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Configuration utils

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Lysandre 2020-01-24 15:26:18 -05:00 committed by Lysandre Debut
parent 11b13e94a3
commit 009fcb0ec1
1 changed files with 103 additions and 44 deletions
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src/transformers/configuration_utils.py
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@ -40,12 +40,17 @@ class PretrainedConfig(object):
- ``pretrained_config_archive_map``: a python ``dict`` with `shortcut names` (string) as keys and `url` (string) of associated pretrained model configurations as values.
- ``model_type``: a string that identifies the model type, that we serialize into the JSON file, and that we use to recreate the correct object in :class:`~transformers.AutoConfig`.
Parameters:
``finetuning_task``: string, default `None`. Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
``num_labels``: integer, default `2`. Number of classes to use when the model is a classification model (sequences/tokens)
``output_attentions``: boolean, default `False`. Should the model returns attentions weights.
``output_hidden_states``: string, default `False`. Should the model returns all hidden-states.
``torchscript``: string, default `False`. Is the model used with Torchscript.
Args:
finetuning_task (:obj:`string` or :obj:`None`, `optional`, defaults to :obj:`None`):
Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
num_labels (:obj:`int`, `optional`, defaults to `2`):
Number of classes to use when the model is a classification model (sequences/tokens)
output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
Should the model returns attentions weights.
output_hidden_states (:obj:`string`, `optional`, defaults to :obj:`False`):
Should the model returns all hidden-states.
torchscript (:obj:`bool`, `optional`, defaults to :obj:`False`):
Is the model used with Torchscript (for PyTorch models).
"""
pretrained_config_archive_map = {} # type: Dict[str, str]
model_type = "" # type: str
@ -93,8 +98,13 @@ class PretrainedConfig(object):
raise err
def save_pretrained(self, save_directory):
""" Save a configuration object to the directory `save_directory`, so that it
can be re-loaded using the :func:`~transformers.PretrainedConfig.from_pretrained` class method.
"""
Save a configuration object to the directory `save_directory`, so that it
can be re-loaded using the :func:`~transformers.PretrainedConfig.from_pretrained` class method.
Args:
save_directory (:obj:`string`):
Directory where the configuration JSON file will be saved.
"""
assert os.path.isdir(
save_directory
@ -107,40 +117,45 @@ class PretrainedConfig(object):
logger.info("Configuration saved in {}".format(output_config_file))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r""" Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs) -> 'PretrainedConfig':
r"""
Parameters:
pretrained_model_name_or_path: either:
Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
- a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a pre-trained model configuration that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing a configuration file saved using the :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
- a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
cache_dir: (`optional`) string:
Args:
pretrained_model_name_or_path (:obj:`string`):
either:
- a string with the `shortcut name` of a pre-trained model configuration to load from cache or
download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a pre-trained model configuration that was user-uploaded to
our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing a configuration file saved using the
:func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
- a path or url to a saved configuration JSON `file`, e.g.:
``./my_model_directory/configuration.json``.
cache_dir (:obj:`string`, `optional`):
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
- The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
- Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
resume_download: (`optional`) boolean, default False:
kwargs (:obj:`Dict[str, any]`, `optional`):
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is
controlled by the `return_unused_kwargs` keyword parameter.
force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Force to (re-)download the model weights and configuration files and override the cached versions if they exist.
resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
proxies (:obj:`Dict`, `optional`):
A dictionary of proxy servers to use by protocol or endpoint, e.g.:
:obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.`
The proxies are used on each request.
return_unused_kwargs: (`optional`) bool:
If False, then this function returns just the final configuration object.
If True, then this functions returns a :obj:`Tuple(config, unused_kwargs)` where `unused_kwargs` is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part
of kwargs which has not been used to update `config` and is otherwise ignored.
- If False, then this function returns just the final configuration object.
- If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
Returns:
:class:`PretrainedConfig`: An instance of a configuration object
Examples::
@ -169,9 +184,14 @@ class PretrainedConfig(object):
for instantiating a Config using `from_dict`.
Parameters:
pretrained_config_archive_map: (`optional`) Dict:
A map of `shortcut names` to `url`.
By default, will use the current class attribute.
pretrained_model_name_or_path (:obj:`string`):
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
pretrained_config_archive_map: (:obj:`Dict[str, str]`, `optional`) Dict:
A map of `shortcut names` to `url`. By default, will use the current class attribute.
Returns:
:obj:`Tuple[Dict, Dict]`: The dictionary that will be used to instantiate the configuration object.
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
@ -235,8 +255,21 @@ class PretrainedConfig(object):
return config_dict, kwargs
@classmethod
def from_dict(cls, config_dict: Dict, **kwargs):
"""Constructs a `Config` from a Python dictionary of parameters."""
def from_dict(cls, config_dict: Dict, **kwargs) -> 'PretrainedConfig':
"""
Constructs a `Config` from a Python dictionary of parameters.
Args:
config_dict (:obj:`Dict[str, any]`):
Dictionary that will be used to instantiate the configuration object. Such a dictionary can be retrieved
from a pre-trained checkpoint by leveraging the :func:`~transformers.PretrainedConfig.get_config_dict`
method.
kwargs (:obj:`Dict[str, any]`):
Additional parameters from which to initialize the configuration object.
Returns:
:class:`PretrainedConfig`: An instance of a configuration object
"""
return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
config = cls(**config_dict)
@ -260,8 +293,18 @@ class PretrainedConfig(object):
return config
@classmethod
def from_json_file(cls, json_file: str):
"""Constructs a `Config` from the path to a json file of parameters."""
def from_json_file(cls, json_file: str) -> 'PretrainedConfig':
"""
Constructs a `Config` from the path to a json file of parameters.
Args:
json_file (:obj:`string`):
Path to the JSON file containing the parameters.
Returns:
:class:`PretrainedConfig`: An instance of a configuration object
"""
config_dict = cls._dict_from_json_file(json_file)
return cls(**config_dict)
@ -278,17 +321,33 @@ class PretrainedConfig(object):
return "{} {}".format(self.__class__.__name__, self.to_json_string())
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
"""
Serializes this instance to a Python dictionary.
Returns:
:obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
if hasattr(self.__class__, "model_type"):
output["model_type"] = self.__class__.model_type
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
"""
Serializes this instance to a JSON string.
Returns:
:obj:`string`: String containing all the attributes that make up this configuration instance in JSON format.
"""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path):
""" Save this instance to a json file."""
"""
Save this instance to a json file.
Args:
json_file_path (:obj:`string`):
Path to the JSON file in which this configuration instance's parameters will be saved.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string())