Remove the need for the config to be in the subfolder (#2044)

* remove the need for the config to be in the subfolder

* fix

* fix for offline mode

* add log

* fix

* enable load local model in subfolder

* fix windows

optimum/modeling_base.py

@@ -380,27 +380,35 @@ def from_pretrained(
                 )
             model_id, revision = model_id.split("@")
 
+        all_files, _ = TasksManager.get_model_files(
+            model_id,
+            subfolder=subfolder,
+            cache_dir=cache_dir,
+            revision=revision,
+            token=token,
+        )
+
+        config_folder = subfolder
+        if cls.config_name not in all_files:
+            logger.info(
+                f"{cls.config_name} not found in the specified subfolder {subfolder}. Using the top level {cls.config_name}."
+            )
+            config_folder = ""
+
         library_name = TasksManager.infer_library_from_model(
-            model_id, subfolder=subfolder, revision=revision, cache_dir=cache_dir, token=token
+            model_id, subfolder=config_folder, revision=revision, cache_dir=cache_dir, token=token
         )
 
         if library_name == "timm":
             config = PretrainedConfig.from_pretrained(
-                model_id, subfolder=subfolder, revision=revision, cache_dir=cache_dir, token=token
+                model_id, subfolder=config_folder, revision=revision, cache_dir=cache_dir, token=token
             )
 
         if config is None:
-            if os.path.isdir(os.path.join(model_id, subfolder)) and cls.config_name == CONFIG_NAME:
-                if CONFIG_NAME in os.listdir(os.path.join(model_id, subfolder)):
-                    config = AutoConfig.from_pretrained(
-                        os.path.join(model_id, subfolder), trust_remote_code=trust_remote_code
-                    )
-                elif CONFIG_NAME in os.listdir(model_id):
+            if os.path.isdir(os.path.join(model_id, config_folder)) and cls.config_name == CONFIG_NAME:
+                if CONFIG_NAME in os.listdir(os.path.join(model_id, config_folder)):
                     config = AutoConfig.from_pretrained(
-                        os.path.join(model_id, CONFIG_NAME), trust_remote_code=trust_remote_code
-                    )
-                    logger.info(
-                        f"config.json not found in the specified subfolder {subfolder}. Using the top level config.json."
+                        os.path.join(model_id, config_folder), trust_remote_code=trust_remote_code
                     )
                 else:
                     raise OSError(f"config.json not found in {model_id} local folder")
@@ -411,7 +419,7 @@ def from_pretrained(
                     cache_dir=cache_dir,
                     token=token,
                     force_download=force_download,
-                    subfolder=subfolder,
+                    subfolder=config_folder,
                     trust_remote_code=trust_remote_code,
                 )
         elif isinstance(config, (str, os.PathLike)):
@@ -421,7 +429,7 @@ def from_pretrained(
                 cache_dir=cache_dir,
                 token=token,
                 force_download=force_download,
-                subfolder=subfolder,
+                subfolder=config_folder,
                 trust_remote_code=trust_remote_code,
             )
 

optimum/onnxruntime/modeling_ort.py

@@ -510,13 +510,12 @@ def _from_pretrained(
 
         if file_name is None:
             if model_path.is_dir():
-                onnx_files = list(model_path.glob("*.onnx"))
+                onnx_files = list((model_path / subfolder).glob("*.onnx"))
             else:
                 repo_files, _ = TasksManager.get_model_files(
                     model_id, revision=revision, cache_dir=cache_dir, token=token
                 )
                 repo_files = map(Path, repo_files)
-
                 pattern = "*.onnx" if subfolder == "" else f"{subfolder}/*.onnx"
                 onnx_files = [p for p in repo_files if p.match(pattern)]
 
@@ -983,10 +982,9 @@ def _cached_file(
             token = use_auth_token
 
         model_path = Path(model_path)
-
         # locates a file in a local folder and repo, downloads and cache it if necessary.
         if model_path.is_dir():
-            model_cache_path = model_path / file_name
+            model_cache_path = model_path / subfolder / file_name
             preprocessors = maybe_load_preprocessors(model_path.as_posix())
         else:
             model_cache_path = hf_hub_download(

tests/onnxruntime/test_modeling.py

@@ -28,6 +28,7 @@
 import requests
 import timm
 import torch
+from huggingface_hub import HfApi
 from huggingface_hub.constants import default_cache_path
 from parameterized import parameterized
 from PIL import Image
@@ -1263,6 +1264,20 @@ def test_trust_remote_code(self):
             torch.allclose(pt_logits, ort_logits, atol=1e-4), f" Maxdiff: {torch.abs(pt_logits - ort_logits).max()}"
         )
 
+    @parameterized.expand(("", "onnx"))
+    def test_loading_with_config_not_from_subfolder(self, subfolder):
+        # config.json file in the root directory and not in the subfolder
+        model_id = "sentence-transformers-testing/stsb-bert-tiny-onnx"
+        # hub model
+        ORTModelForFeatureExtraction.from_pretrained(model_id, subfolder=subfolder, export=subfolder == "")
+        # local model
+        api = HfApi()
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            local_dir = Path(tmpdirname) / "model"
+            api.snapshot_download(repo_id=model_id, local_dir=local_dir)
+            ORTModelForFeatureExtraction.from_pretrained(local_dir, subfolder=subfolder, export=subfolder == "")
+            remove_directory(tmpdirname)
+
 
 class ORTModelForQuestionAnsweringIntegrationTest(ORTModelTestMixin):
     SUPPORTED_ARCHITECTURES = [