def register_statistics(self, model, stats_collector):
model = deepcopy(model)
fqut.insert_fake_quantize_nodes(self._config, model, self.params)
activation_statistics_layout = self.__get_activations_statistics_layout(
model, qscheme=self.params)
layers_mapping = fqut.create_renamed_layers_mapping(
model, activation_statistics_layout)
stats_collector.register(self.name, activation_statistics_layout,
self._sampler, layers_mapping)
self._stats_collector = stats_collector
def get_parameter_meta(self, model, optimizer_state):
param_grid = []
if 'range_estimator' in self._config.tuning_scope:
for variable in self._config.estimator_tuning_scope:
self._config.tuning_scope.append('estimator_' + variable)
config = deepcopy(self._config)
if optimizer_state['first_iteration'] or optimizer_state[
'fully_quantized']:
config['tuning_scope'] = []
hardware_config = load_hardware_config(config)
model = deepcopy(model)
fqut.insert_fake_quantize_nodes(config, model)
fq_configuration = read_all_fake_quantize_configurations(
config, hardware_config, model)
nodes_config = {}
for fq in get_nodes_by_type(model, ['FakeQuantize']):
node_input = get_node_input(fq, 0)
op_type = 'weights' if node_input.type == 'Const' else 'activations'
fq_node_config = fq_configuration[fq.name][op_type]
for child_name, child_config in fq_node_config:
if child_name not in nodes_config:
nodes_config[child_name] = {
'weights': [],
'activations': []
}
nodes_config[child_name][op_type].extend(child_config)
for node_name, node_config in nodes_config.items():
if 'activations' in node_config:
node_config['activations'] = ut.append_estimator_configs(
node_config['activations'], False, config,
self.params[node_name]
if not optimizer_state['fully_quantized']
and node_name in self.params else None)
if 'weights' in node_config:
node_config['weights'] = ut.append_estimator_configs(
node_config['weights'], True, config,
self.params[node_name]
if not optimizer_state['fully_quantized']
and node_name in self.params else None)
for node_name, node_config in nodes_config.items():
op_config = ut.get_quantize_op_config(
node_config, config, self.params[node_name]
if not optimizer_state['fully_quantized']
and node_name in self.params else None)
param_grid.append((node_name, 'choice', op_config))
return param_grid
def get_parameter_meta(self, model):
param_grid = []
config = deepcopy(self._config)
hardware_config = load_hardware_config(config)
model = deepcopy(model)
fqut.insert_fake_quantize_nodes(config, model)
fq_configuration = read_all_fake_quantize_configurations(
config, hardware_config, model)
nodes_config = {}
for fq in get_nodes_by_type(model, ['FakeQuantize']):
node_input = get_node_input(fq, 0)
op_type = 'weights' if node_input.type == 'Const' else 'activations'
fq_node_config = fq_configuration[fq.fullname][op_type]
for child_name, child_config in fq_node_config:
if child_name not in nodes_config:
nodes_config[child_name] = {
'weights': [],
'activations': []
}
nodes_config[child_name][op_type].extend(child_config)
for node_name, node_config in nodes_config.items():
if 'activations' in node_config:
node_config['activations'] = ut.append_estimator_configs(
node_config['activations'], False, config,
self.params[node_name]
if node_name in self.params else None)
if 'weights' in node_config:
node_config['weights'] = ut.append_estimator_configs(
node_config['weights'], True, config,
self.params[node_name]
if node_name in self.params else None)
for node_name, node_config in nodes_config.items():
op_config = ut.get_quantize_op_config(
node_config, config,
self.params[node_name] if node_name in self.params else None)
param_grid.append((node_name, 'choice', op_config))
return param_grid