Comments

nncf/openvino/graph/model_utils.py

@@ -52,9 +52,9 @@ def remove_fq_from_inputs(model: ov.Model, graph: NNCFGraph) -> ov.Model:
     return model_transformer.transform(transformation_layout)
 
 
-def get_input_nodes(nncf_graph: NNCFGraph) -> List[NNCFNode]:
+def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[NNCFNode]:
     """
-    Get all nodes from given nncf_graph that are identified as a input nodes.
+    Get a list of NNCFNodes to use as start nodes for activation path tracing.
 
     :param nncf_graph: NNCFGraph to work with.
     :return: Target NNCFGraph input nodes.

nncf/quantization/algorithms/accuracy_control/openvino_backend.py

@@ -25,7 +25,7 @@
 from nncf.openvino.graph.metatypes.groups import SHAPEOF_OPERATIONS
 from nncf.openvino.graph.metatypes.openvino_metatypes import OVConcatMetatype
 from nncf.openvino.graph.metatypes.openvino_metatypes import OVOpMetatype
-from nncf.openvino.graph.model_utils import get_input_nodes
+from nncf.openvino.graph.model_utils import get_start_nodes_for_activation_path_tracing
 from nncf.openvino.graph.node_utils import get_bias_value
 from nncf.openvino.graph.node_utils import get_weight_value
 from nncf.openvino.graph.node_utils import is_node_with_bias
@@ -61,7 +61,7 @@ def get_shapeof_metatypes() -> List[OVOpMetatype]:
 
     @staticmethod
     def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[NNCFNode]:
-        return get_input_nodes(nncf_graph)
+        return get_start_nodes_for_activation_path_tracing(nncf_graph)
 
     # Manipulations with bias value and weights
 

nncf/quantization/algorithms/accuracy_control/ranker.py

@@ -100,6 +100,7 @@ def find_groups_of_quantizers_to_rank(self, quantized_model_graph: NNCFGraph) ->
 
         quantized_model_graph_without_shapeof = remove_shapeof_subgraphs(
             deepcopy(quantized_model_graph),
+            self._algo_backend.get_shapeof_metatypes(),
             self._algo_backend.get_start_nodes_for_activation_path_tracing(quantized_model_graph),
         )
 

nncf/quantization/algorithms/min_max/backend.py

@@ -148,7 +148,7 @@ def create_quantizer_insertion_command(
 
     @staticmethod
     @abstractmethod
-    def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
+    def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[NNCFNode]:
         """
         Returns a list of NNCFNodes to use as start nodes for activation path tracing.
 

nncf/quantization/algorithms/min_max/onnx_backend.py

@@ -97,7 +97,7 @@ def quant_trait_op_dict(self) -> Dict[int, OperatorMetatype]:
         return DEFAULT_ONNX_QUANT_TRAIT_TO_OP_DICT
 
     @staticmethod
-    def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
+    def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[NNCFNode]:
         return nncf_graph.get_input_nodes()
 
     @staticmethod

nncf/quantization/algorithms/min_max/openvino_backend.py

@@ -26,7 +26,7 @@
 from nncf.openvino.graph.layer_attributes import OVLayerAttributes
 from nncf.openvino.graph.metatypes import openvino_metatypes as om
 from nncf.openvino.graph.metatypes.groups import OPERATIONS_WITH_WEIGHTS
-from nncf.openvino.graph.model_utils import get_input_nodes
+from nncf.openvino.graph.model_utils import get_start_nodes_for_activation_path_tracing
 from nncf.openvino.graph.node_utils import get_channel_agnostic_reduction_axes
 from nncf.openvino.graph.node_utils import get_weight_channel_axes
 from nncf.openvino.graph.transformations.commands import OVQuantizerInsertionCommand
@@ -104,8 +104,8 @@ def quant_trait_op_dict(self) -> Dict[int, OperatorMetatype]:
         return DEFAULT_OV_QUANT_TRAIT_TO_OP_DICT
 
     @staticmethod
-    def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[OperatorMetatype]:
-        return get_input_nodes(nncf_graph)
+    def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[NNCFNode]:
+        return get_start_nodes_for_activation_path_tracing(nncf_graph)
 
     @staticmethod
     def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> OVTargetPoint:

nncf/quantization/algorithms/min_max/torch_backend.py

@@ -31,7 +31,6 @@
 from nncf.quantization.algorithms.min_max.backend import MinMaxAlgoBackend
 from nncf.quantization.fake_quantize import FakeQuantizeParameters
 from nncf.quantization.range_estimator import RangeEstimatorParameters
-from nncf.torch.graph.graph import PTNNCFGraph
 from nncf.torch.graph.graph import PTTargetPoint
 from nncf.torch.graph.transformations.commands import PTQuantizerInsertionCommand
 from nncf.torch.hardware.config import PTHWConfig
@@ -115,7 +114,7 @@ def quant_trait_op_dict(self) -> Dict[int, OperatorMetatype]:
         return DEFAULT_PT_QUANT_TRAIT_TO_OP_DICT
 
     @staticmethod
-    def get_start_nodes_for_activation_path_tracing(nncf_graph: PTNNCFGraph) -> List[OperatorMetatype]:
+    def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> List[NNCFNode]:
         return nncf_graph.get_disconnected_nodes() + nncf_graph.get_input_nodes()
 
     @staticmethod

tests/common/quantization/test_passes.py

@@ -69,7 +69,7 @@ def test_filter_constant_nodes(node_between_const_and_op):
     )
 
     class ConstantMetatype(OperatorMetatype):
-        num_expected_input_edges = 1
+        num_expected_input_edges = 0
         pass
 
     class NodeWithWeightMetatype(OperatorMetatype):