def generate_tensor_like_override_tests(cls):
def test_generator(func, override):
if torch._six.PY3:
args = inspect.getfullargspec(override)
else:
args = inspect.getargspec(override)
nargs = len(args.args)
if args.defaults is not None:
nargs -= len(args.defaults)
func_args = [TensorLike() for _ in range(nargs)]
if args.varargs is not None:
func_args += [TensorLike(), TensorLike()]
def test(self):
self.assertEqual(func(*func_args), -1)
return test
for func, override in get_testing_overrides().items():
test_method = test_generator(func, override)
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 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
implements_tensor_like(func)(override)
def generate_tensor_like_override_tests(cls):
from torch.testing._internal.generated.annotated_fn_args import annotated_args
def test_generator(func, override):
func_args = []
if inspect.isbuiltin(func) and 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':
func_args.append(TensorLike())
elif t == 'TensorList':
func_args.append([TensorLike(), TensorLike()])
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 += [TensorLike() for _ in range(nargs)]
if args.varargs is not None:
func_args += [TensorLike(), TensorLike()]
def test(self):
self.assertEqual(func(*func_args), -1)
return test
for func, override in get_testing_overrides().items():
test_method = test_generator(func, override)
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)