[mypy] nncf/common/quantization (part 2) (openvinotoolkit#3197)

### Changes

Enable mypy for part of files in nncf/common/quantization

nncf/common/quantization/initialization/range.py

@@ -8,8 +8,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from __future__ import annotations
 
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 from nncf.common.graph.utils import get_reduction_axes
 from nncf.common.initialization.dataloader import NNCFDataLoader
@@ -26,7 +27,12 @@ class RangeInitConfig:
     parameters.
     """
 
-    def __init__(self, init_type: str, num_init_samples: int, init_type_specific_params: Dict = None):
+    def __init__(
+        self,
+        init_type: str,
+        num_init_samples: int,
+        init_type_specific_params: Optional[Dict[str, int]] = None,
+    ):
         """
         Initializes the quantization range initialization parameters.
 
@@ -43,11 +49,11 @@ def __init__(self, init_type: str, num_init_samples: int, init_type_specific_par
         if self.init_type_specific_params is None:
             self.init_type_specific_params = {}
 
-    def __eq__(self, other):
+    def __eq__(self, other: object) -> bool:
         return self.__dict__ == other.__dict__
 
     @classmethod
-    def from_dict(cls, dct: Dict) -> "RangeInitConfig":
+    def from_dict(cls, dct: Dict[str, Any]) -> RangeInitConfig:
         num_init_samples = dct.get("num_init_samples", NUM_INIT_SAMPLES)
         if num_init_samples < 0:
             raise ValueError("Number of initialization samples must be >= 0")
@@ -94,10 +100,10 @@ def __init__(
         self.target_group = target_quantizer_group
 
     @classmethod
-    def from_dict(cls, dct: Dict) -> "PerLayerRangeInitConfig":
+    def from_dict(cls, dct: Dict[str, Any]) -> PerLayerRangeInitConfig:
         base_config = RangeInitConfig.from_dict(dct)
 
-        def get_list(dct: Dict, attr_name: str) -> Optional[List[str]]:
+        def get_list(dct: Dict[str, Any], attr_name: str) -> Optional[List[str]]:
             str_or_list = dct.get(attr_name)
             if str_or_list is None:
                 return None
@@ -185,7 +191,7 @@ def is_per_channel(self) -> bool:
         """
         return self._is_per_channel
 
-    def use_per_sample_stats(self, per_sample_stats) -> bool:
+    def use_per_sample_stats(self, per_sample_stats: bool) -> bool:
         """
         For activations, if per_sample_stats is True, statistics will be collected per-sample.
         For weights statistics are always collected per-batch.
@@ -213,7 +219,7 @@ def _get_reduction_axes(
         shape_to_reduce: Union[Tuple[int, ...], List[int]],
         quantization_axes: Union[Tuple[int, ...], List[int]],
         aggregation_axes: Union[Tuple[int, ...], List[int]],
-    ):
+    ) -> Tuple[int, ...]:
         """
         Returns axes for a reducer regarding aggregation axes. As aggregator takes axes counting from stacked tensors,
         from these axes only tensor related axes should be used for reducer.
@@ -225,7 +231,7 @@ def _get_reduction_axes(
         """
         axes_to_keep = set(el - 1 for el in aggregation_axes if el != 0)
         axes_to_keep.update(quantization_axes)
-        return get_reduction_axes(axes_to_keep, shape_to_reduce)
+        return get_reduction_axes(list(axes_to_keep), shape_to_reduce)
 
     def _get_aggregation_axes(self, batchwise_statistics: bool) -> Tuple[int, ...]:
         """

nncf/common/quantization/quantizer_propagation/grouping.py

@@ -21,7 +21,7 @@ class UnifiedScalePropagatingQuantizerGroupManager:
     quantized model.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self._next_gid = 0
         self._group_vs_prop_quants_dict: Dict[int, Set[PropagatingQuantizer]] = {}
 
@@ -46,7 +46,7 @@ def register_group(self, prop_quants: Set[PropagatingQuantizer]) -> int:
         self._group_vs_prop_quants_dict[gid] = prop_quants
         return gid
 
-    def add_to_group(self, target_gid: int, prop_quant: PropagatingQuantizer):
+    def add_to_group(self, target_gid: int, prop_quant: PropagatingQuantizer) -> None:
         """
         Adds a propagating quantizer to an already existing group.
 
@@ -62,7 +62,7 @@ def add_to_group(self, target_gid: int, prop_quant: PropagatingQuantizer):
                 )
         self._group_vs_prop_quants_dict[target_gid].add(prop_quant)
 
-    def remove_from_group(self, group: int, prop_quant: PropagatingQuantizer):
+    def remove_from_group(self, group: int, prop_quant: PropagatingQuantizer) -> None:
         """
         Removes a propagating quantizer from a group.
 
@@ -91,7 +91,7 @@ def get_group_id_by_propagating_quantizer_id(self, requested_pqid: int) -> Optio
                     return gid
         return None
 
-    def merge_groups(self, merge_to_gid: int, merge_from_gid: int):
+    def merge_groups(self, merge_to_gid: int, merge_from_gid: int) -> None:
         """
         Merges two groups into a single one. The `merge_to_gid` group retains its group ID.
 
@@ -110,11 +110,11 @@ class QuantizersWaitingForMergeManager:
     and corresponding node keys.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self._branching_node_keys_vs_quantizers_waiting_for_merge: Dict[str, Set[PropagatingQuantizer]] = {}
         self._quantizers_vs_branching_node_keys: Dict[PropagatingQuantizer, str] = {}
 
-    def add_propagating_quantizer_to_wait_on_node_key(self, pq: PropagatingQuantizer, branching_node_key: str):
+    def add_propagating_quantizer_to_wait_on_node_key(self, pq: PropagatingQuantizer, branching_node_key: str) -> None:
         """
         Registers a propagating quantizer as "waiting" on a node in QuantizerPropagationStateGraph.
 
@@ -146,10 +146,10 @@ def get_waiting_quantizers_for_branching_node_key(self, node_key: str) -> Set[Pr
         """
         return self._branching_node_keys_vs_quantizers_waiting_for_merge[node_key]
 
-    def __contains__(self, item: PropagatingQuantizer):
+    def __contains__(self, item: PropagatingQuantizer) -> bool:
         return item in self._quantizers_vs_branching_node_keys
 
-    def resolve_merged_node(self, branching_node_key: str):
+    def resolve_merged_node(self, branching_node_key: str) -> None:
         """
         De-registers any quantizers that were previously registered to be "waiting" on a given node key.
         :param branching_node_key: The node key in QuantizerPropagationStateGraph that some propagating