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# Owner(s): ["oncall: jit"]
import os
import sys
import torch
from torch.testing import FileCheck
from enum import Enum
from typing import Any, List
# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase, make_global
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 TestEnum(JitTestCase):
def test_enum_value_types(self):
class IntEnum(Enum):
FOO = 1
BAR = 2
class FloatEnum(Enum):
FOO = 1.2
BAR = 2.3
class StringEnum(Enum):
FOO = "foo as in foo bar"
BAR = "bar as in foo bar"
make_global(IntEnum, FloatEnum, StringEnum)
@torch.jit.script
def supported_enum_types(a: IntEnum, b: FloatEnum, c: StringEnum):
return (a.name, b.name, c.name)
FileCheck() \
.check("IntEnum") \
.check("FloatEnum") \
.check("StringEnum") \
.run(str(supported_enum_types.graph))
class TensorEnum(Enum):
FOO = torch.tensor(0)
BAR = torch.tensor(1)
make_global(TensorEnum)
def unsupported_enum_types(a: TensorEnum):
return a.name
# TODO: rewrite code so that the highlight is not empty.
with self.assertRaisesRegexWithHighlight(RuntimeError, "Cannot create Enum with value type 'Tensor'", ""):
torch.jit.script(unsupported_enum_types)
def test_enum_comp(self):
class Color(Enum):
RED = 1
GREEN = 2
make_global(Color)
@torch.jit.script
def enum_comp(x: Color, y: Color) -> bool:
return x == y
FileCheck().check("aten::eq").run(str(enum_comp.graph))
self.assertEqual(enum_comp(Color.RED, Color.RED), True)
self.assertEqual(enum_comp(Color.RED, Color.GREEN), False)
def test_enum_comp_diff_classes(self):
class Foo(Enum):
ITEM1 = 1
ITEM2 = 2
class Bar(Enum):
ITEM1 = 1
ITEM2 = 2
make_global(Foo, Bar)
@torch.jit.script
def enum_comp(x: Foo) -> bool:
return x == Bar.ITEM1
FileCheck() \
.check("prim::Constant") \
.check_same("Bar.ITEM1") \
.check("aten::eq") \
.run(str(enum_comp.graph))
self.assertEqual(enum_comp(Foo.ITEM1), False)
def test_heterogenous_value_type_enum_error(self):
class Color(Enum):
RED = 1
GREEN = "green"
make_global(Color)
def enum_comp(x: Color, y: Color) -> bool:
return x == y
# TODO: rewrite code so that the highlight is not empty.
with self.assertRaisesRegexWithHighlight(RuntimeError, "Could not unify type list", ""):
torch.jit.script(enum_comp)
def test_enum_name(self):
class Color(Enum):
RED = 1
GREEN = 2
make_global(Color)
@torch.jit.script
def enum_name(x: Color) -> str:
return x.name
FileCheck() \
.check("Color") \
.check_next("prim::EnumName") \
.check_next("return") \
.run(str(enum_name.graph))
self.assertEqual(enum_name(Color.RED), Color.RED.name)
self.assertEqual(enum_name(Color.GREEN), Color.GREEN.name)
def test_enum_value(self):
class Color(Enum):
RED = 1
GREEN = 2
make_global(Color)
@torch.jit.script
def enum_value(x: Color) -> int:
return x.value
FileCheck() \
.check("Color") \
.check_next("prim::EnumValue") \
.check_next("return") \
.run(str(enum_value.graph))
self.assertEqual(enum_value(Color.RED), Color.RED.value)
self.assertEqual(enum_value(Color.GREEN), Color.GREEN.value)
def test_enum_as_const(self):
class Color(Enum):
RED = 1
GREEN = 2
make_global(Color)
@torch.jit.script
def enum_const(x: Color) -> bool:
return x == Color.RED
FileCheck() \
.check("prim::Constant[value=__torch__.jit.test_enum.Color.RED]") \
.check_next("aten::eq") \
.check_next("return") \
.run(str(enum_const.graph))
self.assertEqual(enum_const(Color.RED), True)
self.assertEqual(enum_const(Color.GREEN), False)
def test_non_existent_enum_value(self):
class Color(Enum):
RED = 1
GREEN = 2
make_global(Color)
def enum_const(x: Color) -> bool:
if x == Color.PURPLE:
return True
else:
return False
with self.assertRaisesRegexWithHighlight(RuntimeError, "has no attribute 'PURPLE'", "Color.PURPLE"):
torch.jit.script(enum_const)
def test_enum_ivalue_type(self):
class Color(Enum):
RED = 1
GREEN = 2
make_global(Color)
@torch.jit.script
def is_color_enum(x: Any):
return isinstance(x, Color)
FileCheck() \
.check("prim::isinstance[types=[Enum<__torch__.jit.test_enum.Color>]]") \
.check_next("return") \
.run(str(is_color_enum.graph))
self.assertEqual(is_color_enum(Color.RED), True)
self.assertEqual(is_color_enum(Color.GREEN), True)
self.assertEqual(is_color_enum(1), False)
def test_closed_over_enum_constant(self):
class Color(Enum):
RED = 1
GREEN = 2
a = Color
@torch.jit.script
def closed_over_aliased_type():
return a.RED.value
FileCheck() \
.check("prim::Constant[value={}]".format(a.RED.value)) \
.check_next("return") \
.run(str(closed_over_aliased_type.graph))
self.assertEqual(closed_over_aliased_type(), Color.RED.value)
b = Color.RED
@torch.jit.script
def closed_over_aliased_value():
return b.value
FileCheck() \
.check("prim::Constant[value={}]".format(b.value)) \
.check_next("return") \
.run(str(closed_over_aliased_value.graph))
self.assertEqual(closed_over_aliased_value(), Color.RED.value)
def test_enum_as_module_attribute(self):
class Color(Enum):
RED = 1
GREEN = 2
class TestModule(torch.nn.Module):
def __init__(self, e: Color):
super(TestModule, self).__init__()
self.e = e
def forward(self):
return self.e.value
m = TestModule(Color.RED)
scripted = torch.jit.script(m)
FileCheck() \
.check("TestModule") \
.check_next("Color") \
.check_same("prim::GetAttr[name=\"e\"]") \
.check_next("prim::EnumValue") \
.check_next("return") \
.run(str(scripted.graph))
self.assertEqual(scripted(), Color.RED.value)
def test_string_enum_as_module_attribute(self):
class Color(Enum):
RED = "red"
GREEN = "green"
class TestModule(torch.nn.Module):
def __init__(self, e: Color):
super(TestModule, self).__init__()
self.e = e
def forward(self):
return (self.e.name, self.e.value)
make_global(Color)
m = TestModule(Color.RED)
scripted = torch.jit.script(m)
self.assertEqual(scripted(), (Color.RED.name, Color.RED.value))
def test_enum_return(self):
class Color(Enum):
RED = 1
GREEN = 2
make_global(Color)
@torch.jit.script
def return_enum(cond: bool):
if cond:
return Color.RED
else:
return Color.GREEN
self.assertEqual(return_enum(True), Color.RED)
self.assertEqual(return_enum(False), Color.GREEN)
def test_enum_module_return(self):
class Color(Enum):
RED = 1
GREEN = 2
class TestModule(torch.nn.Module):
def __init__(self, e: Color):
super(TestModule, self).__init__()
self.e = e
def forward(self):
return self.e
make_global(Color)
m = TestModule(Color.RED)
scripted = torch.jit.script(m)
FileCheck() \
.check("TestModule") \
.check_next("Color") \
.check_same("prim::GetAttr[name=\"e\"]") \
.check_next("return") \
.run(str(scripted.graph))
self.assertEqual(scripted(), Color.RED)
def test_enum_iterate(self):
class Color(Enum):
RED = 1
GREEN = 2
BLUE = 3
def iterate_enum(x: Color):
res: List[int] = []
for e in Color:
if e != x:
res.append(e.value)
return res
make_global(Color)
scripted = torch.jit.script(iterate_enum)
FileCheck() \
.check("Enum<__torch__.jit.test_enum.Color>[]") \
.check_same("Color.RED") \
.check_same("Color.GREEN") \
.check_same("Color.BLUE") \
.run(str(scripted.graph))
# PURPLE always appears last because we follow Python's Enum definition order.
self.assertEqual(scripted(Color.RED), [Color.GREEN.value, Color.BLUE.value])
self.assertEqual(scripted(Color.GREEN), [Color.RED.value, Color.BLUE.value])
# Tests that explicitly and/or repeatedly scripting an Enum class is permitted.
def test_enum_explicit_script(self):
@torch.jit.script
class Color(Enum):
RED = 1
GREEN = 2
torch.jit.script(Color)
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