frozenleaves/pytorch
1
0
Fork 0
mirror of https://github.com/pytorch/pytorch.git synced 2025-10-20 12:54:11 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
18d8c548f4458c03ae6be60903db3ccf1c0d8d3f
pytorch/test/test_schema_check.py
samdow 18d8c548f4 [Modes] remove enable and rewrite mode stack (squashed) (#84774) 
Based on @ezyang's suggestion, mode stack now has "one true mode" which is the _only_ mode that can ever be active at the C++ level. That mode's torch dispatch is just to take the top mode in the stack, reenable itself (if we aren't at the end of the mode stack), and run the top mode's torch_{dispatch|function}

This maintains that in the middle of a mode's torch dispatch, the mode itself will not be active. It changes the function the user has to call to see what the current mode is (no longer queries the C++, it's python only) but allows the user to also see the entire mode stack easily

Removes `enable_torch_dispatch_mode` and `.restore()` since neither makes sense in this new setup

### Background
Why do we want this? Well, a pretty common pattern that was coming up was that users had to do something like

```python
## PRE-PR UX
def f(mode):
  with mode.restore():  # user needs to understand this restore thing?
    ...

with Mode() as m:
  pass
f(m)
```

Many users were getting error from forgetting to call `.restore` or from forgetting to add the (tbh weird) "mode instantiation"  step where they use the mode as a context manager with an empty body. Really, they wanted to treat modes like context managers and just write
```python
## FROM FEEDBACK, USER DESIRED CODE. POSSIBLE POST-PR
def f(mode):
  with mode:
    ...
f(Mode())
```

** Technical Details **
With the old mode stack, we basically had a linked list so the mode itself could only be used once and had a fixed parent. In this new design, the mode stack is just a python list that we're pushing to and popping from. There's only one mode that's ever active at the C++ level and it runs the next mode in the Python list. The modes don't have state on them anymore
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84774
Approved by: https://github.com/ezyang, https://github.com/zou3519
2022-09-27 01:04:35 +00:00

451 lines
19 KiB
Python
Raw Blame History

# 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()
Reference in New Issue View Git Blame Copy Permalink