simplify forward and save pretrained since no jit support

optimum/intel/ipex/modeling_base.py

@@ -233,39 +233,10 @@ def _from_pretrained(
         return cls(model, config=config, export=True, **kwargs)
 
     def _save_pretrained(self, save_directory: Union[str, Path]):
-        if getattr(self.config, "torchscript", None):
-            output_path = os.path.join(save_directory, WEIGHTS_NAME)
-            torch.jit.save(self.model, output_path)
-        else:
-            logger.warning("The module is not a torchscript model, will be treated as a transformers model.")
-            self.model.save_pretrained(save_directory, safe_serialization=False)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor,
-        token_type_ids: torch.Tensor = None,
-        position_ids: torch.Tensor = None,
-        **kwargs,
-    ):
-        inputs = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-
-        if "token_type_ids" in self.input_names:
-            inputs["token_type_ids"] = token_type_ids
+        self.model.save_pretrained(save_directory, safe_serialization=False)
 
-        if "position_ids" in self.input_names:
-            inputs["position_ids"] = position_ids
-
-        outputs = self._call_model(**inputs)
-        if isinstance(outputs, dict):
-            model_output = ModelOutput(**outputs)
-        else:
-            model_output = ModelOutput()
-            model_output[self.output_name] = outputs[0]
-        return model_output
+    def forward(self, *args, **kwargs):
+        return self.model(*args, **kwargs)
 
     def eval(self):
         self.model.eval()
@@ -336,64 +307,16 @@ class IPEXModelForImageClassification(IPEXModel):
     auto_model_class = AutoModelForImageClassification
     export_feature = "image-classification"
 
-    def forward(
-        self,
-        pixel_values: torch.Tensor,
-        **kwargs,
-    ):
-        inputs = {
-            "pixel_values": pixel_values,
-        }
-
-        outputs = self._call_model(**inputs)
-        return ModelOutput(**outputs) if isinstance(outputs, dict) else ModelOutput(logits=outputs[0])
-
 
 class IPEXModelForAudioClassification(IPEXModel):
     auto_model_class = AutoModelForAudioClassification
     export_feature = "audio-classification"
 
-    def forward(
-        self,
-        input_values: torch.Tensor,
-        attention_mask: torch.Tensor = None,
-        **kwargs,
-    ):
-        inputs = {
-            "input_values": input_values,
-        }
-
-        if "attention_mask" in self.input_names:
-            inputs["attention_mask"] = attention_mask
-
-        outputs = self._call_model(**inputs)
-        return ModelOutput(**outputs) if isinstance(outputs, dict) else ModelOutput(logits=outputs[0])
-
 
 class IPEXModelForQuestionAnswering(IPEXModel):
     auto_model_class = AutoModelForQuestionAnswering
     export_feature = "question-answering"
 
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor,
-        token_type_ids: torch.Tensor = None,
-        **kwargs,
-    ):
-        inputs = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-
-        if "token_type_ids" in self.input_names:
-            inputs["token_type_ids"] = token_type_ids
-
-        outputs = self._call_model(**inputs)
-        start_logits = outputs["start_logits"] if isinstance(outputs, dict) else outputs[0]
-        end_logits = outputs["end_logits"] if isinstance(outputs, dict) else outputs[1]
-        return ModelOutput(start_logits=start_logits, end_logits=end_logits)
-
 
 class IPEXModelForCausalLM(IPEXModel, GenerationMixin):
     auto_model_class = AutoModelForCausalLM