Custom feature extractor

src/transformers/feature_extraction_utils.py

@@ -26,6 +26,7 @@
 
 from requests import HTTPError
 
+from .dynamic_module_utils import custom_object_save
 from .file_utils import (
     FEATURE_EXTRACTOR_NAME,
     EntryNotFoundError,
@@ -205,6 +206,8 @@ class FeatureExtractionMixin:
     extractors.
     """
 
+    _auto_class = None
+
     def __init__(self, **kwargs):
         """Set elements of `kwargs` as attributes."""
         # Pop "processor_class" as it should be saved as private attribute
@@ -316,6 +319,12 @@ def save_pretrained(self, save_directory: Union[str, os.PathLike]):
         """
         if os.path.isfile(save_directory):
             raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
+
+        # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
+        # loaded from the Hub.
+        if self._auto_class is not None:
+            custom_object_save(self, save_directory, config=self)
+
         os.makedirs(save_directory, exist_ok=True)
         # If we save using the predefined names, we can load using `from_pretrained`
         output_feature_extractor_file = os.path.join(save_directory, FEATURE_EXTRACTOR_NAME)
@@ -539,3 +548,29 @@ def to_json_file(self, json_file_path: Union[str, os.PathLike]):
 
     def __repr__(self):
         return f"{self.__class__.__name__} {self.to_json_string()}"
+
+    @classmethod
+    def register_for_auto_class(cls, auto_class="AutoFeatureExtractor"):
+        """
+        Register this class with a given auto class. This should only be used for custom feature extractors as the ones
+        in the library are already mapped with `AutoFeatureExtractor`.
+
+        <Tip warning={true}>
+
+        This API is experimental and may have some slight breaking changes in the next releases.
+
+        </Tip>
+
+        Args:
+            auto_class (`str` or `type`, *optional*, defaults to `"AutoFeatureExtractor"`):
+                The auto class to register this new feature extractor with.
+        """
+        if not isinstance(auto_class, str):
+            auto_class = auto_class.__name__
+
+        import transformers.models.auto as auto_module
+
+        if not hasattr(auto_module, auto_class):
+            raise ValueError(f"{auto_class} is not a valid auto class.")
+
+        cls._auto_class = auto_class

src/transformers/models/auto/feature_extraction_auto.py

@@ -14,23 +14,28 @@
 # limitations under the License.
 """ AutoFeatureExtractor class."""
 import importlib
+import json
 import os
 from collections import OrderedDict
+from typing import Dict, Optional, Union
 
 # Build the list of all feature extractors
 from ...configuration_utils import PretrainedConfig
+from ...dynamic_module_utils import get_class_from_dynamic_module
 from ...feature_extraction_utils import FeatureExtractionMixin
-from ...file_utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME
+from ...file_utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo
+from ...utils import logging
 from .auto_factory import _LazyAutoMapping
 from .configuration_auto import (
     CONFIG_MAPPING_NAMES,
     AutoConfig,
-    config_class_to_model_type,
     model_type_to_module_name,
     replace_list_option_in_docstrings,
 )
 
 
+logger = logging.get_logger(__name__)
+
 FEATURE_EXTRACTOR_MAPPING_NAMES = OrderedDict(
     [
         ("beit", "BeitFeatureExtractor"),
@@ -66,6 +71,96 @@ def feature_extractor_class_from_name(class_name: str):
     return None
 
 
+def get_feature_extractor_config(
+    pretrained_model_name_or_path: Union[str, os.PathLike],
+    cache_dir: Optional[Union[str, os.PathLike]] = None,
+    force_download: bool = False,
+    resume_download: bool = False,
+    proxies: Optional[Dict[str, str]] = None,
+    use_auth_token: Optional[Union[bool, str]] = None,
+    revision: Optional[str] = None,
+    local_files_only: bool = False,
+    **kwargs,
+):
+    """
+    Loads the tokenizer configuration from a pretrained model tokenizer configuration.
+
+    Args:
+        pretrained_model_name_or_path (`str` or `os.PathLike`):
+            This can be either:
+
+            - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
+              huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
+              under a user or organization name, like `dbmdz/bert-base-german-cased`.
+            - a path to a *directory* containing a configuration file saved using the
+              [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
+
+        cache_dir (`str` or `os.PathLike`, *optional*):
+            Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
+            cache should not be used.
+        force_download (`bool`, *optional*, defaults to `False`):
+            Whether or not to force to (re-)download the configuration files and override the cached versions if they
+            exist.
+        resume_download (`bool`, *optional*, defaults to `False`):
+            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
+        proxies (`Dict[str, str]`, *optional*):
+            A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
+            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
+        use_auth_token (`str` or *bool*, *optional*):
+            The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
+            when running `transformers-cli login` (stored in `~/.huggingface`).
+        revision(`str`, *optional*, defaults to `"main"`):
+            The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
+            git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
+            identifier allowed by git.
+        local_files_only (`bool`, *optional*, defaults to `False`):
+            If `True`, will only try to load the tokenizer configuration from local files.
+
+    <Tip>
+
+    Passing `use_auth_token=True` is required when you want to use a private model.
+
+    </Tip>
+
+    Returns:
+        `Dict`: The configuration of the tokenizer.
+
+    Examples:
+
+    ```python
+    # Download configuration from huggingface.co and cache.
+    tokenizer_config = get_tokenizer_config("bert-base-uncased")
+    # This model does not have a tokenizer config so the result will be an empty dict.
+    tokenizer_config = get_tokenizer_config("xlm-roberta-base")
+
+    # Save a pretrained tokenizer locally and you can reload its config
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+    tokenizer.save_pretrained("tokenizer-test")
+    tokenizer_config = get_tokenizer_config("tokenizer-test")
+    ```"""
+    resolved_config_file = get_file_from_repo(
+        pretrained_model_name_or_path,
+        FEATURE_EXTRACTOR_NAME,
+        cache_dir=cache_dir,
+        force_download=force_download,
+        resume_download=resume_download,
+        proxies=proxies,
+        use_auth_token=use_auth_token,
+        revision=revision,
+        local_files_only=local_files_only,
+    )
+    if resolved_config_file is None:
+        logger.info(
+            "Could not locate the feature extractor configuration file, will try to use the model config instead."
+        )
+        return {}
+
+    with open(resolved_config_file, encoding="utf-8") as reader:
+        return json.load(reader)
+
+
 class AutoFeatureExtractor:
     r"""
     This is a generic feature extractor class that will be instantiated as one of the feature extractor classes of the
@@ -128,6 +223,10 @@ def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
                 functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary
                 consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
                 `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.
+            trust_remote_code (`bool`, *optional*, defaults to `False`):
+                Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
+                should only be set to `True` for repositories you trust and in which you have read the code, as it will
+                execute code present on the Hub on your local machine.
             kwargs (`Dict[str, Any]`, *optional*):
                 The values in kwargs of any keys which are feature extractor attributes will be used to override the
                 loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
@@ -151,35 +250,54 @@ def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")
         ```"""
         config = kwargs.pop("config", None)
+        trust_remote_code = kwargs.pop("trust_remote_code", False)
         kwargs["_from_auto"] = True
 
-        is_feature_extraction_file = os.path.isfile(pretrained_model_name_or_path)
-        is_directory = os.path.isdir(pretrained_model_name_or_path) and os.path.exists(
-            os.path.join(pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME)
-        )
-
-        has_local_config = (
-            os.path.exists(os.path.join(pretrained_model_name_or_path, CONFIG_NAME)) if is_directory else False
-        )
+        config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
+        feature_extractor_class = config_dict.get("feature_extractor_type", None)
+        feature_extractor_auto_map = None
+        if "AutoFeatureExtractor" in config_dict.get("auto_map", {}):
+            feature_extractor_auto_map = config_dict["auto_map"]["AutoFeatureExtractor"]
 
-        # load config, if it can be loaded
-        if not is_feature_extraction_file and (has_local_config or not is_directory):
+        # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
+        if feature_extractor_class is None and feature_extractor_auto_map is None:
             if not isinstance(config, PretrainedConfig):
                 config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+            # It could be in `config.feature_extractor_type``
+            feature_extractor_class = getattr(config, "feature_extractor_type", None)
+            if hasattr(config, "auto_map") and "AutoFeatureExtractor" in config.auto_map:
+                feature_extractor_auto_map = config.auto_map["AutoFeatureExtractor"]
 
-        kwargs["_from_auto"] = True
-        config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
+        if feature_extractor_class is not None:
+            # If we have custom code for a feature extractor, we get the proper class.
+            if feature_extractor_auto_map is not None:
+                if not trust_remote_code:
+                    raise ValueError(
+                        f"Loading {pretrained_model_name_or_path} requires you to execute the feature extractor file "
+                        "in that repo on your local machine. Make sure you have read the code there to avoid "
+                        "malicious use, then set the option `trust_remote_code=True` to remove this error."
+                    )
+                if kwargs.get("revision", None) is None:
+                    logger.warning(
+                        "Explicitly passing a `revision` is encouraged when loading a feature extractor with custom "
+                        "code to ensure no malicious code has been contributed in a newer revision."
+                    )
 
-        model_type = config_class_to_model_type(type(config).__name__)
+                module_file, class_name = feature_extractor_auto_map.split(".")
+                feature_extractor_class = get_class_from_dynamic_module(
+                    pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
+                )
+            else:
+                feature_extractor_class = feature_extractor_class_from_name(feature_extractor_class)
 
-        if "feature_extractor_type" in config_dict:
-            feature_extractor_class = feature_extractor_class_from_name(config_dict["feature_extractor_type"])
             return feature_extractor_class.from_dict(config_dict, **kwargs)
-        elif model_type is not None:
-            return FEATURE_EXTRACTOR_MAPPING[type(config)].from_dict(config_dict, **kwargs)
+        # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
+        elif type(config) in FEATURE_EXTRACTOR_MAPPING:
+            feature_extractor_class = FEATURE_EXTRACTOR_MAPPING[type(config)]
+            return feature_extractor_class.from_dict(config_dict, **kwargs)
 
         raise ValueError(
-            f"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a `feature_extractor_type` key in "
-            f"its {FEATURE_EXTRACTOR_NAME}, or one of the following `model_type` keys in its {CONFIG_NAME}: "
-            f"{', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}"
+            f"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
+            f"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
+            "`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}"
         )

tests/test_feature_extraction_auto.py

@@ -82,3 +82,9 @@ def test_feature_extractor_not_found(self):
             "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.",
         ):
             _ = AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model")
+
+    def test_from_pretrained_dynamic_feature_extractor(self):
+        model = AutoFeatureExtractor.from_pretrained(
+            "hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=True
+        )
+        self.assertEqual(model.__class__.__name__, "NewFeatureExtractor")

tests/test_feature_extraction_common.py

@@ -16,9 +16,21 @@
 
 import json
 import os
+import sys
 import tempfile
+import unittest
+from pathlib import Path
 
+from huggingface_hub import Repository, delete_repo, login
+from requests.exceptions import HTTPError
+from transformers import AutoFeatureExtractor
 from transformers.file_utils import is_torch_available, is_vision_available
+from transformers.testing_utils import PASS, USER, is_staging_test
+
+
+sys.path.append(str(Path(__file__).parent.parent / "utils"))
+
+from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
 
 
 if is_torch_available():
@@ -29,6 +41,9 @@
     from PIL import Image
 
 
+SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
+
+
 def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
     """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
     or a list of PyTorch tensors if one specifies torchify=True.
@@ -99,3 +114,41 @@ def test_feat_extract_from_and_save_pretrained(self):
     def test_init_without_params(self):
         feat_extract = self.feature_extraction_class()
         self.assertIsNotNone(feat_extract)
+
+
+@is_staging_test
+class ConfigPushToHubTester(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls._token = login(username=USER, password=PASS)
+
+    @classmethod
+    def tearDownClass(cls):
+        try:
+            delete_repo(token=cls._token, name="test-dynamic-feature-extractor")
+        except HTTPError:
+            pass
+
+    def test_push_to_hub_dynamic_feature_extractor(self):
+        CustomFeatureExtractor.register_for_auto_class()
+        feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-feature-extractor", use_auth_token=self._token)
+            feature_extractor.save_pretrained(tmp_dir)
+
+            # This has added the proper auto_map field to the config
+            self.assertDictEqual(
+                feature_extractor.auto_map,
+                {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"},
+            )
+            # The code has been copied from fixtures
+            self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_feature_extraction.py")))
+
+            repo.push_to_hub()
+
+        new_feature_extractor = AutoFeatureExtractor.from_pretrained(
+            f"{USER}/test-dynamic-feature-extractor", trust_remote_code=True
+        )
+        # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
+        self.assertEqual(new_feature_extractor.__class__.__name__, "CustomFeatureExtractor")

utils/test_module/custom_feature_extraction.py

@@ -0,0 +1,5 @@
+from transformers import Wav2Vec2FeatureExtractor
+
+
+class CustomFeatureExtractor(Wav2Vec2FeatureExtractor):
+    pass