Docstrings

Author: daniil-lyakhov

nncf/onnx/graph/nncf_graph_builder.py

@@ -119,7 +119,14 @@ def get_bias_tensor_port_id(metatype: ONNXOpWithWeightsMetatype) -> Optional[int
     return None
 
 
-def _get_common_layer_attributes(node, metatype: ONNXOpMetatype):
+def _get_common_layer_attributes(node, metatype: ONNXOpMetatype) -> Optional[BaseLayerAttributes]:
+    """
+    Returns layer-specific layer attributes for the given node.
+
+    :param node: Target Node to get layer attributes for.
+    :param metatype: Target node metatype.
+    :return: Target node layer attributes or None.
+    """
     if metatype == ONNXConcatMetatype:
         axis = [attr.i for attr in node.attribute if attr.name == "axis"][0]
         num_inputs = len(node.input)

nncf/openvino/graph/model_utils.py

@@ -53,4 +53,10 @@ def remove_fq_from_inputs(model: ov.Model, graph: NNCFGraph) -> ov.Model:
 
 
 def get_input_nodes(nncf_graph: NNCFGraph) -> List[NNCFNode]:
+    """
+    Get all nodes from given nncf_graph that are identified as a input nodes.
+
+    :param nncf_graph: NNCFGraph to work with.
+    :return: Target NNCFGraph input nodes.
+    """
     return list(set(nncf_graph.get_input_nodes()).upadte(nncf_graph.get_nodes_by_metatypes([OVReadValueMetatype])))

nncf/openvino/graph/nncf_graph_builder.py

@@ -189,14 +189,31 @@ def create_nncf_graph(model: ov.Model) -> NNCFGraph:
         GraphConverter._add_edges_to_nncf_graph(model, nncf_graph)
         return nncf_graph
 
-    def _set_non_weighted_layer_attributes(node: ov.Node, metatype: OVOpMetatype, nncf_graph: NNCFGraph):
+    def _set_non_weighted_layer_attributes(node: ov.Node, metatype: OVOpMetatype, nncf_graph: NNCFGraph) -> None:
+        """
+        Sets layer attributes for a non weighted node.
+
+        :param node: Target node.
+        :param metatype: Target node metatype.
+        :param nncf_graph: NNCFGraph to work with.
+        """
         if metatype == OVConcatMetatype:
             nncf_node = nncf_graph.get_node_by_name(node.get_friendly_name())
             nncf_node.layer_attributes = OVLayerAttributes(
                 {}, MultipleInputLayerAttributes(axis=node.get_axis(), num_inputs=len(node.inputs()))
             )
 
-    def _set_weighted_layer_attributes(node: ov.Node, metatype: OVOpMetatype, nncf_graph: NNCFGraph, visited: Set[str]):
+    def _set_weighted_layer_attributes(
+        node: ov.Node, metatype: OVOpMetatype, nncf_graph: NNCFGraph, visited: Set[str]
+    ) -> None:
+        """
+        Sets layer attributes for a weighted node.
+
+        :param node: Target node.
+        :param metatype: Target node metatype.
+        :param nncf_graph: NNCFGraph to work with.
+        :param visited: Set with node names that were already processed by the GraphConverter.
+        """
         const_attrs, act_attrs = {}, {}
         for inp in GraphConverter._filter_weight_input_ports(node.inputs(), metatype):
             inp_name = inp.get_source_output().get_node().get_friendly_name()

nncf/quantization/algorithms/accuracy_control/backend.py

@@ -54,7 +54,12 @@ def get_quantizable_metatypes() -> List[OperatorMetatype]:
     @staticmethod
     @abstractmethod
     def get_graph_inputs(nncf_graph: NNCFGraph) -> List[NNCFNode]:
-        pass
+        """
+        Returns a list of NNCFNodes that are identified as an inputs.
+
+        :param nncf_graph: The NNCF graph.
+        :return: List of NNCFNodes that are identified as an inputs.
+        """
 
     @staticmethod
     @abstractmethod

nncf/quantization/algorithms/min_max/backend.py

@@ -67,10 +67,6 @@ def dropout_metatypes(self) -> List[OperatorMetatype]:
         Property for the backend-specific Dropout metatypes.
         """
 
-    @abstractmethod
-    def get_input_nodes(self, nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
-        pass
-
     @property
     @abstractmethod
     def overflow_fix_metatypes(self) -> List[OperatorMetatype]:
@@ -143,6 +139,16 @@ def create_quantizer_insertion_command(
         :return: Backend-specific TransformationCommand for the quantizer insertion operation.
         """
 
+    @staticmethod
+    @abstractmethod
+    def get_input_nodes(nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
+        """
+        Returns a list of NNCFNodes that are identified as an inputs.
+
+        :param nncf_graph: NNCFGraph to get input nodes from.
+        :return: List of NNCFNodes that are identified as an inputs.
+        """
+
     @staticmethod
     @abstractmethod
     def unify_statistics(statistics: List[MinMaxTensorStatistic]) -> MinMaxTensorStatistic:

nncf/quantization/algorithms/min_max/onnx_backend.py

@@ -64,9 +64,6 @@ def overflow_fix_metatypes(self) -> List[OperatorMetatype]:
     def add_metatypes(self) -> List[OperatorMetatype]:
         return [om.ONNXAddLayerMetatype]
 
-    def get_input_nodes(self, nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
-        return nncf_graph.get_input_nodes()
-
     @property
     def group_conv_metatypes(self) -> List[OperatorMetatype]:
         return self.conv_metatypes
@@ -95,6 +92,10 @@ def hw_config(self) -> HWConfig:
     def quant_trait_op_dict(self) -> Dict[int, OperatorMetatype]:
         return DEFAULT_ONNX_QUANT_TRAIT_TO_OP_DICT
 
+    @staticmethod
+    def get_input_nodes(nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
+        return nncf_graph.get_input_nodes()
+
     @staticmethod
     def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> ONNXTargetPoint:
         return ONNXTargetPoint(target_type, target_node_name, port_id)

nncf/quantization/algorithms/min_max/openvino_backend.py

@@ -87,9 +87,6 @@ def dropout_metatypes(self) -> List[OperatorMetatype]:
     def read_variable_metatypes(self) -> List[OperatorMetatype]:
         return [om.OVReadValueMetatype]
 
-    def get_input_nodes(self, nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
-        return get_input_nodes(nncf_graph)
-
     @property
     def scales_unification_map(self) -> Dict[OperatorMetatype, OperatorMetatype]:
         return {om.OVConcatMetatype: self.overflow_fix_metatypes}
@@ -102,6 +99,10 @@ def hw_config(self) -> HWConfig:
     def quant_trait_op_dict(self) -> Dict[int, OperatorMetatype]:
         return DEFAULT_OV_QUANT_TRAIT_TO_OP_DICT
 
+    @staticmethod
+    def get_input_nodes(nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
+        return get_input_nodes(nncf_graph)
+
     @staticmethod
     def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> OVTargetPoint:
         return OVTargetPoint(target_type, target_node_name, port_id)

nncf/quantization/algorithms/min_max/torch_backend.py

@@ -74,9 +74,6 @@ def dropout_metatypes(self) -> List[OperatorMetatype]:
     def read_variable_metatypes(self) -> List[OperatorMetatype]:
         return []
 
-    def get_input_nodes(self, nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
-        return get_inputs_for_graph_with_several_connected_components(nncf_graph)
-
     @property
     def conv_metatypes(self) -> List[OperatorMetatype]:
         return [om.PTModuleConv1dMetatype, om.PTModuleConv2dMetatype, om.PTModuleConv3dMetatype]
@@ -113,6 +110,10 @@ def hw_config(self) -> HWConfig:
     def quant_trait_op_dict(self) -> Dict[int, OperatorMetatype]:
         return DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT
 
+    @staticmethod
+    def get_input_nodes(nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
+        return get_inputs_for_graph_with_several_connected_components(nncf_graph)
+
     @staticmethod
     def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> PTTargetPoint:
         if NNCFGraphNodeType.INPUT_NODE in target_node_name or target_type == TargetType.POST_LAYER_OPERATION:

nncf/quantization/passes.py

@@ -29,11 +29,9 @@ def transform_to_inference_graph(
     This method contains inplace pipeline of the passes that uses to provide inference graph without constant flows.
 
     :param nncf_graph: NNCFGraph instance for the transformation.
+    :param input_nodes: List of input nodes for the given NNCFGraph.
     :param shapeof_metatypes: List of backend-specific ShapeOf metatypes.
     :param dropout_metatypes: List of backend-specific Dropout metatypes.
-    :param read_variable_metatypes: List of backend-specific metatypes
-        that also can be interpreted as inputs (ReadValue).
-    :param nncf_graph_contains_constants: Whether NNCFGraph contains constant nodes or not.
     :return: NNCFGraph in the inference style.
     """
     remove_shapeof_subgraphs(nncf_graph, shapeof_metatypes, input_nodes)
@@ -53,8 +51,7 @@ def remove_shapeof_subgraphs(
 
     :param nncf_graph: NNCFGraph instance for the transformation.
     :param shapeof_metatypes: List of backend-specific ShapeOf metatypes.
-    :param read_variable_metatypes: List of backend-specific metatypes
-        that also can be interpreted as inputs (ReadValue).
+    :param input_nodes: List of input nodes for the given NNCFGraph.
     :return: NNCFGraph without ShapeOf subgraphs.
     """
     nodes_to_drop = set()
@@ -149,8 +146,7 @@ def filter_constant_nodes(
     The traversing starts from the input nodes and nodes with weights.
 
     :param nncf_graph: NNCFGraph instance for the transformation.
-    :param read_variable_metatypes: List of backend-specific metatypes
-        that also can be interpreted as inputs (ReadValue).
+    :param input_nodes: List of input nodes for the given NNCFGraph.
     :return: NNCFGraph without Constant nodes.
     """
     if not input_nodes:

nncf/torch/graph/graph.py

@@ -71,7 +71,15 @@ def get_scope_by_node_name(self, node_name: NNCFNodeName) -> Scope:
         return matches[0]
 
 
-def get_inputs_for_graph_with_several_connected_components(nncf_graph: PTNNCFGraph):
+def get_inputs_for_graph_with_several_connected_components(nncf_graph: PTNNCFGraph) -> List[NNCFNode]:
+    """
+    Returns a list of NNCFNodes that are identified as an inputs. Requires MultipleInputLayerAttributes
+    for nodes with several inputs and right `input_edges_num_expected` parameter setted for
+    nncf nodes metatypes.
+
+    :param nncf_graph: NNCFGraph to get input nodes from.
+    :return: List of NNCFNodes that are identified as an inputs.
+    """
     input_nodes = set()
     for node in nncf_graph.get_all_nodes():
         input_edges_num_expected = None
@@ -84,6 +92,8 @@ def get_inputs_for_graph_with_several_connected_components(nncf_graph: PTNNCFGra
         if input_edges_num_expected:
             input_edges = nncf_graph.get_input_edges(node)
             if len(input_edges) < input_edges_num_expected:
+                # If node has missed input edges we assume this node is an input node
+                # that was disconected from an activation input.
                 input_nodes.add(node)
     input_nodes.update(nncf_graph.get_input_nodes())
     return list(input_nodes)