def insert_observer(
node: Node, observer: torch.quantization.ObserverBase,
model: torch.nn.Module,
activation_post_process_map: Dict[str, torch.quantization.ObserverBase],
env: Dict[Any, Any], observed_graph: Graph, load_arg: Callable,
observed_node_names_set: Set[str]):
"""Insert observer for node by modifying the observed_graph and
attach observer module to the model
Args:
node: Node
observer: observer/fake_quantize module instance
"""
# respect device affinity when adding observers
model_device = assert_and_get_unique_device(model)
if model_device:
observer.to(model_device)
# add observer module as attribute
prefix = node.name + '_activation_post_process_'
get_new_observer_name = get_new_attr_name_with_prefix(prefix)
observer_name = get_new_observer_name(model)
setattr(model, observer_name, observer)
# put observer instance activation_post_process map
assert activation_post_process_map is not None
activation_post_process_map[node.name] = observer
# insert observer call
env[node.name] = observed_graph.create_node(
'call_module', observer_name, (load_arg(node),), {})
observed_node_names_set.add(node.name)
def insert_observer(
node: Node,
observer: torch.quantization.ObserverBase,
model: torch.nn.Module,
modules: Dict[str, torch.nn.Module],
graph: Graph,
) -> Node:
"""
Attaches `observer` to `model`, and creates a node which calls
`observer` on the output of `node`.
"""
model_device = assert_and_get_unique_device(model)
if model_device:
observer.to(model_device)
# add observer module as attribute
if is_equalization_observer(observer):
prefix = node.name + '_equalization_process_'
else:
prefix = node.name + '_activation_post_process_'
get_new_observer_name = get_new_attr_name_with_prefix(prefix)
observer_name = get_new_observer_name(model)
setattr(model, observer_name, observer)
modules[observer_name] = observer
with graph.inserting_after(node):
new_obs = graph.create_node(
'call_module', observer_name, (node,), {})
return new_obs