Source code for transformers.configuration_utils

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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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""" Configuration base class and utilities."""


import copy
import json
import logging
import os
from typing import Dict, Optional, Tuple

from .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url


logger = logging.getLogger(__name__)


[docs]class PretrainedConfig(object): r""" Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving configurations. Note: A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to initialize a model does **not** load the model weights. It only affects the model's configuration. Class attributes (overridden by derived classes): - ``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`. 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 def __init__(self, **kwargs): # Attributes with defaults self.output_attentions = kwargs.pop("output_attentions", False) self.output_hidden_states = kwargs.pop("output_hidden_states", False) self.output_past = kwargs.pop("output_past", True) # Not used by all models self.torchscript = kwargs.pop("torchscript", False) # Only used by PyTorch models self.use_bfloat16 = kwargs.pop("use_bfloat16", False) self.pruned_heads = kwargs.pop("pruned_heads", {}) # Is decoder is used in encoder-decoder models to differentiate encoder from decoder self.is_decoder = kwargs.pop("is_decoder", False) # Parameters for sequence generation self.max_length = kwargs.pop("max_length", 20) self.do_sample = kwargs.pop("do_sample", False) self.num_beams = kwargs.pop("num_beams", 1) self.temperature = kwargs.pop("temperature", 1.0) self.top_k = kwargs.pop("top_k", 50) self.top_p = kwargs.pop("top_p", 1.0) self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0) self.bos_token_id = kwargs.pop("bos_token_id", 0) self.pad_token_id = kwargs.pop("pad_token_id", 0) self.eos_token_ids = kwargs.pop("eos_token_ids", 0) self.length_penalty = kwargs.pop("length_penalty", 1.0) self.num_return_sequences = kwargs.pop("num_return_sequences", 1) # Fine-tuning task arguments self.architectures = kwargs.pop("architectures", None) self.finetuning_task = kwargs.pop("finetuning_task", None) self.num_labels = kwargs.pop("num_labels", 2) self.id2label = kwargs.pop("id2label", {i: "LABEL_{}".format(i) for i in range(self.num_labels)}) self.id2label = dict((int(key), value) for key, value in self.id2label.items()) self.label2id = kwargs.pop("label2id", dict(zip(self.id2label.values(), self.id2label.keys()))) self.label2id = dict((key, int(value)) for key, value in self.label2id.items()) # Additional attributes without default values for key, value in kwargs.items(): try: setattr(self, key, value) except AttributeError as err: logger.error("Can't set {} with value {} for {}".format(key, value, self)) raise err
[docs] 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. Args: save_directory (:obj:`string`): Directory where the configuration JSON file will be saved. """ assert os.path.isdir( save_directory ), "Saving path should be a directory where the model and configuration can be saved" # If we save using the predefined names, we can load using `from_pretrained` output_config_file = os.path.join(save_directory, CONFIG_NAME) self.to_json_file(output_config_file) logger.info("Configuration saved in {}".format(output_config_file))
[docs] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, **kwargs) -> "PretrainedConfig": r""" Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration. 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 (: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 (: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. Returns: :class:`PretrainedConfig`: An instance of a configuration object Examples:: # We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a # derived class: BertConfig config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache. config = BertConfig.from_pretrained('./test/saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')` config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json') config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False) assert config.output_attention == True config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True) assert config.output_attention == True assert unused_kwargs == {'foo': False} """ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) return cls.from_dict(config_dict, **kwargs)
[docs] @classmethod def get_config_dict( cls, pretrained_model_name_or_path: str, pretrained_config_archive_map: Optional[Dict] = None, **kwargs ) -> Tuple[Dict, Dict]: """ From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a Config using `from_dict`. Parameters: 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) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) if pretrained_config_archive_map is None: pretrained_config_archive_map = cls.pretrained_config_archive_map if pretrained_model_name_or_path in pretrained_config_archive_map: config_file = pretrained_config_archive_map[pretrained_model_name_or_path] elif os.path.isdir(pretrained_model_name_or_path): config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME) elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): config_file = pretrained_model_name_or_path else: config_file = hf_bucket_url(pretrained_model_name_or_path, postfix=CONFIG_NAME) try: # Load from URL or cache if already cached resolved_config_file = cached_path( config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, ) # Load config dict if resolved_config_file is None: raise EnvironmentError config_dict = cls._dict_from_json_file(resolved_config_file) except EnvironmentError: if pretrained_model_name_or_path in pretrained_config_archive_map: msg = "Couldn't reach server at '{}' to download pretrained model configuration file.".format( config_file ) else: msg = ( "Model name '{}' was not found in model name list. " "We assumed '{}' was a path, a model identifier, or url to a configuration file named {} or " "a directory containing such a file but couldn't find any such file at this path or url.".format( pretrained_model_name_or_path, config_file, CONFIG_NAME, ) ) raise EnvironmentError(msg) except json.JSONDecodeError: msg = ( "Couldn't reach server at '{}' to download configuration file or " "configuration file is not a valid JSON file. " "Please check network or file content here: {}.".format(config_file, resolved_config_file) ) raise EnvironmentError(msg) if resolved_config_file == config_file: logger.info("loading configuration file {}".format(config_file)) else: logger.info("loading configuration file {} from cache at {}".format(config_file, resolved_config_file)) return config_dict, kwargs
[docs] @classmethod 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) if hasattr(config, "pruned_heads"): config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items()) # Update config with kwargs if needed to_remove = [] for key, value in kwargs.items(): if hasattr(config, key): setattr(config, key, value) to_remove.append(key) for key in to_remove: kwargs.pop(key, None) logger.info("Model config %s", str(config)) if return_unused_kwargs: return config, kwargs else: return config
[docs] @classmethod 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)
@classmethod def _dict_from_json_file(cls, json_file: str): with open(json_file, "r", encoding="utf-8") as reader: text = reader.read() return json.loads(text) def __eq__(self, other): return self.__dict__ == other.__dict__ def __repr__(self): return "{} {}".format(self.__class__.__name__, self.to_json_string())
[docs] def to_dict(self): """ 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
[docs] def to_json_string(self): """ 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"
[docs] def to_json_file(self, json_file_path): """ 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())