size, model), eval(my_size, model)))
raise ExpIsNotWf(e, e, "non-polynomial-sized map")
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 _enumerate_core(self, context: Context, size: int,
pool: Pool) -> [Exp]:
"""Build new expressions of the given size.
Arguments:
context : a Context object describing the vars in scope
size : size of expressions to enumerate; each expression in
the output will have this size
pool : pool to enumerate
This function is not cached. Clients should call `enumerate` instead.
This function tries to be a clean description of the Cozy grammar. It
does not concern itself with deduplication (which is handled
efficiently by equivalence class deduplication).
"""
if size < 0:
return
if size == 0:
for e in LITERALS:
yield e
all_interesting_types = OrderedSet(self.hint_types)
for v, _ in context.vars():
all_interesting_types |= all_types(v.type)
for t in all_interesting_types:
l = construct_value(t)
if l not in LITERALS:
yield l
for (v, p) in context.vars():
if p == pool:
yield v
for (e, ctx, p) in self.hints:
if p == pool:
fvs = free_vars(e)
if ctx.alpha_equivalent(context.generalize(fvs)):
yield context.adapt(e, ctx, e_fvs=fvs)
return
if not do_enumerate.value:
return
def build_lambdas(bag, pool, body_size):
v = fresh_var(bag.type.elem_type,
omit=set(v for v, p in context.vars()))
inner_context = UnderBinder(context, v=v, bag=bag, bag_pool=pool)
for lam_body in self.enumerate(inner_context, body_size, pool):
yield ELambda(v, lam_body)
# Load all smaller expressions in this context and pool.
# cache[S] contains expressions of size S in this context and pool.
cache = [list(self.enumerate(context, sz, pool)) for sz in range(size)]
# Enable use of a state-pool expression at runtime
if pool == RUNTIME_POOL:
for e in self.enumerate(context.root(), size - 1, STATE_POOL):
yield EStateVar(e).with_type(e.type)
# Arity-1 expressions
for e in cache[size - 1]:
if is_collection(e.type):
elem_type = e.type.elem_type
# This method of generating EEmptyList() ensures that we visit
# empty collections of all possible types.
yield EEmptyList().with_type(e.type)
if is_numeric(elem_type):
yield EUnaryOp(UOp.Sum, e).with_type(elem_type)
yield EUnaryOp(UOp.Length, e).with_type(INT)
yield EUnaryOp(UOp.Empty, e).with_type(BOOL)
yield EUnaryOp(UOp.Exists, e).with_type(BOOL)
yield EUnaryOp(UOp.The, e).with_type(elem_type)
yield EUnaryOp(UOp.Distinct, e).with_type(e.type)
yield EUnaryOp(UOp.AreUnique, e).with_type(BOOL)
if elem_type == BOOL:
yield EUnaryOp(UOp.Any, e).with_type(BOOL)
yield EUnaryOp(UOp.All, e).with_type(BOOL)
yield ESingleton(e).with_type(TBag(e.type))
if isinstance(e.type, TRecord):
for (f, t) in e.type.fields:
yield EGetField(e, f).with_type(t)
if isinstance(e.type, THandle):
yield EGetField(e, "val").with_type(e.type.value_type)
if isinstance(e.type, TTuple):
for n in range(len(e.type.ts)):
yield ETupleGet(e, n).with_type(e.type.ts[n])
if e.type == BOOL:
yield EUnaryOp(UOp.Not, e).with_type(BOOL)
if is_numeric(e.type):
yield EUnaryOp("-", e).with_type(e.type)
if isinstance(e.type, TMap):
yield EMapKeys(e).with_type(TBag(e.type.k))
# Arity-2 expressions
for (sz1, sz2) in pick_to_sum(2, size - 1):
# sz1 + sz2 = size - 1
for e1 in cache[sz1]:
t = e1.type
if is_numeric(t):
for a2 in of_type(cache[sz2], t):
yield EBinOp(e1, "+", a2).with_type(t)
yield EBinOp(e1, "-", a2).with_type(t)
if is_ordered(t):
for a2 in of_type(cache[sz2], t):
yield EBinOp(e1, ">", a2).with_type(BOOL)
yield EBinOp(e1, "<", a2).with_type(BOOL)
yield EBinOp(e1, ">=", a2).with_type(BOOL)
yield EBinOp(e1, "<=", a2).with_type(BOOL)
if t == BOOL:
for a2 in of_type(cache[sz2], BOOL):
yield EBinOp(e1, BOp.And, a2).with_type(BOOL)
yield EBinOp(e1, BOp.Or, a2).with_type(BOOL)
# Cozy supports the implication operator "=>", but this
# function does not enumerate it because
# - (a => b) is equivalent to ((not a) or b)
# - there isn't an implication operator in any of our
# current target languages, so we would need to
# desugar it to ((not a) or b) anyway.
if not isinstance(t, TMap):
for a2 in of_type(cache[sz2], t):
yield EEq(e1, a2)
yield EBinOp(e1, "!=", a2).with_type(BOOL)
if isinstance(t, TMap):
for k in of_type(cache[sz2], t.k):
yield EMapGet(e1, k).with_type(t.v)
yield EHasKey(e1, k).with_type(BOOL)
if isinstance(t, TList):
for i in of_type(cache[sz2], INT):
yield EListGet(e1, i).with_type(e1.type.elem_type)
if is_collection(t):
elem_type = t.elem_type
for e2 in of_type(cache[sz2], t):
yield EBinOp(e1, "+", e2).with_type(t)
yield EBinOp(e1, "-", e2).with_type(t)
for e2 in of_type(cache[sz2], elem_type):
yield EBinOp(e2, BOp.In, e1).with_type(BOOL)
for f in build_lambdas(e1, pool, sz2):
body_type = f.body.type
yield EMap(e1, f).with_type(TBag(body_type))
if body_type == BOOL:
yield EFilter(e1, f).with_type(t)
if is_numeric(body_type):
yield EArgMin(e1, f).with_type(elem_type)
yield EArgMax(e1, f).with_type(elem_type)
if is_collection(body_type):
yield EFlatMap(e1, f).with_type(
TBag(body_type.elem_type))
if pool == STATE_POOL and is_hashable(elem_type):
yield EMakeMap2(e1, f).with_type(
TMap(elem_type, body_type))
e1_singleton = ESingleton(e1).with_type(TBag(e1.type))
for f in build_lambdas(e1_singleton, pool, sz2):
yield ELet(e1, f).with_type(f.body.type)
# Arity-3 expressions
for (sz1, sz2, sz3) in pick_to_sum(3, size - 1):
# sz1 + sz2 + sz3 = size - 1
for e1 in cache[sz1]:
if e1.type == BOOL:
cond = e1
for then_branch in cache[sz2]:
for else_branch in of_type(cache[sz3],
then_branch.type):
yield ECond(cond, then_branch,
else_branch).with_type(
then_branch.type)
if isinstance(e1.type, TList):
for start in of_type(cache[sz2], INT):
for end in of_type(cache[sz3], INT):
yield EListSlice(e1, start, end).with_type(e1.type)
# It is not necessary to create slice expressions of
# the form a[:i] or a[i:]. Those are desugared
# after parsing to a[0:i] and a[i:len(a)]
# respectively, and Cozy is perfectly capable of
# discovering these expanded forms as well.
for h in all_extension_handlers():
yield from h.enumerate(context, size, pool, self.enumerate,
build_lambdas)
def _enumerate_with_info(self, context: Context, size: int,
pool: Pool) -> [EnumeratedExp]:
"""Helper for enumerate_with_info that bypasses the cache.
Note that this method DOES affect the cache: it writes its output into
the cache and may do evictions. The enumerate_with_info method ensures
that there is only ever one call to this method for a given (context,
size, pool).
"""
examples = context.instantiate_examples(self.examples)
cache = self.cache
queue = self._enumerate_core(context, size, pool)
cost_model = self.cost_model
while True:
if self.stop_callback():
raise StopException()
try:
e = next(queue)
except StopIteration:
# StopIteration is a "control flow exception" indicating that
# there isn't a next element. Since the queue is exhausted,
# breaking out of the loop is the right thing to do.
break
self.stat_timer.check()
e = freshen_binders(e, context)
_consider(e, size, context, pool)
wf = self.check_wf(e, context, pool)
if not wf:
_skip(e, size, context, pool, "wf={}".format(wf))
continue
fp = Fingerprint.of(e, examples)
# Collect all expressions from parent contexts that are
# fingerprint-equivalent to this one. There might be more than one
# because of how `retention_policy` works.
known_equivalents = list(
cache.find_equivalent_expressions(context, pool, fp))
to_evict = []
if any(e.type == prev_entry.e.type
and alpha_equivalent(e, prev_entry.e)
for prev_entry in known_equivalents):
_skip(e, size, context, pool, "duplicate")
should_keep = False
else:
# decide whether to keep this expression
should_keep = True
if known_equivalents:
with task("comparing to cached equivalents",
count=len(known_equivalents)):
for entry in known_equivalents:
prev_exp = entry.e
event("previous: {}".format(pprint(prev_exp)))
to_keep = retention_policy(e, context, prev_exp,
context, pool,
cost_model)
if e not in to_keep:
_skip(e, size, context, pool,
"preferring {}".format(pprint(prev_exp)))
should_keep = False
break
if prev_exp not in to_keep:
to_evict.append(entry)
assert not (to_evict and not should_keep)
if should_keep:
if self.do_eviction and to_evict:
with task("evicting", count=to_evict):
for entry in to_evict:
_evict(entry.e, entry.size, context, pool, e, size)
cache.remove(context, pool, entry)
_accept(e, size, context, pool, fp)
info = EnumeratedExp(e=e, fingerprint=fp, size=size)
yield info
cache.add(context, pool, info)
if size == 0:
with task("accelerating"):
to_try = make_random_access(
self.heuristics(e, context, pool))
if to_try:
event("trying {} accelerations of {}".format(
len(to_try), pprint(e)))
queue = itertools.chain(to_try, queue)
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 enumerate_with_info(self, context: Context, size: int,
pool: Pool) -> [EnumeratedExp]:
canonical_context = self.canonical_context(context)
if canonical_context is not context:
print("adapting request: {} ---> {}".format(
context, canonical_context))
for info in self.enumerate_with_info(canonical_context, size,
pool):
yield info._replace(e=context.adapt(info.e, canonical_context))
return
if context.parent() is not None:
yield from self.enumerate_with_info(context.parent(), size, pool)
k = (pool, size, context)
res = self.cache.get(k)
if res is not None:
# print("[[{} cached @ size={}]]".format(len(res), size))
for e in res:
yield e
else:
# print("ENTER {}".format(k))
examples = context.instantiate_examples(self.examples)
assert k not in self.in_progress, "recursive enumeration?? {}".format(
k)
self.in_progress.add(k)
res = []
self.cache[k] = res
queue = self.enumerate_core(context, size, pool)
cost_model = self.cost_model
while True:
if self.stop_callback():
raise StopException()
try:
e = next(queue)
except StopIteration:
break
fvs = free_vars(e)
if not belongs_in_context(fvs, context):
continue
e = freshen_binders(e, context)
_consider(e, context, pool)
wf = self.check_wf(e, context, pool)
if not wf:
_skip(e, context, pool, "wf={}".format(wf))
continue
fp = fingerprint(e, examples)
# collect all expressions from parent contexts
with task("collecting prev exps",
size=size,
context=context,
pool=pool_name(pool)):
prev = []
for sz in range(0, size + 1):
prev.extend(self.enumerate_with_info(
context, sz, pool))
prev = [p.e for p in prev if p.fingerprint == fp]
if any(alpha_equivalent(e, p) for p in prev):
_skip(e, context, pool, "duplicate")
should_keep = False
else:
# decide whether to keep this expression,
# decide which can be evicted
should_keep = True
# cost = self.cost_model.cost(e, pool)
# print("prev={}".format(prev))
# print("seen={}".format(self.seen))
with task("comparing to cached equivalents"):
for prev_exp in prev:
event("previous: {}".format(pprint(prev_exp)))
# prev_cost = self.cost_model.cost(prev_exp, pool)
# ordering = cost.compare_to(prev_cost)
to_keep = eviction_policy(e, context, prev_exp,
context, pool,
cost_model)
if e not in to_keep:
_skip(e, context, pool,
"preferring {}".format(pprint(prev_exp)))
should_keep = False
break
# if ordering == Order.LT:
# pass
# elif ordering == Order.GT:
# self.blacklist.add(e_key)
# _skip(e, context, pool, "worse than {}".format(pprint(prev_exp)))
# should_keep = False
# break
# else:
# self.blacklist.add(e_key)
# _skip(e, context, pool, "{} to cached {}".format(
# "equal" if ordering == Order.EQUAL else "similar",
# pprint(prev_exp)))
# assert ordering in (Order.EQUAL, Order.AMBIGUOUS)
# should_keep = False
# break
if should_keep:
with task("evicting"):
to_evict = []
for (key, exps) in self.cache.items():
(p, s, c) = key
if p == pool and c in itertools.chain(
[context], parent_contexts(context)):
for ee in exps:
if ee.fingerprint == fp: # and cost_model.compare(e, ee.e, context, pool) == Order.LT:
# to_evict.append((key, ee))
to_keep = eviction_policy(
e, context, ee.e, c, pool,
cost_model)
if ee.e not in to_keep:
to_evict.append((key, ee))
for key, ee in to_evict:
(p, s, c) = key
# self.blacklist.add((ee.e, c, pool))
_evict(ee.e, c, pool, e)
self.cache[key].remove(ee)
self.seen[(c, p, fp)].remove(ee.e)
_accept(e, context, pool)
seen_key = (context, pool, fp)
if seen_key not in self.seen:
self.seen[seen_key] = []
self.seen[seen_key].append(e)
info = EnumeratedExp(e=e, fingerprint=fp, cost=None)
res.append(info)
yield info
with task("accelerating"):
to_try = make_random_access(
self.heuristics(e, context, pool))
if to_try:
# print("trying {} accelerations".format(len(to_try)))
queue = itertools.chain(to_try, queue)
# print("EXIT {}".format(k))
self.in_progress.remove(k)
def enumerate_with_info(self, context: Context, size: int,
pool: Pool) -> [EnumeratedExp]:
canonical_context = self.canonical_context(context)
if canonical_context is not context:
print("adapting request: {} ---> {}".format(
context, canonical_context))
for info in self.enumerate_with_info(canonical_context, size,
pool):
yield info._replace(e=context.adapt(info.e, canonical_context))
return
examples = context.instantiate_examples(self.examples)
if context.parent() is not None:
for info in self.enumerate_with_info(context.parent(), size, pool):
e = info.e
yield EnumeratedExp(e=e, fingerprint=fingerprint(e, examples))
k = (pool, size, context)
res = self.cache.get(k)
if res is not None:
for e in res:
yield e
else:
assert k not in self.in_progress, "recursive enumeration?? {}".format(
k)
self.in_progress.add(k)
res = []
self.cache[k] = res
queue = self.enumerate_core(context, size, pool)
cost_model = self.cost_model
while True:
if self.stop_callback():
raise StopException()
try:
e = next(queue)
except StopIteration:
break
fvs = free_vars(e)
if not belongs_in_context(fvs, context):
continue
e = freshen_binders(e, context)
_consider(e, size, context, pool)
wf = self.check_wf(e, context, pool)
if not wf:
_skip(e, size, context, pool, "wf={}".format(wf))
continue
fp = fingerprint(e, examples)
# collect all expressions from parent contexts
with task("collecting prev exps",
size=size,
context=context,
pool=pool_name(pool)):
prev = []
for sz in range(0, size + 1):
prev.extend(self.enumerate_with_info(
context, sz, pool))
prev = [p.e for p in prev if p.fingerprint == fp]
if any(alpha_equivalent(e, p) for p in prev):
_skip(e, size, context, pool, "duplicate")
should_keep = False
else:
# decide whether to keep this expression
should_keep = True
with task("comparing to cached equivalents"):
for prev_exp in prev:
event("previous: {}".format(pprint(prev_exp)))
to_keep = eviction_policy(e, context, prev_exp,
context, pool,
cost_model)
if e not in to_keep:
_skip(e, size, context, pool,
"preferring {}".format(pprint(prev_exp)))
should_keep = False
break
if should_keep:
if self.do_eviction:
with task("evicting"):
to_evict = []
for (key, exps) in self.cache.items():
(p, s, c) = key
if p == pool and c == context:
for ee in exps:
if ee.fingerprint == fp:
event("considering eviction of {}".
format(pprint(ee.e)))
to_keep = eviction_policy(
e, context, ee.e, c, pool,
cost_model)
if ee.e not in to_keep:
to_evict.append((key, ee))
for key, ee in to_evict:
(p, s, c) = key
_evict(ee.e, s, c, pool, e)
self.cache[key].remove(ee)
self.seen[(c, p, fp)].remove(ee.e)
_accept(e, size, context, pool)
seen_key = (context, pool, fp)
if seen_key not in self.seen:
self.seen[seen_key] = []
self.seen[seen_key].append(e)
info = EnumeratedExp(e=e, fingerprint=fp)
res.append(info)
yield info
with task("accelerating"):
to_try = make_random_access(
self.heuristics(e, context, pool))
if to_try:
event("trying {} accelerations of {}".format(
len(to_try), pprint(e)))
queue = itertools.chain(to_try, queue)
self.in_progress.remove(k)
def good_idea(solver,
e: Exp,
context: Context,
pool=RUNTIME_POOL,
assumptions: Exp = T) -> bool:
"""Heuristic filter to ignore expressions that are almost certainly useless."""
state_vars = OrderedSet(v for v, p in context.vars() if p == STATE_POOL)
args = OrderedSet(v for v, p in context.vars() if p == RUNTIME_POOL)
assumptions = EAll([assumptions, context.path_condition()])
at_runtime = pool == RUNTIME_POOL
if isinstance(e, EStateVar) and not free_vars(e.e):
return No("constant value in state position")
if (isinstance(e, EDropFront)
or isinstance(e, EDropBack)) and not at_runtime:
return No("EDrop* in state position")
if not allow_big_sets.value and isinstance(e, EFlatMap) and not at_runtime:
return No("EFlatMap in state position")
if not allow_int_arithmetic_state.value and not at_runtime and isinstance(
e, EBinOp) and e.type == INT:
return No("integer arithmetic in state position")
if is_collection(e.type) and not is_scalar(e.type.t):
return No("collection of nonscalar")
if isinstance(e.type, TMap) and not is_scalar(e.type.k):
return No("bad key type {}".format(pprint(e.type.k)))
if isinstance(e.type, TMap) and isinstance(e.type.v, TMap):
return No("map to map")
# This check is probably a bad idea: whether `the` is legal may depend on
# the contex that the expression is embedded within, so we can't skip it
# during synthesis just because it looks invalid now.
# if isinstance(e, EUnaryOp) and e.op == UOp.The:
# len = EUnaryOp(UOp.Length, e.e).with_type(INT)
# if not valid(EImplies(assumptions, EBinOp(len, "<=", ENum(1).with_type(INT)).with_type(BOOL))):
# return No("illegal application of 'the': could have >1 elems")
if not at_runtime and isinstance(
e, EBinOp) and e.op == "-" and is_collection(e.type):
return No("collection subtraction in state position")
# if not at_runtime and isinstance(e, ESingleton):
# return No("singleton in state position")
# if not at_runtime and isinstance(e, ENum) and e.val != 0 and e.type == INT:
# return No("nonzero integer constant in state position")
if at_runtime and isinstance(e, EStateVar) and isinstance(
e.e, EBinOp) and is_scalar(e.e.e1.type) and is_scalar(e.e.e2.type):
return No("constant-time binary operator in state position")
if not allow_conditional_state.value and not at_runtime and isinstance(
e, ECond):
return No("conditional in state position")
if isinstance(e, EMakeMap2) and isinstance(e.e, EEmptyList):
return No("trivially empty map")
if not allow_peels.value and not at_runtime and isinstance(e, EFilter):
# catch "peels": removal of zero or one elements
if solver.valid(
EImplies(
assumptions,
ELe(
ELen(
EFilter(e.e, ELambda(e.p.arg, ENot(
e.p.body))).with_type(e.type)), ONE))):
return No("filter is a peel")
if not allow_big_maps.value and not at_runtime and isinstance(
e, EMakeMap2) and is_collection(e.type.v):
all_collections = [sv for sv in state_vars if is_collection(sv.type)]
total_size = ENum(0).with_type(INT)
for c in all_collections:
total_size = EBinOp(total_size, "+",
EUnaryOp(UOp.Length,
c).with_type(INT)).with_type(INT)
my_size = EUnaryOp(
UOp.Length,
EFlatMap(EUnaryOp(UOp.Distinct, e.e).with_type(e.e.type),
e.value).with_type(e.type.v)).with_type(INT)
s = EImplies(assumptions,
EBinOp(total_size, ">=", my_size).with_type(BOOL))
if not solver.valid(s):
# from cozy.evaluation import eval
# from cozy.solver import satisfy
# model = satisfy(EAll([assumptions, EBinOp(total_size, "<", my_size).with_type(BOOL)]), collection_depth=3, validate_model=True)
# assert model is not None
# return No("non-polynomial-sized map ({}); total_size={}, this_size={}".format(model, eval(total_size, model), eval(my_size, model)))
return No("non-polynomial-sized map")
return True
def _try_optimize(e: Exp, context: Context, pool: Pool):
if not accelerate.value:
return
if pool != RUNTIME_POOL:
return
state_vars = [v for v, p in context.vars() if p == STATE_POOL]
args = [v for v, p in context.vars() if p == RUNTIME_POOL]
# ---------------------------------------------------------------------
# "Rewrite schemes": these trigger on many different AST shapes
# They are listed first because they are more powerful than the
# specific rewrite rules below.
if not free_vars(e) and not free_funcs(e):
try:
yield _check(uneval(e.type, eval(e, {})), context, RUNTIME_POOL)
except NotImplementedError:
print("Unable to evaluate {!r}".format(e))
if all(v in state_vars for v in free_vars(e)):
nsv = strip_EStateVar(e)
sv = EStateVar(nsv).with_type(e.type)
yield _check(sv, context, RUNTIME_POOL)
for ee in fold_into_map(e, context):
yield _check(ee, context, pool)
# ---------------------------------------------------------------------
# "Rewrites": these trigger on specific AST nodes
if isinstance(e, EBinOp):
if e.op == "-" and is_collection(e.type):
ee = optimized_bag_difference(e.e1, e.e2)
yield _check(ee, context, RUNTIME_POOL)
if e.op == "===" and isinstance(e.e1.type, THandle):
yield _check(
EAll([
optimized_eq(optimized_addr(e.e1), optimized_addr(e.e2)),
optimized_eq(optimized_val(e.e1),
optimized_val(e.e2)).with_type(BOOL)
]), context, RUNTIME_POOL)
if e.op == BOp.In:
ee = optimized_in(e.e1, e.e2)
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, ECond):
yield _check(optimized_cond(e.cond, e.then_branch, e.else_branch),
context, RUNTIME_POOL)
if isinstance(e, EGetField):
for ee in optimized_get_field(e.e, e.field_name, args):
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EListGet) and e.index == ZERO:
for res in optimized_the(e.e, args):
yield _check(res, context, RUNTIME_POOL)
if isinstance(e, EListGet) and isinstance(e.e, ECond):
yield optimized_cond(
e.e.cond,
EListGet(e.e.then_branch, e.index).with_type(e.type),
EListGet(e.e.else_branch, e.index).with_type(e.type))
from cozy.structures.treemultiset import ETreeMultisetElems, ETreeMultisetPeek
if isinstance(e, EListGet) and isinstance(e.e, ETreeMultisetElems):
yield ETreeMultisetPeek(e.e.e, e.index).with_type(e.type)
if isinstance(e, EMapGet):
ee = inline_mapget(e, context)
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EUnaryOp):
if e.op == UOp.Sum:
for ee in optimized_sum(e.e, args):
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.Length:
ee = optimized_len(e.e)
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.Empty:
ee = optimized_empty(e.e)
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.Exists:
ee = optimized_exists(e.e)
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.Distinct:
for ee in optimized_distinct(e.e, args):
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.The:
for ee in optimized_the(e.e, args):
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EArgMin) or isinstance(e, EArgMax):
for ee in optimized_best(e.e,
e.key_function,
"<" if isinstance(e, EArgMin) else ">",
args=args):
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EFilter):
for ee in optimized_filter(e.e, e.predicate, args=args):
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EMap):
for ee in optimized_map(e.e, e.transform_function, args=args):
yield _check(ee, context, RUNTIME_POOL)
from cozy.syntax import ESorted
from cozy.structures.treemultiset import EMakeMaxTreeMultiset, TMaxTreeMultiset, EMakeMinTreeMultiset, TMinTreeMultiset, ETreeMultisetElems
target = e
if isinstance(target, ESorted) and isinstance(target.e, EStateVar):
e_max = EMakeMaxTreeMultiset(target.e.e).with_type(
TMaxTreeMultiset(target.e.e.type.elem_type))
e_min = EMakeMinTreeMultiset(target.e.e).with_type(
TMinTreeMultiset(target.e.e.type.elem_type))
ee = optimized_cond(
target.asc,
ETreeMultisetElems(EStateVar(e_min).with_type(
e_min.type)).with_type(target.type),
ETreeMultisetElems(EStateVar(e_max).with_type(
e_max.type)).with_type(target.type))
yield _check(ee, context, RUNTIME_POOL)
def enumerate_core(self, context: Context, size: int, pool: Pool) -> [Exp]:
"""
Arguments:
conext : a Context object describing the vars in scope
size : size to enumerate
pool : pool to enumerate
Yields all expressions of the given size legal in the given context and
pool.
"""
if size < 0:
return
if size == 0:
for (e, p) in LITERALS:
if p == pool:
yield e
for (v, p) in context.vars():
if p == pool:
yield v
for t in all_types(v):
yield construct_value(t)
for (e, ctx, p) in self.hints:
if p == pool and ctx.alpha_equivalent(context):
yield context.adapt(e, ctx)
for t in all_types(e):
yield construct_value(t)
return
yield from self.heuristic_enumeration(context, size, pool)
for e in collections(self.enumerate(context, size - 1, pool)):
yield EEmptyList().with_type(e.type)
if is_numeric(e.type.t):
yield EUnaryOp(UOp.Sum, e).with_type(e.type.t)
for e in self.enumerate(context, size - 1, pool):
yield ESingleton(e).with_type(TBag(e.type))
for e in self.enumerate(context, size - 1, pool):
if isinstance(e.type, TRecord):
for (f, t) in e.type.fields:
yield EGetField(e, f).with_type(t)
for e in self.enumerate(context, size - 1, pool):
if isinstance(e.type, THandle):
yield EGetField(e, "val").with_type(e.type.value_type)
for e in self.enumerate(context, size - 1, pool):
if isinstance(e.type, TTuple):
for n in range(len(e.type.ts)):
yield ETupleGet(e, n).with_type(e.type.ts[n])
for e in of_type(self.enumerate(context, size - 1, pool), BOOL):
yield EUnaryOp(UOp.Not, e).with_type(BOOL)
for e in self.enumerate(context, size - 1, pool):
if is_numeric(e.type):
yield EUnaryOp("-", e).with_type(e.type)
for m in self.enumerate(context, size - 1, pool):
if isinstance(m.type, TMap):
yield EMapKeys(m).with_type(TBag(m.type.k))
for (sz1, sz2) in pick_to_sum(2, size - 1):
for a1 in self.enumerate(context, sz1, pool):
t = a1.type
if not is_numeric(t):
continue
for a2 in of_type(self.enumerate(context, sz2, pool), t):
yield EBinOp(a1, "+", a2).with_type(t)
yield EBinOp(a1, "-", a2).with_type(t)
yield EBinOp(a1, ">", a2).with_type(BOOL)
yield EBinOp(a1, "<", a2).with_type(BOOL)
yield EBinOp(a1, ">=", a2).with_type(BOOL)
yield EBinOp(a1, "<=", a2).with_type(BOOL)
for a1 in collections(self.enumerate(context, sz1, pool)):
for a2 in of_type(self.enumerate(context, sz2, pool), a1.type):
yield EBinOp(a1, "+", a2).with_type(a1.type)
yield EBinOp(a1, "-", a2).with_type(a1.type)
for a2 in of_type(self.enumerate(context, sz2, pool),
a1.type.t):
yield EBinOp(a2, BOp.In, a1).with_type(BOOL)
for a1 in of_type(self.enumerate(context, sz1, pool), BOOL):
for a2 in of_type(self.enumerate(context, sz2, pool), BOOL):
yield EBinOp(a1, BOp.And, a2).with_type(BOOL)
yield EBinOp(a1, BOp.Or, a2).with_type(BOOL)
for a1 in self.enumerate(context, sz1, pool):
if not isinstance(a1.type, TMap):
for a2 in of_type(self.enumerate(context, sz2, pool),
a1.type):
yield EEq(a1, a2)
yield EBinOp(a1, "!=", a2).with_type(BOOL)
for m in self.enumerate(context, sz1, pool):
if isinstance(m.type, TMap):
for k in of_type(self.enumerate(context, sz2, pool),
m.type.k):
yield EMapGet(m, k).with_type(m.type.v)
yield EHasKey(m, k).with_type(BOOL)
for l in self.enumerate(context, sz1, pool):
if not isinstance(l.type, TList):
continue
for i in of_type(self.enumerate(context, sz2, pool), INT):
yield EListGet(l, i).with_type(l.type.t)
for (sz1, sz2, sz3) in pick_to_sum(3, size - 1):
for cond in of_type(self.enumerate(context, sz1, pool), BOOL):
for then_branch in self.enumerate(context, sz2, pool):
for else_branch in of_type(
self.enumerate(context, sz2, pool),
then_branch.type):
yield ECond(cond, then_branch,
else_branch).with_type(then_branch.type)
for l in self.enumerate(context, sz1, pool):
if not isinstance(l.type, TList):
continue
for st in of_type(self.enumerate(context, sz2, pool), INT):
for ed in of_type(self.enumerate(context, sz3, pool), INT):
yield EListSlice(l, st, ed).with_type(l.type)
for bag in collections(self.enumerate(context, size - 1, pool)):
# len of bag
count = EUnaryOp(UOp.Length, bag).with_type(INT)
yield count
# empty?
yield EUnaryOp(UOp.Empty, bag).with_type(BOOL)
# exists?
yield EUnaryOp(UOp.Exists, bag).with_type(BOOL)
# singleton?
yield EEq(count, ONE)
yield EUnaryOp(UOp.The, bag).with_type(bag.type.t)
yield EUnaryOp(UOp.Distinct, bag).with_type(bag.type)
yield EUnaryOp(UOp.AreUnique, bag).with_type(BOOL)
if bag.type.t == BOOL:
yield EUnaryOp(UOp.Any, bag).with_type(BOOL)
yield EUnaryOp(UOp.All, bag).with_type(BOOL)
def build_lambdas(bag, pool, body_size):
v = fresh_var(bag.type.t, omit=set(v for v, p in context.vars()))
inner_context = UnderBinder(context, v=v, bag=bag, bag_pool=pool)
for lam_body in self.enumerate(inner_context, body_size, pool):
yield ELambda(v, lam_body)
# Iteration
for (sz1, sz2) in pick_to_sum(2, size - 1):
for bag in collections(self.enumerate(context, sz1, pool)):
for lam in build_lambdas(bag, pool, sz2):
body_type = lam.body.type
yield EMap(bag, lam).with_type(TBag(body_type))
if body_type == BOOL:
yield EFilter(bag, lam).with_type(bag.type)
if is_numeric(body_type):
yield EArgMin(bag, lam).with_type(bag.type.t)
yield EArgMax(bag, lam).with_type(bag.type.t)
if is_collection(body_type):
yield EFlatMap(bag, lam).with_type(TBag(body_type.t))
# Enable use of a state-pool expression at runtime
if pool == RUNTIME_POOL:
for e in self.enumerate(context, size - 1, STATE_POOL):
yield EStateVar(e).with_type(e.type)
# Create maps
if pool == STATE_POOL:
for (sz1, sz2) in pick_to_sum(2, size - 1):
for bag in collections(self.enumerate(context, sz1,
STATE_POOL)):
if not is_scalar(bag.type.t):
continue
for lam in build_lambdas(bag, STATE_POOL, sz2):
t = TMap(bag.type.t, lam.body.type)
m = EMakeMap2(bag, lam).with_type(t)
yield m
def possibly_useful_nonrecursive(
solver,
e: Exp,
context: Context,
pool=RUNTIME_POOL,
assumptions: Exp = ETRUE,
ops: [Op] = ()) -> bool:
"""Heuristic filter to ignore expressions that are almost certainly useless."""
state_vars = OrderedSet(v for v, p in context.vars() if p == STATE_POOL)
args = OrderedSet(v for v, p in context.vars() if p == RUNTIME_POOL)
assumptions = EAll([assumptions, context.path_condition()])
at_runtime = pool == RUNTIME_POOL
h = extension_handler(type(e))
if h is not None:
res = h.possibly_useful(e, context, pool, assumptions, ops, solver)
if not res:
return res
if isinstance(e, EStateVar) and not free_vars(e.e):
return No("constant value in state position")
if (isinstance(e, EDropFront)
or isinstance(e, EDropBack)) and not at_runtime:
return No("EDrop* in state position")
if not allow_big_sets.value and isinstance(e, EFlatMap) and not at_runtime:
return No("EFlatMap in state position")
if not allow_int_arithmetic_state.value and not at_runtime and isinstance(
e, EBinOp) and e.type == INT:
return No("integer arithmetic in state position")
if is_collection(e.type) and not is_scalar(e.type.elem_type):
return No("collection of nonscalar: e {}\n elem_type: {}\n".format(
e, e.type.elem_type))
if isinstance(e.type, TMap) and not is_scalar(e.type.k):
return No("bad key type {}".format(pprint(e.type.k)))
if isinstance(e.type, TMap) and isinstance(e.type.v, TMap):
return No("map to map")
# This check is probably a bad idea: whether `the` is legal may depend on
# the contex that the expression is embedded within, so we can't skip it
# during synthesis just because it looks invalid now.
# if isinstance(e, EUnaryOp) and e.op == UOp.The:
# len = EUnaryOp(UOp.Length, e.e).with_type(INT)
# if not valid(EImplies(assumptions, EBinOp(len, "<=", ENum(1).with_type(INT)).with_type(BOOL))):
# return No("illegal application of 'the': could have >1 elems")
if not at_runtime and isinstance(
e, EBinOp) and e.op == "-" and is_collection(e.type):
return No("collection subtraction in state position")
# if not at_runtime and isinstance(e, ESingleton):
# return No("singleton in state position")
if not allow_nonzero_state_constants.value and not at_runtime and isinstance(
e, ENum) and e.val != 0:
return No("nonzero integer constant in state position")
if not allow_binop_state.value and at_runtime and isinstance(
e, EStateVar) and isinstance(e.e, EBinOp) and is_scalar(
e.e.e1.type) and is_scalar(e.e.e2.type):
return No(
"constant-time binary operator {!r} in state position".format(
e.e.op))
if not allow_conditional_state.value and not at_runtime and isinstance(
e, ECond):
return No("conditional in state position")
if isinstance(e, EMakeMap2) and isinstance(e.e, EEmptyList):
return No("trivially empty map")
if isinstance(e, EMakeMap2) and isinstance(e.e, ESingleton):
return No("really tiny map")
if not at_runtime and (isinstance(e, EArgMin) or isinstance(e, EArgMax)):
# Cozy has no way to efficiently implement mins/maxes when more than
# one element may leave the collection.
from cozy.state_maintenance import mutate
for op in ops:
elems = e.e
elems_prime = mutate(elems, op.body)
formula = EAll([assumptions] + list(op.assumptions) + [
EGt(
ELen(
EBinOp(elems, "-", elems_prime).with_type(elems.type)),
ONE)
])
if solver.satisfiable(formula):
return No(
"more than one element might be removed during {}".format(
op.name))
if not allow_peels.value and not at_runtime and isinstance(e, EFilter):
# catch "peels": removal of zero or one elements
if solver.valid(
EImplies(
assumptions,
ELe(
ELen(
EFilter(
e.e,
ELambda(e.predicate.arg, ENot(
e.predicate.body))).with_type(e.type)),
ONE))):
return No("filter is a peel")
if not allow_big_maps.value and not at_runtime and isinstance(
e, EMakeMap2) and is_collection(e.type.v):
all_collections = [sv for sv in state_vars if is_collection(sv.type)]
total_size = ENum(0).with_type(INT)
for c in all_collections:
total_size = EBinOp(total_size, "+",
EUnaryOp(UOp.Length,
c).with_type(INT)).with_type(INT)
my_size = EUnaryOp(
UOp.Length,
EFlatMap(
EUnaryOp(UOp.Distinct, e.e).with_type(e.e.type),
e.value_function).with_type(e.type.v)).with_type(INT)
s = EImplies(assumptions,
EBinOp(total_size, ">=", my_size).with_type(BOOL))
if not solver.valid(s):
return No("non-polynomial-sized map")
return True
def _try_optimize(e : Exp, context : Context, pool : Pool):
if not accelerate.value:
return
if pool != RUNTIME_POOL:
return
state_vars = [v for v, p in context.vars() if p == STATE_POOL]
args = [v for v, p in context.vars() if p == RUNTIME_POOL]
# ---------------------------------------------------------------------
# "Rewrite schemes": these trigger on many different AST shapes
# They are listed first because they are more powerful than the
# specific rewrite rules below.
if not free_vars(e) and not free_funcs(e):
try:
yield _check(uneval(e.type, eval(e, {})), context, RUNTIME_POOL)
except NotImplementedError:
print("Unable to evaluate {!r}".format(e))
if all(v in state_vars for v in free_vars(e)):
nsv = strip_EStateVar(e)
sv = EStateVar(nsv).with_type(e.type)
yield _check(sv, context, RUNTIME_POOL)
for ee in fold_into_map(e, context):
yield _check(ee, context, pool)
# ---------------------------------------------------------------------
# "Rewrites": these trigger on specific AST nodes
if isinstance(e, EBinOp):
if e.op == "-" and is_collection(e.type):
ee = optimized_bag_difference(e.e1, e.e2)
yield _check(ee, context, RUNTIME_POOL)
if e.op == "===" and isinstance(e.e1.type, THandle):
yield _check(EAll([
optimized_eq(optimized_addr(e.e1), optimized_addr(e.e2)),
optimized_eq(optimized_val(e.e1), optimized_val(e.e2)).with_type(BOOL)]), context, RUNTIME_POOL)
if e.op == BOp.In:
ee = optimized_in(e.e1, e.e2)
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, ECond):
yield _check(optimized_cond(e.cond, e.then_branch, e.else_branch), context, RUNTIME_POOL)
if isinstance(e, EGetField):
for ee in optimized_get_field(e.e, e.field_name, args):
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EListGet) and e.index == ZERO:
for res in optimized_the(e.e, args):
yield _check(res, context, RUNTIME_POOL)
if isinstance(e, EListGet) and isinstance(e.e, ECond):
yield optimized_cond(e.e.cond,
EListGet(e.e.then_branch, e.index).with_type(e.type),
EListGet(e.e.else_branch, e.index).with_type(e.type))
from cozy.structures.treemultiset import ETreeMultisetElems, ETreeMultisetPeek
if isinstance(e, EListGet) and isinstance(e.e, ETreeMultisetElems):
yield ETreeMultisetPeek(e.e.e, e.index).with_type(e.type)
if isinstance(e, EMapGet):
ee = inline_mapget(e, context)
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EUnaryOp):
if e.op == UOp.Sum:
for ee in optimized_sum(e.e, args):
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.Length:
ee = optimized_len(e.e)
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.Empty:
ee = optimized_empty(e.e)
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.Exists:
ee = optimized_exists(e.e)
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.Distinct:
for ee in optimized_distinct(e.e, args):
yield _check(ee, context, RUNTIME_POOL)
if e.op == UOp.The:
for ee in optimized_the(e.e, args):
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EArgMin) or isinstance(e, EArgMax):
for ee in optimized_best(e.e, e.key_function, "<" if isinstance(e, EArgMin) else ">", args=args):
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EFilter):
for ee in optimized_filter(e.e, e.predicate, args=args):
yield _check(ee, context, RUNTIME_POOL)
if isinstance(e, EMap):
for ee in optimized_map(e.e, e.transform_function, args=args):
yield _check(ee, context, RUNTIME_POOL)
from cozy.syntax import ESorted
from cozy.structures.treemultiset import EMakeMaxTreeMultiset, TMaxTreeMultiset, EMakeMinTreeMultiset, TMinTreeMultiset, ETreeMultisetElems
target = e
if isinstance(target, ESorted) and isinstance(target.e, EStateVar):
e_max = EMakeMaxTreeMultiset(target.e.e).with_type(TMaxTreeMultiset(target.e.e.type.elem_type))
e_min = EMakeMinTreeMultiset(target.e.e).with_type(TMinTreeMultiset(target.e.e.type.elem_type))
ee = optimized_cond(target.asc,
ETreeMultisetElems(EStateVar(e_min).with_type(e_min.type)).with_type(target.type),
ETreeMultisetElems(EStateVar(e_max).with_type(e_max.type)).with_type(target.type))
yield _check(ee, context, RUNTIME_POOL)
def possibly_useful_nonrecursive(solver, e : Exp, context : Context, pool = RUNTIME_POOL, assumptions : Exp = ETRUE, ops : [Op] = ()) -> bool:
"""Heuristic filter to ignore expressions that are almost certainly useless."""
state_vars = OrderedSet(v for v, p in context.vars() if p == STATE_POOL)
args = OrderedSet(v for v, p in context.vars() if p == RUNTIME_POOL)
assumptions = EAll([assumptions, context.path_condition()])
at_runtime = pool == RUNTIME_POOL
h = extension_handler(type(e))
if h is not None:
res = h.possibly_useful(e, context, pool, assumptions, ops, solver)
if not res:
return res
if isinstance(e, EStateVar) and not free_vars(e.e):
return No("constant value in state position")
if (isinstance(e, EDropFront) or isinstance(e, EDropBack)) and not at_runtime:
return No("EDrop* in state position")
if not allow_big_sets.value and isinstance(e, EFlatMap) and not at_runtime:
return No("EFlatMap in state position")
if not allow_int_arithmetic_state.value and not at_runtime and isinstance(e, EBinOp) and e.type == INT:
return No("integer arithmetic in state position")
if is_collection(e.type) and not is_scalar(e.type.elem_type):
return No("collection of nonscalar: e {}\n elem_type: {}\n".format(e, e.type.elem_type))
if isinstance(e.type, TMap) and not is_scalar(e.type.k):
return No("bad key type {}".format(pprint(e.type.k)))
if isinstance(e.type, TMap) and isinstance(e.type.v, TMap):
return No("map to map")
# This check is probably a bad idea: whether `the` is legal may depend on
# the contex that the expression is embedded within, so we can't skip it
# during synthesis just because it looks invalid now.
# if isinstance(e, EUnaryOp) and e.op == UOp.The:
# len = EUnaryOp(UOp.Length, e.e).with_type(INT)
# if not valid(EImplies(assumptions, EBinOp(len, "<=", ENum(1).with_type(INT)).with_type(BOOL))):
# return No("illegal application of 'the': could have >1 elems")
if not at_runtime and isinstance(e, EBinOp) and e.op == "-" and is_collection(e.type):
return No("collection subtraction in state position")
# if not at_runtime and isinstance(e, ESingleton):
# return No("singleton in state position")
if not allow_nonzero_state_constants.value and not at_runtime and isinstance(e, ENum) and e.val != 0:
return No("nonzero integer constant in state position")
if not allow_binop_state.value and at_runtime and isinstance(e, EStateVar) and isinstance(e.e, EBinOp) and is_scalar(e.e.e1.type) and is_scalar(e.e.e2.type):
return No("constant-time binary operator {!r} in state position".format(e.e.op))
if not allow_conditional_state.value and not at_runtime and isinstance(e, ECond):
return No("conditional in state position")
if isinstance(e, EMakeMap2) and isinstance(e.e, EEmptyList):
return No("trivially empty map")
if isinstance(e, EMakeMap2) and isinstance(e.e, ESingleton):
return No("really tiny map")
if not at_runtime and (isinstance(e, EArgMin) or isinstance(e, EArgMax)):
# Cozy has no way to efficiently implement mins/maxes when more than
# one element may leave the collection.
from cozy.state_maintenance import mutate
for op in ops:
elems = e.e
elems_prime = mutate(elems, op.body)
formula = EAll([assumptions] + list(op.assumptions) + [EGt(ELen(EBinOp(elems, "-", elems_prime).with_type(elems.type)), ONE)])
if solver.satisfiable(formula):
return No("more than one element might be removed during {}".format(op.name))
if not allow_peels.value and not at_runtime and isinstance(e, EFilter):
# catch "peels": removal of zero or one elements
if solver.valid(EImplies(assumptions, ELe(ELen(EFilter(e.e, ELambda(e.predicate.arg, ENot(e.predicate.body))).with_type(e.type)), ONE))):
return No("filter is a peel")
if not allow_big_maps.value and not at_runtime and isinstance(e, EMakeMap2) and is_collection(e.type.v):
all_collections = [sv for sv in state_vars if is_collection(sv.type)]
total_size = ENum(0).with_type(INT)
for c in all_collections:
total_size = EBinOp(total_size, "+", EUnaryOp(UOp.Length, c).with_type(INT)).with_type(INT)
my_size = EUnaryOp(UOp.Length, EFlatMap(EUnaryOp(UOp.Distinct, e.e).with_type(e.e.type), e.value_function).with_type(e.type.v)).with_type(INT)
s = EImplies(
assumptions,
EBinOp(total_size, ">=", my_size).with_type(BOOL))
if not solver.valid(s):
return No("non-polynomial-sized map")
return True