def test_mutation_check_fail_multiple_operators(self):
with self.assertRaises(RuntimeError):
x = torch.rand((3, 3), requires_grad=True)
x.sinh_()
x.tanh_()
x.relu_()
batch = torch.nn.BatchNorm1d(3, track_running_stats=True)
batch(SchemaCheckTensor(x))
def test_mutation_check_fail(self):
with self.assertRaises(RuntimeError):
x = torch.rand((3, 3), requires_grad=True)
batch = torch.nn.BatchNorm1d(3, track_running_stats=True)
batch(SchemaCheckTensor(x))
def test_schema_check_tensor_functionality_mutable_inputs(self):
x = torch.rand((3, 3), requires_grad=False)
y = torch.clone(x)
x.sinh_()
SchemaCheckTensor(y).sinh_()
self.assertEqual(x, y)
def test_schema_check_tensor_functionality_default_replaced(self):
x = torch.rand((3, 3), requires_grad=True)
self.assertEqual(
x.add(x, alpha=2),
SchemaCheckTensor(x).add(SchemaCheckTensor(x), alpha=2).elem)
def test_schema_check_tensor_functionality(self):
x = torch.rand((3, 3), requires_grad=True)
self.assertEqual(x.relu().sin(),
SchemaCheckTensor(x).relu().sin().elem)
def test_schema_check_tensor_operator_order_no_grad(self):
x = torch.rand((3, 3), requires_grad=False)
SchemaCheckTensor(x).relu().sin()
self.assertEqual(["aten::relu", "aten::sin"],
SchemaCheckTensor.recorded_ops)
def setUp(self):
SchemaCheckTensor.reset_cache()