Allow custom code for Processors (#15649)

* Allow custom code for Processors

* Add more test

* Test all auto_map configs are properly set

src/transformers/dynamic_module_utils.py

@@ -395,8 +395,8 @@ def custom_object_save(obj, folder, config=None):
             "this code in a separate module so we can include it in the saved folder and make it easier to share via "
             "the Hub."
         )
-    # Add object class to the config auto_map
-    if config is not None:
+
+    def _set_auto_map_in_config(_config):
         module_name = obj.__class__.__module__
         last_module = module_name.split(".")[-1]
         full_name = f"{last_module}.{obj.__class__.__name__}"
@@ -418,12 +418,21 @@ def custom_object_save(obj, folder, config=None):
 
             full_name = (slow_tokenizer_class, fast_tokenizer_class)
 
-        if isinstance(config, dict):
-            config["auto_map"] = full_name
-        elif getattr(config, "auto_map", None) is not None:
-            config.auto_map[obj._auto_class] = full_name
+        if isinstance(_config, dict):
+            auto_map = _config.get("auto_map", {})
+            auto_map[obj._auto_class] = full_name
+            _config["auto_map"] = auto_map
+        elif getattr(_config, "auto_map", None) is not None:
+            _config.auto_map[obj._auto_class] = full_name
         else:
-            config.auto_map = {obj._auto_class: full_name}
+            _config.auto_map = {obj._auto_class: full_name}
+
+    # Add object class to the config auto_map
+    if isinstance(config, (list, tuple)):
+        for cfg in config:
+            _set_auto_map_in_config(cfg)
+    elif config is not None:
+        _set_auto_map_in_config(config)
 
     # Copy module file to the output folder.
     object_file = sys.modules[obj.__module__].__file__

src/transformers/models/auto/processing_auto.py

@@ -20,19 +20,22 @@ from collections import OrderedDict
 
 # 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, get_file_from_repo
 from ...tokenization_utils import TOKENIZER_CONFIG_FILE
+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__)
+
 PROCESSOR_MAPPING_NAMES = OrderedDict(
     [
         ("clip", "CLIPProcessor"),
@@ -120,6 +123,10 @@ class AutoProcessor:
                 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
@@ -143,10 +150,14 @@ class AutoProcessor:
         >>> processor = AutoProcessor.from_pretrained("./test/saved_model/")
         ```"""
         config = kwargs.pop("config", None)
+        trust_remote_code = kwargs.pop("trust_remote_code", False)
         kwargs["_from_auto"] = True
 
+        processor_class = None
+        processor_auto_map = None
+
         # First, let's see if we have a preprocessor config.
-        # Filter the kwargs for `get_file_from_repo``.
+        # Filter the kwargs for `get_file_from_repo`.
         get_file_from_repo_kwargs = {
             key: kwargs[key] for key in inspect.signature(get_file_from_repo).parameters.keys() if key in kwargs
         }
@@ -156,35 +167,63 @@ class AutoProcessor:
         )
         if preprocessor_config_file is not None:
             config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
-            if "processor_class" in config_dict:
-                processor_class = processor_class_from_name(config_dict["processor_class"])
-                return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
+            processor_class = config_dict.get("processor_class", None)
+            if "AutoProcessor" in config_dict.get("auto_map", {}):
+                processor_auto_map = config_dict["auto_map"]["AutoProcessor"]
 
-        # Next, let's check whether the processor class is saved in a tokenizer
-        # Let's start by checking whether the processor class is saved in a feature extractor
-        tokenizer_config_file = get_file_from_repo(
-            pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE, **get_file_from_repo_kwargs
-        )
-        if tokenizer_config_file is not None:
-            with open(tokenizer_config_file, encoding="utf-8") as reader:
-                config_dict = json.load(reader)
+        if processor_class is None:
+            # Next, let's check whether the processor class is saved in a tokenizer
+            tokenizer_config_file = get_file_from_repo(
+                pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE, **get_file_from_repo_kwargs
+            )
+            if tokenizer_config_file is not None:
+                with open(tokenizer_config_file, encoding="utf-8") as reader:
+                    config_dict = json.load(reader)
 
-            if "processor_class" in config_dict:
-                processor_class = processor_class_from_name(config_dict["processor_class"])
-                return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
+                processor_class = config_dict.get("processor_class", None)
+                if "AutoProcessor" in config_dict.get("auto_map", {}):
+                    processor_auto_map = config_dict["auto_map"]["AutoProcessor"]
 
-        # Otherwise, load config, if it can be loaded.
-        if not isinstance(config, PretrainedConfig):
-            config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        if processor_class is None:
+            # Otherwise, load config, if it can be loaded.
+            if not isinstance(config, PretrainedConfig):
+                config = AutoConfig.from_pretrained(
+                    pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
+                )
 
-        model_type = config_class_to_model_type(type(config).__name__)
+            # And check if the config contains the processor class.
+            processor_class = getattr(config, "processor_class", None)
+            if hasattr(config, "auto_map") and "AutoProcessor" in config.auto_map:
+                processor_auto_map = config.auto_map["AutoProcessor"]
 
-        if getattr(config, "processor_class", None) is not None:
-            processor_class = processor_class_from_name(config.processor_class)
-            return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        if processor_class is not None:
+            # If we have custom code for a feature extractor, we get the proper class.
+            if processor_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__)
-        if model_type is not None:
+                module_file, class_name = processor_auto_map.split(".")
+                processor_class = get_class_from_dynamic_module(
+                    pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
+                )
+            else:
+                processor_class = processor_class_from_name(processor_class)
+
+            return processor_class.from_pretrained(
+                pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
+            )
+
+        # Last try: we use the PROCESSOR_MAPPING.
+        if type(config) in PROCESSOR_MAPPING:
             return PROCESSOR_MAPPING[type(config)].from_pretrained(pretrained_model_name_or_path, **kwargs)
 
         raise ValueError(

src/transformers/models/auto/tokenization_auto.py

@@ -469,7 +469,13 @@ class AutoTokenizer:
         # Next, let's try to use the tokenizer_config file to get the tokenizer class.
         tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
         config_tokenizer_class = tokenizer_config.get("tokenizer_class")
-        tokenizer_auto_map = tokenizer_config.get("auto_map")
+        tokenizer_auto_map = None
+        if "auto_map" in tokenizer_config:
+            if isinstance(tokenizer_config["auto_map"], (tuple, list)):
+                # Legacy format for dynamic tokenizers
+                tokenizer_auto_map = tokenizer_config["auto_map"]
+            else:
+                tokenizer_auto_map = tokenizer_config["auto_map"].get("AutoTokenizer", None)
 
         # If that did not work, let's try to use the config.
         if config_tokenizer_class is None:

src/transformers/processing_utils.py

@@ -17,10 +17,14 @@
 """
 
 import importlib.util
+import os
 from pathlib import Path
 
+from .dynamic_module_utils import custom_object_save
+from .tokenization_utils_base import PreTrainedTokenizerBase
 
-# Comment to write
+
+# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
 spec = importlib.util.spec_from_file_location(
     "transformers", Path(__file__).parent / "__init__.py", submodule_search_locations=[Path(__file__).parent]
 )
@@ -42,6 +46,7 @@ class ProcessorMixin:
     # Names need to be attr_class for attr in attributes
     feature_extractor_class = None
     tokenizer_class = None
+    _auto_class = None
 
     # args have to match the attributes class attribute
     def __init__(self, *args, **kwargs):
@@ -101,6 +106,14 @@ class ProcessorMixin:
                 Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
                 be created if it does not exist).
         """
+        os.makedirs(save_directory, exist_ok=True)
+        # 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:
+            attrs = [getattr(self, attribute_name) for attribute_name in self.attributes]
+            configs = [(a.init_kwargs if isinstance(a, PreTrainedTokenizerBase) else a) for a in attrs]
+            custom_object_save(self, save_directory, config=configs)
+
         for attribute_name in self.attributes:
             attribute = getattr(self, attribute_name)
             # Include the processor class in the attribute config so this processor can then be reloaded with the
@@ -109,6 +122,13 @@ class ProcessorMixin:
                 attribute._set_processor_class(self.__class__.__name__)
             attribute.save_pretrained(save_directory)
 
+        if self._auto_class is not None:
+            # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
+            for attribute_name in self.attributes:
+                attribute = getattr(self, attribute_name)
+                if isinstance(attribute, PreTrainedTokenizerBase):
+                    del attribute.init_kwargs["auto_map"]
+
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
         r"""
@@ -142,6 +162,32 @@ class ProcessorMixin:
         args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
         return cls(*args)
 
+    @classmethod
+    def register_for_auto_class(cls, auto_class="AutoProcessor"):
+        """
+        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 `AutoProcessor`.
+
+        <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 `"AutoProcessor"`):
+                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
+
     @classmethod
     def _get_arguments_from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
         args = []

tests/test_feature_extraction_common.py

@@ -40,8 +40,6 @@ if is_torch_available():
 if is_vision_available():
     from PIL import Image
 
-SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
-
 
 SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
 
@@ -119,7 +117,7 @@ class FeatureExtractionSavingTestMixin:
 
 
 @is_staging_test
-class ConfigPushToHubTester(unittest.TestCase):
+class FeatureExtractorPushToHubTester(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         cls._token = login(username=USER, password=PASS)

tests/test_processor_auto.py

@@ -15,20 +15,34 @@
 
 import json
 import os
+import sys
 import tempfile
 import unittest
+from pathlib import Path
 from shutil import copyfile
 
+from huggingface_hub import Repository, delete_repo, login
+from requests.exceptions import HTTPError
 from transformers import AutoProcessor, AutoTokenizer, Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2Processor
-from transformers.file_utils import FEATURE_EXTRACTOR_NAME
+from transformers.file_utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
+from transformers.testing_utils import PASS, USER, is_staging_test
 from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
 
 
+sys.path.append(str(Path(__file__).parent.parent / "utils"))
+
+from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
+from test_module.custom_processing import CustomProcessor  # noqa E402
+from test_module.custom_tokenization import CustomTokenizer  # noqa E402
+
+
 SAMPLE_PROCESSOR_CONFIG = os.path.join(
     os.path.dirname(os.path.abspath(__file__)), "fixtures/dummy_feature_extractor_config.json"
 )
 SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/vocab.json")
 
+SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
+
 
 class AutoFeatureExtractorTest(unittest.TestCase):
     def test_processor_from_model_shortcut(self):
@@ -115,3 +129,93 @@ class AutoFeatureExtractorTest(unittest.TestCase):
             processor = AutoProcessor.from_pretrained(tmpdirname)
 
         self.assertIsInstance(processor, Wav2Vec2Processor)
+
+    def test_from_pretrained_dynamic_processor(self):
+        processor = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor", trust_remote_code=True)
+        self.assertTrue(processor.special_attribute_present)
+        self.assertEqual(processor.__class__.__name__, "NewProcessor")
+
+        feature_extractor = processor.feature_extractor
+        self.assertTrue(feature_extractor.special_attribute_present)
+        self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor")
+
+        tokenizer = processor.tokenizer
+        self.assertTrue(tokenizer.special_attribute_present)
+        if is_tokenizers_available():
+            self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast")
+
+            # Test we can also load the slow version
+            processor = AutoProcessor.from_pretrained(
+                "hf-internal-testing/test_dynamic_processor", trust_remote_code=True, use_fast=False
+            )
+            tokenizer = processor.tokenizer
+            self.assertTrue(tokenizer.special_attribute_present)
+            self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
+        else:
+            self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
+
+
+@is_staging_test
+class ProcessorPushToHubTester(unittest.TestCase):
+    vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
+
+    @classmethod
+    def setUpClass(cls):
+        cls._token = login(username=USER, password=PASS)
+
+    @classmethod
+    def tearDownClass(cls):
+        try:
+            delete_repo(token=cls._token, name="test-dynamic-processor")
+        except HTTPError:
+            pass
+
+    def test_push_to_hub_dynamic_processor(self):
+        CustomFeatureExtractor.register_for_auto_class()
+        CustomTokenizer.register_for_auto_class()
+        CustomProcessor.register_for_auto_class()
+
+        feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            vocab_file = os.path.join(tmp_dir, "vocab.txt")
+            with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
+                vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
+            tokenizer = CustomTokenizer(vocab_file)
+
+        processor = CustomProcessor(feature_extractor, tokenizer)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-processor", use_auth_token=self._token)
+            processor.save_pretrained(tmp_dir)
+
+            # This has added the proper auto_map field to the feature extractor config
+            self.assertDictEqual(
+                processor.feature_extractor.auto_map,
+                {
+                    "AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
+                    "AutoProcessor": "custom_processing.CustomProcessor",
+                },
+            )
+
+            # This has added the proper auto_map field to the tokenizer config
+            with open(os.path.join(tmp_dir, "tokenizer_config.json")) as f:
+                tokenizer_config = json.load(f)
+            self.assertDictEqual(
+                tokenizer_config["auto_map"],
+                {
+                    "AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
+                    "AutoProcessor": "custom_processing.CustomProcessor",
+                },
+            )
+
+            # The code has been copied from fixtures
+            self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_feature_extraction.py")))
+            self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_tokenization.py")))
+            self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_processing.py")))
+
+            repo.push_to_hub()
+
+        new_processor = AutoProcessor.from_pretrained(f"{USER}/test-dynamic-processor", trust_remote_code=True)
+        # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
+        self.assertEqual(new_processor.__class__.__name__, "CustomProcessor")

tests/test_tokenization_auto.py

@@ -310,6 +310,38 @@ class AutoTokenizerTest(unittest.TestCase):
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                 del TOKENIZER_MAPPING._extra_content[CustomConfig]
 
+    def test_from_pretrained_dynamic_tokenizer(self):
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True)
+        self.assertTrue(tokenizer.special_attribute_present)
+        if is_tokenizers_available():
+            self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast")
+
+            # Test we can also load the slow version
+            tokenizer = AutoTokenizer.from_pretrained(
+                "hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True, use_fast=False
+            )
+            self.assertTrue(tokenizer.special_attribute_present)
+            self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
+        else:
+            self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
+
+    def test_from_pretrained_dynamic_tokenizer_legacy_format(self):
+        tokenizer = AutoTokenizer.from_pretrained(
+            "hf-internal-testing/test_dynamic_tokenizer_legacy", trust_remote_code=True
+        )
+        self.assertTrue(tokenizer.special_attribute_present)
+        if is_tokenizers_available():
+            self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast")
+
+            # Test we can also load the slow version
+            tokenizer = AutoTokenizer.from_pretrained(
+                "hf-internal-testing/test_dynamic_tokenizer_legacy", trust_remote_code=True, use_fast=False
+            )
+            self.assertTrue(tokenizer.special_attribute_present)
+            self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
+        else:
+            self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
+
     def test_repo_not_found(self):
         with self.assertRaisesRegex(
             EnvironmentError, "bert-base is not a local folder and is not a valid model identifier"

tests/test_tokenization_common.py

@@ -3812,7 +3812,9 @@ class TokenizerPushToHubTester(unittest.TestCase):
 
             with open(os.path.join(tmp_dir, "tokenizer_config.json")) as f:
                 tokenizer_config = json.load(f)
-            self.assertEqual(tokenizer_config["auto_map"], ["custom_tokenization.CustomTokenizer", None])
+            self.assertDictEqual(
+                tokenizer_config["auto_map"], {"AutoTokenizer": ["custom_tokenization.CustomTokenizer", None]}
+            )
 
             repo.push_to_hub()
 
@@ -3837,9 +3839,14 @@ class TokenizerPushToHubTester(unittest.TestCase):
 
             with open(os.path.join(tmp_dir, "tokenizer_config.json")) as f:
                 tokenizer_config = json.load(f)
-            self.assertEqual(
+            self.assertDictEqual(
                 tokenizer_config["auto_map"],
-                ["custom_tokenization.CustomTokenizer", "custom_tokenization_fast.CustomTokenizerFast"],
+                {
+                    "AutoTokenizer": [
+                        "custom_tokenization.CustomTokenizer",
+                        "custom_tokenization_fast.CustomTokenizerFast",
+                    ]
+                },
             )
 
             repo.push_to_hub()

utils/test_module/custom_processing.py

@@ -0,0 +1,6 @@
+from transformers import ProcessorMixin
+
+
+class CustomProcessor(ProcessorMixin):
+    feature_extractor_class = "AutoFeatureExtractor"
+    tokenizer_class = "AutoTokenizer"