def type_inference(self):
list_elem_type = self.ls.elem_type
value_type = self.value.sym_type
dynamic_length = self.ls.dynamic_length
init_length = self.ls.init_length
if list_elem_type is None:
# fill in the elem type using value's type info.
return types.list(value_type,
init_length=init_length,
dynamic_length=dynamic_length)
if list_elem_type == types.unknown:
msg = "Input ls elem type unknown. Override with {}"
logging.warning(msg.format(value_type))
return types.list(value_type,
init_length=init_length,
dynamic_length=dynamic_length)
if not types.is_subtype(value_type, list_elem_type):
msg = "Elem type mismatch: ls elem type {} vs " + "value type {}"
raise ValueError(msg.format(list_elem_type, value_type))
return self.ls.sym_type
def type_inference(self):
num_indices = self.indices.shape[0]
num_values = self.value.shape[0]
if num_values != num_indices:
raise ValueError("Cannot scatter {} values to {} indices".format(
num_values, num_indices))
list_elem_type = self.ls.elem_type
value_type = self.value.sym_type
dynamic_length = self.ls.dynamic_length
init_length = self.ls.init_length
elem_type = types.tensor(value_type.get_primitive(),
value_type.get_shape()[1:])
if list_elem_type == types.unknown:
# fill in the elem type using value's type info.
return types.list(elem_type,
dynamic_length=dynamic_length,
init_length=init_length)
if not types.is_subtype(elem_type, list_elem_type):
msg = "Elem type mismatch: ls elem type {} vs " + "value type {}"
raise ValueError(msg.format(list_elem_type, elem_type))
return self.ls.sym_type
def _check_is_compatible_type(type1, type2):
if not types.is_subtype(type1, type2):
is_comp, _ = types.is_tensor_and_is_compatible(type1, type2)
return is_comp
return True
