Fix use_auth_token with ORTModel

optimum/exporters/tasks.py

@@ -1376,6 +1376,7 @@ def get_model_files(
         model_name_or_path: Union[str, Path],
         subfolder: str = "",
         cache_dir: str = huggingface_hub.constants.HUGGINGFACE_HUB_CACHE,
+        use_auth_token: Optional[str] = None,
     ):
         request_exception = None
         full_model_path = Path(model_name_or_path) / subfolder
@@ -1389,7 +1390,9 @@ def get_model_files(
             try:
                 if not isinstance(model_name_or_path, str):
                     model_name_or_path = str(model_name_or_path)
-                all_files = huggingface_hub.list_repo_files(model_name_or_path, repo_type="model")
+                all_files = huggingface_hub.list_repo_files(
+                    model_name_or_path, repo_type="model", token=use_auth_token
+                )
                 if subfolder != "":
                     all_files = [file[len(subfolder) + 1 :] for file in all_files if file.startswith(subfolder)]
             except RequestsConnectionError as e:  # Hub not accessible
@@ -1670,6 +1673,7 @@ def infer_library_from_model(
         revision: Optional[str] = None,
         cache_dir: str = huggingface_hub.constants.HUGGINGFACE_HUB_CACHE,
         library_name: Optional[str] = None,
+        use_auth_token: Optional[str] = None,
     ):
         """
         Infers the library from the model repo.
@@ -1687,13 +1691,17 @@ def infer_library_from_model(
                 Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used.
             library_name (`Optional[str]`, *optional*):
                 The library name of the model. Can be any of "transformers", "timm", "diffusers", "sentence_transformers".
+            use_auth_token (`Optional[str]`, defaults to `None`):
+                The token to use as HTTP bearer authorization for remote files.
         Returns:
             `str`: The library name automatically detected from the model repo.
         """
         if library_name is not None:
             return library_name
 
-        all_files, _ = TasksManager.get_model_files(model_name_or_path, subfolder, cache_dir)
+        all_files, _ = TasksManager.get_model_files(
+            model_name_or_path, subfolder, cache_dir, use_auth_token=use_auth_token
+        )
 
         if "model_index.json" in all_files:
             library_name = "diffusers"
@@ -1708,6 +1716,7 @@ def infer_library_from_model(
                 "subfolder": subfolder,
                 "revision": revision,
                 "cache_dir": cache_dir,
+                "use_auth_token": use_auth_token,
             }
             config_dict, kwargs = PretrainedConfig.get_config_dict(model_name_or_path, **kwargs)
             model_config = PretrainedConfig.from_dict(config_dict, **kwargs)

optimum/modeling_base.py

@@ -346,7 +346,9 @@ def from_pretrained(
                 )
             model_id, revision = model_id.split("@")
 
-        library_name = TasksManager.infer_library_from_model(model_id, subfolder, revision, cache_dir)
+        library_name = TasksManager.infer_library_from_model(
+            model_id, subfolder, revision, cache_dir, use_auth_token=use_auth_token
+        )
 
         if library_name == "timm":
             config = PretrainedConfig.from_pretrained(model_id, subfolder, revision)

tests/onnxruntime/test_modeling.py

@@ -937,9 +937,12 @@ def test_stable_diffusion_model_on_rocm_ep_str(self):
         self.assertEqual(model.vae_encoder.session.get_providers()[0], "ROCMExecutionProvider")
         self.assertListEqual(model.providers, ["ROCMExecutionProvider", "CPUExecutionProvider"])
 
-    @require_hf_token
     def test_load_model_from_hub_private(self):
-        model = ORTModel.from_pretrained(self.ONNX_MODEL_ID, use_auth_token=os.environ.get("HF_AUTH_TOKEN", None))
+        subprocess.run("huggingface-cli logout", shell=True)
+        # Read token of fxmartyclone (dummy user).
+        token = "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"
+
+        model = ORTModelForCustomTasks.from_pretrained("fxmartyclone/tiny-onnx-private-2", use_auth_token=token)
         self.assertIsInstance(model.model, onnxruntime.InferenceSession)
         self.assertIsInstance(model.config, PretrainedConfig)