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Preserve hub-related kwargs in AutoModel.from_pretrained (#18545) 

* Preserve hub-related kwargs in AutoModel.from_pretrained

* Fix tests

* Remove debug statement
This commit is contained in:
Sylvain Gugger 2022-08-10 08:00:18 -04:00 committed by GitHub
parent 34aad0dac0
commit d7e2d7b40b
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 27 additions and 8 deletions
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29
src/transformers/models/auto/auto_factory.py
View File

@ -419,9 +419,24 @@ class _BaseAutoModelClass:
config = kwargs.pop("config", None)
trust_remote_code = kwargs.pop("trust_remote_code", False)
kwargs["_from_auto"] = True
hub_kwargs_names = [
"cache_dir",
"force_download",
"local_files_only",
"proxies",
"resume_download",
"revision",
"subfolder",
"use_auth_token",
]
hub_kwargs = {name: kwargs.pop(name) for name in hub_kwargs_names if name in kwargs}
if not isinstance(config, PretrainedConfig):
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path, return_unused_kwargs=True, trust_remote_code=trust_remote_code, **kwargs
pretrained_model_name_or_path,
return_unused_kwargs=True,
trust_remote_code=trust_remote_code,
**hub_kwargs,
**kwargs,
)
if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
if not trust_remote_code:
@ -430,7 +445,7 @@ class _BaseAutoModelClass:
"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:
if hub_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."
@ -438,12 +453,16 @@ class _BaseAutoModelClass:
class_ref = config.auto_map[cls.__name__]
module_file, class_name = class_ref.split(".")
model_class = get_class_from_dynamic_module(
pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
pretrained_model_name_or_path, module_file + ".py", class_name, **hub_kwargs, **kwargs
)
return model_class.from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
)
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
elif type(config) in cls._model_mapping.keys():
model_class = _get_model_class(config, cls._model_mapping)
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
return model_class.from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
)
raise ValueError(
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."

6
src/transformers/models/auto/configuration_auto.py
View File

@ -728,7 +728,7 @@ class AutoConfig:
kwargs["_from_auto"] = True
kwargs["name_or_path"] = pretrained_model_name_or_path
trust_remote_code = kwargs.pop("trust_remote_code", False)
config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
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(
@ -749,13 +749,13 @@ class AutoConfig:
return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif "model_type" in config_dict:
config_class = CONFIG_MAPPING[config_dict["model_type"]]
return config_class.from_dict(config_dict, **kwargs)
return config_class.from_dict(config_dict, **unused_kwargs)
else:
# Fallback: use pattern matching on the string.
# We go from longer names to shorter names to catch roberta before bert (for instance)
for pattern in sorted(CONFIG_MAPPING.keys(), key=len, reverse=True):
if pattern in str(pretrained_model_name_or_path):
return CONFIG_MAPPING[pattern].from_dict(config_dict, **kwargs)
return CONFIG_MAPPING[pattern].from_dict(config_dict, **unused_kwargs)
raise ValueError(
f"Unrecognized model in {pretrained_model_name_or_path}. "