Fix IPEXModel input names

optimum/intel/ipex/modeling_base.py

@@ -90,7 +90,7 @@ def ipex_jit_trace(model, task, use_cache):
 
     if _is_patched_with_ipex(model, task):
         model = _patch_model(model)
-        # Todo: integerate in prepare_jit_inputs.
+        # TODO: integerate in prepare_jit_inputs.
         sample_inputs = get_dummy_input(model, return_dict=True)
         # Use Tensor Processing Primitives to accelerate linear, see https://arxiv.org/abs/2104.05755.
         _enable_tpp()
@@ -151,7 +151,7 @@ def __init__(
                 logger.warning("The model has been exported already.")
             else:
                 config = model.config if config is None else config
-                use_cache = kwargs.get("use_cache", None)
+                use_cache = kwargs.get("use_cache", True)
                 model = ipex_jit_trace(model, self.export_feature, use_cache)
                 config.torchscript = True
 
@@ -162,11 +162,13 @@ def __init__(
         self.model_save_dir = model_save_dir
         self._is_ipex_exported = _is_patched_with_ipex(model, self.export_feature)
 
-        self.input_names = (
-            {inputs.debugName().split(".")[0] for inputs in model.graph.inputs() if inputs.debugName() != "self"}
-            if isinstance(model, torch.jit.RecursiveScriptModule)
-            else inspect.signature(model.forward).parameters
-        )
+        if isinstance(model, torch.jit.RecursiveScriptModule):
+            self.input_names = {
+                inputs.debugName().split(".")[0] for inputs in model.graph.inputs() if inputs.debugName() != "self"
+            }
+        else:
+            self.input_names = set(inspect.signature(model.forward).parameters)
+
         # Registers the IPEXModelForXXX classes into the transformers AutoModel classes to avoid warnings when creating
         # a pipeline https://github.com/huggingface/transformers/blob/cad61b68396a1a387287a8e2e2fef78a25b79383/src/transformers/pipelines/base.py#L863
         AutoConfig.register(self.base_model_prefix, AutoConfig)
@@ -184,7 +186,6 @@ def _from_pretrained(
         cls,
         model_id: Union[str, Path],
         config: PretrainedConfig,
-        use_cache: bool = True,
         use_auth_token: Optional[Union[bool, str]] = None,
         token: Optional[Union[bool, str]] = None,
         revision: Optional[str] = None,
@@ -194,7 +195,6 @@ def _from_pretrained(
         local_files_only: bool = False,
         torch_dtype: Optional[Union[str, "torch.dtype"]] = None,
         trust_remote_code: bool = False,
-        _commit_hash: str = None,
         file_name: Optional[str] = WEIGHTS_NAME,
         **kwargs,
     ):
@@ -209,6 +209,17 @@ def _from_pretrained(
                 )
             token = use_auth_token
 
+        commit_hash = kwargs.pop("_commit_hash", None)
+
+        model_kwargs = {
+            "revision": revision,
+            "token": token,
+            "cache_dir": cache_dir,
+            "subfolder": subfolder,
+            "local_files_only": local_files_only,
+            "force_download": force_download,
+        }
+
         if not getattr(config, "torchscript", False):
             logger.warning("Detect torchscript is false. Convert to torchscript model!")
 
@@ -217,44 +228,30 @@ def _from_pretrained(
 
             task = cls.export_feature
             config.torch_dtype = torch_dtype
-            model_kwargs = {
-                "revision": revision,
-                "token": token,
-                "cache_dir": cache_dir,
-                "subfolder": subfolder,
-                "local_files_only": local_files_only,
-                "force_download": force_download,
-                "torch_dtype": torch_dtype,
-                "trust_remote_code": trust_remote_code,
-                "_commit_hash": _commit_hash,
-            }
-
-            model = TasksManager.get_model_from_task(task, model_id, **model_kwargs)
+            model = TasksManager.get_model_from_task(
+                task,
+                model_id,
+                trust_remote_code=trust_remote_code,
+                torch_dtype=torch_dtype,
+                _commit_hash=commit_hash,
+                **model_kwargs,
+            )
 
-            return cls(model, config=config, export=True, use_cache=use_cache, **kwargs)
+            return cls(model, config=config, export=True, **kwargs)
 
         # Load the model from local directory
         if os.path.isdir(model_id):
             model_cache_path = os.path.join(model_id, file_name)
             model_save_dir = model_id
         # Download the model from the hub
         else:
-            model_cache_path = hf_hub_download(
-                repo_id=model_id,
-                filename=file_name,
-                token=token,
-                revision=revision,
-                cache_dir=cache_dir,
-                force_download=force_download,
-                local_files_only=local_files_only,
-                subfolder=subfolder,
-            )
+            model_cache_path = hf_hub_download(repo_id=model_id, filename=file_name, **model_kwargs)
             model_save_dir = Path(model_cache_path).parent
 
         model = torch.jit.load(model_cache_path)
         torch.jit.freeze(model.eval())
 
-        return cls(model, config=config, model_save_dir=model_save_dir, use_cache=use_cache, **kwargs)
+        return cls(model, config=config, model_save_dir=model_save_dir, **kwargs)
 
     def _save_pretrained(self, save_directory: Union[str, Path]):
         output_path = os.path.join(save_directory, WEIGHTS_NAME)