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046e88a291
Rewrite Python built-in class `super()` calls. Only non-semantic changes should be applied. - #94587 - #94588 - #94592 Also, methods with only a `super()` call are removed: ```diff class MyModule(nn.Module): - def __init__(self): - super().__init__() - def forward(self, ...): ... ``` Some cases that change the semantics should be kept unchanged. E.g.:f152a79be9/caffe2/python/net_printer.py (L184-L190)f152a79be9/test/test_jit_fuser_te.py (L2628-L2635)Pull Request resolved: https://github.com/pytorch/pytorch/pull/94592 Approved by: https://github.com/ezyang, https://github.com/seemethere
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
# Owner(s): ["oncall: jit"]
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from torch.testing import FileCheck
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from torch.testing._internal.jit_utils import JitTestCase
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import torch
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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 TestGetDefaultAttr(JitTestCase):
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def test_getattr_with_default(self):
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class A(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.init_attr_val = 1.0
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def forward(self, x):
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y = getattr(self, "init_attr_val") # noqa: B009
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w : list[float] = [1.0]
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z = getattr(self, "missing", w) # noqa: B009
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z.append(y)
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return z
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result = A().forward(0.0)
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self.assertEqual(2, len(result))
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graph = torch.jit.script(A()).graph
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# The "init_attr_val" attribute exists
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FileCheck().check("prim::GetAttr[name=\"init_attr_val\"]").run(graph)
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# The "missing" attribute does not exist, so there should be no corresponding GetAttr in AST
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FileCheck().check_not("missing").run(graph)
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# instead the getattr call will emit the default value, which is a list with one float element
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FileCheck().check("float[] = prim::ListConstruct").run(graph)
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