def __init__(self, algo: 'QuantizationController', config: Config,
default_activation_bitwidth: int, default_weight_bitwidth: int, criterion: _Loss,
data_loader: DataLoader, is_distributed: bool = False):
super().__init__(algo, config, default_activation_bitwidth, default_weight_bitwidth,
criterion, data_loader, is_distributed)
self._traces_per_layer_path = config.get('traces_per_layer_path', None)
self._num_data_points = config.get('num_data_points', 200)
self._iter_number = config.get('iter_number', 200)
self._tolerance = config.get('tolerance', 1e-5)
self._bits = config.get('bits', [4, 8])
def __init__(self, algo: 'QuantizationController', config: Config,
default_activation_bitwidth: int, default_weight_bitwidth: int,
criterion: _Loss, data_loader: DataLoader, is_distributed: bool = False):
self._algo = algo
self._model = self._algo._model # type: NNCFNetwork
self._bitwidth_per_scope = config.get('bitwidth_per_scope', {}) # type: List[List]
self._default_activation_bitwidth = default_activation_bitwidth
self._default_weight_bitwidth = default_weight_bitwidth
self._criterion = criterion
self._data_loader = data_loader
self._is_distributed = is_distributed
self._all_quantizations = {}
self._ordered_weight_quantizations = []
for class_type in QUANTIZATION_MODULES.registry_dict.values():
quantization_type = class_type.__name__
act_module_dict = self._model.get_compression_modules_by_type(
CompressionModuleType.ACTIVATION_QUANTIZER)
func_module_dict = self._model.get_compression_modules_by_type(CompressionModuleType.FUNCTION_QUANTIZER)
weight_module_dict = self._model.get_nncf_wrapped_model()
self._all_quantizations.update(get_all_modules_by_type(act_module_dict, quantization_type))
self._all_quantizations.update(get_all_modules_by_type(func_module_dict, quantization_type))
ops_quantizations = get_all_modules_by_type(weight_module_dict, quantization_type)
self._ordered_weight_quantizations.extend([q for q in ops_quantizations.values() if q.is_weights])
self._all_quantizations.update(ops_quantizations)
class PruningScheduler(CompressionScheduler):
def __init__(self, pruning_algo, params: Config = None):
super().__init__()
if params is None:
self._params = Config()
else:
self._params = params
self.algo = pruning_algo
# Number of initial steps of training before pruning
self.num_init_steps = self._params.get('num_init_steps', 0)
self.pruning_steps = self._params.get('pruning_steps', 100)
# Pruning rates
self.initial_pruning = self._params.get('pruning_init', 0)
self.pruning_target = self._params.get('pruning_target', 0.5)
def load_state_dict(self, state_dict):
super().load_state_dict(state_dict)
self._set_pruning_level()
def epoch_step(self, epoch=None):
super().epoch_step(epoch)
self._set_pruning_level()
def _set_pruning_level(self):
self.algo.set_pruning_rate(self.current_pruning_level)
if self.last_epoch >= (self.pruning_steps + self.num_init_steps):
self.algo.freeze()
def _calc_density_level(self):
raise NotImplementedError
@property
def current_pruning_level(self):
if self.last_epoch >= self.num_init_steps:
return 1 - self._calc_density_level()
return 0
def create_compressed_model(
model: Module,
config: Config,
dummy_forward_fn: Callable[[Module], Any] = None,
dump_graphs=True
) -> Tuple[CompressionAlgorithmController, NNCFNetwork]:
"""
The main function used to produce a model ready for compression fine-tuning from an original PyTorch
model and a configuration object.
dummy_forward_fn
:param model: The original model. Should have its parameters already loaded from a checkpoint or another
source.
:param config: A configuration object used to determine the exact compression modifications to be applied
to the model
:param dummy_forward_fn: will be used instead of a *forward* function call to build
the internal graph representation via tracing. Specifying this is useful when the original training pipeline
has special formats of data loader output or has additional *forward* arguments other than input tensors.
Otherwise, the *forward* call of the model during graph tracing will be made with mock tensors according
to the shape specified in the config object.
:param dump_graphs: Whether or not should also dump the internal graph representation of the
original and compressed models in the .dot format into the log directory.
:return: A controller for the compression algorithm (or algorithms, in which case the controller
is an instance of CompositeCompressionController) and the model ready for compression wrapped
as an object of NNCFNetwork."""
if dump_graphs:
if dummy_forward_fn is None:
input_info_list = create_input_infos(config)
graph_builder = GraphBuilder(
custom_forward_fn=create_dummy_forward_fn(
input_info_list, with_input_tracing=True))
else:
graph_builder = GraphBuilder(custom_forward_fn=dummy_forward_fn)
if is_main_process():
graph = graph_builder.build_graph(model)
graph.dump_graph(osp.join(config.log_dir, "original_graph.dot"),
extended=True)
if is_debug():
set_debug_log_dir(config.log_dir)
input_info_list = create_input_infos(config)
scopes_without_shape_matching = config.get('scopes_without_shape_matching',
[])
ignored_scopes = config.get('ignored_scopes')
target_scopes = config.get('target_scopes')
compressed_model = NNCFNetwork(
model,
input_infos=input_info_list,
dummy_forward_fn=dummy_forward_fn,
ignored_scopes=ignored_scopes,
target_scopes=target_scopes,
scopes_without_shape_matching=scopes_without_shape_matching)
compression_algo_builder_list = create_compression_algorithm_builders(
config)
for builder in compression_algo_builder_list:
compressed_model = builder.apply_to(compressed_model)
compression_ctrl = compressed_model.commit_compression_changes()
if dump_graphs and is_main_process() and compression_algo_builder_list:
if dummy_forward_fn is None:
compressed_graph_builder = GraphBuilder(
custom_forward_fn=create_dummy_forward_fn(
input_info_list, with_input_tracing=False))
else:
compressed_graph_builder = GraphBuilder(
custom_forward_fn=dummy_forward_fn)
graph = compressed_graph_builder.build_graph(
compressed_model, compressed_model.get_tracing_context())
graph.dump_graph(osp.join(config.log_dir, "compressed_graph.dot"),
extended=True)
return compression_ctrl, compressed_model
