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test_typing.py
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test_typing.py
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# Owner(s): ["oncall: jit"]
import os
import sys
import torch
from torch.testing._internal.jit_utils import JitTestCase
from torch.testing._internal.common_utils import IS_WINDOWS
from collections import namedtuple
from typing import List, Tuple, Optional, Dict
# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
if __name__ == "__main__":
raise RuntimeError(
"This test file is not meant to be run directly, use:\n\n"
"\tpython test/test_jit.py TESTNAME\n\n"
"instead."
)
class TestTyping(JitTestCase):
def test_dict_in_not_in(self):
def test_in_dict(x):
# type: (Dict[str, int]) -> bool
return 'hi' in x
self.checkScript(test_in_dict, ({'hi': 2, 'bye': 3},))
self.checkScript(test_in_dict, ({'bye': 3},))
# Check evaluation order
@torch.jit.script
def a():
print("a")
return 3
@torch.jit.script
def b():
print("b")
return {3: 2, 4: 1}
@torch.jit.script
def fn():
return a() in b()
with self.capture_stdout() as captured:
self.assertTrue(fn())
if not IS_WINDOWS:
# no stdout capturing on windows
self.assertEqual(captured[0], "a\nb\n")
def test_not_in_dict(a):
# type: (Dict[str, int]) -> bool
if "hello" not in a:
return False
else:
return True
self.checkScript(test_not_in_dict, ({"hello": 1, "world": 2}, ))
self.checkScript(test_not_in_dict, ({"world": 2}, ))
def test_dict_tensor_key(a, t):
# type: (Dict[Tensor, int], Tensor) -> bool
if t in a:
return True
else:
return False
inp1 = torch.tensor(3)
inp2 = torch.tensor(5)
dict_a = {inp1: 1, inp2: 3}
self.checkScript(test_dict_tensor_key, (dict_a, torch.tensor(4)))
self.checkScript(test_dict_tensor_key, (dict_a, torch.tensor(3)))
self.checkScript(test_dict_tensor_key, (dict_a, inp1))
self.checkScript(test_dict_tensor_key, (dict_a, inp2))
def test_list_type_refinement_annotation_element_mismatch(self):
def fn():
l: List[int] = [1, 2, "foo", 3]
return l
with self.assertRaisesRegex(RuntimeError, "List type annotation"
r" `List\[int\]` did not match the "
"types of the given list elements"):
torch.jit.script(fn)
def test_dict_type_refinement_annotation_key_mismatch(self):
def fn():
l1 = [1, 2, "foo", 3]
l2 = ["foo", "bar", "baz", "qux"]
d: Dict[int, str] = {k : v for k, v in zip(l1, l2)}
return d
with self.assertRaisesRegex(RuntimeError, "Dicts may only "
"contain homogeneous keys, but the "
"type of the first generated key "
r"was Union\[int, str\]"):
torch.jit.script(fn)
def test_dict_type_refinement_annotation_value_mismatch(self):
def fn():
l1 = ["foo", "bar", "baz", "qux"]
l2 = [1, 2, "foo", 3]
d: Dict[str, int] = {k : v for k, v in zip(l1, l2)}
return d
with self.assertRaisesRegex(RuntimeError, "Dict type annotation"
r" `Dict\[str, int\]` did not match"
" the type of an actual value type"
r" `Union\[int, str\]`"):
torch.jit.script(fn)
def test_dict_invalid_annotations(self):
# Check for invalid value type annotation
def wrong_value_type(dictionary: Dict[str, torch.jit.ScriptModule]):
return
with self.assertRaisesRegex(ValueError, "Unknown type annotation"):
torch.jit.script(wrong_value_type)
# Check for invalid key type annotation
def wrong_key_type(dictionary: Dict[torch.jit.ScriptModule, str]):
return
with self.assertRaisesRegex(ValueError, "Unknown type annotation"):
torch.jit.script(wrong_key_type)
# Check for invalid key and value type annotation
def wrong_key_value_type(dictionary: Dict[torch.jit.ScriptModule, torch.jit.ScriptModule]):
return
with self.assertRaisesRegex(ValueError, "Unknown type annotation"):
torch.jit.script(wrong_key_value_type)
def test_tuple_specialization(self):
@torch.jit.script
def f(t, s):
# type: (Tuple[Tensor, Tuple[int, Tensor]], str) -> Tensor
x, t2 = t
_, y = t2
return x + y
t = torch.randn(2, 2), (1, torch.randn(2, 2)),
f(t, "hi")
graph = f.graph_for(t, "hi")
input_types = list(next(graph.inputs()).type().elements())
w = input_types[0]
self.assertEqual(input_types[0].kind(), 'TensorType')
self.assertEqual(input_types[1].elements()[1].kind(), 'TensorType')
def test_tuple_io(self):
def stuff(x):
# type: (Tuple[Tensor, Tensor]) -> Tuple[Tensor, Tensor]
a, b = x
return b, a
a = (torch.rand(3), torch.rand(3))
self.checkScript(stuff, (a,))
def test_tuple_keyword(self):
def bar():
f = tuple((1, 2)) # noqa: C409
return f
self.checkScript(bar, ())
def foo():
return tuple(1, 2)
self.checkScriptRaisesRegex(foo, (), Exception,
"1 argument")
def cant_infer_size():
return tuple([1, 2, 3]) # noqa: C409
with self.assertRaisesRegex(Exception, "cannot statically infer the expected"):
torch.jit.script(cant_infer_size)
def test_tuple_create_return(self):
def stuff2(x):
# type: (int) -> Tuple[Tensor, Tensor]
a = (torch.ones(x), torch.zeros(x))
return a
self.checkScript(stuff2, (3,))
def test_list_io(self):
def stuff3(x):
# type: (List[int]) -> Tuple[Tensor, List[int]]
return torch.ones(x), x
self.checkScript(stuff3, ([3, 2],))
def test_bool_list_io(self):
@torch.jit.script
def stuff4(x):
# type: (List[bool]) -> Tuple[List[bool], List[bool], List[List[bool]]]
return x, [True, False], [[True]]
li_1, li_2, li_3 = stuff4([True])
li_3 = li_3[0]
for li in [li_1, li_2, li_3]:
self.assertTrue(type(li[0]) == type(True))
def test_nested_list(self):
def foo(z):
# type: (Tuple[int, List[List[int]]]) -> int
x, y = z
return y[0][1]
self.checkScript(foo, ((1, [[1, 2], [3, 4]]),))
def test_list_sum(self):
def fn(x: List[int]) -> int:
return sum(x)
def fn1(x: List[float]):
return sum(x)
def fn2(x: List[bool]):
return sum(x)
self.checkScript(fn, ([1, 2, 3], ))
self.checkScript(fn1, ([1.0, 2.0, 3.0], ))
self.checkScript(fn1, ([1, 2.8, 3], ))
self.checkScript(fn2, ([True, False, False], ))
self.checkScript(fn2, ([False, False, False], ))
self.checkScript(fn2, ([0, 1, 1, 0], ))
def test_list_unification(self):
def fn():
return [1, None, 2]
def fn2(x):
return [torch.ones(2, 2), None, x]
self.checkScript(fn, [])
self.checkScript(fn2, (torch.ones(2, 2),))
# to avoid defining sum_list in multiple tests
def get_sum_list_fn(self):
def sum_list(a):
# type: (List[int]) -> int
sum = 0
for i in a:
sum += i
return sum
return sum_list
def test_sum_list_diff_elms(self):
self.checkScript(self.get_sum_list_fn(), ([1, 2, 3, 4, 5],))
def test_sum_list_empty(self):
self.checkScript(self.get_sum_list_fn(), ([],))
def test_sum_list_one(self):
self.checkScript(self.get_sum_list_fn(), ([1],))
def test_sum_list_literal(self):
def sum_list():
# type: () -> int
sum = 0
for i in [1, 2, 3, 4, 5]:
sum += i
return sum
self.checkScript(sum_list, ())
def test_sum_list_wrong_type(self):
with self.assertRaisesRegex(RuntimeError, "'int' object is not iterable"):
@torch.jit.script
def sum_list(a):
# type: (int) -> int
sum = 0
for i in a: # noqa: T484
sum += i
return sum
sum_list(1)
def test_list_iterables(self):
with self.assertRaisesRegex(RuntimeError, 'List of iterables is not supported currently'):
cu = torch.jit.CompilationUnit('''
def list_iterables(x):
for i, j in [2, 3, 4], [5, 6, 7]:
x += i
x += j
return x
''')
def test_for_in_string(self):
def test_strings(x):
# type: (str) -> str
reverse = ""
for c in x:
reverse = c + reverse
return reverse
self.checkScript(test_strings, ("hello",))
self.checkScript(test_strings, ("",))
def test_list_strings(x):
# type: (List[str]) -> str
result = ""
for sub_str in x:
result += sub_str
return result
self.checkScript(test_list_strings, (["hello", "world"],))
self.checkScript(test_list_strings, (["hello", " ", "world", ""],))
def test_for_in_dict(self):
def test_dicts(x):
# type: (Dict[str, int]) -> int
sum = 0
for key in x:
sum += x[key]
return sum
self.checkScript(test_dicts, ({"a": 1, "b": 2, "c": 3},))
def test_dict_keys_values(x):
# type: (Dict[str, int]) -> Tuple[str, int]
key_str = ""
sum = 0
for key in x.keys():
key_str += key
for val in x.values():
sum += val
return key_str, sum
self.checkScript(test_dicts, ({"a": 1, "b": 2, "c": 3},))
def test_for_tuple_unpack(self):
def for_tuple_unpack(x, y):
for i, j in [[3, 4], [5, 6], [7, 8]]:
x += i
y += j
return x, y
self.checkScript(for_tuple_unpack, (torch.tensor(3), torch.tensor(5)))
def nested_tuple_unpack(x, y):
# type: (List[int], List[int]) -> int
sum = 0
for i, (j, k), v in zip(x, enumerate(x), y):
sum += i + j + k + v
return sum
self.checkScript(nested_tuple_unpack, ([1, 3, 5], [2, 4, 6]))
def test_dict_comprehension(self):
def fn():
return {i : chr(i + 65) for i in range(4)}
self.checkScript(fn, ())
def test_dict_comprehension_with_type_annotation(self):
def fn():
d: Dict[int, str] = {i : chr(i + 65) for i in range(4)}
return d
self.checkScript(fn, ())
with self.assertRaisesRegex(RuntimeError, ""):
with self.assertRaisesRegex(AssertionError, "Expected Dict "
"type annotation for dict "
"comprehension, found "
"Tuple[int, str]"):
@torch.jit.script
def fn():
d: Tuple[int, str] = {i : chr(i + 65) for i in range(4)}
return d
def test_dict_comprehension_scope(self):
def comprehension_can_access_outer_scope_variables():
lst = ["foo", "bar", "baz"]
return {l : len(l) for l in lst}
self.checkScript(comprehension_can_access_outer_scope_variables, ())
with self.assertRaisesRegex(RuntimeError, "undefined value i"):
@torch.jit.script
def outer_scope_cannot_access_comprehension_variables():
d = {i : chr(i + 65) for i in range(4)}
i = i + 1
def test_for_tuple_assign(self):
def test_simple_assign(x):
# type: (Tuple[int, float]) -> float
sum = 0.0
for a in x:
sum += float(a)
return sum
self.checkScript(test_simple_assign, ((1, 2.5),))
def test_tuple_assign(x):
# type: (Tuple[Tuple[int, int], Tuple[int, int]]) -> int
sum = 0
for a in x:
sum += a[0]
sum += a[1]
return sum
self.checkScript(test_tuple_assign, (((1, 2), (4, 7)), ))
def test_single_starred_lhs(self):
with self.assertRaisesRegex(RuntimeError, 'A Starred expression may only appear on the lhs within the presence'
' of another non-starred expression'):
cu = torch.jit.CompilationUnit('''
def single_starred_lhs(x):
a = (x, x, x)
*b, = a
return b
''')
def test_singleton_tuple_unpack(self):
def foo(a):
b, = (a,)
return b + 1
self.checkScript(foo, (torch.rand(3),))
def test_tuple_assignments(self):
def var_tuple_assign(x, y):
# type: (Tuple[Tensor, Tensor], Tensor) -> Tensor
(a, b), c = x, y
return a + b + c
tuple_inputs = (torch.randn(1, 4), torch.randn(3, 4))
self.checkScript(var_tuple_assign, (tuple_inputs, torch.randn(3, 4)))
def nested_tuple_assign(x, y, z):
# type: (int, Tuple[int, Tuple[int, int]], Tuple[int, int]) -> int
a, (b, (c, d)), (e, f) = x, y, z
return a + b + c + d + e + f
self.checkScript(nested_tuple_assign, ((1, (2, (3, 4)), (5, 6))))
def subscript_tuple_assign(a, x, i):
# type: (List[int], Tensor, int) -> Tuple[int, Tensor, int]
a[i], (x[i], b) = 1, (2, 3)
return a[i] + 1, x + 5, b
self.checkScript(subscript_tuple_assign, ([12, 7, 9, 11], torch.tensor((3, 13, 17)), 0))
def star_tuple_assign():
# type: () -> Tuple[int, int, Tuple[int, int], Tuple[int, int]]
a, (b, *c), *d = 1, (2, 3, 4), 5, 6
return a, b, c, d
self.checkScript(star_tuple_assign, ())
def subscript_tuple_augmented_assign(a):
# type: (Tuple[int, int]) -> Tuple[int, int]
a[0] += 1
return a
with self.assertRaisesRegex(RuntimeError, 'does not support augmented assign'):
scripted_aug_assign = torch.jit.script(subscript_tuple_augmented_assign)
def test_multiple_assign(self):
def test():
a = b, c = d, f = (1, 1)
# side effect
ten = torch.tensor(1)
ten1 = ten2 = ten.add_(1)
# ordering
x = 1
y = 3
x, y = y, x + y
return a, b, c, d, f, ten, ten1, ten2, x, y
self.checkScript(test, ())
def test_opt_opt_refinement(self):
@torch.jit.script
def test_unify(weight, bias):
# type: (Optional[int], Optional[int]) -> Optional[int]
if weight is not None:
opt = None
else:
if bias is not None:
opt = 1
else:
opt = None
return opt
def test_optional_refinement(self):
@torch.jit.script
def test_if_none_assignment(x):
# type: (Optional[int]) -> int
if x is None:
x = 1
return x + 1
self.assertEqual(test_if_none_assignment(1), 2)
def test_optional_conversion(self):
@torch.jit.script
def other_fn(x=None):
# type: (Optional[int]) -> int
return torch.jit._unwrap_optional(x)
@torch.jit.script
def fn(x):
# type: (int) -> int
return other_fn(x)
self.assertEqual(fn(2), 2)
@torch.jit.script
def unify_to_optional(x):
# type: (bool) -> Optional[int]
if x:
a = None
else:
a = 2
return a
self.assertEqual(unify_to_optional(True), None)
self.assertEqual(unify_to_optional(False), 2)
@torch.jit.script
def opt_list(x):
# type: (Optional[List[float]]) -> int
return 2
@torch.jit.script
def broadcast_opt_list(x):
# type: (Optional[BroadcastingList2[float]]) -> int
return 2
@torch.jit.script
def opt_list_tuple_caller(x):
# type: (Tuple[float, float]) -> int
return opt_list(x) + broadcast_opt_list(x)
self.assertEqual(opt_list_tuple_caller((2., 3.)), 4)
def test_optional_tuple(self):
def fn(x=None):
# type: (Optional[Tuple[int, int]]) -> Tuple[int, int]
if x is None:
new_x = (1, 2)
else:
new_x = x
return new_x
self.checkScript(fn, ((3, 4),))
self.checkScript(fn, ())
def test_namedtuple_redefine(self):
global _1, _2
_1 = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
_2 = namedtuple('GoogLeNetOutputs', ['different'])
with self.assertRaisesRegex(RuntimeError, r'redefine'):
@torch.jit.script
def foo(x, y):
# type: (_1, _2) -> _1
return x
def test_namedtuple_py2(self):
global _GoogLeNetOutputs # see [local resolution in python]
_GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
@torch.jit.script
def foo(x):
# type: (_GoogLeNetOutputs) -> _GoogLeNetOutputs
return x
vals = torch.rand(3), torch.rand(4), torch.rand(5)
out = foo(_GoogLeNetOutputs(logits=vals[0], aux_logits2=vals[1], aux_logits1=vals[2]))
self.assertEqual(out.logits, vals[0])
self.assertEqual(out.aux_logits2, vals[1])
self.assertEqual(out.aux_logits1, vals[2])
def test_namedtuple_good_error(self):
global _GoogLeNetOutputs # see [local resolution in python]
_GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
@torch.jit.script
def foo(x):
# type: (_GoogLeNetOutputs) -> _GoogLeNetOutputs
return x
with self.assertRaisesRegex(RuntimeError,
r'aka NamedTuple\(logits, aux_logits2, aux_logits1\)'):
out = foo(_GoogLeNetOutputs(logits="3", aux_logits2="4", aux_logits1="5"))