[mypy] hwconfig (#3189)

### Changes

Enable mypy check for `nncf/common/hardware/config.py`

nncf/common/hardware/config.py

@@ -15,7 +15,7 @@
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Set, Type
 
-import jstyleson as json
+import jstyleson as json  # type: ignore[import-untyped]
 
 import nncf
 from nncf.common.graph.operator_metatypes import OperatorMetatype
@@ -60,7 +60,7 @@ def get_hw_config_type(target_device: str) -> Optional[HWConfigType]:
     return HWConfigType(HW_CONFIG_TYPE_TARGET_DEVICE_MAP[target_device])
 
 
-class HWConfig(list, ABC):
+class HWConfig(list[Dict[str, Any]], ABC):
     QUANTIZATION_ALGORITHM_NAME = "quantization"
     ATTRIBUTES_NAME = "attributes"
     SCALE_ATTRIBUTE_NAME = "scales"
@@ -69,23 +69,23 @@ class HWConfig(list, ABC):
 
     TYPE_TO_CONF_NAME_DICT = {HWConfigType.CPU: "cpu.json", HWConfigType.NPU: "npu.json", HWConfigType.GPU: "gpu.json"}
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
-        self.registered_algorithm_configs = {}
+        self.registered_algorithm_configs: Dict[str, Any] = {}
         self.target_device = None
 
     @abstractmethod
     def _get_available_operator_metatypes_for_matching(self) -> List[Type[OperatorMetatype]]:
         pass
 
     @staticmethod
-    def get_path_to_hw_config(hw_config_type: HWConfigType):
+    def get_path_to_hw_config(hw_config_type: HWConfigType) -> str:
         return "/".join(
             [NNCF_PACKAGE_ROOT_DIR, HW_CONFIG_RELATIVE_DIR, HWConfig.TYPE_TO_CONF_NAME_DICT[hw_config_type]]
         )
 
     @classmethod
-    def from_dict(cls, dct: dict):
+    def from_dict(cls, dct: Dict[str, Any]) -> "HWConfig":
         hw_config = cls()
         hw_config.target_device = dct["target_device"]
 
@@ -104,7 +104,7 @@ def from_dict(cls, dct: dict):
             for algorithm_name in op_dict:
                 if algorithm_name not in hw_config.registered_algorithm_configs:
                     continue
-                tmp_config = {}
+                tmp_config: Dict[str, List[Dict[str, Any]]] = {}
                 for algo_and_op_specific_field_name, algorithm_configs in op_dict[algorithm_name].items():
                     if not isinstance(algorithm_configs, list):
                         algorithm_configs = [algorithm_configs]
@@ -129,30 +129,30 @@ def from_dict(cls, dct: dict):
         return hw_config
 
     @classmethod
-    def from_json(cls, path):
+    def from_json(cls: type["HWConfig"], path: str) -> List[Dict[str, Any]]:
         file_path = Path(path).resolve()
         with safe_open(file_path) as f:
             json_config = json.load(f, object_pairs_hook=OrderedDict)
             return cls.from_dict(json_config)
 
     @staticmethod
-    def get_quantization_mode_from_config_value(str_val: str):
+    def get_quantization_mode_from_config_value(str_val: str) -> str:
         if str_val == "symmetric":
             return QuantizationMode.SYMMETRIC
         if str_val == "asymmetric":
             return QuantizationMode.ASYMMETRIC
         raise nncf.ValidationError("Invalid quantization type specified in HW config")
 
     @staticmethod
-    def get_is_per_channel_from_config_value(str_val: str):
+    def get_is_per_channel_from_config_value(str_val: str) -> bool:
         if str_val == "perchannel":
             return True
         if str_val == "pertensor":
             return False
         raise nncf.ValidationError("Invalid quantization granularity specified in HW config")
 
     @staticmethod
-    def get_qconf_from_hw_config_subdict(quantization_subdict: Dict):
+    def get_qconf_from_hw_config_subdict(quantization_subdict: Dict[str, Any]) -> QuantizerConfig:
         bits = quantization_subdict["bits"]
         mode = HWConfig.get_quantization_mode_from_config_value(quantization_subdict["mode"])
         is_per_channel = HWConfig.get_is_per_channel_from_config_value(quantization_subdict["granularity"])
@@ -181,20 +181,22 @@ def get_qconf_from_hw_config_subdict(quantization_subdict: Dict):
         )
 
     @staticmethod
-    def is_qconf_list_corresponding_to_unspecified_op(qconf_list: Optional[List[QuantizerConfig]]):
+    def is_qconf_list_corresponding_to_unspecified_op(qconf_list: Optional[List[QuantizerConfig]]) -> bool:
         return qconf_list is None
 
     @staticmethod
-    def is_wildcard_quantization(qconf_list: Optional[List[QuantizerConfig]]):
+    def is_wildcard_quantization(qconf_list: Optional[List[QuantizerConfig]]) -> bool:
         # Corresponds to an op itself being specified in the HW config, but having no associated quantization
         # configs specified
         return qconf_list is not None and len(qconf_list) == 0
 
     def get_metatype_vs_quantizer_configs_map(
-        self, for_weights=False
+        self, for_weights: bool = False
     ) -> Dict[Type[OperatorMetatype], Optional[List[QuantizerConfig]]]:
         # 'None' for ops unspecified in HW config, empty list for wildcard quantization ops
-        retval = {k: None for k in self._get_available_operator_metatypes_for_matching()}
+        retval: Dict[Type[OperatorMetatype], Optional[List[QuantizerConfig]]] = {
+            k: None for k in self._get_available_operator_metatypes_for_matching()
+        }
         config_key = "weights" if for_weights else "activations"
         for op_dict in self:
             hw_config_op_name = op_dict["type"]

nncf/common/quantization/structs.py

@@ -11,7 +11,7 @@
 
 from copy import deepcopy
 from enum import Enum
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 import nncf
 from nncf.common.graph import NNCFNode
@@ -45,7 +45,7 @@ class QuantizerConfig:
     def __init__(
         self,
         num_bits: int = QUANTIZATION_BITS,
-        mode: QuantizationScheme = QuantizationScheme.SYMMETRIC,
+        mode: Union[QuantizationScheme, str] = QuantizationScheme.SYMMETRIC,  # TODO(AlexanderDokuchaev): use enum
         signedness_to_force: Optional[bool] = None,
         per_channel: bool = QUANTIZATION_PER_CHANNEL,
     ):

nncf/common/utils/helpers.py

@@ -12,13 +12,15 @@
 import itertools
 import os
 import os.path as osp
-import pathlib
-from typing import Any, Dict, Hashable, Iterable, List, Optional, Union
+from pathlib import Path
+from typing import Any, Dict, Hashable, Iterable, List, Optional, TypeVar, Union
 
 from tabulate import tabulate
 
 from nncf.common.utils.os import is_windows
 
+TKey = TypeVar("TKey", bound=Hashable)
+
 
 def create_table(
     header: List[str],
@@ -44,7 +46,7 @@ def create_table(
     return tabulate(tabular_data=rows, headers=header, tablefmt=table_fmt, maxcolwidths=max_col_widths, floatfmt=".3f")
 
 
-def configure_accuracy_aware_paths(log_dir: Union[str, pathlib.Path]) -> Union[str, pathlib.Path]:
+def configure_accuracy_aware_paths(log_dir: Union[str, Path]) -> Union[str, Path]:
     """
     Create a subdirectory inside of the passed log directory
     to save checkpoints from the accuracy-aware training loop to.
@@ -59,7 +61,7 @@ def configure_accuracy_aware_paths(log_dir: Union[str, pathlib.Path]) -> Union[s
     return acc_aware_log_dir
 
 
-def product_dict(d: Dict[Hashable, List[str]]) -> Iterable[Dict[Hashable, str]]:
+def product_dict(d: Dict[TKey, List[Any]]) -> Iterable[Dict[TKey, Any]]:
     """
     Generates dicts which enumerate the options for keys given in the input dict;
     options are represented by list values in the input dict.

pyproject.toml

@@ -109,7 +109,6 @@ exclude = [
     "nncf/common/composite_compression.py",
     "nncf/common/compression.py",
     "nncf/common/deprecation.py",
-    "nncf/common/hardware/config.py",
     "nncf/common/logging/progress_bar.py",
     "nncf/common/logging/track_progress.py",
     "nncf/common/pruning/clusterization.py",