# coding=utf-8 # Copyright 2020, The RAG Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """RAG model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import add_start_docstrings RAG_CONFIG_DOC = r""" [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: title_sep (`str`, *optional*, defaults to `" / "`): Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`]. doc_sep (`str`, *optional*, defaults to `" // "`): Separator inserted between the text of the retrieved document and the original input when calling [`RagRetriever`]. n_docs (`int`, *optional*, defaults to 5): Number of documents to retrieve. max_combined_length (`int`, *optional*, defaults to 300): Max length of contextualized input returned by [`~RagRetriever.__call__`]. retrieval_vector_size (`int`, *optional*, defaults to 768): Dimensionality of the document embeddings indexed by [`RagRetriever`]. retrieval_batch_size (`int`, *optional*, defaults to 8): Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated [`RagRetriever`]. dataset (`str`, *optional*, defaults to `"wiki_dpr"`): A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids using `datasets.list_datasets()`). dataset_split (`str`, *optional*, defaults to `"train"`) Which split of the `dataset` to load. index_name (`str`, *optional*, defaults to `"compressed"`) The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and `"compressed"`. index_path (`str`, *optional*) The path to the serialized faiss index on disk. passages_path (`str`, *optional*): A path to text passages compatible with the faiss index. Required if using [`~models.rag.retrieval_rag.LegacyIndex`] use_dummy_dataset (`bool`, *optional*, defaults to `False`) Whether to load a "dummy" variant of the dataset specified by `dataset`. label_smoothing (`float`, *optional*, defaults to 0.0): Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label smoothing is performed. do_marginalize (`bool`, *optional*, defaults to `False`): If `True`, the logits are marginalized over all documents by making use of `torch.nn.functional.log_softmax`. reduce_loss (`bool`, *optional*, defaults to `False`): Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation. do_deduplication (`bool`, *optional*, defaults to `True`): Whether or not to deduplicate the generations from different context documents for a given input. Has to be set to `False` if used while training with distributed backend. exclude_bos_score (`bool`, *optional*, defaults to `False`): Whether or not to disregard the BOS token when computing the loss. output_retrieved(`bool`, *optional*, defaults to `False`): If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and `context_attention_mask` are returned. See returned tensors for more detail. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). forced_eos_token_id (`int`, *optional*): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. """ @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, use_cache=True, forced_eos_token_id=None, dataset_revision=None, **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, forced_eos_token_id=forced_eos_token_id, is_encoder_decoder=is_encoder_decoder, prefix=prefix, vocab_size=vocab_size, **kwargs, ) if "question_encoder" not in kwargs or "generator" not in kwargs: raise ValueError( f"A configuraton of type {self.model_type} cannot be instantiated because " f"both `question_encoder` and `generator` sub-configurations were not passed, only {kwargs}" ) 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 ..auto.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.dataset_revision = dataset_revision self.output_retrieved = output_retrieved self.do_deduplication = do_deduplication self.use_cache = use_cache if self.forced_eos_token_id is None: self.forced_eos_token_id = getattr(self.generator, "forced_eos_token_id", None) @classmethod def from_question_encoder_generator_configs( cls, question_encoder_config: PretrainedConfig, generator_config: PretrainedConfig, **kwargs ) -> PretrainedConfig: r""" Instantiate a [`EncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model configuration and decoder model configuration. Returns: [`EncoderDecoderConfig`]: An instance of a configuration object """ return cls(question_encoder=question_encoder_config.to_dict(), generator=generator_config.to_dict(), **kwargs) __all__ = ["RagConfig"]