[mypy] Fix mypy issue for some files

pyproject.toml

@@ -125,9 +125,7 @@ exclude = [
     "src/nncf/quantization/algorithms/accuracy_control/rank_functions.py",
     "src/nncf/quantization/algorithms/accuracy_control/ranker.py",
     "src/nncf/quantization/algorithms/accuracy_control/subset_selection.py",
-    "src/nncf/quantization/algorithms/algorithm.py",
     "src/nncf/quantization/algorithms/bias_correction/algorithm.py",
-    "src/nncf/quantization/algorithms/bias_correction/backend.py",
     "src/nncf/quantization/algorithms/bias_correction/onnx_backend.py",
     "src/nncf/quantization/algorithms/bias_correction/openvino_backend.py",
     "src/nncf/quantization/algorithms/bias_correction/torch_fx_backend.py",

src/nncf/common/utils/os.py

@@ -67,7 +67,7 @@ def get_available_cpu_count(logical: bool = True) -> int:
         return 1
 
 
-def get_available_memory_amount() -> float:
+def get_available_memory_amount() -> int:
     """
     :return: Available memory amount (bytes)
     """

src/nncf/quantization/algorithms/accuracy_control/algorithm.py

@@ -29,6 +29,7 @@
 from nncf.quantization.algorithms.accuracy_control.backend import AccuracyControlAlgoBackend
 from nncf.quantization.algorithms.accuracy_control.evaluator import Evaluator
 from nncf.quantization.algorithms.accuracy_control.evaluator import MetricResults
+from nncf.quantization.algorithms.accuracy_control.ranker import GroupToRank
 from nncf.quantization.algorithms.accuracy_control.ranker import Ranker
 
 TModel = TypeVar("TModel")
@@ -110,8 +111,8 @@ class QuantizationAccuracyRestorerReport:
     :param num_iterations: Number of iterations performed.
     """
 
-    def __init__(self):
-        self.removed_groups = []
+    def __init__(self) -> None:
+        self.removed_groups: list[GroupToRank] = []
         self.removed_all = False
         self.reached_required_drop = False
         self.num_quantized_operations = None
@@ -355,7 +356,7 @@ def _apply(
                 break
 
             nncf_logger.info(
-                f"Accuracy drop with the new quantization scope is {float(current_accuracy_drop)} ({self.drop_type})"
+                f"Accuracy drop with the new quantization scope is {current_accuracy_drop} ({self.drop_type})"
             )
 
             # Continue greedy quantizer remove
@@ -479,9 +480,9 @@ def _print_report(report: QuantizationAccuracyRestorerReport, max_num_iterations
             )
 
     @staticmethod
-    def _print_completion_message(accuracy_drop: float, drop_type: DropType) -> None:
+    def _print_completion_message(accuracy_drop: float | None, drop_type: DropType) -> None:
         if accuracy_drop is None or accuracy_drop < 0:
             reason = "metric of the quantized model is greater than the metric of the initial model"
         else:
-            reason = f"achieved required accuracy drop {float(accuracy_drop)} ({drop_type})"
+            reason = f"achieved required accuracy drop {accuracy_drop} ({drop_type})"
         nncf_logger.info(f"Algorithm completed: {reason}")

src/nncf/quantization/algorithms/accuracy_control/backend.py

@@ -56,7 +56,7 @@ class AccuracyControlAlgoBackend(ABC):
 
     @staticmethod
     @abstractmethod
-    def get_op_with_weights_metatypes() -> list[OperatorMetatype]:
+    def get_op_with_weights_metatypes() -> list[type[OperatorMetatype]]:
         """
         Returns a list of operation metatypes that can be reverted to representation
         with int8 weights.
@@ -67,7 +67,7 @@ def get_op_with_weights_metatypes() -> list[OperatorMetatype]:
 
     @staticmethod
     @abstractmethod
-    def get_quantizer_metatypes() -> list[OperatorMetatype]:
+    def get_quantizer_metatypes() -> list[type[OperatorMetatype]]:
         """
         Returns a list of quantizer metatypes.
 
@@ -76,7 +76,7 @@ def get_quantizer_metatypes() -> list[OperatorMetatype]:
 
     @staticmethod
     @abstractmethod
-    def get_const_metatypes() -> list[OperatorMetatype]:
+    def get_const_metatypes() -> list[type[OperatorMetatype]]:
         """
         Returns a list of constant metatypes.
 
@@ -85,7 +85,7 @@ def get_const_metatypes() -> list[OperatorMetatype]:
 
     @staticmethod
     @abstractmethod
-    def get_quantizable_metatypes() -> list[OperatorMetatype]:
+    def get_quantizable_metatypes() -> list[type[OperatorMetatype]]:
         """
         Returns a list of metatypes for operations that may be quantized.
 
@@ -104,7 +104,7 @@ def get_start_nodes_for_activation_path_tracing(nncf_graph: NNCFGraph) -> list[N
 
     @staticmethod
     @abstractmethod
-    def get_quantize_agnostic_metatypes() -> list[OperatorMetatype]:
+    def get_quantize_agnostic_metatypes() -> list[type[OperatorMetatype]]:
         """
         Returns a list of quantize agnostic metatypes.
 
@@ -113,7 +113,7 @@ def get_quantize_agnostic_metatypes() -> list[OperatorMetatype]:
 
     @staticmethod
     @abstractmethod
-    def get_shapeof_metatypes() -> list[OperatorMetatype]:
+    def get_shapeof_metatypes() -> list[type[OperatorMetatype]]:
         """
         Returns a list of shape of metatypes.
 
@@ -171,7 +171,7 @@ def get_weight_value(node_with_weight: NNCFNode, model: TModel, port_id: int) ->
 
     @staticmethod
     @abstractmethod
-    def get_weight_tensor_port_ids(node: NNCFNode) -> list[int | None]:
+    def get_weight_tensor_port_ids(node: NNCFNode) -> list[int]:
         """
         Returns node's input port indices with weights tensors.
 

src/nncf/quantization/algorithms/accuracy_control/onnx_backend.py

@@ -9,6 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Any
 
 import numpy as np
 import onnx
@@ -58,27 +59,27 @@ class ONNXAccuracyControlAlgoBackend(AccuracyControlAlgoBackend):
     # Metatypes
 
     @staticmethod
-    def get_op_with_weights_metatypes() -> list[ONNXOpMetatype]:
+    def get_op_with_weights_metatypes() -> list[type[ONNXOpMetatype]]:
         return OPERATIONS_WITH_WEIGHTS
 
     @staticmethod
-    def get_quantizer_metatypes() -> list[ONNXOpMetatype]:
+    def get_quantizer_metatypes() -> list[type[ONNXOpMetatype]]:
         return QUANTIZE_DEQUANTIZE_OPERATIONS
 
     @staticmethod
-    def get_const_metatypes() -> list[ONNXOpMetatype]:
+    def get_const_metatypes() -> list[type[ONNXOpMetatype]]:
         return [onnx_metatypes.ONNXConstantMetatype]
 
     @staticmethod
-    def get_quantizable_metatypes() -> list[ONNXOpMetatype]:
+    def get_quantizable_metatypes() -> list[type[ONNXOpMetatype]]:
         return INPUTS_QUANTIZABLE_OPERATIONS
 
     @staticmethod
-    def get_quantize_agnostic_metatypes() -> list[ONNXOpMetatype]:
+    def get_quantize_agnostic_metatypes() -> list[type[ONNXOpMetatype]]:
         return QUANTIZE_AGNOSTIC_OPERATIONS + [onnx_metatypes.ONNXConcatMetatype]
 
     @staticmethod
-    def get_shapeof_metatypes() -> list[ONNXOpMetatype]:
+    def get_shapeof_metatypes() -> list[type[ONNXOpMetatype]]:
         return [onnx_metatypes.ONNXShapeMetatype]
 
     @staticmethod
@@ -96,17 +97,17 @@ def is_node_with_weight(node: NNCFNode) -> bool:
         return node.metatype in OPERATIONS_WITH_WEIGHTS and node.layer_attributes.has_weight()
 
     @staticmethod
-    def get_bias_value(node_with_bias: NNCFNode, nncf_graph: NNCFGraph, model: onnx.ModelProto) -> np.ndarray:
+    def get_bias_value(node_with_bias: NNCFNode, nncf_graph: NNCFGraph, model: onnx.ModelProto) -> np.ndarray[Any, Any]:
         return get_bias_value(node_with_bias, model)
 
     @staticmethod
-    def get_weight_value(node_with_weight: NNCFNode, model: onnx.ModelProto, port_id: int) -> np.ndarray:
+    def get_weight_value(node_with_weight: NNCFNode, model: onnx.ModelProto, port_id: int) -> np.ndarray[Any, Any]:
         assert node_with_weight.layer_attributes.has_weight()
         weight_name = node_with_weight.layer_attributes.weight_attrs[port_id]["name"]
         return get_tensor_value(model, weight_name)
 
     @staticmethod
-    def get_weight_tensor_port_ids(node: NNCFNode) -> list[int | None]:
+    def get_weight_tensor_port_ids(node: NNCFNode) -> list[int]:
         return list(node.layer_attributes.weight_attrs.keys())
 
     @staticmethod

src/nncf/quantization/algorithms/accuracy_control/openvino_backend.py

@@ -10,6 +10,8 @@
 # limitations under the License.
 
 
+from typing import Any
+
 import numpy as np
 import openvino as ov
 from openvino import Type
@@ -47,7 +49,7 @@ def __init__(self, model: ov.Model, use_fp32_precision: bool = True):
         if use_fp32_precision:
             config = {inference_precision: Type.f32}
         self._compiled_model = ov.compile_model(model, device_name="CPU", config=config)
-        self._engine = None
+        self._engine: OVCompiledModelEngine | None = None
 
     @property
     def model_for_inference(self) -> ov.CompiledModel:
@@ -68,27 +70,27 @@ class OVAccuracyControlAlgoBackend(AccuracyControlAlgoBackend):
     # Metatypes
 
     @staticmethod
-    def get_op_with_weights_metatypes() -> list[OVOpMetatype]:
+    def get_op_with_weights_metatypes() -> list[type[OVOpMetatype]]:
         return OPERATIONS_WITH_WEIGHTS
 
     @staticmethod
-    def get_quantizer_metatypes() -> list[OVOpMetatype]:
+    def get_quantizer_metatypes() -> list[type[OVOpMetatype]]:
         return FAKE_QUANTIZE_OPERATIONS
 
     @staticmethod
-    def get_const_metatypes() -> list[OVOpMetatype]:
+    def get_const_metatypes() -> list[type[OVOpMetatype]]:
         return CONSTANT_OPERATIONS
 
     @staticmethod
-    def get_quantizable_metatypes() -> list[OVOpMetatype]:
+    def get_quantizable_metatypes() -> list[type[OVOpMetatype]]:
         return INPUTS_QUANTIZABLE_OPERATIONS
 
     @staticmethod
-    def get_quantize_agnostic_metatypes() -> list[OVOpMetatype]:
+    def get_quantize_agnostic_metatypes() -> list[type[OVOpMetatype]]:
         return QUANTIZE_AGNOSTIC_OPERATIONS + [OVConcatMetatype]
 
     @staticmethod
-    def get_shapeof_metatypes() -> list[OVOpMetatype]:
+    def get_shapeof_metatypes() -> list[type[OVOpMetatype]]:
         return SHAPEOF_OPERATIONS
 
     @staticmethod
@@ -106,15 +108,15 @@ def is_node_with_weight(node: NNCFNode) -> bool:
         return node.metatype in OPERATIONS_WITH_WEIGHTS and isinstance(node.layer_attributes, OVLayerAttributes)
 
     @staticmethod
-    def get_bias_value(node_with_bias: NNCFNode, nncf_graph: NNCFGraph, model: ov.Model) -> np.ndarray:
+    def get_bias_value(node_with_bias: NNCFNode, nncf_graph: NNCFGraph, model: ov.Model) -> np.ndarray[Any, Any]:
         return get_bias_value(node_with_bias, nncf_graph, model)
 
     @staticmethod
-    def get_weight_value(node_with_weight: NNCFNode, model: ov.Model, port_id: int) -> np.ndarray:
+    def get_weight_value(node_with_weight: NNCFNode, model: ov.Model, port_id: int) -> np.ndarray[Any, Any]:
         return get_weight_value(node_with_weight, model, port_id)
 
     @staticmethod
-    def get_weight_tensor_port_ids(node: NNCFNode) -> list[int | None]:
+    def get_weight_tensor_port_ids(node: NNCFNode) -> list[int]:
         return node.layer_attributes.get_const_port_ids()
 
     @staticmethod