mirror of
https://github.com/zebrajr/pytorch.git
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09c7771e9c
Summary: Action following https://github.com/pytorch/pytorch/issues/66232 Pull Request resolved: https://github.com/pytorch/pytorch/pull/66808 Reviewed By: mrshenli Differential Revision: D31761414 Pulled By: janeyx99 fbshipit-source-id: baf8c49ff9c4bcda7b0ea0f6aafd26380586e72d
261 lines
9.1 KiB
Python
261 lines
9.1 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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import torch
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from typing import List, Dict, Optional
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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.jit_utils import JitTestCase
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if __name__ == '__main__':
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raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
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"\tpython test/test_jit.py TESTNAME\n\n"
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"instead.")
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class TestScriptModuleInstanceAttributeTypeAnnotation(JitTestCase):
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# NB: There are no tests for `Tuple` or `NamedTuple` here. In fact,
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# reassigning a non-empty Tuple to an attribute previously typed
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# as containing an empty Tuple SHOULD fail. See note in `_check.py`
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def test_annotated_falsy_base_type(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x: int = 0
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def forward(self, x: int):
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self.x = x
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return 1
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with warnings.catch_warnings(record=True) as w:
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self.checkModule(M(), (1,))
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assert(len(w) == 0)
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def test_annotated_nonempty_container(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x: List[int] = [1, 2, 3]
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def forward(self, x: List[int]):
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self.x = x
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return 1
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with warnings.catch_warnings(record=True) as w:
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self.checkModule(M(), ([1, 2, 3],))
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assert(len(w) == 0)
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def test_annotated_empty_tensor(self):
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class M(torch.nn.Module):
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def __init__(self):
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super(M, self).__init__()
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self.x: torch.Tensor = torch.empty(0)
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def forward(self, x: torch.Tensor):
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self.x = x
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return self.x
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with warnings.catch_warnings(record=True) as w:
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self.checkModule(M(), (torch.rand(2, 3),))
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assert(len(w) == 0)
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def test_annotated_with_jit_attribute(self):
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class M(torch.nn.Module):
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def __init__(self):
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super(M, self).__init__()
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self.x = torch.jit.Attribute([], List[int])
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def forward(self, x: List[int]):
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self.x = x
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return self.x
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with warnings.catch_warnings(record=True) as w:
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self.checkModule(M(), ([1, 2, 3],))
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assert(len(w) == 0)
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def test_annotated_class_level_annotation_only(self):
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class M(torch.nn.Module):
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x: List[int]
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def __init__(self):
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super().__init__()
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self.x = []
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def forward(self, y: List[int]):
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self.x = y
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return self.x
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with warnings.catch_warnings(record=True) as w:
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self.checkModule(M(), ([1, 2, 3],))
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assert(len(w) == 0)
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def test_annotated_class_level_annotation_and_init_annotation(self):
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class M(torch.nn.Module):
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x: List[int]
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def __init__(self):
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super().__init__()
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self.x: List[int] = []
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def forward(self, y: List[int]):
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self.x = y
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return self.x
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with warnings.catch_warnings(record=True) as w:
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self.checkModule(M(), ([1, 2, 3],))
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assert(len(w) == 0)
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def test_annotated_class_level_jit_annotation(self):
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class M(torch.nn.Module):
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x: List[int]
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def __init__(self):
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super().__init__()
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self.x: List[int] = torch.jit.annotate(List[int], [])
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def forward(self, y: List[int]):
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self.x = y
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return self.x
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with warnings.catch_warnings(record=True) as w:
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self.checkModule(M(), ([1, 2, 3],))
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assert(len(w) == 0)
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def test_annotated_empty_list(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x: List[int] = []
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def forward(self, x: List[int]):
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self.x = x
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return 1
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with self.assertRaisesRegexWithHighlight(RuntimeError,
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"Tried to set nonexistent attribute",
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"self.x = x"):
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with self.assertWarnsRegex(UserWarning, "doesn't support "
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"instance-level annotations on "
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"empty non-base types"):
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torch.jit.script(M())
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def test_annotated_empty_dict(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x: Dict[str, int] = {}
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def forward(self, x: Dict[str, int]):
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self.x = x
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return 1
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with self.assertRaisesRegexWithHighlight(RuntimeError,
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"Tried to set nonexistent attribute",
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"self.x = x"):
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with self.assertWarnsRegex(UserWarning, "doesn't support "
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"instance-level annotations on "
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"empty non-base types"):
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torch.jit.script(M())
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def test_annotated_empty_optional(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x: Optional[str] = None
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def forward(self, x: Optional[str]):
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self.x = x
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return 1
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with self.assertRaisesRegexWithHighlight(RuntimeError,
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"Wrong type for attribute assignment",
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"self.x = x"):
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with self.assertWarnsRegex(UserWarning, "doesn't support "
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"instance-level annotations on "
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"empty non-base types"):
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torch.jit.script(M())
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def test_annotated_with_jit_empty_list(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x = torch.jit.annotate(List[int], [])
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def forward(self, x: List[int]):
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self.x = x
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return 1
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with self.assertRaisesRegexWithHighlight(RuntimeError,
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"Tried to set nonexistent attribute",
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"self.x = x"):
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with self.assertWarnsRegex(UserWarning, "doesn't support "
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"instance-level annotations on "
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"empty non-base types"):
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torch.jit.script(M())
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def test_annotated_with_jit_empty_dict(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x = torch.jit.annotate(Dict[str, int], {})
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def forward(self, x: Dict[str, int]):
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self.x = x
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return 1
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with self.assertRaisesRegexWithHighlight(RuntimeError,
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"Tried to set nonexistent attribute",
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"self.x = x"):
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with self.assertWarnsRegex(UserWarning, "doesn't support "
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"instance-level annotations on "
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"empty non-base types"):
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torch.jit.script(M())
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def test_annotated_with_jit_empty_optional(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x = torch.jit.annotate(Optional[str], None)
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def forward(self, x: Optional[str]):
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self.x = x
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return 1
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with self.assertRaisesRegexWithHighlight(RuntimeError,
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"Wrong type for attribute assignment",
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"self.x = x"):
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with self.assertWarnsRegex(UserWarning, "doesn't support "
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"instance-level annotations on "
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"empty non-base types"):
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torch.jit.script(M())
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def test_annotated_with_torch_jit_import(self):
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from torch import jit
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.x = jit.annotate(Optional[str], None)
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def forward(self, x: Optional[str]):
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self.x = x
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return 1
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with self.assertRaisesRegexWithHighlight(RuntimeError,
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"Wrong type for attribute assignment",
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"self.x = x"):
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with self.assertWarnsRegex(UserWarning, "doesn't support "
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"instance-level annotations on "
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"empty non-base types"):
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torch.jit.script(M())
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