Minor: redundant get_raw_statistic_collector backend methods are removed

src/nncf/quantization/algorithms/bias_correction/algorithm.py

@@ -25,6 +25,7 @@
 from nncf.common.graph.transformations.commands import TransformationCommand
 from nncf.common.graph.transformations.layout import TransformationLayout
 from nncf.common.logging.track_progress import track
+from nncf.common.tensor_statistics.builders import get_raw_stat_collector
 from nncf.common.tensor_statistics.statistic_point import StatisticPoint
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
 from nncf.common.tensor_statistics.statistical_functions import mean_per_channel
@@ -599,7 +600,7 @@ def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPoin
             statistic_point = self._backend_entity.target_point(
                 TargetType.POST_LAYER_OPERATION, edge.from_node.node_name, port_id=edge.output_port_id
             )
-            stat_collector = self._backend_entity.raw_statistic_collector(num_samples=self.subset_size)
+            stat_collector = get_raw_stat_collector(num_samples=self.subset_size)
             statistic_container.add_statistic_point(
                 StatisticPoint(
                     target_point=statistic_point, tensor_collector=stat_collector, algorithm=self._algorithm_key
@@ -620,7 +621,7 @@ def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPoin
             statistic_point = self._backend_entity.target_point(
                 TargetType.POST_LAYER_OPERATION, input_node.node_name, port_id=OUTPUT_PORT_OF_NODE
             )
-            stat_collector = self._backend_entity.raw_statistic_collector(num_samples=self.subset_size)
+            stat_collector = get_raw_stat_collector(num_samples=self.subset_size)
             statistic_container.add_statistic_point(
                 StatisticPoint(
                     target_point=statistic_point, tensor_collector=stat_collector, algorithm=self._algorithm_key

src/nncf/quantization/algorithms/bias_correction/backend.py

@@ -95,17 +95,6 @@ def mean_statistic_collector(
         :return: Backend-specific TensorCollector for the statistics calculation.
         """
 
-    @staticmethod
-    @abstractmethod
-    def raw_statistic_collector(num_samples: int | None = None) -> TensorCollector:
-        """
-        Returns backend-specific raw statistic collector.
-        This statistic collector is used for raw data calculation, without aggregating.
-
-        :param num_samples: Maximum number of samples to collect.
-        :return: Backend-specific TensorCollector for the statistics calculation.
-        """
-
     @staticmethod
     @abstractmethod
     def process_model_output(raw_data: OutputType, output_name: str | int) -> Tensor:

src/nncf/quantization/algorithms/bias_correction/onnx_backend.py

@@ -16,7 +16,6 @@
 from nncf.common.graph import NNCFNode
 from nncf.common.graph.transformations.commands import TargetType
 from nncf.common.tensor_statistics.builders import get_mean_statistic_collector
-from nncf.common.tensor_statistics.builders import get_raw_stat_collector
 from nncf.common.tensor_statistics.collectors import TensorCollector
 from nncf.onnx.graph.model_utils import remove_fq_from_inputs
 from nncf.onnx.graph.node_utils import get_bias_value
@@ -66,10 +65,6 @@ def mean_statistic_collector(
     ) -> TensorCollector:
         return get_mean_statistic_collector(num_samples, channel_axis, window_size)
 
-    @staticmethod
-    def raw_statistic_collector(num_samples: int = None) -> TensorCollector:
-        return get_raw_stat_collector(num_samples)
-
     @staticmethod
     def process_model_output(raw_data: dict, output_name: str) -> Tensor:
         return Tensor(raw_data[output_name])

src/nncf/quantization/algorithms/bias_correction/openvino_backend.py

@@ -15,7 +15,6 @@
 from nncf.common.graph import NNCFGraph
 from nncf.common.graph import NNCFNode
 from nncf.common.graph.transformations.commands import TargetType
-from nncf.common.tensor_statistics.builders import get_raw_stat_collector
 from nncf.common.tensor_statistics.collectors import TensorCollector
 from nncf.openvino.graph.metatypes.groups import FAKE_QUANTIZE_OPERATIONS
 from nncf.openvino.graph.model_utils import remove_fq_from_inputs
@@ -63,10 +62,6 @@ def mean_statistic_collector(
     ) -> TensorCollector:
         return get_mean_statistic_collector(num_samples, channel_axis, window_size, inplace)
 
-    @staticmethod
-    def raw_statistic_collector(num_samples: int | None = None) -> TensorCollector:
-        return get_raw_stat_collector(num_samples)
-
     @staticmethod
     def process_model_output(raw_data: dict, output_name: str) -> Tensor:
         return Tensor(raw_data[output_name])

src/nncf/quantization/algorithms/bias_correction/torch_fx_backend.py

@@ -17,7 +17,6 @@
 from nncf.common.graph import NNCFNode
 from nncf.common.graph.transformations.commands import TargetType
 from nncf.common.tensor_statistics.builders import get_mean_statistic_collector
-from nncf.common.tensor_statistics.builders import get_raw_stat_collector
 from nncf.common.tensor_statistics.collectors import TensorCollector
 from nncf.experimental.torch.fx.commands import FXApplyTransformationCommand
 from nncf.experimental.torch.fx.model_utils import get_target_point
@@ -66,10 +65,6 @@ def mean_statistic_collector(
     ) -> TensorCollector:
         return get_mean_statistic_collector(num_samples, channel_axis, window_size)
 
-    @staticmethod
-    def raw_statistic_collector(num_samples: int | None = None) -> TensorCollector:
-        return get_raw_stat_collector(num_samples)
-
     @staticmethod
     def process_model_output(raw_data: dict, output_name: int) -> Tensor:
         return Tensor(raw_data[output_name])

src/nncf/quantization/algorithms/layerwise/backend.py

@@ -16,7 +16,6 @@
 from nncf.common.graph import NNCFGraph
 from nncf.common.graph.transformations.commands import TargetPoint
 from nncf.common.graph.transformations.commands import TargetType
-from nncf.common.tensor_statistics.collectors import TensorCollector
 from nncf.data.dataset import Dataset
 from nncf.quantization.algorithms.layerwise.iterator import LayerwiseIterator
 from nncf.quantization.algorithms.layerwise.scheduler import LayerwiseStep
@@ -64,14 +63,3 @@ def target_point(target_type: TargetType, target_node_name: str, port_id: int) -
         :param port_id: id of the port for the statistics distribution.
         :return: Backend-specific TargetPoint.
         """
-
-    @staticmethod
-    @abstractmethod
-    def raw_statistic_collector(num_samples: int | None = None) -> TensorCollector:
-        """
-        Returns backend-specific raw statistic collector.
-        This statistic collector is used for raw data calculation, without aggregating.
-
-        :param num_samples: Maximum number of samples to collect.
-        :return: Backend-specific TensorCollector for the statistics calculation.
-        """

src/nncf/quantization/algorithms/layerwise/engine.py

@@ -14,6 +14,7 @@
 from nncf.common.graph.graph import NNCFGraph
 from nncf.common.graph.graph import NNCFNode
 from nncf.common.graph.transformations.commands import TargetType
+from nncf.common.tensor_statistics.builders import get_raw_stat_collector
 from nncf.common.tensor_statistics.statistic_point import StatisticPoint
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
 from nncf.common.utils.backend import BackendType
@@ -188,7 +189,7 @@ def get_statistic_points(
             statistic_point = self._backend_entity.target_point(
                 TargetType.POST_LAYER_OPERATION, node_name, port_id=output_port_id
             )
-            stat_collector = self._backend_entity.raw_statistic_collector(num_samples=self._subset_size)
+            stat_collector = get_raw_stat_collector(num_samples=self._subset_size)
             statistic_container.add_statistic_point(
                 StatisticPoint(
                     target_point=statistic_point, tensor_collector=stat_collector, algorithm=self._algorithm_key

src/nncf/quantization/algorithms/layerwise/openvino_backend.py

@@ -14,8 +14,6 @@
 
 from nncf.common.graph import NNCFGraph
 from nncf.common.graph.transformations.commands import TargetType
-from nncf.common.tensor_statistics.builders import get_raw_stat_collector
-from nncf.common.tensor_statistics.collectors import TensorCollector
 from nncf.data.dataset import Dataset
 from nncf.openvino.graph.transformations.commands import OVTargetPoint
 from nncf.quantization.algorithms.layerwise.backend import LayerwiseEngineBackend
@@ -40,7 +38,3 @@ def create_layerwise_iterator(
     @staticmethod
     def target_point(target_type: TargetType, target_node_name: str, port_id: int) -> OVTargetPoint:
         return OVTargetPoint(target_type, target_node_name, port_id)
-
-    @staticmethod
-    def raw_statistic_collector(num_samples: int | None = None) -> TensorCollector:
-        return get_raw_stat_collector(num_samples)

tests/common/test_statistics_aggregator.py

@@ -24,6 +24,7 @@
 from nncf.common.quantization.structs import QuantizationScheme as QuantizationMode
 from nncf.common.quantization.structs import QuantizerConfig
 from nncf.common.tensor_statistics.aggregator import EMPTY_DATASET_ERROR
+from nncf.common.tensor_statistics.builders import get_raw_stat_collector
 from nncf.common.tensor_statistics.collectors import NoopAggregator
 from nncf.common.tensor_statistics.collectors import TensorCollector
 from nncf.common.tensor_statistics.collectors import TensorReducerBase
@@ -596,7 +597,7 @@ def test_statistics_aggregator_bias_correction(
                 test_params.axis, inplace_statistics, len(dataset_samples)
             )
         elif test_params.collector_type == BCStatsCollectors.RAW:
-            tensor_collector = algo_backend.raw_statistic_collector(len(dataset_samples))
+            tensor_collector = get_raw_stat_collector(len(dataset_samples))
         else:
             msg = f"Invalid collector type: {test_params.collector_type}"
             raise nncf.InvalidCollectorTypeError(msg)