pytorch/test/jit/test_alias_analysis.py
Jane Xu b40dbdc49f Fix test ownership lint (#71554)
Summary:
I noticed after creating https://github.com/pytorch/pytorch/issues/71553 that the test ownership lint was not working properly.

This fixes my egregious mistake and fixes the broken lints.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/71554

Reviewed By: malfet

Differential Revision: D33690732

Pulled By: janeyx99

fbshipit-source-id: ba4dfbcd98038e4afd63e326832ae40935d2501e
(cherry picked from commit 1bbc3d343a)
2022-01-21 18:24:42 +00:00

45 lines
1.8 KiB
Python

# Owner(s): ["oncall: jit"]
from torch.testing._internal.jit_utils import JitTestCase
from torch._C import parse_ir
import torch
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 TestAliasAnalysis(JitTestCase):
def test_becomes_wildcard_annotations(self):
graph_str = """
graph(%a.1 : Tensor, %b.1 : Tensor):
%11 : NoneType = prim::Constant()
%8 : int = prim::Constant[value=0]()
%7 : int = prim::Constant[value=1]()
%x.1 : Tensor = aten::add(%a.1, %b.1, %7)
%y.1 : Tensor[] = aten::split(%x.1, %7, %8)
return ()
"""
graph = parse_ir(graph_str)
alias_db = graph.alias_db()
split_node = graph.findNode("aten::split")
# split input enters wildcard set, list initalized as containing wildcard set
self.assertTrue(alias_db.may_contain_alias(next(split_node.inputs()), split_node.output()))
# because %x.1 enters wildcard set, it now aliases other members of wildcard set (graph inputs)
self.assertTrue(alias_db.may_contain_alias(next(split_node.inputs()), next(graph.inputs())))
def test_nested_list_construct_not_wildcard(self):
@torch.jit.script
def foo(x):
y = torch.rand([2, 2])
return [y]
graph = foo.graph
graph.alias_db()
alias_db = graph.alias_db()
ten_construct = graph.findNode("aten::rand").output()
output = next(graph.outputs())
self.assertTrue(alias_db.may_contain_alias(ten_construct, output))
self.assertFalse(alias_db.may_contain_alias(next(graph.inputs()), ten_construct))