[NNCF]: Optimized memory footprint by removing redundant collected statistics

nncf/common/tensor_statistics/statistic_point.py

@@ -71,6 +71,22 @@ def add_statistic_point(self, statistic_point: StatisticPoint) -> None:
 
             self.data[target_node_name].append(statistic_point)
 
+    def remove_statistic_point(self, algorithm: str) -> None:
+        """
+        Method to remove statistic point associated with a given algorithm
+        from statistic point container.
+
+        :param algorithm: Algorithm name
+        """
+        for target_node_name, statistic_points in list(self.data.items()):
+            # Reassign every target node name IF it doesn't contain the given algorithm
+            self.data[target_node_name] = [
+                _statistic_point for _statistic_point in statistic_points if algorithm not in _statistic_point.algorithm_to_tensor_collectors
+            ]
+
+            if not self.data[target_node_name]:
+                del self.data[target_node_name]
+
     def iter_through_statistic_points_in_target_node(
         self, target_node_name: str, filter_fn: Callable[[StatisticPoint], bool]
     ) -> StatisticPoint:

nncf/quantization/algorithms/pipeline.py

@@ -118,6 +118,8 @@ def run_step(
             current_graph = NNCFGraphFactory.create(current_model)
         current_model = pipeline_step[-1].apply(current_model, current_graph, step_statistics)
 
+        step_statistics.remove_statistic_point(algorithm)
+
         return current_model
 
     def run_from_step(