Move activations_to_wc_statistics to SE class (#3022)

### Changes

As in the PR title.

Author: nikita-savelyevv

nncf/quantization/algorithms/weight_compression/gptq.py

@@ -18,7 +18,6 @@
 from nncf.common.graph import NNCFNode
 from nncf.common.logging.track_progress import track
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
-from nncf.common.tensor_statistics.statistics import WCTensorStatistic
 from nncf.common.utils.backend import BackendType
 from nncf.common.utils.backend import get_backend
 from nncf.parameters import CompressWeightsMode
@@ -266,7 +265,7 @@ def _quantize_weights(
                     else:
                         if self._scale_estimation and block_compression_config.num_bits == 4:
                             activations = [inp.squeeze()[:, (i1 + i) : (i1 + i + group_size)] for inp in inputs]
-                            wc_statistics = self._activations_to_wc_statistics(activations)
+                            wc_statistics = ScaleEstimation.activations_to_wc_statistics(activations)
                             scale, zero_point = ScaleEstimation.calculate_quantization_params(
                                 self._backend_entity,
                                 wc_statistics,
@@ -327,15 +326,3 @@ def _quantize_weights(
         else:
             zero_points = None
         return scales, zero_points
-
-    @staticmethod
-    def _activations_to_wc_statistics(activations: List[Tensor]) -> WCTensorStatistic:
-        # The code below mimics the logic from WeightCompression.get_statistic_points
-        mean_values = []
-        shapes = []
-        for act in activations:
-            shapes.append(act.shape)
-            reduction_shape = tuple(range(act.ndim - 1))
-            mean_values.append(fns.mean(act, axis=reduction_shape))
-        wc_statistics = WCTensorStatistic(mean_values, shapes)
-        return wc_statistics

nncf/quantization/algorithms/weight_compression/scale_estimation.py

@@ -371,6 +371,23 @@ def calculate_quantization_params(
 
         return result_scale, zp
 
+    @staticmethod
+    def activations_to_wc_statistics(activations: List[Tensor]) -> WCTensorStatistic:
+        """
+        Mimic the activation reducing logic from WeightCompression.get_statistic_points.
+
+        :param activations: List of raw activations.
+        :return: Instance of WCTensorStatistic class containing reduced activations and shapes.
+        """
+        mean_values = []
+        shapes = []
+        for act in activations:
+            shapes.append(act.shape)
+            reduction_shape = tuple(range(act.ndim - 1))
+            mean_values.append(fns.mean(act, axis=reduction_shape))
+        wc_statistics = WCTensorStatistic(mean_values, shapes)
+        return wc_statistics
+
 
 def get_target_zero_mask(compressed_weights: Tensor, zp: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
     """