[mypy] extend mypy check (#3154)

### Changes

Added some files to mypy check and fix errors:

- nncf/quantization/passes.py
- nncf/quantization/advanced_parameters.py
- nncf/quantization/range_estimator.py
- nncf/quantization/telemetry_extractors.py

Author: AlexanderDokuchaev

nncf/common/graph/graph.py

@@ -11,7 +11,21 @@
 import pathlib
 from collections import defaultdict
 from copy import deepcopy
-from typing import Any, Callable, Dict, Generator, KeysView, List, Optional, Tuple, Type, Union, ValuesView, cast
+from typing import (
+    Any,
+    Callable,
+    Collection,
+    Dict,
+    Generator,
+    KeysView,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+    ValuesView,
+    cast,
+)
 
 import networkx as nx  # type:ignore
 import networkx.algorithms.isomorphism as iso  # type:ignore
@@ -245,7 +259,7 @@ def get_nodes_by_types(self, type_list: List[str]) -> List[NNCFNode]:
                 all_nodes_of_type.append(nncf_node)
         return all_nodes_of_type
 
-    def get_nodes_by_metatypes(self, metatype_list: List[Type[OperatorMetatype]]) -> List[NNCFNode]:
+    def get_nodes_by_metatypes(self, metatype_list: Collection[Type[OperatorMetatype]]) -> List[NNCFNode]:
         """
         Return a list of nodes with provided metatypes.
 
@@ -766,7 +780,7 @@ def get_all_edges(self) -> Generator[NNCFGraphEdge, None, None]:
         for nx_edge in self._nx_graph.in_edges:
             yield self.get_edge(self.get_node_by_key(nx_edge[0]), self.get_node_by_key(nx_edge[1]))
 
-    def remove_nodes_from(self, nodes: List[NNCFNode]) -> None:
+    def remove_nodes_from(self, nodes: Collection[NNCFNode]) -> None:
         """
         Removes nodes from the current NNCFGraph instance.
         We use the remove_node method here because remove_nodes_from uses a silent fail instead of an exception.

nncf/quantization/advanced_parameters.py

@@ -182,7 +182,7 @@ class AdvancedQuantizationParameters:
 
     :param overflow_fix: This option controls whether to apply the overflow issue fix
         for the 8-bit quantization.
-    :type overflow_fix: nncf.quantization.advanced_parameters.OverflowFix
+    :type overflow_fix: Optional[nncf.quantization.advanced_parameters.OverflowFix]
     :param quantize_outputs: Whether to insert additional quantizers right before each
         of the model outputs.
     :type quantize_outputs: bool
@@ -232,16 +232,16 @@ class AdvancedQuantizationParameters:
     """
 
     # General parameters
-    overflow_fix: OverflowFix = None
+    overflow_fix: Optional[OverflowFix] = None
     quantize_outputs: bool = False
     inplace_statistics: bool = True
     disable_channel_alignment: bool = True
     disable_bias_correction: bool = False
     batchwise_statistics: Optional[bool] = None
 
     # Advanced Quantization parameters
-    activations_quantization_params: Union[QuantizationParameters, FP8QuantizationParameters] = None
-    weights_quantization_params: Union[QuantizationParameters, FP8QuantizationParameters] = None
+    activations_quantization_params: Optional[Union[QuantizationParameters, FP8QuantizationParameters]] = None
+    weights_quantization_params: Optional[Union[QuantizationParameters, FP8QuantizationParameters]] = None
     quantizer_propagation_rule: QuantizerPropagationRule = QuantizerPropagationRule.MERGE_ALL_IN_ONE
 
     # Range estimator parameters
@@ -254,7 +254,7 @@ class AdvancedQuantizationParameters:
     # Advanced SmoothQuant algorithm parameters
     smooth_quant_alphas: AdvancedSmoothQuantParameters = field(default_factory=AdvancedSmoothQuantParameters)
     # Deprecated parameter
-    smooth_quant_alpha: float = None
+    smooth_quant_alpha: Optional[float] = None
 
     # Backend specific parameters
     backend_params: Dict[str, Any] = field(default_factory=dict)
@@ -460,14 +460,14 @@ def convert_to_dict_recursively(params: Any) -> Dict[str, Any]:
     return result
 
 
-def convert_quantization_parameters_to_dict(params: QuantizationParameters) -> Dict[str, Any]:
+def convert_quantization_parameters_to_dict(params: Optional[QuantizationParameters]) -> Dict[str, Any]:
     """
     Converts quantization parameters to the dict in the legacy format
 
     :param params: Quantization parameters
     :return: Quantization parameters as dict in the legacy format
     """
-    result = {}
+    result: Dict[str, Any] = {}
     if params is not None:
         if params.num_bits is not None:
             result["bits"] = params.num_bits
@@ -492,7 +492,7 @@ def convert_range_estimator_parameters_to_dict(params: RangeEstimatorParameters)
     if params.min.clipping_value is not None or params.max.clipping_value is not None:
         raise nncf.ParameterNotSupportedError("clipping_value parameter is not supported in the legacy format")
 
-    result = {}
+    result: Dict[str, Any] = {}
     if (
         params.min.statistics_type == StatisticsType.MIN
         and params.min.aggregator_type == AggregatorType.MIN
@@ -551,13 +551,15 @@ def apply_advanced_parameters_to_config(
         initializer["batchnorm_adaptation"] = {"num_bn_adaptation_samples": 0}
         config["initializer"] = initializer
 
-    activations_config = convert_quantization_parameters_to_dict(params.activations_quantization_params)
-    if activations_config:
-        config["activations"] = activations_config
+    if isinstance(params.activations_quantization_params, QuantizationParameters):
+        activations_config = convert_quantization_parameters_to_dict(params.activations_quantization_params)
+        if activations_config:
+            config["activations"] = activations_config
 
-    weights_config = convert_quantization_parameters_to_dict(params.weights_quantization_params)
-    if weights_config:
-        config["weights"] = weights_config
+    if isinstance(params.weights_quantization_params, QuantizationParameters):
+        weights_config = convert_quantization_parameters_to_dict(params.weights_quantization_params)
+        if weights_config:
+            config["weights"] = weights_config
 
     activations_init_range_config = convert_range_estimator_parameters_to_dict(
         params.activations_range_estimator_params

nncf/quantization/passes.py

@@ -10,7 +10,7 @@
 # limitations under the License.
 
 import collections
-from typing import List, TypeVar
+from typing import Deque, List, Type, TypeVar
 
 from nncf.common.graph.graph import NNCFGraph
 from nncf.common.graph.graph import NNCFNode
@@ -23,9 +23,9 @@
 def transform_to_inference_graph(
     nncf_graph: NNCFGraph,
     input_nodes: List[NNCFNode],
-    shapeof_metatypes: List[OperatorMetatype],
-    dropout_metatypes: List[OperatorMetatype],
-    preserved_metatypes: List[OperatorMetatype],
+    shapeof_metatypes: List[Type[OperatorMetatype]],
+    dropout_metatypes: List[Type[OperatorMetatype]],
+    preserved_metatypes: List[Type[OperatorMetatype]],
 ) -> NNCFGraph:
     """
     This method contains inplace pipeline of the passes that uses to provide inference graph without constant flows.
@@ -49,7 +49,7 @@ def transform_to_inference_graph(
 
 def find_shapeof_subgraphs(
     nncf_graph: NNCFGraph,
-    shapeof_metatypes: List[OperatorMetatype],
+    shapeof_metatypes: List[Type[OperatorMetatype]],
     input_nodes: List[NNCFNode],
 ) -> List[NNCFNode]:
     """
@@ -80,7 +80,7 @@ def find_shapeof_subgraphs(
     for shape_of_node in shape_of_nodes:
         shapeof_subgraphs.add(shape_of_node)
 
-        shape_of_queue = collections.deque()
+        shape_of_queue: Deque[NNCFNode] = collections.deque()
         shape_of_queue.extend(nncf_graph.get_next_nodes(shape_of_node))
         while shape_of_queue:
             node = shape_of_queue.pop()
@@ -97,7 +97,7 @@ def find_shapeof_subgraphs(
 def find_preserved_nodes(
     graph: NNCFGraph,
     shapeof_subgraphs: List[NNCFNode],
-    preserved_metatypes: List[OperatorMetatype],
+    preserved_metatypes: List[Type[OperatorMetatype]],
 ) -> List[NNCFNode]:
     """
     :param graph: The input graph to be analyzed.
@@ -129,7 +129,7 @@ def find_preserved_nodes(
 
 def remove_nodes_and_reconnect_graph(
     nncf_graph: NNCFGraph,
-    metatypes: List[OperatorMetatype],
+    metatypes: List[Type[OperatorMetatype]],
 ) -> NNCFGraph:
     """
     Removes nodes with metatypes specified by `metatypes` parameter from

nncf/quantization/quantize_model.py

@@ -67,7 +67,7 @@ def warning_model_no_batchwise_support(
     :param graph: Model's NNCFGraph.
     :param advanced_quantization_parameters: AdvancedQuantizationParameters.
     :param model_type: Model type algorithm option.
-    :param no_batchwise_support_metatypes: Meatypes having no batchwise statistics support.
+    :param no_batchwise_support_metatypes: Metatypes having no batchwise statistics support.
     """
     if is_model_no_batchwise_support(
         graph, advanced_quantization_parameters, model_type, no_batchwise_support_metatypes
@@ -80,16 +80,16 @@ def is_model_no_batchwise_support(
     advanced_quantization_parameters: Optional[AdvancedQuantizationParameters],
     model_type: ModelType,
     no_batchwise_support_metatypes: List[OperatorMetatype],
-) -> None:
+) -> bool:
     """
     Returns True if batchwise statistics could lead to a significant accuracy drop.
 
     :param graph: Model's NNCFGraph.
     :param advanced_quantization_parameters: AdvancedQuantizationParameters.
     :param model_type: Model type algorithm option.
-    :param no_batchwise_support_metatypes: Meatypes having no batchwise statistics support.
+    :param no_batchwise_support_metatypes: Metatypes having no batchwise statistics support.
     """
-    return (
+    return bool(
         advanced_quantization_parameters
         and advanced_quantization_parameters.batchwise_statistics
         and (graph.get_nodes_by_metatypes(no_batchwise_support_metatypes) or model_type == ModelType.TRANSFORMER)

pyproject.toml

@@ -100,6 +100,10 @@ files = [
     "nncf/common/utils/",
     "nncf/common/tensor_statistics",
     "nncf/experimental/torch2",
+    "nncf/quantization/passes.py",
+    "nncf/quantization/advanced_parameters.py",
+    "nncf/quantization/range_estimator.py",
+    "nncf/quantization/telemetry_extractors.py",
     "nncf/telemetry/",
 ]