def wrap(obj: 'OperatorMetatype'):
cls_name = name_
if cls_name is None:
cls_name = obj.__name__
super_register(obj, cls_name)
op_names = obj.get_all_aliases()
for name in op_names:
name = get_version_agnostic_name(name)
if name not in self._op_name_to_op_meta_dict:
self._op_name_to_op_meta_dict[name] = obj
else:
assert self._op_name_to_op_meta_dict[name] == obj, \
"Inconsistent operator metatype registry - single patched op name maps to multiple metatypes!"
return obj
def get_caller_context(
self,
operator_type: str) -> InputAgnosticOperationExecutionContext:
"""
Designed to work in the following way - for each scope the context will track the number of the calls to the
operators with the name operator_type (call_order). The counter values are preserved until reset by a
corresponding member function of the context, which must be called after each model iteration - this is
usually handled inside NNCF. This mechanism allows to discern between multiple function calls inside the same
module that would each require their own instance of compression layers - for instance, multiple `relu`
function calls (either on their own or inside a `for` cycle), and at the same moment allow the checkpoints to
be loaded if the model had changed in the meantime in a way that does not impact the major function call
order (e.g. if comments were added to the .py file with the model)
"""
version_agnostic_operator_type = get_version_agnostic_name(
operator_type)
call_order = self.get_operator_call_count_in_scope(
version_agnostic_operator_type, self.scope)
ia_op_exec_context = InputAgnosticOperationExecutionContext(
version_agnostic_operator_type, self.scope, call_order)
return ia_op_exec_context
