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# Owner(s): ["module: unknown"]
from typing import Optional, List
import torch
from torch.testing._internal.common_utils import TestCase, run_tests, skipIfTorchDynamo
# End-to-end tests of features in native_functions.yaml
class FloatListWrapperModule(torch.nn.Module):
def forward(self, values, incr: Optional[List[float]]):
return torch._C._nn._test_optional_floatlist(values, incr)
class IntListWrapperModule(torch.nn.Module):
def forward(self, values, incr: Optional[List[int]]):
return torch._C._nn._test_optional_intlist(values, incr)
class TestNativeFunctions(TestCase):
def _lists_with_str(self):
return [
("foo",),
(2, "foo"),
("foo", 3),
["foo"],
[2, "foo"],
["foo", 3],
"foo",
]
def _test_raises_str_typeerror(self, fn):
for arg in self._lists_with_str():
self.assertRaisesRegex(TypeError, "str", lambda: fn(arg))
try:
fn(arg)
except TypeError as e:
print(e)
def test_symintlist_error(self):
x = torch.randn(1)
self._test_raises_str_typeerror(lambda arg: torch._C._nn.pad(x, arg))
def test_vararg_symintlist_error(self):
self._test_raises_str_typeerror(lambda arg: torch.rand(arg))
self._test_raises_str_typeerror(lambda arg: torch.rand(*arg))
def test_symintlist_error_with_overload_but_is_unique(self):
x = torch.randn(1)
y = torch.randn(1)
self._test_raises_str_typeerror(lambda arg: x.set_(y, 0, arg))
def test_symintlist_error_with_overload(self):
x = torch.randn(1)
self._test_raises_str_typeerror(lambda arg: x.view(arg))
def test_intlist_error_with_overload(self):
x = torch.randn(1)
self._test_raises_str_typeerror(lambda arg: torch._C._nn.pad(x, arg))
#
# optional float list
#
def do_test_optional_floatlist_with_module(self, module):
values = torch.tensor([1.5, 2.5], dtype=torch.float)
returned = module(values, None)
self.assertEqual(values, returned)
# Make sure that it's an alias, indicating that the operator saw a nullopt.
values[0] = 3.5
self.assertEqual(values, returned)
returned = module(values, [5.1, 4.1])
self.assertEqual(values, torch.tensor([3.5, 2.5], dtype=torch.float))
self.assertEqual(returned, torch.tensor([8.6, 6.6], dtype=torch.float))
def trace_optional_floatlist(self, const):
def wrapper(values):
return torch._C._nn._test_optional_floatlist(values, const)
return torch.jit.trace(wrapper, torch.tensor([1.5, 2.5], dtype=torch.float))
@skipIfTorchDynamo("Not a suitable test for TorchDynamo")
def test_optional_floatlist(self):
self.do_test_optional_floatlist_with_module(FloatListWrapperModule())
self.do_test_optional_floatlist_with_module(torch.jit.script(FloatListWrapperModule()))
traced_none = self.trace_optional_floatlist(None)
traced_list = self.trace_optional_floatlist([5.1, 4.1])
# Not really a module, just lets us use our two traced functions to handle
# the specific cases of passing None and [5.1, 4.1].
def fake_module(values, const):
if const is None:
return traced_none(values)
if const == [5.1, 4.1]:
return traced_list(values)
raise Exception("Invalid argument") # noqa: TRY002
self.do_test_optional_floatlist_with_module(fake_module)
def test_optional_floatlist_invalid(self):
with self.assertRaisesRegex(TypeError, "must be tuple of floats, not list"):
FloatListWrapperModule()(torch.zeros(1), ["hi"])
with self.assertRaisesRegex(RuntimeError, "value of type .* instead found type"):
torch.jit.script(FloatListWrapperModule())(torch.zeros(1), ["hi"])
with self.assertRaisesRegex(TypeError, "must be .* Tensor"):
FloatListWrapperModule()(torch.zeros(1), torch.zeros(1))
with self.assertRaisesRegex(RuntimeError, "value of type .* instead found type"):
torch.jit.script(FloatListWrapperModule())(torch.zeros(1), torch.zeros(1))
#
# optional int list
#
def do_test_optional_intlist_with_module(self, module):
values = torch.tensor([1, 2], dtype=torch.int)
returned = module(values, None)
self.assertEqual(values, returned)
# Make sure that it's an alias, indicating that the operator saw a nullopt.
values[0] = 3
self.assertEqual(values, returned)
returned = module(values, [5, 4])
self.assertEqual(values, torch.tensor([3, 2], dtype=torch.int))
self.assertEqual(returned, torch.tensor([8, 6], dtype=torch.int))
def trace_optional_intlist(self, const):
def wrapper(values):
return torch._C._nn._test_optional_intlist(values, const)
return torch.jit.trace(wrapper, torch.tensor([1, 2], dtype=torch.int))
@skipIfTorchDynamo("Not a suitable test for TorchDynamo")
def test_optional_intlist(self):
self.do_test_optional_intlist_with_module(IntListWrapperModule())
self.do_test_optional_intlist_with_module(torch.jit.script(IntListWrapperModule()))
traced_none = self.trace_optional_intlist(None)
traced_list = self.trace_optional_intlist([5, 4])
# Not really a module, just lets us use our two traced functions to handle
# the specific cases of passing None and [5, 4].
def fake_module(values, const):
if const is None:
return traced_none(values)
if const == [5, 4]:
return traced_list(values)
raise Exception("Invalid argument") # noqa: TRY002
self.do_test_optional_intlist_with_module(fake_module)
def test_optional_intlist_invalid(self):
with self.assertRaisesRegex(TypeError, "must be .* but found"):
IntListWrapperModule()(torch.zeros(1), [0.5])
with self.assertRaisesRegex(RuntimeError, "value of type .* instead found type"):
torch.jit.script(IntListWrapperModule())(torch.zeros(1), [0.5])
with self.assertRaisesRegex(TypeError, "must be .* Tensor"):
IntListWrapperModule()(torch.zeros(1), torch.zeros(1))
with self.assertRaisesRegex(RuntimeError, "value of type .* instead found type"):
torch.jit.script(IntListWrapperModule())(torch.zeros(1), torch.zeros(1))
#
# optional filled int list
#
def do_test_optional_filled_intlist_with_module(self, module):
values = torch.tensor([1, 2], dtype=torch.int)
returned = module(values, None)
self.assertEqual(values, returned)
# Make sure that it's an alias, indicating that the operator saw a nullopt.
values[0] = 3
self.assertEqual(values, returned)
returned = module(values, 10)
self.assertEqual(values, torch.tensor([3, 2], dtype=torch.int))
self.assertEqual(returned, torch.tensor([13, 12], dtype=torch.int))
def trace_optional_filled_intlist(self, const):
def wrapper(values):
return torch._C._nn._test_optional_filled_intlist(values, const)
return torch.jit.trace(wrapper, torch.tensor([1, 2], dtype=torch.int))
@skipIfTorchDynamo("Not a suitable test for TorchDynamo")
def test_optional_filled_intlist(self):
def f(n: int):
x = torch._C._nn._test_optional_filled_intlist(torch.tensor([1, 1], dtype=torch.int), (n, n))
y = torch._C._nn._test_optional_filled_intlist(torch.tensor([1, 1], dtype=torch.int), n)
return x, y
# eager
returned = f(10)
self.assertEqual(returned[0], returned[1])
# scripted
s = torch.jit.script(f)
returned = s(10)
self.assertEqual(returned[0], returned[1])
# traced
traced_none = self.trace_optional_filled_intlist(None)
traced_int = self.trace_optional_filled_intlist(10)
# Not really a module, just lets us use our two traced functions to handle
# the specific cases of passing None and 10.
def fake_module(values, const):
if const is None:
return traced_none(values)
if const == 10:
return traced_int(values)
raise Exception("Invalid argument") # noqa: TRY002
self.do_test_optional_filled_intlist_with_module(fake_module)
def test_string_defaults(self):
dummy = torch.rand(1)
fn = torch._C._nn._test_string_default
fn(dummy)
with self.assertRaisesRegex(RuntimeError, "A"):
fn(dummy, a="")
with self.assertRaisesRegex(RuntimeError, "B"):
fn(dummy, b="")
def f(x):
torch._C._nn._test_string_default(x)
scripted_fn = torch.jit.script(f)
scripted_fn(dummy)
if __name__ == '__main__':
run_tests()
|
