mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
bf7e290854
This PR is part of a series attempting to re-submit https://github.com/pytorch/pytorch/pull/134592 as smaller PRs. In jit tests: - Add and use a common raise_on_run_directly method for when a user runs a test file directly which should not be run this way. Print the file which the user should have run. - Raise a RuntimeError on tests which have been disabled (not run) Pull Request resolved: https://github.com/pytorch/pytorch/pull/154725 Approved by: https://github.com/clee2000
354 lines
11 KiB
Python
354 lines
11 KiB
Python
# Owner(s): ["oncall: jit"]
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import os
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import sys
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import warnings
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from typing import Any, Dict, List, Optional, Tuple
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import torch
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# Make the helper files in test/ importable
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(pytorch_test_dir)
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from torch.testing._internal.common_utils import raise_on_run_directly
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from torch.testing._internal.jit_utils import JitTestCase
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# Tests for torch.jit.isinstance
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class TestIsinstance(JitTestCase):
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def test_int(self):
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def int_test(x: Any):
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assert torch.jit.isinstance(x, int)
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assert not torch.jit.isinstance(x, float)
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x = 1
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self.checkScript(int_test, (x,))
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def test_float(self):
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def float_test(x: Any):
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assert torch.jit.isinstance(x, float)
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assert not torch.jit.isinstance(x, int)
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x = 1.0
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self.checkScript(float_test, (x,))
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def test_bool(self):
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def bool_test(x: Any):
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assert torch.jit.isinstance(x, bool)
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assert not torch.jit.isinstance(x, float)
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x = False
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self.checkScript(bool_test, (x,))
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def test_list(self):
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def list_str_test(x: Any):
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assert torch.jit.isinstance(x, List[str])
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assert not torch.jit.isinstance(x, List[int])
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assert not torch.jit.isinstance(x, Tuple[int])
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x = ["1", "2", "3"]
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self.checkScript(list_str_test, (x,))
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def test_list_tensor(self):
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def list_tensor_test(x: Any):
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assert torch.jit.isinstance(x, List[torch.Tensor])
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assert not torch.jit.isinstance(x, Tuple[int])
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x = [torch.tensor([1]), torch.tensor([2]), torch.tensor([3])]
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self.checkScript(list_tensor_test, (x,))
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def test_dict(self):
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def dict_str_int_test(x: Any):
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assert torch.jit.isinstance(x, Dict[str, int])
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assert not torch.jit.isinstance(x, Dict[int, str])
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assert not torch.jit.isinstance(x, Dict[str, str])
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x = {"a": 1, "b": 2}
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self.checkScript(dict_str_int_test, (x,))
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def test_dict_tensor(self):
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def dict_int_tensor_test(x: Any):
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assert torch.jit.isinstance(x, Dict[int, torch.Tensor])
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x = {2: torch.tensor([2])}
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self.checkScript(dict_int_tensor_test, (x,))
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def test_tuple(self):
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def tuple_test(x: Any):
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assert torch.jit.isinstance(x, Tuple[str, int, str])
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assert not torch.jit.isinstance(x, Tuple[int, str, str])
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assert not torch.jit.isinstance(x, Tuple[str])
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x = ("a", 1, "b")
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self.checkScript(tuple_test, (x,))
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def test_tuple_tensor(self):
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def tuple_tensor_test(x: Any):
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assert torch.jit.isinstance(x, Tuple[torch.Tensor, torch.Tensor])
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x = (torch.tensor([1]), torch.tensor([[2], [3]]))
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self.checkScript(tuple_tensor_test, (x,))
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def test_optional(self):
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def optional_test(x: Any):
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assert torch.jit.isinstance(x, Optional[torch.Tensor])
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assert not torch.jit.isinstance(x, Optional[str])
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x = torch.ones(3, 3)
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self.checkScript(optional_test, (x,))
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def test_optional_none(self):
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def optional_test_none(x: Any):
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assert torch.jit.isinstance(x, Optional[torch.Tensor])
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# assert torch.jit.isinstance(x, Optional[str])
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# TODO: above line in eager will evaluate to True while in
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# the TS interpreter will evaluate to False as the
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# first torch.jit.isinstance refines the 'None' type
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x = None
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self.checkScript(optional_test_none, (x,))
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def test_list_nested(self):
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def list_nested(x: Any):
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assert torch.jit.isinstance(x, List[Dict[str, int]])
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assert not torch.jit.isinstance(x, List[List[str]])
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x = [{"a": 1, "b": 2}, {"aa": 11, "bb": 22}]
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self.checkScript(list_nested, (x,))
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def test_dict_nested(self):
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def dict_nested(x: Any):
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assert torch.jit.isinstance(x, Dict[str, Tuple[str, str, str]])
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assert not torch.jit.isinstance(x, Dict[str, Tuple[int, int, int]])
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x = {"a": ("aa", "aa", "aa"), "b": ("bb", "bb", "bb")}
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self.checkScript(dict_nested, (x,))
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def test_tuple_nested(self):
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def tuple_nested(x: Any):
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assert torch.jit.isinstance(
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x, Tuple[Dict[str, Tuple[str, str, str]], List[bool], Optional[str]]
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)
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assert not torch.jit.isinstance(x, Dict[str, Tuple[int, int, int]])
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assert not torch.jit.isinstance(x, Tuple[str])
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assert not torch.jit.isinstance(x, Tuple[List[bool], List[str], List[int]])
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x = (
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{"a": ("aa", "aa", "aa"), "b": ("bb", "bb", "bb")},
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[True, False, True],
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None,
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)
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self.checkScript(tuple_nested, (x,))
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def test_optional_nested(self):
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def optional_nested(x: Any):
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assert torch.jit.isinstance(x, Optional[List[str]])
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x = ["a", "b", "c"]
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self.checkScript(optional_nested, (x,))
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def test_list_tensor_type_true(self):
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def list_tensor_type_true(x: Any):
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assert torch.jit.isinstance(x, List[torch.Tensor])
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x = [torch.rand(3, 3), torch.rand(4, 3)]
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self.checkScript(list_tensor_type_true, (x,))
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def test_tensor_type_false(self):
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def list_tensor_type_false(x: Any):
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assert not torch.jit.isinstance(x, List[torch.Tensor])
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x = [1, 2, 3]
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self.checkScript(list_tensor_type_false, (x,))
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def test_in_if(self):
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def list_in_if(x: Any):
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if torch.jit.isinstance(x, List[int]):
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assert True
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if torch.jit.isinstance(x, List[str]):
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assert not True
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x = [1, 2, 3]
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self.checkScript(list_in_if, (x,))
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def test_if_else(self):
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def list_in_if_else(x: Any):
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if torch.jit.isinstance(x, Tuple[str, str, str]):
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assert True
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else:
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assert not True
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x = ("a", "b", "c")
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self.checkScript(list_in_if_else, (x,))
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def test_in_while_loop(self):
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def list_in_while_loop(x: Any):
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count = 0
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while torch.jit.isinstance(x, List[Dict[str, int]]) and count <= 0:
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count = count + 1
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assert count == 1
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x = [{"a": 1, "b": 2}, {"aa": 11, "bb": 22}]
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self.checkScript(list_in_while_loop, (x,))
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def test_type_refinement(self):
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def type_refinement(obj: Any):
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hit = False
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if torch.jit.isinstance(obj, List[torch.Tensor]):
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hit = not hit
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for el in obj:
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# perform some tensor operation
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y = el.clamp(0, 0.5) # noqa: F841
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if torch.jit.isinstance(obj, Dict[str, str]):
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hit = not hit
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str_cat = ""
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for val in obj.values():
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str_cat = str_cat + val
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assert "111222" == str_cat
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assert hit
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x = [torch.rand(3, 3), torch.rand(4, 3)]
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self.checkScript(type_refinement, (x,))
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x = {"1": "111", "2": "222"}
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self.checkScript(type_refinement, (x,))
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def test_list_no_contained_type(self):
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def list_no_contained_type(x: Any):
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assert torch.jit.isinstance(x, List)
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x = ["1", "2", "3"]
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err_msg = (
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"Attempted to use List without a contained type. "
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r"Please add a contained type, e.g. List\[int\]"
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)
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with self.assertRaisesRegex(
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RuntimeError,
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err_msg,
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):
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torch.jit.script(list_no_contained_type)
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with self.assertRaisesRegex(
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RuntimeError,
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err_msg,
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):
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list_no_contained_type(x)
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def test_tuple_no_contained_type(self):
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def tuple_no_contained_type(x: Any):
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assert torch.jit.isinstance(x, Tuple)
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x = ("1", "2", "3")
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err_msg = (
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"Attempted to use Tuple without a contained type. "
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r"Please add a contained type, e.g. Tuple\[int\]"
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)
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with self.assertRaisesRegex(
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RuntimeError,
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err_msg,
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):
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torch.jit.script(tuple_no_contained_type)
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with self.assertRaisesRegex(
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RuntimeError,
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err_msg,
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):
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tuple_no_contained_type(x)
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def test_optional_no_contained_type(self):
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def optional_no_contained_type(x: Any):
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assert torch.jit.isinstance(x, Optional)
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x = ("1", "2", "3")
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err_msg = (
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"Attempted to use Optional without a contained type. "
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r"Please add a contained type, e.g. Optional\[int\]"
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)
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with self.assertRaisesRegex(
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RuntimeError,
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err_msg,
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):
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torch.jit.script(optional_no_contained_type)
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with self.assertRaisesRegex(
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RuntimeError,
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err_msg,
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):
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optional_no_contained_type(x)
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def test_dict_no_contained_type(self):
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def dict_no_contained_type(x: Any):
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assert torch.jit.isinstance(x, Dict)
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x = {"a": "aa"}
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err_msg = (
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"Attempted to use Dict without contained types. "
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r"Please add contained type, e.g. Dict\[int, int\]"
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)
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with self.assertRaisesRegex(
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RuntimeError,
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err_msg,
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):
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torch.jit.script(dict_no_contained_type)
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with self.assertRaisesRegex(
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RuntimeError,
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err_msg,
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):
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dict_no_contained_type(x)
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def test_tuple_rhs(self):
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def fn(x: Any):
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assert torch.jit.isinstance(x, (int, List[str]))
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assert not torch.jit.isinstance(x, (List[float], Tuple[int, str]))
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assert not torch.jit.isinstance(x, (List[float], str))
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self.checkScript(fn, (2,))
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self.checkScript(fn, (["foo", "bar", "baz"],))
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def test_nontuple_container_rhs_throws_in_eager(self):
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def fn1(x: Any):
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assert torch.jit.isinstance(x, [int, List[str]])
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def fn2(x: Any):
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assert not torch.jit.isinstance(x, {List[str], Tuple[int, str]})
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err_highlight = "must be a type or a tuple of types"
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with self.assertRaisesRegex(RuntimeError, err_highlight):
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fn1(2)
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with self.assertRaisesRegex(RuntimeError, err_highlight):
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fn2(2)
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def test_empty_container_throws_warning_in_eager(self):
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def fn(x: Any):
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torch.jit.isinstance(x, List[int])
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with warnings.catch_warnings(record=True) as w:
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x: List[int] = []
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fn(x)
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self.assertEqual(len(w), 1)
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with warnings.catch_warnings(record=True) as w:
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x: int = 2
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fn(x)
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self.assertEqual(len(w), 0)
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def test_empty_container_special_cases(self):
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# Should not throw "Boolean value of Tensor with no values is
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# ambiguous" error
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torch._jit_internal.check_empty_containers(torch.Tensor([]))
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# Should not throw "Boolean value of Tensor with more than
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# one value is ambiguous" error
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torch._jit_internal.check_empty_containers(torch.rand(2, 3))
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if __name__ == "__main__":
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raise_on_run_directly("test/test_jit.py")
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