def generate_tensor_like_torch_implementations():
torch_vars = vars(torch)
untested_funcs = []
testing_overrides = get_testing_overrides()
# test/test_cpp_api_parity.py monkeypatches torch.nn to have a new
# function sample_functional. Depending on what order you run pytest
# collection, this may trigger the error here. This is a hack to fix
# the problem. A more proper fix is to make the "not tested" check
# a test on its own, and to make sure the monkeypatch is only installed
# for the span of the relevant test (and deleted afterwards)
testing_ignore = {"sample_functional"}
for namespace, funcs in get_overridable_functions().items():
for func in funcs:
if func not in testing_overrides and func.__name__ not in testing_ignore:
untested_funcs.append("{}.{}".format(namespace, func.__name__))
msg = (
"The following functions are not tested for __torch_function__ "
"support, please ensure there is an entry in the dict returned by "
"torch._overrides.get_testing_overrides for this function or if a "
"__torch_function__ override does not make sense, add an entry to "
"the tuple returned by torch._overrides.get_ignored_functions.\n\n{}")
assert len(untested_funcs) == 0, msg.format(pprint.pformat(untested_funcs))
for func, override in testing_overrides.items():
# decorate the overrides with implements_tensor_like if it's not a
# torch.Tensor method
wrapped = triggered_wrapper(override)
# See note: "_triggered wrapper"
WRAPPED_TRIGGERED_IMPLS[func] = wrapped
if is_tensor_method_or_property(func):
implements_sub(func)(wrapped)
else:
implements_tensor_like(func)(wrapped)
def generate_tensor_like_torch_implementations():
torch_vars = vars(torch)
untested_funcs = []
testing_overrides = get_testing_overrides()
for namespace, funcs in get_overridable_functions().items():
for func in funcs:
if func not in testing_overrides:
untested_funcs.append("{}.{}".format(namespace, func.__name__))
msg = (
"The following functions are not tested for __torch_function__ "
"support, please ensure there is an entry in the dict returned by "
"torch._overrides.get_testing_overrides for this function or if a "
"__torch_function__ override does not make sense, add an entry to "
"the tuple returned by torch._overrides.get_ignored_functions.\n\n{}"
)
assert len(untested_funcs) == 0, msg.format(pprint.pformat(untested_funcs))
for func, override in testing_overrides.items():
# decorate the overrides with implements_tensor_like if it's not a
# torch.Tensor method
wrapped = triggered_wrapper(override)
# See note: "_triggered wrapper"
WRAPPED_TRIGGERED_IMPLS[func] = wrapped
if is_tensor_method_or_property(func):
implements_sub(func)(wrapped)
else:
implements_tensor_like(func)(wrapped)
def test_generator(func, override):
# If func corresponds to a torch.Tensor method or property.
if is_tensor_method_or_property(func):
# Generate an instance by using SubTensor,
def instance_gen():
return SubTensor([5])
else:
# Otherwise, TensorLike.
def instance_gen():
return TensorLike()
func_args = []
is_method = is_tensor_method_or_property(func)
if func in annotated_args:
for arg in annotated_args[func]:
# Guess valid input to aten function based on type of argument
t = arg['simple_type']
if t.endswith('?'):
t = t[:-1]
if t == 'Tensor':
if is_method and arg['name'] == 'self':
# See "Note: properties and __get__"
func = func.__get__(instance_gen())
continue
func_args.append(instance_gen())
elif t == 'TensorList':
func_args.append([instance_gen(), instance_gen()])
elif t == 'c10::List<c10::optional<Tensor>>':
func_args.append([instance_gen(), instance_gen()])
elif t == 'IntArrayRef':
size = arg.get('size', 2)
if size == 1:
func_args.append(1)
else:
func_args.append([1] * size)
elif t == 'Scalar':
func_args.append(3.5)
elif t == 'bool':
func_args.append(False)
elif t.startswith('int') or t in {'Dimname', 'DimnameList'}:
func_args.append(0)
elif t in {'Stream'}:
func_args.append(torch.Stream())
elif t.startswith('float') or t == 'double':
func_args.append(1.0)
elif t in {'Generator', 'MemoryFormat', 'TensorOptions'}:
func_args.append(None)
elif t == 'ScalarType':
func_args.append(torch.float32)
elif t == 'std::string':
func_args.append('')
else:
raise RuntimeError(
f"Unsupported argument type {t} for {arg['name']} of function {func}"
)
else:
args = inspect.getfullargspec(override)
try:
func_args = inspect.getfullargspec(func)
# Remove annotations from argspec
func_args = type(func_args)(**{
**func_args, 'annotations': None
})
if func_args != args:
raise RuntimeError(
f"Override for {func} doesn't match its argspec.\n" +
f"Original: {inspect.signature(func)}\n" +
f"Override: {inspect.signature(override)}")
except TypeError:
pass
nargs = len(args.args)
if args.defaults is not None:
nargs -= len(args.defaults)
func_args = [instance_gen() for _ in range(nargs)]
if args.varargs is not None:
func_args += [instance_gen(), instance_gen()]
def test(self):
ret = func(*func_args)
# ret is None for certain protocols, e.g., `__weakref__` and `__setitem__`
# This is currently the best check but doesn't work for, for example,
# Tensor.__add__ because it redirects to Tensor.add.
# See note "_triggered wrapper"
if not is_method or ret is None:
self.assertTrue(WRAPPED_TRIGGERED_IMPLS[func]._triggered)
return
self.assertEqual(ret, -1)
return test
def generate_tensor_like_override_tests(cls):
from torch.testing._internal.generated.annotated_fn_args import annotated_args
def test_generator(func, override):
# If func corresponds to a torch.Tensor method or property.
if is_tensor_method_or_property(func):
# Generate an instance by using SubTensor,
def instance_gen():
return SubTensor([5])
else:
# Otherwise, TensorLike.
def instance_gen():
return TensorLike()
func_args = []
is_method = is_tensor_method_or_property(func)
if func in annotated_args:
for arg in annotated_args[func]:
# Guess valid input to aten function based on type of argument
t = arg['simple_type']
if t.endswith('?'):
t = t[:-1]
if t == 'Tensor':
if is_method and arg['name'] == 'self':
# See "Note: properties and __get__"
func = func.__get__(instance_gen())
continue
func_args.append(instance_gen())
elif t == 'TensorList':
func_args.append([instance_gen(), instance_gen()])
elif t == 'c10::List<c10::optional<Tensor>>':
func_args.append([instance_gen(), instance_gen()])
elif t == 'IntArrayRef':
size = arg.get('size', 2)
if size == 1:
func_args.append(1)
else:
func_args.append([1] * size)
elif t == 'Scalar':
func_args.append(3.5)
elif t == 'bool':
func_args.append(False)
elif t.startswith('int') or t in {'Dimname', 'DimnameList'}:
func_args.append(0)
elif t in {'Stream'}:
func_args.append(torch.Stream())
elif t.startswith('float') or t == 'double':
func_args.append(1.0)
elif t in {'Generator', 'MemoryFormat', 'TensorOptions'}:
func_args.append(None)
elif t == 'ScalarType':
func_args.append(torch.float32)
elif t == 'std::string':
func_args.append('')
else:
raise RuntimeError(
f"Unsupported argument type {t} for {arg['name']} of function {func}"
)
else:
args = inspect.getfullargspec(override)
try:
func_args = inspect.getfullargspec(func)
# Remove annotations from argspec
func_args = type(func_args)(**{
**func_args, 'annotations': None
})
if func_args != args:
raise RuntimeError(
f"Override for {func} doesn't match its argspec.\n" +
f"Original: {inspect.signature(func)}\n" +
f"Override: {inspect.signature(override)}")
except TypeError:
pass
nargs = len(args.args)
if args.defaults is not None:
nargs -= len(args.defaults)
func_args = [instance_gen() for _ in range(nargs)]
if args.varargs is not None:
func_args += [instance_gen(), instance_gen()]
def test(self):
ret = func(*func_args)
# ret is None for certain protocols, e.g., `__weakref__` and `__setitem__`
# This is currently the best check but doesn't work for, for example,
# Tensor.__add__ because it redirects to Tensor.add.
# See note "_triggered wrapper"
if not is_method or ret is None:
self.assertTrue(WRAPPED_TRIGGERED_IMPLS[func]._triggered)
return
self.assertEqual(ret, -1)
return test
for func, override in get_testing_overrides().items():
test_method = test_generator(func, override)
if func.__name__ == "__get__":
# Note: properties and __get__
# __get__ is part of the descriptor protocol.
# https://docs.python.org/3/howto/descriptor.html
# This is used for properties of the form
# torch.Tensor.<property>, with the method __get__
# In this case we get the property name in two ways:
# This case for properties defined in C.
module = getattr(func.__self__, "__qualname__", None)
# This one for properties defined in Python.
if module is None:
module = "Tensor." + func.__self__.fget.__name__
# Unfortunately I couldn't find a way to unify these two cases
# and there is no way for general descriptors.
elif is_tensor_method_or_property(func):
module = "Tensor"
else:
module = func.__module__
if module:
name = 'test_{}_{}'.format(module.replace('.', '_'), func.__name__)
else:
name = 'test_{}'.format(func.__name__)
test_method.__name__ = name
setattr(cls, name, test_method)
def test_generator(func, override):
# If func corresponds to a torch.Tensor method or property.
if is_tensor_method_or_property(func):
# Generate an instance by using SubTensor,
def instance_gen():
return SubTensor([5])
else:
# Otherwise, TensorLike.
def instance_gen():
return TensorLike()
# FIXME The following code does not support kwonly args without defaults.
# The fix is easy, as one just needs to save these args when generating the variable
# annotated_args. The problem is that, if one does so, one finds a number
# of functions that have problematic signatures in native_functions.yaml.
# Fixing these would be BC breaking, so hence this terrible hack
# https://github.com/pytorch/pytorch/issues/67008
kwargs = {}
if hasattr(func,
"__name__") and "linalg_solve_triangular" in func.__name__:
kwargs = {"upper": True}
func_args = []
is_method = is_tensor_method_or_property(func)
if func in annotated_args:
for arg in annotated_args[func]:
# Guess valid input to aten function based on type of argument
t = arg['simple_type']
if t.endswith('?'):
t = t[:-1]
if t == 'Tensor':
if is_method and arg['name'] == 'self':
# See "Note: properties and __get__"
func = func.__get__(instance_gen())
continue
func_args.append(instance_gen())
elif t == 'TensorList':
func_args.append([instance_gen(), instance_gen()])
elif t == 'c10::List<c10::optional<Tensor>>':
func_args.append([instance_gen(), instance_gen()])
elif t == 'IntArrayRef' or t == 'SymIntArrayRef':
size = arg.get('size', 2)
if size == 1:
func_args.append(1)
else:
func_args.append([1] * size)
elif t == 'Scalar':
func_args.append(3.5)
elif t == 'bool':
func_args.append(False)
elif t.startswith('int') or t in {'Dimname', 'DimnameList'}:
func_args.append(0)
elif t in {'Stream'}:
func_args.append(torch.Stream())
elif t.startswith('float') or t == 'double':
func_args.append(1.0)
elif t in {'Generator', 'MemoryFormat', 'TensorOptions'}:
func_args.append(None)
elif t == 'ScalarType':
func_args.append(torch.float32)
elif t == 'c10::string_view':
func_args.append('')
elif t == 'SymInt':
# TODO: generate actual SymbolicInt
func_args.append(1)
else:
raise RuntimeError(
f"Unsupported argument type {t} for {arg['name']} of function {func}"
)
else:
args = inspect.getfullargspec(override)
try:
func_args = inspect.getfullargspec(func)
# Remove annotations from argspec
func_args = type(func_args)(**{
**func_args, 'annotations': None
})
if func_args != args:
raise RuntimeError(
f"Override for {func} doesn't match its argspec.\n" +
f"Original: {inspect.signature(func)}\n" +
f"Override: {inspect.signature(override)}")
except TypeError:
pass
nargs = len(args.args)
if args.defaults is not None:
nargs -= len(args.defaults)
func_args = [instance_gen() for _ in range(nargs)]
if args.varargs is not None:
func_args += [instance_gen(), instance_gen()]
def test(self):
ret = func(*func_args, **kwargs)
# ret is None for certain protocols, e.g., `__weakref__` and `__setitem__`
# This is currently the best check but doesn't work for, for example,
# Tensor.__add__ because it redirects to Tensor.add.
# See note "_triggered wrapper"
if not is_method or ret is None:
self.assertTrue(WRAPPED_TRIGGERED_IMPLS[func]._triggered)
return
self.assertEqual(ret, -1)
return test
def test_generator(func, override):
# If func corresponds to a torch.Tensor method or property.
if is_tensor_method_or_property(func):
# Generate an instance by using SubTensor,
def instance_gen():
return SubTensor([5])
else:
# Otherwise, TensorLike.
def instance_gen():
return TensorLike()
func_args = []
if func in annotated_args:
for arg in annotated_args[func]:
# Guess valid input to aten function based on type of argument
t = arg['simple_type']
if t.endswith('?'):
t = t[:-1]
if t == 'Tensor':
if arg['name'] == 'self' and is_tensor_method_or_property(
func):
func = func.__get__(instance_gen())
continue
func_args.append(instance_gen())
elif t == 'TensorList':
func_args.append([instance_gen(), instance_gen()])
elif t == 'IntArrayRef':
size = arg.get('size', 2)
if size == 1:
func_args.append(1)
else:
func_args.append([1] * size)
elif t == 'Scalar':
func_args.append(3.5)
elif t == 'bool':
func_args.append(False)
elif t.startswith('int') or t in {'Dimname', 'DimnameList'}:
func_args.append(0)
elif t.startswith('float') or t == 'double':
func_args.append(1.0)
elif t in {'Generator', 'MemoryFormat', 'TensorOptions'}:
func_args.append(None)
elif t == 'ScalarType':
func_args.append(torch.float32)
elif t == 'std::string':
func_args.append('')
else:
raise RuntimeError(
f"Unsupported argument type {t} for {arg['name']} of function {func}"
)
else:
args = inspect.getfullargspec(override)
nargs = len(args.args)
if args.defaults is not None:
nargs -= len(args.defaults)
func_args = [instance_gen() for _ in range(nargs)]
if args.varargs is not None:
func_args += [instance_gen(), instance_gen()]
def test(self):
ret = func(*func_args)
# ret is None for certain protocols, e.g., `__weakref__` and `__setitem__`
# This is currently the best check but doesn't work for, for example,
# Tensor.__add__ because it redirects to Tensor.add.
if ret is None:
self.assertTrue(HANDLED_FUNCTIONS_WRAPPERS[func]._triggered)
return
self.assertEqual(ret, -1)
return test
