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# Owner(s): ["oncall: jit"]
import os
import sys
import torch
import warnings
from typing import List, Any, Dict, Tuple, Optional
# 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)
from torch.testing._internal.jit_utils import JitTestCase
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."
)
# Tests for torch.jit.isinstance
class TestIsinstance(JitTestCase):
def test_int(self):
def int_test(x: Any):
assert torch.jit.isinstance(x, int)
assert not torch.jit.isinstance(x, float)
x = 1
self.checkScript(int_test, (x,))
def test_float(self):
def float_test(x: Any):
assert torch.jit.isinstance(x, float)
assert not torch.jit.isinstance(x, int)
x = 1.0
self.checkScript(float_test, (x,))
def test_bool(self):
def bool_test(x: Any):
assert torch.jit.isinstance(x, bool)
assert not torch.jit.isinstance(x, float)
x = False
self.checkScript(bool_test, (x,))
def test_list(self):
def list_str_test(x: Any):
assert torch.jit.isinstance(x, List[str])
assert not torch.jit.isinstance(x, List[int])
assert not torch.jit.isinstance(x, Tuple[int])
x = ["1", "2", "3"]
self.checkScript(list_str_test, (x,))
def test_list_tensor(self):
def list_tensor_test(x: Any):
assert torch.jit.isinstance(x, List[torch.Tensor])
assert not torch.jit.isinstance(x, Tuple[int])
x = [torch.tensor([1]), torch.tensor([2]), torch.tensor([3])]
self.checkScript(list_tensor_test, (x,))
def test_dict(self):
def dict_str_int_test(x: Any):
assert torch.jit.isinstance(x, Dict[str, int])
assert not torch.jit.isinstance(x, Dict[int, str])
assert not torch.jit.isinstance(x, Dict[str, str])
x = {"a": 1, "b": 2}
self.checkScript(dict_str_int_test, (x,))
def test_dict_tensor(self):
def dict_int_tensor_test(x: Any):
assert torch.jit.isinstance(x, Dict[int, torch.Tensor])
x = {2: torch.tensor([2])}
self.checkScript(dict_int_tensor_test, (x,))
def test_tuple(self):
def tuple_test(x: Any):
assert torch.jit.isinstance(x, Tuple[str, int, str])
assert not torch.jit.isinstance(x, Tuple[int, str, str])
assert not torch.jit.isinstance(x, Tuple[str])
x = ("a", 1, "b")
self.checkScript(tuple_test, (x,))
def test_tuple_tensor(self):
def tuple_tensor_test(x: Any):
assert torch.jit.isinstance(x, Tuple[torch.Tensor, torch.Tensor])
x = (torch.tensor([1]), torch.tensor([[2], [3]]))
self.checkScript(tuple_tensor_test, (x,))
def test_optional(self):
def optional_test(x: Any):
assert torch.jit.isinstance(x, Optional[torch.Tensor])
assert not torch.jit.isinstance(x, Optional[str])
x = torch.ones(3, 3)
self.checkScript(optional_test, (x,))
def test_optional_none(self):
def optional_test_none(x: Any):
assert torch.jit.isinstance(x, Optional[torch.Tensor])
# assert torch.jit.isinstance(x, Optional[str])
# TODO: above line in eager will evaluate to True while in
# the TS interpreter will evaluate to False as the
# first torch.jit.isinstance refines the 'None' type
x = None
self.checkScript(optional_test_none, (x,))
def test_list_nested(self):
def list_nested(x: Any):
assert torch.jit.isinstance(x, List[Dict[str, int]])
assert not torch.jit.isinstance(x, List[List[str]])
x = [{"a": 1, "b": 2}, {"aa": 11, "bb": 22}]
self.checkScript(list_nested, (x,))
def test_dict_nested(self):
def dict_nested(x: Any):
assert torch.jit.isinstance(x, Dict[str, Tuple[str, str, str]])
assert not torch.jit.isinstance(x, Dict[str, Tuple[int, int, int]])
x = {"a": ("aa", "aa", "aa"), "b": ("bb", "bb", "bb")}
self.checkScript(dict_nested, (x,))
def test_tuple_nested(self):
def tuple_nested(x: Any):
assert torch.jit.isinstance(
x, Tuple[Dict[str, Tuple[str, str, str]], List[bool], Optional[str]]
)
assert not torch.jit.isinstance(x, Dict[str, Tuple[int, int, int]])
assert not torch.jit.isinstance(x, Tuple[str])
assert not torch.jit.isinstance(x, Tuple[List[bool], List[str], List[int]])
x = (
{"a": ("aa", "aa", "aa"), "b": ("bb", "bb", "bb")},
[True, False, True],
None,
)
self.checkScript(tuple_nested, (x,))
def test_optional_nested(self):
def optional_nested(x: Any):
assert torch.jit.isinstance(x, Optional[List[str]])
x = ["a", "b", "c"]
self.checkScript(optional_nested, (x,))
def test_list_tensor_type_true(self):
def list_tensor_type_true(x: Any):
assert torch.jit.isinstance(x, List[torch.Tensor])
x = [torch.rand(3, 3), torch.rand(4, 3)]
self.checkScript(list_tensor_type_true, (x,))
def test_tensor_type_false(self):
def list_tensor_type_false(x: Any):
assert not torch.jit.isinstance(x, List[torch.Tensor])
x = [1, 2, 3]
self.checkScript(list_tensor_type_false, (x,))
def test_in_if(self):
def list_in_if(x: Any):
if torch.jit.isinstance(x, List[int]):
assert True
if torch.jit.isinstance(x, List[str]):
assert not True
x = [1, 2, 3]
self.checkScript(list_in_if, (x,))
def test_if_else(self):
def list_in_if_else(x: Any):
if torch.jit.isinstance(x, Tuple[str, str, str]):
assert True
else:
assert not True
x = ("a", "b", "c")
self.checkScript(list_in_if_else, (x,))
def test_in_while_loop(self):
def list_in_while_loop(x: Any):
count = 0
while torch.jit.isinstance(x, List[Dict[str, int]]) and count <= 0:
count = count + 1
assert count == 1
x = [{"a": 1, "b": 2}, {"aa": 11, "bb": 22}]
self.checkScript(list_in_while_loop, (x,))
def test_type_refinement(self):
def type_refinement(obj: Any):
hit = False
if torch.jit.isinstance(obj, List[torch.Tensor]):
hit = not hit
for el in obj:
# perform some tensor operation
y = el.clamp(0, 0.5)
if torch.jit.isinstance(obj, Dict[str, str]):
hit = not hit
str_cat = ""
for val in obj.values():
str_cat = str_cat + val
assert "111222" == str_cat
assert hit
x = [torch.rand(3, 3), torch.rand(4, 3)]
self.checkScript(type_refinement, (x,))
x = {"1": "111", "2": "222"}
self.checkScript(type_refinement, (x,))
def test_list_no_contained_type(self):
def list_no_contained_type(x: Any):
assert torch.jit.isinstance(x, List)
x = ["1", "2", "3"]
err_msg = "Attempted to use List without a contained type. " \
r"Please add a contained type, e.g. List\[int\]"
with self.assertRaisesRegex(RuntimeError, err_msg,):
torch.jit.script(list_no_contained_type)
with self.assertRaisesRegex(RuntimeError, err_msg,):
list_no_contained_type(x)
def test_tuple_no_contained_type(self):
def tuple_no_contained_type(x: Any):
assert torch.jit.isinstance(x, Tuple)
x = ("1", "2", "3")
err_msg = "Attempted to use Tuple without a contained type. " \
r"Please add a contained type, e.g. Tuple\[int\]"
with self.assertRaisesRegex(RuntimeError, err_msg,):
torch.jit.script(tuple_no_contained_type)
with self.assertRaisesRegex(RuntimeError, err_msg,):
tuple_no_contained_type(x)
def test_optional_no_contained_type(self):
def optional_no_contained_type(x: Any):
assert torch.jit.isinstance(x, Optional)
x = ("1", "2", "3")
err_msg = "Attempted to use Optional without a contained type. " \
r"Please add a contained type, e.g. Optional\[int\]"
with self.assertRaisesRegex(RuntimeError, err_msg,):
torch.jit.script(optional_no_contained_type)
with self.assertRaisesRegex(RuntimeError, err_msg,):
optional_no_contained_type(x)
def test_dict_no_contained_type(self):
def dict_no_contained_type(x: Any):
assert torch.jit.isinstance(x, Dict)
x = {"a": "aa"}
err_msg = "Attempted to use Dict without contained types. " \
r"Please add contained type, e.g. Dict\[int, int\]"
with self.assertRaisesRegex(RuntimeError, err_msg,):
torch.jit.script(dict_no_contained_type)
with self.assertRaisesRegex(RuntimeError, err_msg,):
dict_no_contained_type(x)
def test_tuple_rhs(self):
def fn(x: Any):
assert torch.jit.isinstance(x, (int, List[str]))
assert not torch.jit.isinstance(x, (List[float], Tuple[int, str]))
assert not torch.jit.isinstance(x, (List[float], str))
self.checkScript(fn, (2,))
self.checkScript(fn, (["foo", "bar", "baz"],))
def test_nontuple_container_rhs_throws_in_eager(self):
def fn1(x: Any):
assert torch.jit.isinstance(x, [int, List[str]])
def fn2(x: Any):
assert not torch.jit.isinstance(x, {List[str], Tuple[int, str]})
err_highlight = "must be a type or a tuple of types"
with self.assertRaisesRegex(RuntimeError, err_highlight):
fn1(2)
with self.assertRaisesRegex(RuntimeError, err_highlight):
fn2(2)
def test_empty_container_throws_warning_in_eager(self):
def fn(x: Any):
torch.jit.isinstance(x, List[int])
with warnings.catch_warnings(record=True) as w:
x: List[int] = []
fn(x)
self.assertEqual(len(w), 1)
with warnings.catch_warnings(record=True) as w:
x: int = 2
fn(x)
self.assertEqual(len(w), 0)
def test_empty_container_special_cases(self):
# Should not throw "Boolean value of Tensor with no values is
# ambiguous" error
torch._jit_internal.check_empty_containers(torch.Tensor([]))
# Should not throw "Boolean value of Tensor with more than
# one value is ambiguous" error
torch._jit_internal.check_empty_containers(torch.rand(2, 3))
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