From 239474da960d691e33b481f5b47aa0783ff556b6 Mon Sep 17 00:00:00 2001
From: dlyakhov <daniil.lyakhov@intel.com>
Date: Tue, 16 May 2023 13:45:13 +0200
Subject: [PATCH] TensorCollectorAdapter

---
 .../common/tensor_statistics/collectors.py    | 211 ++++++------------
 nncf/openvino/quantization/quantize_model.py  |   4 -
 nncf/openvino/statistics/aggregator.py        |   5 +-
 nncf/openvino/statistics/collectors.py        |   5 +-
 nncf/parameters.py                            |   1 -
 .../algorithms/bias_correction/algorithm.py   |   9 +-
 .../algorithms/bias_correction/backend.py     |   2 -
 .../bias_correction/onnx_backend.py           |   2 -
 .../bias_correction/openvino_backend.py       |   4 +-
 .../fast_bias_correction/algorithm.py         |   8 +-
 .../fast_bias_correction/backend.py           |   2 -
 .../fast_bias_correction/onnx_backend.py      |   2 -
 .../fast_bias_correction/openvino_backend.py  |   4 +-
 .../algorithms/min_max/algorithm.py           |   1 -
 .../algorithms/min_max/backend.py             |   1 -
 .../algorithms/min_max/onnx_backend.py        |   1 -
 .../algorithms/min_max/openvino_backend.py    |   4 -
 .../algorithms/min_max/torch_backend.py       |   1 -
 .../algorithms/post_training/algorithm.py     |   2 -
 19 files changed, 82 insertions(+), 187 deletions(-)

diff --git a/nncf/experimental/common/tensor_statistics/collectors.py b/nncf/experimental/common/tensor_statistics/collectors.py
index dcb83949bc6..1b9ef5db7ef 100644
--- a/nncf/experimental/common/tensor_statistics/collectors.py
+++ b/nncf/experimental/common/tensor_statistics/collectors.py
@@ -27,52 +27,7 @@
 InplaceInsertionFNType = TypeVar("InplaceInsertionFNType")
 
 
-class TensorReducerInterface(ABC):
-    @abstractproperty
-    def inplace(self):
-        pass
-
-    @abstractproperty
-    def output_port_id(self) -> int:
-        pass
-
-    @abstractproperty
-    def name(self):
-        pass
-
-    @abstractmethod
-    def get_output_names(self, target_node_name: str, port_id: int) -> List[str]:
-        """
-        Returns target output names from target model that is
-            modified for statistic collection.
-
-        :param target_node_name: Target node name for reducer.
-        :param port_id: Target port id for target node name for reducer.
-        :return: Target output names for reducer.
-        """
-
-    @abstractmethod
-    def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]:
-        """
-        Returns correspondent inplace operation builder if inplace operations are available in backend.
-
-        :return: Inplace operation builder if possible else None.
-        """
-
-    @abstractmethod
-    def __call__(self, x: List[NNCFTensor]):
-        pass
-
-    @abstractmethod
-    def __eq__(self, __o: object) -> bool:
-        pass
-
-    @abstractmethod
-    def __hash__(self) -> int:
-        pass
-
-
-class TensorReducerBase(TensorReducerInterface, ABC):
+class TensorReducerBase(ABC):
     """
     Tensor reducer is a callable object that reduces tensors according to
     the specified rule. Could handle tensors inplace or out of place.
@@ -106,6 +61,25 @@ def name(self):
     def _get_processor() -> NNCFCollectorTensorProcessor:
         pass
 
+    @abstractmethod
+    def get_output_names(self, target_node_name: str, port_id: int) -> List[str]:
+        """
+        Returns target output names from target model that is
+            modified for statistic collection.
+
+        :param target_node_name: Target node name for reducer.
+        :param port_id: Target port id for target node name for reducer.
+        :return: Target output names for reducer.
+        """
+
+    @abstractmethod
+    def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]:
+        """
+        Returns correspondent inplace operation builder if inplace operations are available in backend.
+
+        :return: Inplace operation builder if possible else None.
+        """
+
     @abstractstaticmethod
     def reduce_out_of_place(x: List[TensorType], **kwargs) -> List[TensorType]:
         """
@@ -121,9 +95,6 @@ def get_kwargs(self):
             "keepdims": self._keepdims,
         }
 
-    def __call__(self, x: List[NNCFTensor]):
-        return self._adapter.reduce(x, self)
-
     def __eq__(self, __o: object) -> bool:
         return (
             isinstance(__o, self.__class__)
@@ -136,86 +107,6 @@ def __hash__(self) -> int:
         return hash((self.__class__.__name__, self.inplace, self._init_reduction_shape, self._keepdims))
 
 
-class BaseReducerAdapter(ABC):
-    @abstractmethod
-    def reduce(self, x: List[NNCFTensor], reducer: TensorReducerBase):
-        pass
-
-
-class DefaultReducerAdapter(BaseReducerAdapter):
-    def reduce(self, x: List[NNCFTensor], reducer: TensorReducerBase):
-        if reducer.inplace:
-            return x
-
-        kwargs = reducer.get_kwargs()
-        reduction_shape_key = "reduction_shape"
-        if reduction_shape_key in kwargs and kwargs[reduction_shape_key] is None:
-            kwargs[reduction_shape_key] = tuple(range(len(x[0].shape)))
-
-        return reducer.reduce_out_of_place(x, **kwargs)
-
-
-class SequentialReducerAdapter(DefaultReducerAdapter):
-    def __init__(self, stack_axis: int) -> None:
-        super().__init__()
-        self._stack_axis = stack_axis
-
-    def reduce(self, x: List[NNCFTensor], reducer: TensorReducerBase):
-        per_element_result = super().reduce(x, reducer)
-        kwargs = reducer.get_kwargs()
-        new_params = {"reduction_shape": self._stack_axis, "keepdims": False}
-        if not all(k in kwargs for k in new_params):
-            return per_element_result
-
-        kwargs.update(new_params)
-        return reducer.reduce_out_of_place(per_element_result, **kwargs)
-
-
-class TensorReducersSequence(TensorReducerInterface):
-    def __init__(self, *args):
-        if any(reducer.inplace for reducer in args[1:]):
-            raise RuntimeError(f"Only first reducer of sequential tensor reducer could not be inplace.")
-        self._reducers = args
-
-    @property
-    def inplace(self):
-        return self._reducers[0].inplace
-
-    @property
-    def output_port_id(self) -> int:
-        return self._reducers[0].output_port_id
-
-    @property
-    def name(self):
-        name = ""
-        for i, reducer in enumerate(self._reducers):
-            name += f"{i}_{reducer.name}"
-        return name
-
-    def get_output_names(self, target_node_name: str, port_id: int) -> List[str]:
-        return self._reducers[0].get_output_names(target_node_name, port_id)
-
-    def get_inplace_fn(self) -> Optional[InplaceInsertionFNType]:
-        return self._reducers[0].get_inplace_fn()
-
-    def __call__(self, x: List[NNCFTensor]):
-        if not self._reducers[0].inplace:
-            x = self._reducers[0](x)
-
-        for reducer in self._reducers[1:]:
-            x = reducer(x)
-        return x
-
-    def __eq__(self, __o: object) -> bool:
-        return (
-            isinstance(__o, self.__class__)
-            and len(self._reducers) == len(__o.reducers)
-            and all(self_r == o_r for self_r, o_r in zip(self._reducers, __o.reducers))
-        )
-
-    def __hash__(self) -> int:
-        return hash(tuple(hash(reducer) for reducer in self._reducers))
-
 
 class TensorAggregatorBase:
     """
@@ -273,6 +164,54 @@ def __hash__(self) -> int:
         return hash(self.__class__.__name__)
 
 
+class BaseTensorCollectorAdapter(ABC):
+    @abstractmethod
+    def reduce(self, x: List[NNCFTensor], reducer: TensorReducerBase):
+        pass
+
+    @abstractmethod
+    def register_reduced_input(self, x: NNCFTensor, aggregator: TensorAggregatorBase):
+        pass
+
+
+class DefaultTensorCollectorAdapter(BaseTensorCollectorAdapter):
+    def reduce(self, x: List[NNCFTensor], reducer: TensorReducerBase):
+        if reducer.inplace:
+            return x
+
+        kwargs = reducer.get_kwargs()
+        reduction_shape_key = "reduction_shape"
+        if reduction_shape_key in kwargs and kwargs[reduction_shape_key] is None:
+            kwargs[reduction_shape_key] = tuple(range(len(x[0].shape)))
+
+        return reducer.reduce_out_of_place(x, **kwargs)
+
+    def register_reduced_input(self, x: NNCFTensor, aggregator: TensorAggregatorBase):
+        aggregator.register_reduced_input(x)
+
+
+class SequentialTensorCollectorAdapter(DefaultTensorCollectorAdapter):
+    def __init__(self, stack_axis: int, tensor_processor: NNCFCollectorTensorProcessor) -> None:
+        super().__init__()
+        self._stack_axis = stack_axis
+        self._tensor_processor = tensor_processor
+
+    def reduce(self, x: List[NNCFTensor], reducer: TensorReducerBase):
+        per_element_result = super().reduce(x, reducer)
+        kwargs = reducer.get_kwargs()
+        new_params = {"reduction_shape": self._stack_axis, "keepdims": False}
+        if not all(k in kwargs for k in new_params):
+            return per_element_result
+
+        kwargs.update(new_params)
+        return reducer.reduce_out_of_place(per_element_result, **kwargs)
+
+    def register_reduced_input(self, x: NNCFTensor, aggregator: TensorAggregatorBase):
+        if isinstance(aggregator, ShapeAggregator):
+            x = self._tensor_processor.unstack(x, axis=0)[0]
+        super().register_reduced_input(x, aggregator)
+
+
 class TensorCollector:
     """
     Calculates statistics at given tensors according to registered statistic branches.
@@ -289,7 +228,7 @@ def __init__(self, statistic_container: Optional[TensorStatistic] = None) -> Non
         self._stat_container_kwargs_map: Dict[str, Tuple[int, int]] = {}
         self._stat_container = statistic_container
         self._enabled = True
-        self._adapter = DefaultReducerAdapter()
+        self._adapter = DefaultTensorCollectorAdapter()
 
     @property
     def num_samples(self) -> Optional[int]:
@@ -313,7 +252,7 @@ def reducers(self):
     def aggregators(self):
         return self._aggregators.copy()
 
-    def set_adapter(self, adapter: "BaseReducerAdapter"):
+    def set_adapter(self, adapter: "BaseTensorCollectorAdapter"):
         self._adapter = adapter
 
     def enable(self):
@@ -390,7 +329,7 @@ def register_inputs(self, inputs: Dict[int, List[NNCFTensor]]) -> None:
             aggregator,
         ) in self._aggregators.items():
             if reducer_hash in reduced_inputs:
-                aggregator.register_reduced_input(reduced_inputs[reducer_hash][reducer_port_id])
+                self._adapter.register_reduced_input(reduced_inputs[reducer_hash][reducer_port_id], aggregator)
 
     def _aggregate(self) -> None:
         result = {}
@@ -683,18 +622,14 @@ def aggregate(self):
 
 
 class ShapeAggregator(TensorAggregatorBase):
-    def __init__(self, slice_=None):
+    def __init__(self):
         super().__init__(None, 1)
-        self._slice = slice_
 
     def _register_reduced_input_impl(self, x: TensorType) -> None:
         self._container = x
 
     def aggregate(self):
-        shape = self._container.shape
-        if self._slice is not None:
-            return shape[self._slice]
-        return shape
+        return self._container.shape
 
 
 class MinAggregator(TensorAggregatorBase):
diff --git a/nncf/openvino/quantization/quantize_model.py b/nncf/openvino/quantization/quantize_model.py
index 61ba10d07a4..aa344e95f4d 100644
--- a/nncf/openvino/quantization/quantize_model.py
+++ b/nncf/openvino/quantization/quantize_model.py
@@ -21,7 +21,6 @@
 from nncf.common.utils.backend import get_backend
 from nncf.common.utils.timer import timer
 from nncf.data import Dataset
-from nncf.data import RecurentDataset
 from nncf.openvino.quantization.backend_parameters import BackendParameters
 from nncf.openvino.quantization.backend_parameters import is_weight_compression_needed
 from nncf.parameters import DropType
@@ -103,9 +102,6 @@ def native_quantize_impl(
     """
     Implementation of the `quantize()` method for the OpenVINO backend via the OpenVINO Runtime API.
     """
-    if isinstance(calibration_dataset, RecurentDataset):
-        model_type = ModelType.SEQUENTIAL
-
     quantization_algorithm = PostTrainingQuantization(
         preset=preset,
         target_device=target_device,
diff --git a/nncf/openvino/statistics/aggregator.py b/nncf/openvino/statistics/aggregator.py
index a8d15e9f7b9..44a364e47a2 100644
--- a/nncf/openvino/statistics/aggregator.py
+++ b/nncf/openvino/statistics/aggregator.py
@@ -21,11 +21,12 @@
 from nncf.common.tensor_statistics.statistic_point import StatisticPoint
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
 from nncf.experimental.common.tensor_statistics.collectors import MergedTensorCollector
-from nncf.experimental.common.tensor_statistics.collectors import SequentialReducerAdapter
+from nncf.experimental.common.tensor_statistics.collectors import SequentialTensorCollectorAdapter
 from nncf.experimental.common.tensor_statistics.collectors import TensorCollector
 from nncf.openvino.graph.nncf_graph_builder import GraphConverter
 from nncf.openvino.graph.transformations.commands import OVInplaceFnInsertionCommand
 from nncf.openvino.graph.transformations.commands import OVOutputInsertionCommand
+from nncf.openvino.statistics.collectors import OVNNCFCollectorTensorProcessor
 from nncf.openvino.tensor import OVNNCFTensor
 
 
@@ -77,7 +78,7 @@ def _get_transformation_layout_extra_outputs(
     # TODO(dlyakhov) Move this to common part
     def _adapt_collectors(statistic_points: StatisticPointsContainer, stack_axis: int):
         for _, _, tensor_collector in statistic_points.get_tensor_collectors():
-            tensor_collector.set_adapter(SequentialReducerAdapter(stack_axis))
+            tensor_collector.set_adapter(SequentialTensorCollectorAdapter(stack_axis, OVNNCFCollectorTensorProcessor))
 
         return statistic_points
 
diff --git a/nncf/openvino/statistics/collectors.py b/nncf/openvino/statistics/collectors.py
index 5f8254b6a46..3898f0436d3 100644
--- a/nncf/openvino/statistics/collectors.py
+++ b/nncf/openvino/statistics/collectors.py
@@ -40,7 +40,6 @@
 from nncf.openvino.statistics.statistics import OVBatchTensorStatistic
 from nncf.openvino.statistics.statistics import OVMeanTensorStatistic
 from nncf.openvino.tensor import OVNNCFTensor
-from nncf.parameters import ModelType
 from nncf.quantization.advanced_parameters import StatisticsType
 
 
@@ -243,7 +242,7 @@ def get_output_names(self, target_node_name: str, port_id: int) -> List[str]:
         return get_reducer_output_node_names(self.name, target_node_name, port_id, self.output_port_id, self.inplace)
 
 
-def get_mean_stat_collector(num_samples, channel_axis, window_size=None, inplace=True, model_type=None):
+def get_mean_stat_collector(num_samples, channel_axis, window_size=None, inplace=True):
     # TODO(dlyakhov): use inplace OVBatchMeanReducer and OVMeanPerChanelReducer
     # after migration on openvino-dev=2023.0
     inplace = False
@@ -260,7 +259,7 @@ def get_mean_stat_collector(num_samples, channel_axis, window_size=None, inplace
         "window_size": window_size,
     }
     aggregate_mean = MeanAggregator(**kwargs)
-    aggregate_shape = ShapeAggregator(slice(1, None) if model_type == ModelType.SEQUENTIAL else None)
+    aggregate_shape = ShapeAggregator()
 
     collector = TensorCollector(OVMeanTensorStatistic)
     collector.register_statistic_branch(OVMeanTensorStatistic.MEAN_STAT, reducer, aggregate_mean)
diff --git a/nncf/parameters.py b/nncf/parameters.py
index 4c1dc2b61b2..28ae264834e 100644
--- a/nncf/parameters.py
+++ b/nncf/parameters.py
@@ -40,7 +40,6 @@ class ModelType(Enum):
     """
 
     TRANSFORMER = "transformer"
-    SEQUENTIAL = "sequential"
 
 
 @api(canonical_alias="nncf.DropType")
diff --git a/nncf/quantization/algorithms/bias_correction/algorithm.py b/nncf/quantization/algorithms/bias_correction/algorithm.py
index b042db80c77..d66175e3fce 100644
--- a/nncf/quantization/algorithms/bias_correction/algorithm.py
+++ b/nncf/quantization/algorithms/bias_correction/algorithm.py
@@ -29,7 +29,6 @@
 from nncf.common.utils.backend import BackendType
 from nncf.common.utils.backend import copy_model
 from nncf.common.utils.backend import get_backend
-from nncf.parameters import ModelType
 from nncf.quantization.algorithms.algorithm import Algorithm
 from nncf.quantization.algorithms.bias_correction.backend import ALGO_BACKENDS
 
@@ -69,7 +68,6 @@ def __init__(
         apply_for_all_nodes: bool = False,
         inplace_statistics: bool = True,
         backend_params: Optional[Dict[str, Any]] = None,
-        model_type: ModelType = None,
     ):
         """
         :param subset_size: Size of a subset for the statistics collection,
@@ -93,8 +91,6 @@ def __init__(
         self.apply_for_all_nodes = apply_for_all_nodes
         self.inplace_statistics = inplace_statistics
         self.backend_params = backend_params
-        self.model_type = model_type
-
         self.nncf_graph = None
         self._backend_entity = None
         self._collected_stat_inputs = set()
@@ -480,10 +476,7 @@ def get_statistic_points(self, model: TModel) -> StatisticPointsContainer:
                 TargetType.POST_LAYER_OPERATION, node_name, output_port_id
             )
             stat_collector = self._backend_entity.mean_statistic_collector(
-                reduction_shape=channel_axis,
-                num_samples=self.subset_size,
-                inplace=self.inplace_statistics,
-                model_type=self.model_type,
+                reduction_shape=channel_axis, num_samples=self.subset_size, inplace=self.inplace_statistics
             )
             statistic_container.add_statistic_point(
                 StatisticPoint(target_point=statistic_point, tensor_collector=stat_collector, algorithm=BiasCorrection)
diff --git a/nncf/quantization/algorithms/bias_correction/backend.py b/nncf/quantization/algorithms/bias_correction/backend.py
index 9c36e94675c..c189bd2bf20 100644
--- a/nncf/quantization/algorithms/bias_correction/backend.py
+++ b/nncf/quantization/algorithms/bias_correction/backend.py
@@ -24,7 +24,6 @@
 from nncf.common.tensor_statistics.collectors import ReductionShape
 from nncf.common.tensor_statistics.collectors import TensorStatisticCollectorBase
 from nncf.common.utils.registry import Registry
-from nncf.parameters import ModelType
 
 TModel = TypeVar("TModel")
 OutputType = TypeVar("OutputType")
@@ -109,7 +108,6 @@ def mean_statistic_collector(
         inplace: bool,
         num_samples: Optional[int] = None,
         window_size: Optional[int] = None,
-        model_type: Optional[ModelType] = None,
     ) -> TensorStatisticCollectorBase:
         """
         Returns backend-specific mean statistic collector.
diff --git a/nncf/quantization/algorithms/bias_correction/onnx_backend.py b/nncf/quantization/algorithms/bias_correction/onnx_backend.py
index 1cdbf16ef24..e4a92fc43f4 100644
--- a/nncf/quantization/algorithms/bias_correction/onnx_backend.py
+++ b/nncf/quantization/algorithms/bias_correction/onnx_backend.py
@@ -35,7 +35,6 @@
 from nncf.onnx.statistics.collectors import ONNXMeanStatisticCollector
 from nncf.onnx.statistics.collectors import ONNXNNCFCollectorTensorProcessor
 from nncf.onnx.tensor import ONNXNNCFTensor
-from nncf.parameters import ModelType
 from nncf.quantization.algorithms.bias_correction.backend import ALGO_BACKENDS
 from nncf.quantization.algorithms.bias_correction.backend import BiasCorrectionAlgoBackend
 
@@ -83,7 +82,6 @@ def mean_statistic_collector(
         inplace: bool,
         num_samples: Optional[int] = None,
         window_size: Optional[int] = None,
-        model_type: Optional[ModelType] = None,
     ) -> ONNXMeanStatisticCollector:
         return ONNXMeanStatisticCollector(reduction_shape, num_samples, window_size)
 
diff --git a/nncf/quantization/algorithms/bias_correction/openvino_backend.py b/nncf/quantization/algorithms/bias_correction/openvino_backend.py
index c88aac12835..1a7d10b887b 100644
--- a/nncf/quantization/algorithms/bias_correction/openvino_backend.py
+++ b/nncf/quantization/algorithms/bias_correction/openvino_backend.py
@@ -34,7 +34,6 @@
 from nncf.openvino.statistics.collectors import get_mean_batch_stat_collector
 from nncf.openvino.statistics.collectors import get_mean_stat_collector
 from nncf.openvino.tensor import OVNNCFTensor
-from nncf.parameters import ModelType
 from nncf.quantization.algorithms.bias_correction.backend import ALGO_BACKENDS
 from nncf.quantization.algorithms.bias_correction.backend import BiasCorrectionAlgoBackend
 
@@ -78,9 +77,8 @@ def mean_statistic_collector(
         inplace: bool,
         num_samples: Optional[int] = None,
         window_size: Optional[int] = None,
-        model_type: Optional[ModelType] = None,
     ) -> TensorCollector:
-        return get_mean_stat_collector(num_samples, reduction_shape, window_size, inplace, model_type)
+        return get_mean_stat_collector(num_samples, reduction_shape, window_size, inplace)
 
     @staticmethod
     def batch_statistic_collector(inplace: bool, num_samples: int = None) -> TensorCollector:
diff --git a/nncf/quantization/algorithms/fast_bias_correction/algorithm.py b/nncf/quantization/algorithms/fast_bias_correction/algorithm.py
index 0492df2dd98..3320c1bfe0a 100644
--- a/nncf/quantization/algorithms/fast_bias_correction/algorithm.py
+++ b/nncf/quantization/algorithms/fast_bias_correction/algorithm.py
@@ -27,7 +27,6 @@
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
 from nncf.common.utils.backend import BackendType
 from nncf.common.utils.backend import get_backend
-from nncf.parameters import ModelType
 from nncf.quantization.algorithms.algorithm import Algorithm
 from nncf.quantization.algorithms.fast_bias_correction.backend import ALGO_BACKENDS
 
@@ -60,7 +59,6 @@ def __init__(
         apply_for_all_nodes: bool = False,
         inplace_statistics: bool = True,
         backend_params: Optional[Dict[str, Any]] = None,
-        model_type: ModelType = None,
     ):
         """
         :param subset_size: Size of a subset for the statistics collection,
@@ -84,7 +82,6 @@ def __init__(
         self.apply_for_all_nodes = apply_for_all_nodes
         self.inplace_statistics = inplace_statistics
         self.backend_params = backend_params
-        self.model_type = model_type
         self.nncf_graph = None
         self._backend_entity = None
 
@@ -255,10 +252,7 @@ def _add_statistic_point(self, container: StatisticPointsContainer, point: Targe
         :param axis: Channel axis for the statistics calculation.
         """
         stat_collector = self._backend_entity.mean_statistic_collector(
-            reduction_shape=axis,
-            num_samples=self.subset_size,
-            inplace=self.inplace_statistics,
-            model_type=self.model_type,
+            reduction_shape=axis, num_samples=self.subset_size, inplace=self.inplace_statistics
         )
         container.add_statistic_point(
             StatisticPoint(target_point=point, tensor_collector=stat_collector, algorithm=FastBiasCorrection)
diff --git a/nncf/quantization/algorithms/fast_bias_correction/backend.py b/nncf/quantization/algorithms/fast_bias_correction/backend.py
index 86748218b75..136c6f4f800 100644
--- a/nncf/quantization/algorithms/fast_bias_correction/backend.py
+++ b/nncf/quantization/algorithms/fast_bias_correction/backend.py
@@ -24,7 +24,6 @@
 from nncf.common.tensor_statistics.collectors import ReductionShape
 from nncf.common.tensor_statistics.collectors import TensorStatisticCollectorBase
 from nncf.common.utils.registry import Registry
-from nncf.parameters import ModelType
 
 TModel = TypeVar("TModel")
 OutputType = TypeVar("OutputType")
@@ -88,7 +87,6 @@ def mean_statistic_collector(
         inplace: bool,
         num_samples: Optional[int] = None,
         window_size: Optional[int] = None,
-        model_type: Optional[ModelType] = None,
     ) -> TensorStatisticCollectorBase:
         """
         Returns backend-specific mean statistic collector.
diff --git a/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py b/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py
index 47d1daee8c4..71ac7468d4f 100644
--- a/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py
+++ b/nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py
@@ -31,7 +31,6 @@
 from nncf.onnx.statistics.collectors import ONNXMeanStatisticCollector
 from nncf.onnx.statistics.collectors import ONNXNNCFCollectorTensorProcessor
 from nncf.onnx.tensor import ONNXNNCFTensor
-from nncf.parameters import ModelType
 from nncf.quantization.algorithms.fast_bias_correction.backend import ALGO_BACKENDS
 from nncf.quantization.algorithms.fast_bias_correction.backend import FastBiasCorrectionAlgoBackend
 
@@ -66,7 +65,6 @@ def mean_statistic_collector(
         inplace: bool,
         num_samples: Optional[int] = None,
         window_size: Optional[int] = None,
-        model_type: Optional[ModelType] = None,
     ) -> ONNXMeanStatisticCollector:
         return ONNXMeanStatisticCollector(reduction_shape, num_samples, window_size)
 
diff --git a/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py b/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py
index c8be93ff4cc..1057b7cbc17 100644
--- a/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py
+++ b/nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py
@@ -32,7 +32,6 @@
 from nncf.openvino.statistics.collectors import OVNNCFCollectorTensorProcessor
 from nncf.openvino.statistics.collectors import get_mean_stat_collector
 from nncf.openvino.tensor import OVNNCFTensor
-from nncf.parameters import ModelType
 from nncf.quantization.algorithms.fast_bias_correction.backend import ALGO_BACKENDS
 from nncf.quantization.algorithms.fast_bias_correction.backend import FastBiasCorrectionAlgoBackend
 
@@ -67,9 +66,8 @@ def mean_statistic_collector(
         inplace: bool,
         num_samples: Optional[int] = None,
         window_size: Optional[int] = None,
-        model_type: Optional[ModelType] = None,
     ) -> TensorCollector:
-        return get_mean_stat_collector(num_samples, reduction_shape, window_size, inplace, model_type)
+        return get_mean_stat_collector(num_samples, reduction_shape, window_size, inplace)
 
     @staticmethod
     def get_sub_input_output_names(subgraph: ov.Model) -> Tuple[str, str]:
diff --git a/nncf/quantization/algorithms/min_max/algorithm.py b/nncf/quantization/algorithms/min_max/algorithm.py
index a3da13fe905..953fda6d579 100644
--- a/nncf/quantization/algorithms/min_max/algorithm.py
+++ b/nncf/quantization/algorithms/min_max/algorithm.py
@@ -275,7 +275,6 @@ def _get_stat_collector(
             target_point,
             quantizer_config,
             inplace=self._inplace_statistics,
-            model_type=self._model_type,
             num_samples=self._subset_size,
         )
 
diff --git a/nncf/quantization/algorithms/min_max/backend.py b/nncf/quantization/algorithms/min_max/backend.py
index 2c063c644db..07cd9990b62 100644
--- a/nncf/quantization/algorithms/min_max/backend.py
+++ b/nncf/quantization/algorithms/min_max/backend.py
@@ -157,7 +157,6 @@ def get_statistic_collector(
         target_point: TargetPoint,
         quantizer_config: QuantizerConfig,
         inplace: bool,
-        model_type: ModelType,
         num_samples: int = None,
     ) -> TensorStatisticCollectorBase:
         """
diff --git a/nncf/quantization/algorithms/min_max/onnx_backend.py b/nncf/quantization/algorithms/min_max/onnx_backend.py
index f908115d3fd..c04b8cd4038 100644
--- a/nncf/quantization/algorithms/min_max/onnx_backend.py
+++ b/nncf/quantization/algorithms/min_max/onnx_backend.py
@@ -178,7 +178,6 @@ def get_statistic_collector(
         target_point: ONNXTargetPoint,
         quantizer_config: QuantizerConfig,
         inplace: bool,
-        model_type: ModelType,
         num_samples: int = None,
     ) -> Union[ONNXMinMaxStatisticCollector, ONNXMeanMinMaxStatisticCollector]:
         reduction_shape, use_abs_max = ONNXMinMaxAlgoBackend._get_reduction_shape_and_use_abs_max(
diff --git a/nncf/quantization/algorithms/min_max/openvino_backend.py b/nncf/quantization/algorithms/min_max/openvino_backend.py
index 360b5f902c0..50973520947 100644
--- a/nncf/quantization/algorithms/min_max/openvino_backend.py
+++ b/nncf/quantization/algorithms/min_max/openvino_backend.py
@@ -24,8 +24,6 @@
 from nncf.common.utils.backend import BackendType
 from nncf.experimental.common.tensor_statistics.collectors import AGGREGATORS_MAP
 from nncf.experimental.common.tensor_statistics.collectors import TensorCollector
-from nncf.experimental.common.tensor_statistics.collectors import TensorReducersSequence
-from nncf.openvino.engine import SEQUENTIAL_SAMPLE_STACK_AXIS
 from nncf.openvino.graph.metatypes.openvino_metatypes import GENERAL_WEIGHT_LAYER_METATYPES
 from nncf.openvino.graph.metatypes.openvino_metatypes import OVAddMetatype
 from nncf.openvino.graph.metatypes.openvino_metatypes import OVConvolutionBackpropDataMetatype
@@ -181,7 +179,6 @@ def get_statistic_collector(
         target_point: OVTargetPoint,
         quantizer_config: QuantizerConfig,
         inplace: bool,
-        model_type: ModelType,
         num_samples: int = None,
     ) -> TensorCollector:
         reduction_shape, use_abs_max = OVMinMaxAlgoBackend._get_reduction_shape_and_use_abs_max(
@@ -215,7 +212,6 @@ def get_statistic_collector(
             statistic_type = params.statistics_type
             if use_abs_max and statistic_type == StatisticsType.MAX:
                 statistic_type = StatisticsType.ABS_MAX
-
             reducer = OV_REDUCERS_MAP[statistic_type](**kwargs)
 
             kwargs = {"num_samples": _num_samples, "tensor_processor": OVNNCFCollectorTensorProcessor}
diff --git a/nncf/quantization/algorithms/min_max/torch_backend.py b/nncf/quantization/algorithms/min_max/torch_backend.py
index 0875941e2ce..7315574fe96 100644
--- a/nncf/quantization/algorithms/min_max/torch_backend.py
+++ b/nncf/quantization/algorithms/min_max/torch_backend.py
@@ -154,7 +154,6 @@ def get_statistic_collector(
         target_point: PTTargetPoint,
         quantizer_config: QuantizerConfig,
         inplace: bool,
-        model_type: ModelType,
         num_samples: int = None,
     ) -> Union[PTMinMaxStatisticCollector, PTMeanMinMaxStatisticCollector]:
         if (
diff --git a/nncf/quantization/algorithms/post_training/algorithm.py b/nncf/quantization/algorithms/post_training/algorithm.py
index 0d1e9c625f0..60ab4b02edf 100644
--- a/nncf/quantization/algorithms/post_training/algorithm.py
+++ b/nncf/quantization/algorithms/post_training/algorithm.py
@@ -114,7 +114,6 @@ def __init__(
                 apply_for_all_nodes=bias_correction_params.apply_for_all_nodes,
                 inplace_statistics=advanced_parameters.inplace_statistics,
                 backend_params=advanced_parameters.backend_params,
-                model_type=model_type,
             )
         else:
             threshold = BIAS_CORRECTION_THRESHOLD
@@ -127,7 +126,6 @@ def __init__(
                 apply_for_all_nodes=bias_correction_params.apply_for_all_nodes,
                 inplace_statistics=advanced_parameters.inplace_statistics,
                 backend_params=advanced_parameters.backend_params,
-                model_type=model_type,
             )
 
         self.algorithms.append(bias_correction)