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_resolve_name(self):
for cs in get_overridable_functions().values():
for c in cs:
self.assertEqual(eval(torch.overrides.resolve_name(c)),
c,
msg=f"{c}, {torch.overrides.resolve_name(c)}")
