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52456b2eba
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29332 Even though we're statically typed, this can be useful, e.g. as shorthand when iterating through a module list. Test Plan: Imported from OSS Differential Revision: D18393097 Pulled By: suo fbshipit-source-id: aa42e955f88d1b8a876d0727055eb596453b9839
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
import os
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import sys
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from typing import List
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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 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 TestBuiltins(JitTestCase):
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"""
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Tests for TorchScript support of Python builtin functions.
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"""
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def test_has_attr(self):
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class HasA(torch.nn.Module):
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def __init__(self):
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super(HasA, self).__init__()
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self.a = 0
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class HasB(torch.nn.Module):
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def __init__(self):
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super(HasB, self).__init__()
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self.b = 1
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class Mod(torch.nn.Module):
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def __init__(self):
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super(Mod, self).__init__()
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self.mods = torch.nn.ModuleList([HasA(), HasB()])
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def forward(self):
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# use a list to encode hasattr results
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l = torch.jit.annotate(List[int], [])
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for mod in self.mods:
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l.append(int(hasattr(mod, "a")))
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l.append(int(hasattr(mod, "b")))
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# actually retrieve the attr to test static refinement
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if hasattr(mod, "a"):
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l.append(mod.a)
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if hasattr(mod, "b"):
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l.append(mod.b)
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return l
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self.checkModule(Mod(), ())
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def test_has_attr_invalid_args(self):
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class Mod(torch.nn.Module):
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def __init__(self):
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super(Mod, self).__init__()
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self.mod = torch.nn.Linear(1, 1)
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def forward(self, name):
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# not allowed, `name` must be static.
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return hasattr(self.mod, name)
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with self.assertRaisesRegex(RuntimeError, "hasattr"):
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torch.jit.script(Mod())
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class Mod(torch.nn.Module):
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def __init__(self):
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super(Mod, self).__init__()
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def forward(self, name):
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# not allowed, `torch.rand` is not a class type
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return hasattr(torch.rand(2, 3), name)
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with self.assertRaisesRegex(RuntimeError, "hasattr"):
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torch.jit.script(Mod())
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