def test_alias_check_fail_multiple_operators(self):
with self.assertRaisesRegex(
RuntimeError,
"Argument input is not defined to alias output but was aliasing"
):
x = torch.rand((3, 3), requires_grad=True)
y = torch.zeros((3, 3), requires_grad=True)
with enable_torch_dispatch_mode(SchemaCheckMode()):
IncorrectAliasTensor(x).sin().relu().add(
IncorrectAliasTensor(y), alpha=2)
def test_schema_check_mode_mutated_aliasing_aliasing_inputs(self):
actual = torch.rand((3, 3))
y = actual
schema_check = SchemaCheckMode()
with enable_torch_dispatch_mode(schema_check):
actual.add_(y)
self.assertEqual([('aten::add_', 'input'), ('aten::add_', 'other')],
schema_check.mutated)
self.assertEqual([('aten::add_', 'input', 'output_0'),
('aten::add_', 'other', 'output_0')],
schema_check.aliasing)
def test_mutation_check_fail_multiple_operators(self):
with self.assertRaisesRegex(
RuntimeError,
"Argument running_mean is not defined as mutable but was mutated"
):
x = torch.rand((3, 3), requires_grad=True)
batch = torch.nn.BatchNorm1d(3, track_running_stats=True)
with enable_torch_dispatch_mode(SchemaCheckMode()):
x = x.sinh()
x = x.tanh()
x = x.relu()
batch(x)
def test_schema_check_mode_mutated_aliasing_multiple_outputs(self):
x = torch.arange(9.)
m_actual = torch.arange(9.)
e_actual = torch.zeros([9], dtype=torch.int32)
schema_check = SchemaCheckMode()
with enable_torch_dispatch_mode(schema_check):
torch.frexp(x, out=(m_actual, e_actual))
self.assertEqual([('aten::frexp', 'mantissa'),
('aten::frexp', 'exponent')], schema_check.mutated)
self.assertEqual([('aten::frexp', 'mantissa', 'output_0'),
('aten::frexp', 'exponent', 'output_1')],
schema_check.aliasing)
def test_schema_check_mode_mutated_aliasing_aliasing_outputs(self):
x = torch.rand((3, 3))
actual = torch.zeros(3)
schema_check = SchemaCheckMode()
with enable_torch_dispatch_mode(schema_check):
torch.aminmax(x, dim=0, out=[actual, actual])
self.assertEqual([('aten::aminmax', 'min'), ('aten::aminmax', 'max')],
schema_check.mutated)
self.assertEqual([('aten::aminmax', 'min', 'output_0'),
('aten::aminmax', 'min', 'output_1'),
('aten::aminmax', 'max', 'output_0'),
('aten::aminmax', 'max', 'output_1')],
schema_check.aliasing)
def test_schema_check_mode_functionality_nested_training_op(self):
actual = torch.rand((3, 3))
batch = torch.nn.BatchNorm1d(3, track_running_stats=True)
expected = torch.clone(actual)
expected.sinh_()
expected.tanh_()
expected.relu_()
expected = batch(expected)
with enable_torch_dispatch_mode(SchemaCheckMode()):
actual.sinh_()
actual.tanh_()
actual.relu_()
actual = batch(actual)
self.assertEqual(expected, actual)
def test_schema_check_mode_functionality_kwarg_tensor(self):
x = torch.rand((3, 5))
w = torch.rand((4))
expected = torch.stft(x,
4,
win_length=4,
window=w,
return_complex=True)
with enable_torch_dispatch_mode(SchemaCheckMode()):
actual = torch.stft(x,
4,
win_length=4,
window=w,
return_complex=True)
self.assertEqual(expected, actual)
def test_schema_check_mode_mutated_aliasing_as_strided(self):
x = torch.rand((3, 6, 4))
schema_check = SchemaCheckMode()
with enable_torch_dispatch_mode(schema_check):
x.as_strided_([3, 6, 4], [9, 1, 1])
self.assertEqual(
[
('aten::as_strided_', 'input')
],
schema_check.mutated
)
self.assertEqual(
[
('aten::as_strided_', 'input', 'output_0')
],
schema_check.aliasing
)
def test_schema_check_tensor_operator_order_without_grad(self):
schema_check = SchemaCheckMode()
with enable_torch_dispatch_mode(schema_check):
x = torch.rand((3, 3), requires_grad=False)
x.relu().sin()
self.assertEqual(["aten::rand", "aten::relu", "aten::sin"], schema_check.ops)
def test_alias_check_fail_outputs_unexpectedly_aliasing(self):
with self.assertRaisesRegex(RuntimeError, "Outputs 0 and 1 alias unexpectedly"):
x = torch.rand((3, 3))
s = SchemaCheckMode()
with enable_torch_dispatch_mode(s):
IncorrectAliasTensor(x).aminmax(dim=0)
def test_mutation_check_fail_multiple_operators(self):
with self.assertRaisesRegex(RuntimeError, "Argument input is not defined as mutable but was mutated"):
x = torch.rand((3, 3))
y = torch.rand((3, 3))
with enable_torch_dispatch_mode(SchemaCheckMode()):
IncorrectAliasTensor(x).sin().cos().sub(IncorrectAliasTensor(y))
def test_schema_check_mode_empty_list_input(self):
expected = torch.atleast_1d([])
with enable_torch_dispatch_mode(SchemaCheckMode()):
actual = torch.atleast_1d([])
self.assertEqual(expected, actual)
def test_schema_check_mode_functionality_device_input(self):
with enable_torch_dispatch_mode(SchemaCheckMode()):
x = torch.rand((3, 3), device="cpu", dtype=torch.double)
y = x + x
self.assertEqual(x + x, y)
def test_schema_check_mode_functionality_wildcard_after(self):
x = torch.rand((3, 3))
expected = x.chunk(6)
with enable_torch_dispatch_mode(SchemaCheckMode()):
actual = x.chunk(6)
self.assertEqual(expected, actual)
def test_schema_check_tensor_functionality(self):
x = torch.rand((3, 3), requires_grad=True)
expected = x.relu().sin()
with enable_torch_dispatch_mode(SchemaCheckMode()):
actual = x.relu().sin()
self.assertEqual(expected, actual)
def test_schema_check_tensor_functionality_default_replaced(self):
x = torch.rand((3, 3), requires_grad=True)
expected = x.add(x, alpha=2)
with enable_torch_dispatch_mode(SchemaCheckMode()):
actual = x.add(x, alpha=2)
self.assertEqual(expected, actual)
def test_schema_check_mode_functionality_with_multiple_outputs_aliasing(self):
x = torch.rand((3, 3))
actual = torch.zeros(3)
with enable_torch_dispatch_mode(SchemaCheckMode()):
torch.aminmax(x, dim=0, out=[actual, actual])
self.assertEqual(torch.amax(x, dim=0), actual)