Source code for transformers.configuration_rag

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""" RAG model configuration """

import copy

from .configuration_utils import PretrainedConfig
from .file_utils import add_start_docstrings

    :class:`~transformers.RagConfig` stores the configuration of a `RagModel`. Configuration objects inherit from
    :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from
    :class:`~transformers.PretrainedConfig` for more information.

        title_sep (:obj:`str`, `optional`, defaults to  ``" / "``):
            Separator inserted between the title and the text of the retrieved document when calling
        doc_sep (:obj:`str`, `optional`, defaults to  ``" // "``):
            Separator inserted between the the text of the retrieved document and the original input when calling
        n_docs (:obj:`int`, `optional`, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (:obj:`int`, `optional`, defaults to 300):
            Max length of contextualized input returned by :meth:`~transformers.RagRetriever.__call__`.
        retrieval_vector_size (:obj:`int`, `optional`, defaults to 768):
            Dimensionality of the document embeddings indexed by :class:`~transformers.RagRetriever`.
        retrieval_batch_size (:obj:`int`, `optional`, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
        dataset (:obj:`str`, `optional`, defaults to :obj:`"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using :obj:`datasets.list_datasets()`).
        dataset_split (:obj:`str`, `optional`, defaults to :obj:`"train"`)
            Which split of the :obj:`dataset` to load.
        index_name (:obj:`str`, `optional`, defaults to :obj:`"compressed"`)
            The index name of the index associated with the :obj:`dataset`. One can choose between :obj:`"legacy"`,
            :obj:`"exact"` and :obj:`"compressed"`.
        index_path (:obj:`str`, `optional`)
            The path to the serialized faiss index on disk.
        passages_path: (:obj:`str`, `optional`):
            A path to text passages compatible with the faiss index. Required if using
        use_dummy_dataset (:obj:`bool`, `optional`, defaults to ``False``)
            Whether to load a "dummy" variant of the dataset specified by :obj:`dataset`.
        label_smoothing (:obj:`float`, `optional`, defaults to 0.0):
            Only relevant if ``return_loss`` is set to :obj:`True`. Controls the ``epsilon`` parameter value for label
            smoothing in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (:obj:`bool`, `optional`, defaults to :obj:`False`):
            If :obj:`True`, the logits are marginalized over all documents by making use of
        reduce_loss (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to reduce the NLL loss using the ``torch.Tensor.sum`` operation.
        do_deduplication (:obj:`bool`, `optional`, defaults to :obj:`True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to :obj:`False` if used while training with distributed backend.
        exclude_bos_score (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(:obj:`bool`, `optional`, defaults to :obj:`False`):
            If set to ``True``, :obj:`retrieved_doc_embeds`, :obj:`retrieved_doc_ids`, :obj:`context_input_ids` and
            :obj:`context_attention_mask` are returned. See returned tensors for more detail.

[docs]@add_start_docstrings(RAG_CONFIG_DOC) class RagConfig(PretrainedConfig): model_type = "rag" is_composition = True def __init__( self, vocab_size=None, is_encoder_decoder=True, prefix=None, bos_token_id=None, pad_token_id=None, eos_token_id=None, decoder_start_token_id=None, title_sep=" / ", doc_sep=" // ", n_docs=5, max_combined_length=300, retrieval_vector_size=768, retrieval_batch_size=8, dataset="wiki_dpr", dataset_split="train", index_name="compressed", index_path=None, passages_path=None, use_dummy_dataset=False, reduce_loss=False, label_smoothing=0.0, do_deduplication=True, exclude_bos_score=False, do_marginalize=False, output_retrieved=False, **kwargs ): super().__init__( bos_token_id=bos_token_id, pad_token_id=pad_token_id, eos_token_id=eos_token_id, decoder_start_token_id=decoder_start_token_id, is_encoder_decoder=is_encoder_decoder, prefix=prefix, vocab_size=vocab_size, **kwargs, ) assert ( "question_encoder" in kwargs and "generator" in kwargs ), "Config has to be initialized with question_encoder and generator config" question_encoder_config = kwargs.pop("question_encoder") question_encoder_model_type = question_encoder_config.pop("model_type") decoder_config = kwargs.pop("generator") decoder_model_type = decoder_config.pop("model_type") from .configuration_auto import AutoConfig self.question_encoder = AutoConfig.for_model(question_encoder_model_type, **question_encoder_config) self.generator = AutoConfig.for_model(decoder_model_type, **decoder_config) self.reduce_loss = reduce_loss self.label_smoothing = label_smoothing self.exclude_bos_score = exclude_bos_score self.do_marginalize = do_marginalize self.title_sep = title_sep self.doc_sep = doc_sep self.n_docs = n_docs self.max_combined_length = max_combined_length self.dataset = dataset self.dataset_split = dataset_split self.index_name = index_name self.retrieval_vector_size = retrieval_vector_size self.retrieval_batch_size = retrieval_batch_size self.passages_path = passages_path self.index_path = index_path self.use_dummy_dataset = use_dummy_dataset self.output_retrieved = output_retrieved self.do_deduplication = do_deduplication
[docs] @classmethod def from_question_encoder_generator_configs( cls, question_encoder_config: PretrainedConfig, generator_config: PretrainedConfig, **kwargs ) -> PretrainedConfig: r""" Instantiate a :class:`~transformers.EncoderDecoderConfig` (or a derived class) from a pre-trained encoder model configuration and decoder model configuration. Returns: :class:`EncoderDecoderConfig`: An instance of a configuration object """ return cls(question_encoder=question_encoder_config.to_dict(), generator=generator_config.to_dict(), **kwargs)
[docs] def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default :meth:`~transformers.PretrainedConfig.to_dict`. Returns: :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = copy.deepcopy(self.__dict__) output["question_encoder"] = self.question_encoder.to_dict() output["generator"] = self.generator.to_dict() output["model_type"] = self.__class__.model_type return output