Configuration (original) (raw)

The base class PreTrainedConfig implements the common methods for loading/saving a configuration either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace’s AWS S3 repository).

Each derived config class implements model specific attributes. Common attributes present in all config classes are:hidden_size, num_attention_heads, and num_hidden_layers. Text models further implement:vocab_size.

class transformers.PreTrainedConfig

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( output_hidden_states: bool = False output_attentions: bool = False return_dict: bool = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None tie_word_embeddings: bool = True chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False is_decoder: bool = False cross_attention_hidden_size: typing.Optional[int] = None add_cross_attention: bool = False tie_encoder_decoder: bool = False architectures: typing.Optional[list[str]] = None finetuning_task: typing.Optional[str] = None id2label: typing.Optional[dict[int, str]] = None label2id: typing.Optional[dict[str, int]] = None num_labels: typing.Optional[int] = None task_specific_params: typing.Optional[dict[str, typing.Any]] = None problem_type: typing.Optional[str] = None tokenizer_class: typing.Optional[str] = None prefix: typing.Optional[str] = None bos_token_id: typing.Optional[int] = None pad_token_id: typing.Optional[int] = None eos_token_id: typing.Optional[int] = None sep_token_id: typing.Optional[int] = None decoder_start_token_id: typing.Optional[int] = None **kwargs )

Parameters

• name_or_path (str, optional, defaults to "") — Store the string that was passed to PreTrainedModel.from_pretrained() as pretrained_model_name_or_pathif the configuration was created with such a method.
• output_hidden_states (bool, optional, defaults to False) — Whether or not the model should return all hidden-states.
• output_attentions (bool, optional, defaults to False) — Whether or not the model should returns all attentions.
• return_dict (bool, optional, defaults to True) — Whether or not the model should return a ModelOutput instead of a plain tuple.
• is_encoder_decoder (bool, optional, defaults to False) — Whether the model is used as an encoder/decoder or not.
• is_decoder (bool, optional, defaults to False) — Whether to only use the decoder in an encoder-decoder architecture, otherwise it has no effect on decoder-only or encoder-only architectures.
• cross_attention_hidden_size (bool, optional) — The hidden size of the cross-attention layer in case the model is used as a decoder in an encoder-decoder setting and the cross-attention hidden dimension differs from self.config.hidden_size.
• add_cross_attention (bool, optional, defaults to False) — Whether cross-attention layers should be added to the model. Note, this option is only relevant for models that can be used as decoder models within the EncoderDecoderModel class, which consists of all models in AUTO_MODELS_FOR_CAUSAL_LM.
• tie_encoder_decoder (bool, optional, defaults to False) — Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder and decoder model to have the exact same parameter names.
• chunk_size_feed_forward (int, optional, defaults to 0) — The chunk size of all feed forward layers in the residual attention blocks. A chunk size of 0 means that the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes n < sequence_length embeddings at a time. For more information on feed forward chunking, see How does Feed Forward Chunking work?.

Parameters for fine-tuning tasks

• architectures (list[str], optional) — Model architectures that can be used with the model pretrained weights.
• finetuning_task (str, optional) — Name of the task used to fine-tune the model.
• id2label (dict[int, str], optional) — A map from index (for instance prediction index, or target index) to label.
• label2id (dict[str, int], optional) — A map from label to index for the model.
• num_labels (int, optional) — Number of labels to use in the last layer added to the model, typically for a classification task.
• task_specific_params (dict[str, Any], optional) — Additional keyword arguments to store for the current task.
• problem_type (str, optional) — Problem type for XxxForSequenceClassification models. Can be one of "regression","single_label_classification" or "multi_label_classification".

Parameters linked to the tokenizer

• tokenizer_class (str, optional) — The name of the associated tokenizer class to use (if none is set, will use the tokenizer associated to the model by default).
• prefix (str, optional) — A specific prompt that should be added at the beginning of each text before calling the model.
• bos_token_id (int, optional) — The id of the beginning-of-stream token.
• pad_token_id (int, optional) — The id of the padding token.
• eos_token_id (int, optional) — The id of the end-of-stream token.
• decoder_start_token_id (int, optional) — If an encoder-decoder model starts decoding with a different token than bos, the id of that token.
• sep_token_id (int, optional) — The id of the separation token.

PyTorch specific parameters

• tie_word_embeddings (bool, optional, defaults to True) — Whether the model’s input and output word embeddings should be tied. Note that this is only relevant if the model has a output word embedding layer.
• dtype (str, optional) — The dtype of the weights. This attribute can be used to initialize the model to a non-default dtype(which is normally float32) and thus allow for optimal storage allocation. For example, if the saved model is float16, ideally we want to load it back using the minimal amount of memory needed to loadfloat16 weights.

Base class for all configuration classes. Handles a few parameters common to all models’ configurations as well as methods for loading/downloading/saving configurations.

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):

• model_type (str) — An identifier for the model type, serialized into the JSON file, and used to recreate the correct object in AutoConfig.
• has_no_defaults_at_init (bool) — Whether the config class can be initialized without providing input arguments. Some configurations requires inputs to be defined at init and have no default values, usually these are composite configs, (but not necessarily) such as EncoderDecoderConfig or ~RagConfig. They have to be initialized from two or more configs of type PreTrainedConfig.
• keys_to_ignore_at_inference (list[str]) — A list of keys to ignore by default when looking at dictionary outputs of the model during inference.
• attribute_map (dict[str, str]) — A dict that maps model specific attribute names to the standardized naming of attributes.
• base_model_tp_plan (dict[str, Any]) — A dict that maps sub-modules FQNs of a base model to a tensor parallel plan applied to the sub-module when model.tensor_parallel is called.
• base_model_pp_plan (dict[str, tuple[list[str]]]) — A dict that maps child-modules of a base model to a pipeline parallel plan that enables users to place the child-module on the appropriate device.

Common attributes (present in all subclasses):

• vocab_size (int) — The number of tokens in the vocabulary, which is also the first dimension of the embeddings matrix (this attribute may be missing for models that don’t have a text modality like ViT).
• hidden_size (int) — The hidden size of the model.
• num_attention_heads (int) — The number of attention heads used in the multi-head attention layers of the model.
• num_hidden_layers (int) — The number of blocks in the model.

Setting parameters for sequence generation in the model config is deprecated. For backward compatibility, loading some of them will still be possible, but attempting to overwrite them will throw an exception — you should set them in a [~transformers.GenerationConfig]. Check the documentation of [~transformers.GenerationConfig] for more information about the individual parameters.

push_to_hub

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( repo_id: str commit_message: str | None = None commit_description: str | None = None private: bool | None = None token: bool | str | None = None revision: str | None = None create_pr: bool = False max_shard_size: int | str | None = '50GB' tags: list[str] | None = None )

Parameters

• repo_id (str) — The name of the repository you want to push your config to. It should contain your organization name when pushing to a given organization.
• commit_message (str, optional) — Message to commit while pushing. Will default to "Upload config".
• commit_description (str, optional) — The description of the commit that will be created
• private (bool, optional) — Whether to make the repo private. If None (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists.
• token (bool or str, optional) — The token to use as HTTP bearer authorization for remote files. If True (default), will use the token generated when running hf auth login (stored in ~/.huggingface).
• revision (str, optional) — Branch to push the uploaded files to.
• create_pr (bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit.
• max_shard_size (int or str, optional, defaults to "50GB") — Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size lower than this size. If expressed as a string, needs to be digits followed by a unit (like "5MB").
• tags (list[str], optional) — List of tags to push on the Hub.

Upload the configuration file to the 🤗 Model Hub.

Examples:

from transformers import AutoConfig

config = AutoConfig.from_pretrained("google-bert/bert-base-cased")

config.push_to_hub("my-finetuned-bert")

config.push_to_hub("huggingface/my-finetuned-bert")

Checks whether the passed dictionary and its nested dicts have a dtype key and if it’s not None, converts torch.dtype to a string of just the type. For example, torch.float32 get converted into _“float32”_string, which can then be stored in the json format.

from_dict

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( config_dict: dict **kwargs ) → PreTrainedConfig

Parameters

• config_dict (dict[str, Any]) — Dictionary that will be used to instantiate the configuration object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the get_config_dict() method.
• kwargs (dict[str, Any]) — Additional parameters from which to initialize the configuration object.

The configuration object instantiated from those parameters.

Instantiates a PreTrainedConfig from a Python dictionary of parameters.

from_json_file

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( json_file: str | os.PathLike ) → PreTrainedConfig

Parameters

• json_file (str or os.PathLike) — Path to the JSON file containing the parameters.

The configuration object instantiated from that JSON file.

Instantiates a PreTrainedConfig from the path to a JSON file of parameters.

from_pretrained

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( pretrained_model_name_or_path: str | os.PathLike cache_dir: str | os.PathLike | None = None force_download: bool = False local_files_only: bool = False token: str | bool | None = None revision: str = 'main' **kwargs ) → PreTrainedConfig

Parameters

• pretrained_model_name_or_path (str or os.PathLike) — This can be either:
  • a string, the model id of a pretrained model configuration hosted inside a model repo on huggingface.co.
  • a path to a directory containing a configuration file saved using thesave_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 (str or os.PathLike, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
• force_download (bool, optional, defaults to False) — Whether or not to force to (re-)download the configuration files and override the cached versions if they exist.
• proxies (dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.
• token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, or not specified, will use the token generated when running hf auth login (stored in ~/.huggingface).
• revision (str, optional, defaults to "main") — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.
  To test a pull request you made on the Hub, you can pass revision="refs/pr/<pr_number>".
• return_unused_kwargs (bool, optional, defaults to False) — 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: i.e., the part of kwargs which has not been used to update config and is otherwise ignored.
• subfolder (str, optional, defaults to "") — In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here.
• kwargs (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.

The configuration object instantiated from this pretrained model.

Instantiate a PreTrainedConfig (or a derived class) from a pretrained model configuration.

Examples:

config = BertConfig.from_pretrained( "google-bert/bert-base-uncased" )
config = BertConfig.from_pretrained( "./test/saved_model/" )
config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json") config = BertConfig.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False) assert config.output_attentions == True config, unused_kwargs = BertConfig.from_pretrained( "google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True ) assert config.output_attentions == True assert unused_kwargs == {"foo": False}

get_config_dict

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( pretrained_model_name_or_path: str | os.PathLike **kwargs ) → tuple[Dict, Dict]

Parameters

• pretrained_model_name_or_path (str or os.PathLike) — The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.

Returns

tuple[Dict, Dict]

The dictionary(ies) that will be used to instantiate the configuration object.

From a pretrained_model_name_or_path, resolve to a dictionary of parameters, to be used for instantiating aPreTrainedConfig using from_dict.

get_text_config

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( decoder = None encoder = None )

Parameters

• decoder (Optional[bool], optional) — If set to True, then only search for decoder config names.
• encoder (Optional[bool], optional) — If set to True, then only search for encoder config names.

Returns the text config related to the text input (encoder) or text output (decoder) of the model. Thedecoder and encoder input arguments can be used to specify which end of the model we are interested in, which is useful on models that have both text input and output modalities.

There are three possible outcomes of using this method:

1. On most models, it returns the original config instance itself.
2. On newer (2024+) composite models, it returns the text section of the config, which is nested under a set of valid names.
3. On older (2023-) composite models, it discards decoder-only parameters when encoder=True and vice-versa.

register_for_auto_class

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( auto_class = 'AutoConfig' )

Parameters

• auto_class (str or type, optional, defaults to "AutoConfig") — The auto class to register this new configuration with.

Register this class with a given auto class. This should only be used for custom configurations as the ones in the library are already mapped with AutoConfig.

save_pretrained

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( save_directory: str | os.PathLike push_to_hub: bool = False **kwargs )

Parameters

• save_directory (str or os.PathLike) — Directory where the configuration JSON file will be saved (will be created if it does not exist).
• push_to_hub (bool, optional, defaults to False) — Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with repo_id (will default to the name of save_directory in your namespace).
• kwargs (dict[str, Any], optional) — Additional key word arguments passed along to the push_to_hub() method.

Save a configuration object to the directory save_directory, so that it can be re-loaded using thefrom_pretrained() class method.

to_dict

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( ) → dict[str, Any]

Dictionary of all the attributes that make up this configuration instance.

Serializes this instance to a Python dictionary.

to_diff_dict

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( ) → dict[str, Any]

Dictionary of all the attributes that make up this configuration instance.

Removes all attributes from the configuration that correspond to the default config attributes for better readability, while always retaining the config attribute from the class. Serializes to a Python dictionary.

to_json_file

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( json_file_path: str | os.PathLike use_diff: bool = True )

Parameters

• json_file_path (str or os.PathLike) — Path to the JSON file in which this configuration instance’s parameters will be saved.
• use_diff (bool, optional, defaults to True) — If set to True, only the difference between the config instance and the default PreTrainedConfig()is serialized to JSON file.

Save this instance to a JSON file.

to_json_string

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( use_diff: bool = True ) → str

Parameters

• use_diff (bool, optional, defaults to True) — If set to True, only the difference between the config instance and the default PreTrainedConfig()is serialized to JSON string.

String containing all the attributes that make up this configuration instance in JSON format.

Serializes this instance to a JSON string.

update

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( config_dict: dict )

Parameters

• config_dict (dict[str, Any]) — Dictionary of attributes that should be updated for this class.

Updates attributes of this class with attributes from config_dict.

update_from_string

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( update_str: str )

Parameters

• update_str (str) — String with attributes that should be updated for this class.

Updates attributes of this class with attributes from update_str.

The expected format is ints, floats and strings as is, and for booleans use true or false. For example: “n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index”

The keys to change have to already exist in the config object.