| # Owner(s): ["oncall: jit"] |
| |
| import os |
| import sys |
| import torch |
| from torch.utils._pytree import tree_map |
| |
| from torch.testing._internal.common_utils import run_tests |
| from torch.fx.operator_schemas import normalize_function |
| from torch.testing._internal.schema_check_mode import SchemaCheckMode |
| from torch.utils._python_dispatch import TorchDispatchMode |
| from torch.testing._internal.common_methods_invocations import op_db |
| from torch.testing._internal.jit_utils import JitTestCase |
| from torch.testing._internal.common_device_type import ops, OpDTypes, instantiate_device_type_tests |
| pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) |
| sys.path.append(pytorch_test_dir) |
| |
| # This TorchDispatchTensor Subclass is used to simulate an incorrect schema |
| # which is then used to test that SchemaCheckMode behaves as expected |
| |
| class IncorrectAliasTensor(torch.Tensor): |
| ALIAS_ARG_OUT = {"aten::add"} |
| ALIAS_OUT_OUT = {"aten::aminmax"} |
| MUTATE_ARGS_OUT = {"aten::sub"} |
| |
| elem: torch.Tensor |
| |
| __slots__ = ['elem'] |
| |
| __torch_function__ = torch._C._disabled_torch_function_impl |
| |
| @staticmethod |
| def __new__(cls, elem, *args, **kwargs): |
| # The wrapping tensor (IncorrectAliasTensor) shouldn't hold any |
| # memory for the class in question, but it should still |
| # advertise the same device as before |
| r = torch.Tensor._make_wrapper_subclass( # type: ignore[attr-defined] |
| cls, elem.size(), |
| strides=elem.stride(), storage_offset=elem.storage_offset(), |
| # TODO: clone storage aliasing |
| dtype=elem.dtype, layout=elem.layout, |
| device=elem.device, requires_grad=kwargs.get("requires_grad", False) |
| ) |
| # ...the real tensor is held as an element on the tensor. |
| r.elem = elem.detach() if r.requires_grad else elem |
| return r |
| |
| def __repr__(self): |
| return super().__repr__(tensor_contents=f"{self.elem}") |
| |
| @classmethod |
| def __torch_dispatch__(cls, func, types, args=(), kwargs=None): |
| def unwrap(e): |
| return e.elem if isinstance(e, cls) else e |
| |
| def wrap(e): |
| return cls(e) if isinstance(e, torch.Tensor) else e |
| unwrapped_args = tree_map(unwrap, args) |
| out = func(*unwrapped_args, **tree_map(unwrap, kwargs)) |
| if func._schema.name in IncorrectAliasTensor.ALIAS_ARG_OUT: |
| args[0].elem = out |
| if func._schema.name in IncorrectAliasTensor.MUTATE_ARGS_OUT: |
| args[0].elem = torch.rand(args[0].elem.shape) |
| if func._schema.name in IncorrectAliasTensor.ALIAS_OUT_OUT: |
| incorrect_out = list(out) |
| incorrect_out[0] = incorrect_out[1] |
| return tree_map(wrap, tuple(incorrect_out)) |
| |
| return tree_map(wrap, out) |
| |
| # Tests various schema checking functionalities. |
| class TestSchemaCheck(JitTestCase): |
| # Tests that SchemaCheckMode records operator order with grad |
| def test_schema_check_mode_operator_order(self): |
| with SchemaCheckMode() as schema_check: |
| x = torch.rand((3, 3), requires_grad=True) |
| x.relu().sin() |
| self.assertEqual(["aten::rand", "aten::relu", "aten::detach", "aten::sin"], schema_check.ops) |
| |
| # Tests that SchemaCheckMode records operator order without grad |
| def test_schema_check_mode_operator_order_without_grad(self): |
| with SchemaCheckMode() as schema_check: |
| x = torch.rand((3, 3), requires_grad=False) |
| x.relu().sin() |
| self.assertEqual(["aten::rand", "aten::relu", "aten::sin"], schema_check.ops) |
| |
| # Tests that SchemaCheckMode records mutations and aliases with none expected |
| def test_schema_check_mode_mutated_aliasing_none(self): |
| # NB: previously requires_grad=True, but this induces a detach for |
| # saved variable |
| x = torch.rand((3, 3)) |
| with SchemaCheckMode() as schema_check: |
| actual = x.relu().sin() |
| self.assertEqual([], schema_check.mutated) |
| self.assertEqual([], schema_check.aliasing) |
| |
| # Tests that SchemaCheckMode records mutations and aliases with mutation expected |
| def test_schema_check_mode_mutated_aliasing_mutation(self): |
| actual = torch.rand((3, 3), requires_grad=False) |
| with SchemaCheckMode() as schema_check: |
| actual.sinh_() |
| self.assertEqual([('aten::sinh_', 'input')], schema_check.mutated) |
| self.assertEqual([('aten::sinh_', 'input', 'output_0')], schema_check.aliasing) |
| |
| # Tests that SchemaCheckMode records mutations and aliases with resize_ |
| def test_schema_check_mode_mutated_aliasing_resize_(self): |
| actual = torch.rand((3, 3), requires_grad=False) |
| with SchemaCheckMode() as schema_check: |
| actual.resize_(9) |
| self.assertEqual([('aten::resize_', 'input')], schema_check.mutated) |
| self.assertEqual([('aten::resize_', 'input', 'output_0')], schema_check.aliasing) |
| |
| # Tests that SchemaCheckMode records mutations and aliases with aliasing inputs |
| def test_schema_check_mode_mutated_aliasing_aliasing_inputs(self): |
| actual = torch.rand((3, 3)) |
| y = actual |
| with SchemaCheckMode() as 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 |
| ) |
| |
| # Tests that SchemaCheckMode records mutations and alias with as_strided |
| def test_schema_check_mode_mutated_aliasing_as_strided(self): |
| x = torch.rand((3, 6, 4)) |
| with SchemaCheckMode() as 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 |
| ) |
| |
| # Tests that SchemaCheckMode records mutations and aliases with multiple outputs |
| 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) |
| with SchemaCheckMode() as 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 |
| ) |
| |
| # Tests that SchemaCheckMode records mutations and aliases with aliasing outputs |
| def test_schema_check_mode_mutated_aliasing_aliasing_outputs(self): |
| x = torch.rand((3, 3)) |
| actual = torch.zeros(3) |
| with SchemaCheckMode() as 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 |
| ) |
| |
| # Tests that SchemaCheckMode wraps torch.Tensor |
| def test_schema_check_mode_functionality(self): |
| x = torch.rand((3, 3), requires_grad=True) |
| expected = x.relu().sin() |
| with SchemaCheckMode(): |
| actual = x.relu().sin() |
| self.assertEqual(expected, actual) |
| |
| # Tests that SchemaCheckMode wraps torch.Tensor when an argument's default is overriden |
| def test_schema_check_mode_functionality_default_replaced(self): |
| x = torch.rand((3, 3), requires_grad=True) |
| expected = x.add(x, alpha=2) |
| with SchemaCheckMode(): |
| actual = x.add(x, alpha=2) |
| self.assertEqual(expected, actual) |
| |
| # Tests that SchemaCheckMode wraps torch.Tensor when there is a Tensor[] argument |
| def test_schema_check_mode_functionality_list_input(self): |
| a = torch.rand((3, 3)) |
| b = torch.rand((3, 3)) |
| c = torch.rand((3, 3)) |
| expected = torch.linalg.multi_dot([a, b, c]) |
| with SchemaCheckMode(): |
| actual = torch.linalg.multi_dot([a, b, c]) |
| self.assertEqual(expected, actual) |
| |
| # Tests that SchemaCheckMode wraps torch.Tensor with an op that has the (a -> *) notation |
| def test_schema_check_mode_functionality_wildcard_after(self): |
| x = torch.rand((3, 3)) |
| expected = x.chunk(6) |
| with SchemaCheckMode(): |
| actual = x.chunk(6) |
| self.assertEqual(expected, actual) |
| |
| # Tests that SchemaCheckMode wraps torch.Tensor when there is a kwarg tensor input |
| 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 SchemaCheckMode(): |
| actual = torch.stft(x, 4, win_length=4, window=w, return_complex=True) |
| self.assertEqual(expected, actual) |
| |
| # Tests that SchemaCheckMode wraps torch.Tensor with a mutable op |
| def test_schema_check_mode_functionality_mutable_inputs(self): |
| expected = torch.rand((3, 3), requires_grad=False) |
| actual = torch.clone(expected) |
| expected.sinh_() |
| with SchemaCheckMode(): |
| actual.sinh_() |
| self.assertEqual(expected, actual) |
| |
| # Tests that SchemaCheckMode wraps Torch.tensor when inputs alias |
| def test_schema_check_mode_functionality_aliasing_inputs(self): |
| expected = torch.rand((3, 3)) |
| x = expected |
| actual = torch.clone(expected) |
| y = actual |
| expected.add_(x) |
| with SchemaCheckMode(): |
| actual.add_(y) |
| self.assertEqual(expected, actual) |
| |
| # Tests that SchemaCheckMode wraps Torch.tensor with multiple tensor outputs |
| def test_schema_check_mode_functionality_with_multiple_outputs(self): |
| x = torch.arange(9.) |
| m_expected, e_expected = torch.frexp(x) |
| m_actual = torch.arange(9.) |
| e_actual = torch.zeros([9], dtype=torch.int32) |
| with SchemaCheckMode(): |
| torch.frexp(x, out=(m_actual, e_actual)) |
| self.assertEqual(m_expected, m_actual) |
| self.assertEqual(e_expected, e_actual) |
| |
| # Tests that SchemaCheckMode wraps Torch.tensor with aliasing ouputs due to aliasing inputs |
| def test_schema_check_mode_functionality_with_multiple_outputs_aliasing(self): |
| x = torch.rand((3, 3)) |
| actual = torch.zeros(3) |
| with SchemaCheckMode(): |
| torch.aminmax(x, dim=0, out=[actual, actual]) |
| self.assertEqual(torch.amax(x, dim=0), actual) |
| |
| # Tests that SchemaCheckMode wraps Torch.tensor in ops with real Device input |
| def test_schema_check_mode_functionality_device_input(self): |
| with SchemaCheckMode(): |
| x = torch.rand((3, 3), device="cpu", dtype=torch.double) |
| y = x + x |
| self.assertEqual(x + x, y) |
| |
| # Tests that SchemaCheckMode wraps Torch.tensor in special training op edge case |
| def test_schema_check_mode_functionality_training_op(self): |
| x = torch.rand((3, 3), requires_grad=True) |
| batch = torch.nn.BatchNorm1d(3, track_running_stats=True) |
| expected = batch(x) |
| with SchemaCheckMode(): |
| actual = batch(x) |
| self.assertEqual(expected, actual) |
| |
| # Tests that SchemaCheckMode wraps Torch.tensor with nested training op edge case |
| 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 SchemaCheckMode(): |
| actual.sinh_() |
| actual.tanh_() |
| actual.relu_() |
| actual = batch(actual) |
| self.assertEqual(expected, actual) |
| |
| # Tests that SchemaCheckMode wraps Torch.tensor with empty list input |
| def test_schema_check_mode_empty_list_input(self): |
| expected = torch.atleast_1d([]) |
| with SchemaCheckMode(): |
| actual = torch.atleast_1d([]) |
| self.assertEqual(expected, actual) |
| |
| # Tests that an exception is raised for a mismatching mutation |
| def test_mutation_check_fail(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 SchemaCheckMode(): |
| IncorrectAliasTensor(x).sub(IncorrectAliasTensor(y)) |
| |
| # # Tests that an exception is raised for a mismatching mutation over multiple ops |
| 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 SchemaCheckMode(): |
| IncorrectAliasTensor(x).sin().cos().sub(IncorrectAliasTensor(y)) |
| |
| # Tests that an exception is raised for a mismatching alias |
| def test_alias_check_fail_simple(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.rand((3, 3)) |
| with SchemaCheckMode(): |
| IncorrectAliasTensor(x).add(IncorrectAliasTensor(y), alpha=2) |
| |
| # Tests that an exception is raised for a mismatching alias over multiple ops |
| 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 SchemaCheckMode(): |
| IncorrectAliasTensor(x).sin().relu().add(IncorrectAliasTensor(y), alpha=2) |
| |
| # Tests that an exception is raised for a centered mismatching alias over multiple ops |
| def test_alias_check_fail_multiple_operators_centered(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 SchemaCheckMode(): |
| IncorrectAliasTensor(x).sin().add(IncorrectAliasTensor(y), alpha=2).relu() |
| |
| # Tests that an exception is raised for a centered mismatching alias over multiple 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)) |
| with SchemaCheckMode() as s: |
| IncorrectAliasTensor(x).aminmax(dim=0) |
| |
| # Tests that is_alias_of returns as expected |
| def test_is_alias_of_basic(self): |
| x = torch.rand((3, 3), requires_grad=True) |
| y = torch.rand((3, 3), requires_grad=True) |
| y = x.add(x, alpha=2) |
| self.assertTrue(torch._C._is_alias_of(x, x)) |
| self.assertFalse(torch._C._is_alias_of(x, y)) |
| |
| # Tests that is_alias_of returns as expected with empty containers |
| def test_is_alias_of_empty_container(self): |
| x = [] |
| y = torch.rand((3, 3), requires_grad=True) |
| self.assertFalse(torch._C._is_alias_of(x, x)) |
| self.assertFalse(torch._C._is_alias_of(x, y)) |
| |
| # Tests that overlaps returns as expected |
| def test_overlaps_basic(self): |
| x = torch.rand((3, 3), requires_grad=True) |
| y = torch.rand((3, 3), requires_grad=True) |
| z = [x, y] |
| self.assertTrue(torch._C._overlaps(x, x)) |
| self.assertFalse(torch._C._overlaps(x, y)) |
| self.assertTrue(torch._C._overlaps(z, x)) |
| self.assertTrue(torch._C._overlaps(z, y)) |
| |
| # Tests that overlaps returns correctly with empty containers |
| def test_overlaps_empty_container(self): |
| x = [] |
| y = [torch.rand((3, 3), requires_grad=True)] |
| # Empty containers return false |
| self.assertFalse(torch._C._overlaps(y, x)) |
| self.assertTrue(torch._C._overlaps(y, y)) |
| |
| # Tests that SchemaInfo Bindings work as expected |
| def test_schema_info_bind_basic(self): |
| class SchemaInfoBindTestMode(TorchDispatchMode): |
| def __init__(self, test_self): |
| self.test_self = test_self |
| |
| def __torch_dispatch__(self, func, types, args=(), kwargs=None): |
| named_arg_list = normalize_function( |
| func, |
| args, |
| kwargs, |
| normalize_to_only_use_kwargs=True |
| ).kwargs |
| schema_info_value_test = torch._C._SchemaInfo(func._schema) |
| schema_info_values_test = torch._C._SchemaInfo(func._schema) |
| self.test_self.assertFalse(schema_info_value_test.may_alias( |
| torch._C._SchemaArgument(torch._C._SchemaArgType.input, 0), |
| torch._C._SchemaArgument(torch._C._SchemaArgType.input, 1))) |
| self.test_self.assertFalse(schema_info_values_test.may_alias( |
| torch._C._SchemaArgument(torch._C._SchemaArgType.input, 0), |
| torch._C._SchemaArgument(torch._C._SchemaArgType.input, 1))) |
| for i in named_arg_list: |
| schema_info_value_test.add_argument_value(i, named_arg_list[i]) |
| schema_info_values_test.add_argument_values(named_arg_list) |
| self.test_self.assertTrue(schema_info_value_test.may_alias( |
| torch._C._SchemaArgument(torch._C._SchemaArgType.input, 0), |
| torch._C._SchemaArgument(torch._C._SchemaArgType.input, 1))) |
| self.test_self.assertTrue(schema_info_values_test.may_alias( |
| torch._C._SchemaArgument(torch._C._SchemaArgType.input, 0), |
| torch._C._SchemaArgument(torch._C._SchemaArgType.input, 1))) |
| |
| return func(*args, **kwargs) |
| x = torch.rand((3, 3)) |
| with SchemaInfoBindTestMode(self) as schemaInfoCheck: |
| x.add(x) |
| |
| |
| class TestSchemaCheckModeOpInfo(JitTestCase): |
| @ops(op_db, dtypes=OpDTypes.supported) |
| def test_schema_correctness(self, device, dtype, op): |
| # Currently torch.equal isn't supported with torch.complex32 |
| # There's also errors with complex64 and complex128 |
| if (dtype == torch.complex32): |
| return |
| for sample in op.sample_inputs(device, dtype, requires_grad=False): |
| with SchemaCheckMode(): |
| op(sample.input, *sample.args, **sample.kwargs) |
| |
| instantiate_device_type_tests(TestSchemaCheckModeOpInfo, globals(), only_for=("cpu", "cuda")) |
| |
| if __name__ == '__main__': |
| run_tests() |
