# Owner(s): ["oncall: jit"] from typing import NamedTuple, Tuple import torch from torch.testing import FileCheck 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." ) class TestGetDefaultAttr(JitTestCase): def test_getattr_with_default(self): class A(torch.nn.Module): def __init__(self) -> None: super().__init__() self.init_attr_val = 1.0 def forward(self, x): y = getattr(self, "init_attr_val") # noqa: B009 w: list[float] = [1.0] z = getattr(self, "missing", w) # noqa: B009 z.append(y) return z result = A().forward(0.0) self.assertEqual(2, len(result)) graph = torch.jit.script(A()).graph # The "init_attr_val" attribute exists FileCheck().check('prim::GetAttr[name="init_attr_val"]').run(graph) # The "missing" attribute does not exist, so there should be no corresponding GetAttr in AST FileCheck().check_not("missing").run(graph) # instead the getattr call will emit the default value, which is a list with one float element FileCheck().check("float[] = prim::ListConstruct").run(graph) def test_getattr_named_tuple(self): global MyTuple class MyTuple(NamedTuple): x: str y: torch.Tensor def fn(x: MyTuple) -> Tuple[str, torch.Tensor, int]: return ( getattr(x, "x", "fdsa"), getattr(x, "y", torch.ones((3, 3))), getattr(x, "z", 7), ) inp = MyTuple(x="test", y=torch.ones(3, 3) * 2) ref = fn(inp) fn_s = torch.jit.script(fn) res = fn_s(inp) self.assertEqual(res, ref) def test_getattr_tuple(self): def fn(x: Tuple[str, int]) -> int: return getattr(x, "x", 2) with self.assertRaisesRegex(RuntimeError, "but got a normal Tuple"): torch.jit.script(fn)