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Unverified Commit f1660d7e authored by Sylvain Gugger's avatar Sylvain Gugger Committed by GitHub
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Remote code improvements (#23959) 



* Fix model load when it has both code on the Hub and locally

* Add input check with timeout

* Add tests

* Apply suggestions from code review
Co-authored-by: default avatarLysandre Debut <lysandre.debut@reseau.eseo.fr>

* Some non-saved stuff

* Add feature extractors

* Add image processor

* Add model

* Add processor and tokenizer

* Reduce timeout

---------
Co-authored-by: default avatarLysandre Debut <lysandre.debut@reseau.eseo.fr>
parent 60825f2c
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Showing with 435 additions and 103 deletions
src/transformers/dynamic_module_utils.py
View file @ f1660d7e
......@@ -18,6 +18,7 @@ import importlib
import os
import re
import shutil
import signal
import sys
from pathlib import Path
from typing import Dict, Optional, Union
......@@ -513,3 +514,46 @@ def custom_object_save(obj, folder, config=None):
result.append(dest_file)
return result
def _raise_timeout_error(signum, frame):
raise ValueError(
"Loading this model requires you to execute the configuration file in that repo on your local machine. We "
"asked if it was okay but did not get an answer. Make sure you have read the code there to avoid malicious "
"use, then set the option `trust_remote_code=True` to remove this error."
)
TIME_OUT_REMOTE_CODE = 15
def resolve_trust_remote_code(trust_remote_code, model_name, has_local_code, has_remote_code):
if trust_remote_code is None:
if has_local_code:
trust_remote_code = False
elif has_remote_code and TIME_OUT_REMOTE_CODE > 0:
signal.signal(signal.SIGALRM, _raise_timeout_error)
signal.alarm(TIME_OUT_REMOTE_CODE)
while trust_remote_code is None:
answer = input(
f"Loading {model_name} requires to execute some code in that repo, you can inspect the content of "
f"the repository at https://hf.co/{model_name}. You can dismiss this prompt by passing "
"`trust_remote_code=True`.\nDo you accept? [y/N] "
)
if answer.lower() in ["yes", "y", "1"]:
trust_remote_code = True
elif answer.lower() in ["no", "n", "0", ""]:
trust_remote_code = False
signal.alarm(0)
elif has_remote_code:
# For the CI which puts the timeout at 0
_raise_timeout_error(None, None)
if has_remote_code and not has_local_code and not trust_remote_code:
raise ValueError(
f"Loading {model_name} requires you to execute the configuration 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."
)
return trust_remote_code
src/transformers/models/auto/auto_factory.py
View file @ f1660d7e
......@@ -18,7 +18,7 @@ import importlib
from collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...utils import copy_func, logging, requires_backends
from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
......@@ -404,19 +404,14 @@ class _BaseAutoModelClass:
@classmethod
def from_config(cls, config, **kwargs):
trust_remote_code = kwargs.pop("trust_remote_code", False)
if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
if not trust_remote_code:
raise ValueError(
"Loading this model requires you to execute the modeling 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 model with custom code to ensure "
"no malicious code has been contributed in a newer revision."
)
trust_remote_code = kwargs.pop("trust_remote_code", None)
has_remote_code = hasattr(config, "auto_map") and cls.__name__ in config.auto_map
has_local_code = type(config) in cls._model_mapping.keys()
trust_remote_code = resolve_trust_remote_code(
trust_remote_code, config._name_or_path, has_local_code, has_remote_code
)
if has_remote_code and trust_remote_code:
class_ref = config.auto_map[cls.__name__]
if "--" in class_ref:
repo_id, class_ref = class_ref.split("--")
......@@ -437,7 +432,7 @@ class _BaseAutoModelClass:
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
config = kwargs.pop("config", None)
trust_remote_code = kwargs.pop("trust_remote_code", False)
trust_remote_code = kwargs.pop("trust_remote_code", None)
kwargs["_from_auto"] = True
hub_kwargs_names = [
"cache_dir",
......@@ -470,13 +465,12 @@ class _BaseAutoModelClass:
if kwargs_orig.get("torch_dtype", None) == "auto":
kwargs["torch_dtype"] = "auto"
if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
if not trust_remote_code:
raise ValueError(
f"Loading {pretrained_model_name_or_path} requires you to execute the modeling 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."
)
has_remote_code = hasattr(config, "auto_map") and cls.__name__ in config.auto_map
has_local_code = type(config) in cls._model_mapping.keys()
trust_remote_code = resolve_trust_remote_code(
trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
)
if has_remote_code and trust_remote_code:
class_ref = config.auto_map[cls.__name__]
model_class = get_class_from_dynamic_module(
class_ref, pretrained_model_name_or_path, **hub_kwargs, **kwargs
......
src/transformers/models/auto/configuration_auto.py
View file @ f1660d7e
......@@ -20,7 +20,7 @@ from collections import OrderedDict
from typing import List, Union
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...utils import CONFIG_NAME, logging
......@@ -940,15 +940,15 @@ class AutoConfig:
```"""
kwargs["_from_auto"] = True
kwargs["name_or_path"] = pretrained_model_name_or_path
trust_remote_code = kwargs.pop("trust_remote_code", False)
trust_remote_code = kwargs.pop("trust_remote_code", None)
config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]:
if not trust_remote_code:
raise ValueError(
f"Loading {pretrained_model_name_or_path} requires you to execute the configuration 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."
)
has_remote_code = "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]
has_local_code = "model_type" in config_dict and config_dict["model_type"] in CONFIG_MAPPING
trust_remote_code = resolve_trust_remote_code(
trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
)
if has_remote_code and trust_remote_code:
class_ref = config_dict["auto_map"]["AutoConfig"]
config_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs)
_ = kwargs.pop("code_revision", None)
......
src/transformers/models/auto/feature_extraction_auto.py
View file @ f1660d7e
......@@ -21,7 +21,7 @@ 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 ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
......@@ -307,7 +307,7 @@ class AutoFeatureExtractor:
>>> # feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")
```"""
config = kwargs.pop("config", None)
trust_remote_code = kwargs.pop("trust_remote_code", False)
trust_remote_code = kwargs.pop("trust_remote_code", None)
kwargs["_from_auto"] = True
config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
......@@ -326,21 +326,21 @@ class AutoFeatureExtractor:
feature_extractor_auto_map = config.auto_map["AutoFeatureExtractor"]
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."
)
feature_extractor_class = get_class_from_dynamic_module(
feature_extractor_auto_map, pretrained_model_name_or_path, **kwargs
)
_ = kwargs.pop("code_revision", None)
else:
feature_extractor_class = feature_extractor_class_from_name(feature_extractor_class)
feature_extractor_class = feature_extractor_class_from_name(feature_extractor_class)
has_remote_code = feature_extractor_auto_map is not None
has_local_code = feature_extractor_class is not None or type(config) in FEATURE_EXTRACTOR_MAPPING
trust_remote_code = resolve_trust_remote_code(
trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
)
if has_remote_code and trust_remote_code:
feature_extractor_class = get_class_from_dynamic_module(
feature_extractor_auto_map, pretrained_model_name_or_path, **kwargs
)
_ = kwargs.pop("code_revision", None)
return feature_extractor_class.from_dict(config_dict, **kwargs)
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(config_dict, **kwargs)
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(config) in FEATURE_EXTRACTOR_MAPPING:
......
src/transformers/models/auto/image_processing_auto.py
View file @ f1660d7e
......@@ -21,7 +21,7 @@ from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
......@@ -314,7 +314,7 @@ class AutoImageProcessor:
>>> # image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")
```"""
config = kwargs.pop("config", None)
trust_remote_code = kwargs.pop("trust_remote_code", False)
trust_remote_code = kwargs.pop("trust_remote_code", None)
kwargs["_from_auto"] = True
config_dict, _ = ImageProcessingMixin.get_image_processor_dict(pretrained_model_name_or_path, **kwargs)
......@@ -351,21 +351,21 @@ class AutoImageProcessor:
image_processor_auto_map = config.auto_map["AutoImageProcessor"]
if image_processor_class is not None:
# If we have custom code for a image processor, we get the proper class.
if image_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 image processor 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."
)
image_processor_class = get_class_from_dynamic_module(
image_processor_auto_map, pretrained_model_name_or_path, **kwargs
)
_ = kwargs.pop("code_revision", None)
else:
image_processor_class = image_processor_class_from_name(image_processor_class)
image_processor_class = image_processor_class_from_name(image_processor_class)
has_remote_code = image_processor_auto_map is not None
has_local_code = image_processor_class is not None or type(config) in IMAGE_PROCESSOR_MAPPING
trust_remote_code = resolve_trust_remote_code(
trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
)
if has_remote_code and trust_remote_code:
image_processor_class = get_class_from_dynamic_module(
image_processor_auto_map, pretrained_model_name_or_path, **kwargs
)
_ = kwargs.pop("code_revision", None)
return image_processor_class.from_dict(config_dict, **kwargs)
elif image_processor_class is not None:
return image_processor_class.from_dict(config_dict, **kwargs)
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(config) in IMAGE_PROCESSOR_MAPPING:
......
src/transformers/models/auto/processing_auto.py
View file @ f1660d7e
......@@ -20,7 +20,7 @@ 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 ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
......@@ -194,7 +194,7 @@ class AutoProcessor:
>>> # processor = AutoProcessor.from_pretrained("./test/saved_model/")
```"""
config = kwargs.pop("config", None)
trust_remote_code = kwargs.pop("trust_remote_code", False)
trust_remote_code = kwargs.pop("trust_remote_code", None)
kwargs["_from_auto"] = True
processor_class = None
......@@ -248,28 +248,28 @@ class AutoProcessor:
processor_auto_map = config.auto_map["AutoProcessor"]
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."
)
processor_class = get_class_from_dynamic_module(
processor_auto_map, pretrained_model_name_or_path, **kwargs
)
_ = kwargs.pop("code_revision", None)
else:
processor_class = processor_class_from_name(processor_class)
processor_class = processor_class_from_name(processor_class)
has_remote_code = processor_auto_map is not None
has_local_code = processor_class is not None or type(config) in PROCESSOR_MAPPING
trust_remote_code = resolve_trust_remote_code(
trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
)
if has_remote_code and trust_remote_code:
processor_class = get_class_from_dynamic_module(
processor_auto_map, pretrained_model_name_or_path, **kwargs
)
_ = kwargs.pop("code_revision", None)
return processor_class.from_pretrained(
pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
)
elif processor_class is not None:
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:
elif type(config) in PROCESSOR_MAPPING:
return PROCESSOR_MAPPING[type(config)].from_pretrained(pretrained_model_name_or_path, **kwargs)
# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
......
src/transformers/models/auto/tokenization_auto.py
View file @ f1660d7e
......@@ -21,7 +21,7 @@ from collections import OrderedDict
from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE
from ...utils import cached_file, extract_commit_hash, is_sentencepiece_available, is_tokenizers_available, logging
......@@ -608,7 +608,7 @@ class AutoTokenizer:
use_fast = kwargs.pop("use_fast", True)
tokenizer_type = kwargs.pop("tokenizer_type", None)
trust_remote_code = kwargs.pop("trust_remote_code", False)
trust_remote_code = kwargs.pop("trust_remote_code", None)
# First, let's see whether the tokenizer_type is passed so that we can leverage it
if tokenizer_type is not None:
......@@ -662,31 +662,28 @@ class AutoTokenizer:
if hasattr(config, "auto_map") and "AutoTokenizer" in config.auto_map:
tokenizer_auto_map = config.auto_map["AutoTokenizer"]
# If we have the tokenizer class from the tokenizer config or the model config we're good!
if config_tokenizer_class is not None:
tokenizer_class = None
if tokenizer_auto_map is not None:
if not trust_remote_code:
raise ValueError(
f"Loading {pretrained_model_name_or_path} requires you to execute the tokenizer 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 use_fast and tokenizer_auto_map[1] is not None:
class_ref = tokenizer_auto_map[1]
else:
class_ref = tokenizer_auto_map[0]
tokenizer_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs)
_ = kwargs.pop("code_revision", None)
has_remote_code = tokenizer_auto_map is not None
has_local_code = config_tokenizer_class is not None or type(config) in TOKENIZER_MAPPING
trust_remote_code = resolve_trust_remote_code(
trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
)
elif use_fast and not config_tokenizer_class.endswith("Fast"):
if has_remote_code and trust_remote_code:
if use_fast and tokenizer_auto_map[1] is not None:
class_ref = tokenizer_auto_map[1]
else:
class_ref = tokenizer_auto_map[0]
tokenizer_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs)
_ = kwargs.pop("code_revision", None)
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif config_tokenizer_class is not None:
tokenizer_class = None
if use_fast and not config_tokenizer_class.endswith("Fast"):
tokenizer_class_candidate = f"{config_tokenizer_class}Fast"
tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)
if tokenizer_class is None:
tokenizer_class_candidate = config_tokenizer_class
tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)
if tokenizer_class is None:
raise ValueError(
f"Tokenizer class {tokenizer_class_candidate} does not exist or is not currently imported."
......
tests/models/auto/test_configuration_auto.py
View file @ f1660d7e
......@@ -21,6 +21,7 @@ import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
......@@ -37,6 +38,9 @@ SAMPLE_ROBERTA_CONFIG = get_tests_dir("fixtures/dummy-config.json")
class AutoConfigTest(unittest.TestCase):
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
def test_module_spec(self):
self.assertIsNotNone(transformers.models.auto.__spec__)
self.assertIsNotNone(importlib.util.find_spec("transformers.models.auto"))
......@@ -108,6 +112,13 @@ class AutoConfigTest(unittest.TestCase):
_ = AutoConfig.from_pretrained("hf-internal-testing/no-config-test-repo")
def test_from_pretrained_dynamic_config(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model")
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(config.__class__.__name__, "NewModelConfig")
......@@ -116,3 +127,25 @@ class AutoConfigTest(unittest.TestCase):
config.save_pretrained(tmp_dir)
reloaded_config = AutoConfig.from_pretrained(tmp_dir, trust_remote_code=True)
self.assertEqual(reloaded_config.__class__.__name__, "NewModelConfig")
def test_from_pretrained_dynamic_config_conflict(self):
class NewModelConfigLocal(BertConfig):
model_type = "new-model"
try:
AutoConfig.register("new-model", NewModelConfigLocal)
# If remote code is not set, the default is to use local
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model")
self.assertEqual(config.__class__.__name__, "NewModelConfigLocal")
# If remote code is disabled, we load the local one.
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
self.assertEqual(config.__class__.__name__, "NewModelConfigLocal")
# If remote is enabled, we load from the Hub
config = AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(config.__class__.__name__, "NewModelConfig")
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
tests/models/auto/test_feature_extraction_auto.py
View file @ f1660d7e
......@@ -19,6 +19,7 @@ import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
......@@ -42,6 +43,9 @@ SAMPLE_CONFIG = get_tests_dir("fixtures/dummy-config.json")
class AutoFeatureExtractorTest(unittest.TestCase):
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
def test_feature_extractor_from_model_shortcut(self):
config = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
......@@ -96,6 +100,17 @@ class AutoFeatureExtractorTest(unittest.TestCase):
_ = AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model")
def test_from_pretrained_dynamic_feature_extractor(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
feature_extractor = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor"
)
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
feature_extractor = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=False
)
feature_extractor = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=True
)
......@@ -127,3 +142,37 @@ class AutoFeatureExtractorTest(unittest.TestCase):
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def test_from_pretrained_dynamic_feature_extractor_conflict(self):
class NewFeatureExtractor(Wav2Vec2FeatureExtractor):
is_local = True
try:
AutoConfig.register("custom", CustomConfig)
AutoFeatureExtractor.register(CustomConfig, NewFeatureExtractor)
# If remote code is not set, the default is to use local
feature_extractor = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor"
)
self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor")
self.assertTrue(feature_extractor.is_local)
# If remote code is disabled, we load the local one.
feature_extractor = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=False
)
self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor")
self.assertTrue(feature_extractor.is_local)
# If remote is enabled, we load from the Hub
feature_extractor = AutoFeatureExtractor.from_pretrained(
"hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=True
)
self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor")
self.assertTrue(not hasattr(feature_extractor, "is_local"))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
tests/models/auto/test_image_processing_auto.py
View file @ f1660d7e
......@@ -19,6 +19,7 @@ import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
......@@ -37,6 +38,9 @@ from test_module.custom_image_processing import CustomImageProcessor # noqa E40
class AutoImageProcessorTest(unittest.TestCase):
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
def test_image_processor_from_model_shortcut(self):
config = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
self.assertIsInstance(config, CLIPImageProcessor)
......@@ -130,6 +134,15 @@ class AutoImageProcessorTest(unittest.TestCase):
_ = AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model")
def test_from_pretrained_dynamic_image_processor(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
image_processor = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor")
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
image_processor = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=False
)
image_processor = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True
)
......@@ -171,3 +184,35 @@ class AutoImageProcessorTest(unittest.TestCase):
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def test_from_pretrained_dynamic_image_processor_conflict(self):
class NewImageProcessor(CLIPImageProcessor):
is_local = True
try:
AutoConfig.register("custom", CustomConfig)
AutoImageProcessor.register(CustomConfig, NewImageProcessor)
# If remote code is not set, the default is to use local
image_processor = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor")
self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
self.assertTrue(image_processor.is_local)
# If remote code is disabled, we load the local one.
image_processor = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=False
)
self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
self.assertTrue(image_processor.is_local)
# If remote is enabled, we load from the Hub
image_processor = AutoImageProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True
)
self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
self.assertTrue(not hasattr(image_processor, "is_local"))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
tests/models/auto/test_modeling_auto.py
View file @ f1660d7e
......@@ -22,6 +22,7 @@ from pathlib import Path
import pytest
import transformers
from transformers import BertConfig, GPT2Model, is_safetensors_available, is_torch_available
from transformers.models.auto.configuration_auto import CONFIG_MAPPING
from transformers.testing_utils import (
......@@ -92,6 +93,9 @@ if is_torch_available():
@require_torch
class AutoModelTest(unittest.TestCase):
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
@slow
def test_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
......@@ -312,6 +316,13 @@ class AutoModelTest(unittest.TestCase):
del MODEL_MAPPING._extra_content[CustomConfig]
def test_from_pretrained_dynamic_model_distant(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model")
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(model.__class__.__name__, "NewModel")
......@@ -416,6 +427,34 @@ class AutoModelTest(unittest.TestCase):
if CustomConfig in mapping._extra_content:
del mapping._extra_content[CustomConfig]
def test_from_pretrained_dynamic_model_conflict(self):
class NewModelConfigLocal(BertConfig):
model_type = "new-model"
class NewModel(BertModel):
config_class = NewModelConfigLocal
try:
AutoConfig.register("new-model", NewModelConfigLocal)
AutoModel.register(NewModelConfigLocal, NewModel)
# If remote code is not set, the default is to use local
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model")
self.assertEqual(model.config.__class__.__name__, "NewModelConfigLocal")
# If remote code is disabled, we load the local one.
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
self.assertEqual(model.config.__class__.__name__, "NewModelConfigLocal")
# If remote is enabled, we load from the Hub
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(model.config.__class__.__name__, "NewModelConfig")
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
if NewModelConfigLocal in MODEL_MAPPING._extra_content:
del MODEL_MAPPING._extra_content[NewModelConfigLocal]
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/models/auto/test_processor_auto.py
View file @ f1660d7e
......@@ -24,6 +24,7 @@ from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
......@@ -33,6 +34,8 @@ from transformers import (
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
Wav2Vec2Config,
Wav2Vec2FeatureExtractor,
Wav2Vec2Processor,
......@@ -58,6 +61,9 @@ SAMPLE_PROCESSOR_CONFIG_DIR = get_tests_dir("fixtures")
class AutoFeatureExtractorTest(unittest.TestCase):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
def test_processor_from_model_shortcut(self):
processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
self.assertIsInstance(processor, Wav2Vec2Processor)
......@@ -144,6 +150,15 @@ class AutoFeatureExtractorTest(unittest.TestCase):
self.assertIsInstance(processor, Wav2Vec2Processor)
def test_from_pretrained_dynamic_processor(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
processor = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor")
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
processor = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor", trust_remote_code=False
)
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")
......@@ -203,6 +218,58 @@ class AutoFeatureExtractorTest(unittest.TestCase):
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def test_from_pretrained_dynamic_processor_conflict(self):
class NewFeatureExtractor(Wav2Vec2FeatureExtractor):
special_attribute_present = False
class NewTokenizer(BertTokenizer):
special_attribute_present = False
class NewProcessor(ProcessorMixin):
feature_extractor_class = "AutoFeatureExtractor"
tokenizer_class = "AutoTokenizer"
special_attribute_present = False
try:
AutoConfig.register("custom", CustomConfig)
AutoFeatureExtractor.register(CustomConfig, NewFeatureExtractor)
AutoTokenizer.register(CustomConfig, slow_tokenizer_class=NewTokenizer)
AutoProcessor.register(CustomConfig, NewProcessor)
# If remote code is not set, the default is to use local classes.
processor = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor")
self.assertEqual(processor.__class__.__name__, "NewProcessor")
self.assertFalse(processor.special_attribute_present)
self.assertFalse(processor.feature_extractor.special_attribute_present)
self.assertFalse(processor.tokenizer.special_attribute_present)
# If remote code is disabled, we load the local ones.
processor = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor", trust_remote_code=False
)
self.assertEqual(processor.__class__.__name__, "NewProcessor")
self.assertFalse(processor.special_attribute_present)
self.assertFalse(processor.feature_extractor.special_attribute_present)
self.assertFalse(processor.tokenizer.special_attribute_present)
# If remote is enabled, we load from the Hub.
processor = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor", trust_remote_code=True
)
self.assertEqual(processor.__class__.__name__, "NewProcessor")
self.assertTrue(processor.special_attribute_present)
self.assertTrue(processor.feature_extractor.special_attribute_present)
self.assertTrue(processor.tokenizer.special_attribute_present)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def test_auto_processor_creates_tokenizer(self):
processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert")
self.assertEqual(processor.__class__.__name__, "BertTokenizerFast")
......
tests/models/auto/test_tokenization_auto.py
View file @ f1660d7e
......@@ -22,6 +22,7 @@ from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
......@@ -65,6 +66,9 @@ if is_tokenizers_available():
class AutoTokenizerTest(unittest.TestCase):
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
@slow
def test_tokenizer_from_pretrained(self):
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
......@@ -298,6 +302,15 @@ class AutoTokenizerTest(unittest.TestCase):
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def test_from_pretrained_dynamic_tokenizer(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer")
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
tokenizer = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=False
)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True)
self.assertTrue(tokenizer.special_attribute_present)
# Test tokenizer can be reloaded.
......@@ -326,6 +339,57 @@ class AutoTokenizerTest(unittest.TestCase):
self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
self.assertEqual(reloaded_tokenizer.__class__.__name__, "NewTokenizer")
@require_tokenizers
def test_from_pretrained_dynamic_tokenizer_conflict(self):
class NewTokenizer(BertTokenizer):
special_attribute_present = False
class NewTokenizerFast(BertTokenizerFast):
slow_tokenizer_class = NewTokenizer
special_attribute_present = False
try:
AutoConfig.register("custom", CustomConfig)
AutoTokenizer.register(CustomConfig, slow_tokenizer_class=NewTokenizer)
AutoTokenizer.register(CustomConfig, fast_tokenizer_class=NewTokenizerFast)
# If remote code is not set, the default is to use local
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer")
self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast")
self.assertFalse(tokenizer.special_attribute_present)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer", use_fast=False)
self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
self.assertFalse(tokenizer.special_attribute_present)
# If remote code is disabled, we load the local one.
tokenizer = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=False
)
self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast")
self.assertFalse(tokenizer.special_attribute_present)
tokenizer = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=False, use_fast=False
)
self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
self.assertFalse(tokenizer.special_attribute_present)
# If remote is enabled, we load from the Hub
tokenizer = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True
)
self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast")
self.assertTrue(tokenizer.special_attribute_present)
tokenizer = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True, use_fast=False
)
self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
self.assertTrue(tokenizer.special_attribute_present)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def test_from_pretrained_dynamic_tokenizer_legacy_format(self):
tokenizer = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer_legacy", trust_remote_code=True
......
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