def test_in_1(self):
@vsc.randobj
class my_c(object):
def __init__(self):
self.a = vsc.rand_uint32_t()
@vsc.constraint
def ab_c(self):
self.a in vsc.rangelist(1, 4, 8)
my_item = my_c()
model = my_item.get_model()
visitor = VariableBoundVisitor()
visitor.process([model], [])
a_model = model.find_field("a")
self.assertIsNotNone(a_model)
self.assertTrue(a_model in visitor.bound_m.keys())
a_bounds = visitor.bound_m[a_model]
self.assertEqual(3, len(a_bounds.domain.range_l))
# a in 1, 4, 8
self.assertEqual(1, a_bounds.domain.range_l[0][0])
self.assertEqual(1, a_bounds.domain.range_l[0][1])
self.assertEqual(4, a_bounds.domain.range_l[1][0])
self.assertEqual(4, a_bounds.domain.range_l[1][1])
self.assertEqual(8, a_bounds.domain.range_l[2][0])
self.assertEqual(8, a_bounds.domain.range_l[2][1])
def test_in_lt_limited(self):
@vsc.randobj
class my_c(object):
def __init__(self):
self.a = vsc.rand_uint32_t()
self.b = vsc.rand_uint16_t()
self.c = vsc.rand_uint16_t()
@vsc.constraint
def ab_c(self):
self.a < 8
self.a in vsc.rangelist(1, 4, 8)
self.b in vsc.rangelist((2, 4), (8, 12))
self.b < 11
self.c in vsc.rangelist((2, 4), (8, 12))
self.c < 4
my_item = my_c()
model = my_item.get_model()
visitor = VariableBoundVisitor()
visitor.process([model], [])
a_model = model.find_field("a")
b_model = model.find_field("b")
c_model = model.find_field("c")
self.assertIsNotNone(a_model)
self.assertIsNotNone(b_model)
self.assertIsNotNone(c_model)
self.assertTrue(a_model in visitor.bound_m.keys())
self.assertTrue(b_model in visitor.bound_m.keys())
self.assertTrue(c_model in visitor.bound_m.keys())
a_bounds = visitor.bound_m[a_model]
b_bounds = visitor.bound_m[b_model]
c_bounds = visitor.bound_m[c_model]
self.assertEqual(2, len(a_bounds.domain.range_l))
self.assertEqual(2, len(b_bounds.domain.range_l))
self.assertEqual(1, len(c_bounds.domain.range_l))
# a in 1, 4, 8
self.assertEqual(1, a_bounds.domain.range_l[0][0])
self.assertEqual(1, a_bounds.domain.range_l[0][1])
self.assertEqual(4, a_bounds.domain.range_l[1][0])
self.assertEqual(4, a_bounds.domain.range_l[1][1])
# b in [2..4] [8..12]
self.assertEqual(2, b_bounds.domain.range_l[0][0])
self.assertEqual(4, b_bounds.domain.range_l[0][1])
self.assertEqual(8, b_bounds.domain.range_l[1][0])
self.assertEqual(10, b_bounds.domain.range_l[1][1])
self.assertEqual(2, c_bounds.domain.range_l[0][0])
self.assertEqual(3, c_bounds.domain.range_l[0][1])
def do_randomize(field_model_l: List[FieldModel],
constraint_l: List[ConstraintModel] = None):
# All fields passed to do_randomize are treated
# as randomizable
seed = Randomizer._rng.randint(0, (1 << 64) - 1)
for f in field_model_l:
f.set_used_rand(True, 0)
# print("Initial Model:")
# for fm in field_model_l:
# print(" " + ModelPrettyPrinter.print(fm))
if constraint_l is None:
constraint_l = []
# Collect all variables (pre-array) and establish bounds
bounds_v = VariableBoundVisitor()
bounds_v.process(field_model_l, constraint_l, False)
# TODO: need to handle inline constraints that impact arrays
constraints_len = len(constraint_l)
for fm in field_model_l:
constraint_l.extend(
ArrayConstraintBuilder.build(fm, bounds_v.bound_m))
# Now, handle dist constraints
DistConstraintBuilder.build(seed, fm)
# If we made changes during array remodeling,
# re-run bounds checking on the updated model
# if len(constraint_l) != constraints_len:
bounds_v.process(field_model_l, constraint_l)
# print("Final Model:")
# for fm in field_model_l:
# print(" " + ModelPrettyPrinter.print(fm))
# for c in constraint_l:
# print(" " + ModelPrettyPrinter.print(c, show_exp=True))
# First, invoke pre_randomize on all elements
for fm in field_model_l:
fm.pre_randomize()
r = Randomizer()
ri = RandInfoBuilder.build(field_model_l, constraint_l,
Randomizer._rng)
try:
r.randomize(ri, bounds_v.bound_m)
finally:
# Rollback any constraints we've replaced for arrays
for fm in field_model_l:
ConstraintOverrideRollbackVisitor.rollback(fm)
for fm in field_model_l:
fm.post_randomize()
def test_enum_range_limited(self):
class my_e(IntEnum):
A = 0
B = auto()
C = auto()
D = auto()
@vsc.randobj
class my_c(object):
def __init__(self):
self.a = vsc.rand_enum_t(my_e)
@vsc.constraint
def ab_c(self):
self.a.inside(vsc.rangelist(vsc.rng(my_e.A, my_e.C), my_e.D))
self.a >= my_e.A
self.a <= my_e.C
my_item = my_c()
model = my_item.get_model()
visitor = VariableBoundVisitor()
visitor.process([model], [])
a_model = model.find_field("a")
self.assertIsNotNone(a_model)
self.assertTrue(a_model in visitor.bound_m.keys())
a_bounds = visitor.bound_m[a_model]
self.assertEqual(3, len(a_bounds.domain.range_l))
# a in 1, 4, 8
self.assertEqual(0, a_bounds.domain.range_l[0][0])
self.assertEqual(0, a_bounds.domain.range_l[0][1])
self.assertEqual(1, a_bounds.domain.range_l[1][0])
self.assertEqual(1, a_bounds.domain.range_l[1][1])
self.assertEqual(2, a_bounds.domain.range_l[2][0])
self.assertEqual(2, a_bounds.domain.range_l[2][1])
def test_smoke(self):
@vsc.randobj
class my_c(object):
def __init__(self):
self.a = vsc.rand_uint32_t()
self.b = vsc.rand_uint16_t()
@vsc.constraint
def ab_c(self):
self.a < 10
self.b < 4
my_item = my_c()
model = my_item.get_model()
visitor = VariableBoundVisitor()
visitor.process([model], [])
a_model = model.find_field("a")
b_model = model.find_field("b")
self.assertIsNotNone(a_model)
self.assertIsNotNone(b_model)
self.assertTrue(a_model in visitor.bound_m.keys())
self.assertTrue(b_model in visitor.bound_m.keys())
a_bounds = visitor.bound_m[a_model]
b_bounds = visitor.bound_m[b_model]
self.assertEqual(1, len(a_bounds.domain.range_l))
self.assertEqual(1, len(b_bounds.domain.range_l))
# a in [0..9]
self.assertEqual(0, a_bounds.domain.range_l[0][0])
self.assertEqual(9, a_bounds.domain.range_l[0][1])
# b in [0..9]
self.assertEqual(0, b_bounds.domain.range_l[0][0])
self.assertEqual(3, b_bounds.domain.range_l[0][1])
def do_randomize(randstate,
srcinfo: SourceInfo,
field_model_l: List[FieldModel],
constraint_l: List[ConstraintModel] = None,
debug=0,
lint=0,
solve_fail_debug=0):
if profile_on():
solve_info = SolveInfo()
solve_info.totaltime = time.time()
randomize_start(srcinfo, field_model_l, constraint_l)
else:
solve_info = None
clear_soft_priority = ClearSoftPriorityVisitor()
for f in field_model_l:
f.set_used_rand(True, 0)
clear_soft_priority.clear(f)
if debug > 0:
print("Initial Model:")
for fm in field_model_l:
print(" " + ModelPrettyPrinter.print(fm))
# First, invoke pre_randomize on all elements
for fm in field_model_l:
fm.pre_randomize()
if constraint_l is None:
constraint_l = []
for c in constraint_l:
clear_soft_priority.clear(c)
# Collect all variables (pre-array) and establish bounds
bounds_v = VariableBoundVisitor()
bounds_v.process(field_model_l, constraint_l, False)
# TODO: need to handle inline constraints that impact arrays
constraints_len = len(constraint_l)
for fm in field_model_l:
constraint_l.extend(
ArrayConstraintBuilder.build(fm, bounds_v.bound_m))
# Now, handle dist constraints
DistConstraintBuilder.build(randstate, fm)
for c in constraint_l:
constraint_l.extend(
ArrayConstraintBuilder.build(c, bounds_v.bound_m))
# Now, handle dist constraints
DistConstraintBuilder.build(randstate, c)
# If we made changes during array remodeling,
# re-run bounds checking on the updated model
# if len(constraint_l) != constraints_len:
bounds_v.process(field_model_l, constraint_l)
if debug > 0:
print("Final Model:")
for fm in field_model_l:
print(" " + ModelPrettyPrinter.print(fm))
for c in constraint_l:
print(" " + ModelPrettyPrinter.print(c, show_exp=True))
# if lint > 0:
# LintVisitor().lint(
# field_model_l,
# constraint_l)
r = Randomizer(randstate,
solve_info=solve_info,
debug=debug,
lint=lint,
solve_fail_debug=solve_fail_debug)
# if Randomizer._rng is None:
# Randomizer._rng = random.Random(random.randrange(sys.maxsize))
ri = RandInfoBuilder.build(field_model_l, constraint_l,
Randomizer._rng)
try:
r.randomize(ri, bounds_v.bound_m)
finally:
# Rollback any constraints we've replaced for arrays
if solve_info is not None:
solve_info.totaltime = int(
(time.time() - solve_info.totaltime) * 1000)
randomize_done(srcinfo, solve_info)
for fm in field_model_l:
ConstraintOverrideRollbackVisitor.rollback(fm)
for fm in field_model_l:
fm.post_randomize()