ruff

nncf/experimental/torch2/model_transformer.py

@@ -54,7 +54,8 @@ def transform(self, transformation_layout: TransformationLayout) -> GraphModelWr
         for transformation in transformations:
             transformation_cls = transformation.__class__
             if transformation_cls not in [x[0] for x in self._command_transformation_ordered_pairs]:
-                raise ValueError(f"Unsupported transformation: {transformation_cls}")
+                msg = f"Unsupported transformation: {transformation_cls}"
+                raise ValueError(msg)
             aggregated_transformations[transformation.__class__].append(transformation)
 
         model = self._model.model

nncf/experimental/torch2/quantization/quantize_model.py

@@ -45,11 +45,14 @@ def quantize_impl(
     Implementation of the `quantize()` method for the PyTorch backend.
     """
     if fast_bias_correction is False:
-        raise ValueError(f"fast_bias_correction={fast_bias_correction} is not supported")
+        msg = f"fast_bias_correction={fast_bias_correction} is not supported"
+        raise ValueError(msg)
     if target_device == TargetDevice.CPU_SPR:
-        raise nncf.InternalError("target_device == CPU_SPR is not supported")
+        msg = "target_device == CPU_SPR is not supported"
+        raise nncf.InternalError(msg)
     if mode is not None:
-        raise ValueError(f"mode={mode} is not supported")
+        msg = f"mode={mode} is not supported"
+        raise ValueError(msg)
 
     copied_model = deepcopy(model)
     example_input = next(iter(calibration_dataset.get_inference_data()))

nncf/experimental/torch2/statistics/aggregator.py

@@ -100,6 +100,7 @@ def _get_statistics_key(self, statistics: TensorStatistic, target_point: TargetP
         :return: Statistics key.
         """
         if not isinstance(target_point, PTTargetPoint):
-            raise nncf.InternalError(f"Unexpected target point type: {type(target_point)}")
+            msg = f"Unexpected target point type: {type(target_point)}"
+            raise nncf.InternalError(msg)
         target_point_id = f"{target_point.target_node_name}_{target_point.type}_{target_point.input_port_id}"
         return f"{statistics.__class__.__name__}_{target_point_id}"