enhance base Quantizer

Signed-off-by: yuwenzho <yuwen.zhou@intel.com>

Author: yuwenzho

neural_compressor/torch/algorithms/base_algorithm.py

@@ -85,19 +85,18 @@ def quantize(self, model: torch.nn.Module, *args: Any, **kwargs: Any):
         Returns:
             A quantized model.
         """
+        model = self.prepare(model, *args, **kwargs)
+
         run_fn = kwargs.get("run_fn", None)
-        run_args = kwargs.get("run_args", None)
-        assert run_fn is not None, (
-            "Can't find run_func. Please provide run_func to quantize API "
-            "or overwrite quantize member function in your Quantizer class."
-        )
+        if run_fn is not None:
+            run_args = kwargs.get("run_args", None)
+            if run_args:
+                run_fn(model, *run_args)
+            else:
+                run_fn(model)
 
-        model = self.prepare(model, *args, **kwargs)
-        if run_args:
-            run_fn(model, *run_args)
-        else:
-            run_fn(model)
         model = self.convert(model, *args, **kwargs)
+
         return model
 
     def execute(self, model: torch.nn.Module, mode, *args: Any, **kwargs: Any):  # pragma: no cover

neural_compressor/torch/algorithms/weight_only/hqq/quantizer.py

@@ -114,20 +114,6 @@ def convert(self, model: torch.nn.Module, *args, **kwargs) -> Optional[torch.nn.
         )
         return model
 
-    @torch.no_grad()
-    def quantize(self, model: torch.nn.Module, *args, **kwargs):
-        """Quantizes a float torch model.
-
-        Args:
-            model: A float model to be quantized.
-
-        Returns:
-            A quantized model.
-        """
-        model = self.prepare(model, *args, **kwargs)
-        model = self.convert(model, *args, **kwargs)
-        return model
-
     def save(self, model, path):
         # TODO: to implement it in the next PR
         pass

neural_compressor/torch/algorithms/weight_only/rtn.py

@@ -210,17 +210,3 @@ def convert(
                     m.weight.t_().contiguous()
                 m.weight.data.copy_(weight)
         return model
-
-    @torch.no_grad()
-    def quantize(self, model, *args, **kwargs):
-        """Quantizes a given torch model.
-
-        Args:
-            model: A float model to be quantized.
-
-        Returns:
-            A quantized model.
-        """
-        model = self.prepare(model, *args, **kwargs)
-        model = self.convert(model, *args, **kwargs)
-        return model