Skip the verified config

neural_compressor/common/base_config.py

@@ -436,6 +436,13 @@ def _is_op_type(name: str) -> bool:
     def get_config_set_for_tuning(cls):
         raise NotImplementedError
 
+    def __eq__(self, other: BaseConfig) -> bool:
+        if not isinstance(other, type(self)):
+            return False
+        return self.params_list == other.params_list and all(
+            getattr(self, str(attr)) == getattr(other, str(attr)) for attr in self.params_list
+        )
+
 
 class ComposableConfig(BaseConfig):
     name = COMPOSABLE_CONFIG

neural_compressor/common/base_tuning.py

@@ -231,13 +231,29 @@ def __len__(self) -> int:
 
 
 class ConfigLoader:
-    def __init__(self, config_set: ConfigSet, sampler: Sampler = default_sampler) -> None:
+    def __init__(
+        self, config_set: ConfigSet, sampler: Sampler = default_sampler, skip_verified_config: bool = True
+    ) -> None:
         self.config_set = ConfigSet.from_fwk_configs(config_set)
         self._sampler = sampler(self.config_set)
+        self.skip_verified_config = skip_verified_config
+        self.verify_config_list = list()
+
+    def is_verified_config(self, config):
+        for verified_config in self.verify_config_list:
+            if config == verified_config:
+                return True
+        return False
 
     def __iter__(self) -> Generator[BaseConfig, Any, None]:
         for index in self._sampler:
-            yield self.config_set[index]
+            new_config = self.config_set[index]
+            if self.skip_verified_config and self.is_verified_config(new_config):
+                logger.warning("Skip the verified config:")
+                logger.warning(new_config.to_dict())
+                continue
+            self.verify_config_list.append(new_config)
+            yield new_config
 
 
 class TuningConfig:

neural_compressor/common/tuning_param.py

@@ -98,3 +98,6 @@ def is_tunable(self, value: Any) -> bool:
         except Exception as e:
             logger.debug(f"Failed to validate the input_args: {e}")
             return False
+
+    def __str__(self) -> str:
+        return self.name

neural_compressor/torch/quantization/algorithm_entry.py

@@ -41,7 +41,7 @@ def rtn_entry(
     configs_mapping: Dict[Tuple[str, callable], RTNConfig],
     mode: Mode = Mode.QUANTIZE,
     *args,
-    **kwargs
+    **kwargs,
 ) -> torch.nn.Module:
     """The main entry to apply rtn quantization."""
     from neural_compressor.torch.algorithms.weight_only.rtn import RTNQuantizer
@@ -258,7 +258,7 @@ def awq_quantize_entry(
     configs_mapping: Dict[Tuple[str, callable], AWQConfig],
     mode: Mode = Mode.QUANTIZE,
     *args,
-    **kwargs
+    **kwargs,
 ) -> torch.nn.Module:
     logger.info("Quantize model with the AWQ algorithm.")
     from neural_compressor.torch.algorithms.weight_only.awq import AWQQuantizer
@@ -455,7 +455,7 @@ def hqq_entry(
     configs_mapping: Dict[Tuple[str, Callable], HQQConfig],
     mode: Mode = Mode.QUANTIZE,
     *args,
-    **kwargs
+    **kwargs,
 ) -> torch.nn.Module:
     from neural_compressor.torch.algorithms.weight_only.hqq import HQQuantizer
 

test/3x/common/test_common.py

@@ -336,6 +336,14 @@ def test_config_loader(self) -> None:
         for i, config in enumerate(self.loader):
             self.assertEqual(config, self.config_set[i])
 
+    def test_config_loader_skip_verified_config(self) -> None:
+        config_set = [FakeAlgoConfig(weight_bits=[4, 8]), FakeAlgoConfig(weight_bits=8)]
+        config_loader = ConfigLoader(config_set)
+        config_count = 0
+        for i, config in enumerate(config_loader):
+            config_count += 1
+        self.assertEqual(config_count, 2)
+
 
 if __name__ == "__main__":
     unittest.main()