Fix smoothquant minmax observer init (#1421)

Author: changwangss

neural_compressor/adaptor/pytorch.py

@@ -3114,14 +3114,23 @@ def _get_quantizable_ops_recursively(self, model, prefix, quantizable_ops):
                         smooth_quant_args = self.recipes.get("smooth_quant_args", {})
                         folding = smooth_quant_args.get("folding", False)
                         if not folding:
-                            if self.sq_minmax_init or self.version.release >= Version("2.1.1").release:
-                                from torch.ao.quantization.observer import MinMaxObserver
+                            from torch.ao.quantization.observer import MinMaxObserver
 
+                            if self.version.release >= Version("2.1.1").release:
                                 static_qconfig = ipex.quantization.get_smooth_quant_qconfig_mapping(
                                     alpha=0.5, act_observer=MinMaxObserver
                                 )
                             else:
-                                static_qconfig = ipex.quantization.get_smooth_quant_qconfig_mapping(alpha=0.5)
+                                if self.sq_minmax_init:
+                                    static_qconfig = ipex.quantization.get_smooth_quant_qconfig_mapping(
+                                        alpha=0.5, act_observer=MinMaxObserver()
+                                    )
+                                    logger.warning(
+                                        "The int8 model accuracy will be close to 0 with MinMaxobserver, "
+                                        + "the suggested IPEX version is higher or equal than 2.1.100."
+                                    )
+                                else:
+                                    static_qconfig = ipex.quantization.get_smooth_quant_qconfig_mapping(alpha=0.5)
                     if self.example_inputs is None:
                         self.example_inputs = get_example_inputs(model, self.q_dataloader)
                     from neural_compressor.adaptor.torch_utils.util import move_input_device
@@ -3304,14 +3313,23 @@ def qdq_quantize(self, model, q_model, tune_cfg, dataloader, q_func):
         # Check save_qconf_summary part is a workaround for IPEX bug.
         # Sometimes the prepared model from get_op_capablitiy loss this attribute
         if not hasattr(model._model, "save_qconf_summary") or not hasattr(model._model, "load_qconf_summary"):
-            if self.sq_minmax_init or self.version.release >= Version("2.1.1").release:
-                from torch.ao.quantization.observer import MinMaxObserver
+            from torch.ao.quantization.observer import MinMaxObserver
 
+            if self.version.release >= Version("2.1.1").release:
                 static_qconfig = ipex.quantization.get_smooth_quant_qconfig_mapping(
                     alpha=0.5, act_observer=MinMaxObserver
                 )
             else:
-                static_qconfig = ipex.quantization.get_smooth_quant_qconfig_mapping(alpha=0.5)
+                if self.sq_minmax_init:
+                    static_qconfig = ipex.quantization.get_smooth_quant_qconfig_mapping(
+                        alpha=0.5, act_observer=MinMaxObserver()
+                    )
+                    logger.warning(
+                        "The int8 model accuracy will be close to 0 with MinMaxobserver, "
+                        + "the suggested IPEX version is higher or equal than 2.1.100+cpu."
+                    )
+                else:
+                    static_qconfig = ipex.quantization.get_smooth_quant_qconfig_mapping(alpha=0.5)
             if isinstance(self.example_inputs, dict):
                 model._model = ipex.quantization.prepare(
                     model._model, static_qconfig, example_kwarg_inputs=self.example_inputs, inplace=inplace