#
RAG

##
Overview

Retrieval-augmented generation (“RAG”) models combine the powers of pretrained dense retrieval (DPR) and sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing both retrieval and generation to adapt to downstream tasks.

It is based on the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.

The abstract from the paper is the following:

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre-trained models with a differentiable access mechanism to explicit nonparametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state-of-the-art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.

This model was contributed by ola13.

##
Usage tips

Retrieval-augmented generation (“RAG”) models combine the powers of pretrained dense retrieval (DPR) and Seq2Seq models. RAG models retrieve docs, pass them to a seq2seq model, then marginalize to generate outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing both retrieval and generation to adapt to downstream tasks.

##
RagConfig

###
class transformers.RagConfig

< source >

( 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 )

Parameters

- 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 `__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 `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`.

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.

####
from_question_encoder_generator_configs

< source >

( question_encoder_config: PretrainedConfig generator_config: PretrainedConfig **kwargs ) →
EncoderDecoderConfig

Returns

EncoderDecoderConfig

An instance of a configuration object

Instantiate a EncoderDecoderConfig (or a derived class) from a pre-trained encoder model configuration and decoder model configuration.

##
RagTokenizer

###
class transformers.RagTokenizer

< source >

( question_encoder generator )

##
Rag specific outputs

###
class transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput

< source >

( loss: typing.Optional[torch.FloatTensor] = None logits: FloatTensor = None doc_scores: FloatTensor = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None retrieved_doc_embeds: typing.Optional[torch.FloatTensor] = None retrieved_doc_ids: typing.Optional[torch.LongTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None question_encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None question_enc_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None question_enc_attentions: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_enc_last_hidden_state: typing.Optional[torch.FloatTensor] = None generator_enc_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_enc_attentions: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_dec_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_dec_attentions: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None )

Parameters

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Language modeling loss.
- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token.
- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.
- past_key_values (`List[torch.FloatTensor]`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `torch.FloatTensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

  Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.

- retrieved_doc_embeds (`torch.FloatTensor` of shape `(batch_size, config.n_docs, hidden_size)`, optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`.
- retrieved_doc_ids (`torch.LongTensor` of shape `(batch_size, config.n_docs)`, optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.
- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.
- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.
- question_encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model.
- question_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.

- question_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_enc_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.
- generator_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.

- generator_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_dec_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.

- generator_dec_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_cross_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Cross-attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the cross-attention heads.

Base class for retriever augmented marginalized models outputs.

###
class transformers.models.rag.modeling_rag.RetrievAugLMOutput

< source >

( logits: FloatTensor = None doc_scores: FloatTensor = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None retrieved_doc_embeds: typing.Optional[torch.FloatTensor] = None retrieved_doc_ids: typing.Optional[torch.LongTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None question_encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None question_enc_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None question_enc_attentions: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_enc_last_hidden_state: typing.Optional[torch.FloatTensor] = None generator_enc_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_enc_attentions: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_dec_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_dec_attentions: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None generator_cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor, ...]] = None )

Parameters

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token.
- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.
- past_key_values (`List[torch.FloatTensor]`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `torch.FloatTensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

  Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.

- retrieved_doc_embeds (`torch.FloatTensor` of shape `(batch_size, config.n_docs, hidden_size)`, optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`.
- retrieved_doc_ids (`torch.LongTensor` of shape `(batch_size, config.n_docs)`, optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.
- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.
- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.
- question_encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model.
- question_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.

- question_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_enc_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.
- generator_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.

- generator_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_dec_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.

- generator_dec_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_cross_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Cross-attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the cross-attention heads.

##
RagRetriever

###
class transformers.RagRetriever

< source >

( config question_encoder_tokenizer generator_tokenizer index = None init_retrieval = True )

Parameters

- config (RagConfig) — The configuration of the RAG model this Retriever is used with. Contains parameters indicating which `Index` to build. You can load your own custom dataset with `config.index_name="custom"` or use a canonical one (default) from the datasets library with `config.index_name="wiki_dpr"` for example.
- question_encoder_tokenizer (PreTrainedTokenizer) — The tokenizer that was used to tokenize the question. It is used to decode the question and then use the generator_tokenizer.
- generator_tokenizer (PreTrainedTokenizer) — The tokenizer used for the generator part of the RagModel.
- index (`Index`, optional, defaults to the one defined by the configuration) — If specified, use this index instead of the one built using the configuration

Retriever used to get documents from vector queries. It retrieves the documents embeddings as well as the documents contents, and it formats them to be used with a RagModel.

Examples:

    >>> # To load the default "wiki_dpr" dataset with 21M passages from wikipedia (index name is 'compressed' or 'exact')
    >>> from transformers import RagRetriever

    >>> retriever = RagRetriever.from_pretrained(
    ...     "facebook/dpr-ctx_encoder-single-nq-base", dataset="wiki_dpr", index_name="compressed"
    ... )

    >>> # To load your own indexed dataset built with the datasets library. More info on how to build the indexed dataset in examples/rag/use_own_knowledge_dataset.py
    >>> from transformers import RagRetriever

    >>> dataset = (
    ...     ...
    ... )  # dataset must be a datasets.Datasets object with columns "title", "text" and "embeddings", and it must have a faiss index
    >>> retriever = RagRetriever.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", indexed_dataset=dataset)

    >>> # To load your own indexed dataset built with the datasets library that was saved on disk. More info in examples/rag/use_own_knowledge_dataset.py
    >>> from transformers import RagRetriever

    >>> dataset_path = "path/to/my/dataset"  # dataset saved via *dataset.save_to_disk(...)*
    >>> index_path = "path/to/my/index.faiss"  # faiss index saved via *dataset.get_index("embeddings").save(...)*
    >>> retriever = RagRetriever.from_pretrained(
    ...     "facebook/dpr-ctx_encoder-single-nq-base",
    ...     index_name="custom",
    ...     passages_path=dataset_path,
    ...     index_path=index_path,
    ... )

    >>> # To load the legacy index built originally for Rag's paper
    >>> from transformers import RagRetriever

    >>> retriever = RagRetriever.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", index_name="legacy")

####
init_retrieval

< source >

( )

Retriever initialization function. It loads the index into memory.

####
postprocess_docs

< source >

( docs input_strings prefix n_docs return_tensors = None ) →
`tuple(tensors)`

Parameters

- docs (`dict`) — Retrieved documents.
- input_strings (`str`) — Input strings decoded by `preprocess_query`.
- prefix (`str`) — Prefix added at the beginning of each input, typically used with T5-based models.

Returns

`tuple(tensors)`

a tuple consisting of two elements: contextualized `input_ids` and a compatible `attention_mask`.

Postprocessing retrieved `docs` and combining them with `input_strings`.

####
retrieve

< source >

( question_hidden_states: ndarray n_docs: int ) →
`Tuple[np.ndarray, np.ndarray, List[dict]]`

Parameters

- question_hidden_states (`np.ndarray` of shape `(batch_size, vector_size)`) — A batch of query vectors to retrieve with.
- n_docs (`int`) — The number of docs retrieved per query.

Returns

`Tuple[np.ndarray, np.ndarray, List[dict]]`

A tuple with the following objects:

- retrieved_doc_embeds (`np.ndarray` of shape `(batch_size, n_docs, dim)`) — The retrieval embeddings of the retrieved docs per query.
- doc_ids (`np.ndarray` of shape `(batch_size, n_docs)`) — The ids of the documents in the index
- doc_dicts (`List[dict]`): The `retrieved_doc_embeds` examples per query.

Retrieves documents for specified `question_hidden_states`.

Pytorch
Hide Pytorch content

##
RagModel

###
class transformers.RagModel

< source >

( config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None question_encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None generator: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None retriever: typing.Optional[transformers.models.rag.retrieval_rag.RagRetriever] = None **kwargs )

Parameters

- config (RagConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
- question_encoder (PreTrainedModel) — An encoder model compatible with the faiss index encapsulated by the `retriever`.
- generator (PreTrainedModel) — A seq2seq model used as the generator in the RAG architecture.
- retriever (RagRetriever) — A retriever class encapsulating a faiss index queried to obtain context documents for current inputs.

The RagModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

RAG is a seq2seq model which encapsulates two core components: a question encoder and a generator. During a forward pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator.

The question encoder can be any autoencoding model, preferably DPRQuestionEncoder, and the generator can be any seq2seq model, preferably BartForConditionalGeneration.

The model can be initialized with a RagRetriever for end-to-end generation or used in combination with the outputs of a retriever in multiple steps---see examples for more details. The model is compatible any autoencoding model as the `question_encoder` and any seq2seq model with language model head as the `generator`. It has been tested with DPRQuestionEncoder as the `question_encoder` and BartForConditionalGeneration or T5ForConditionalGeneration as the `generator`.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None doc_scores: typing.Optional[torch.FloatTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None output_retrieved: typing.Optional[bool] = None n_docs: typing.Optional[int] = None ) →
transformers.models.rag.modeling_rag.RetrievAugLMOutput or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to obtain the indices.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- encoder_outputs (`tuple(tuple(torch.FloatTensor)`, optional) — Tuple consists of (`generator_enc_last_hidden_state`, optional: `generator_enc_hidden_states`, optional: `generator_enc_attentions`). `generator_enc_last_hidden_state` of shape `(batch_size, n_docs * sequence_length, hidden_size)` is a sequence of hidden-states at the output of the last layer of the generator’s encoder.

  Used by the (RagModel) model during decoding.

- decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional) — Provide for generation tasks. `None` by default, construct as per instructions for the generator model you’re using with your RAG instance.
- decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, optional) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
- past_key_values (`tuple(tuple(torch.FloatTensor))`) — Tuple consists of two elements: `encoder_outputs` of the RAG model (see `encoder_outputs`) and `past_key_values` of the underlying generator. Can be used to speed up decoding. `past_key_values` are used in the (RagTokenForGeneration) model during decoding.
- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever` `doc_scores` has to be provided to the forward pass. `doc_scores` can be computed via `question_encoder_last_hidden_state` and `retrieved_doc_embeds`, see examples for more information.
- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model was not initialized with a `retriever` ``context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`.
- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`,optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model has is not initialized with a `retriever` `context_attention_mask` has to be provided to the forward pass. `context_attention_mask` are returned by `__call__()`.
- use_cache (`bool`, optional, defaults to `True`) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- output_retrieved(`bool`, optional) — Whether or not to return the `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and `context_attention_mask`. See returned tensors for more detail.
- n_docs (`int`, optional, defaults to `config.n_docs“) — Number of documents to retrieve and/or number of documents for which to generate an answer.

Returns

transformers.models.rag.modeling_rag.RetrievAugLMOutput or `tuple(torch.FloatTensor)`

A transformers.models.rag.modeling_rag.RetrievAugLMOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (RagConfig) and inputs.

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token.

- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.

- past_key_values (`List[torch.FloatTensor]`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `torch.FloatTensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

  Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.

- retrieved_doc_embeds (`torch.FloatTensor` of shape `(batch_size, config.n_docs, hidden_size)`, optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`.

- retrieved_doc_ids (`torch.LongTensor` of shape `(batch_size, config.n_docs)`, optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.

- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.

- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

- question_encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model.

- question_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.

- question_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_enc_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.

- generator_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.

- generator_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_dec_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.

- generator_dec_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_cross_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Cross-attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the cross-attention heads.

The RagModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, RagRetriever, RagModel
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-base")
    >>> retriever = RagRetriever.from_pretrained(
    ...     "facebook/rag-token-base", index_name="exact", use_dummy_dataset=True
    ... )
    >>> # initialize with RagRetriever to do everything in one forward call
    >>> model = RagModel.from_pretrained("facebook/rag-token-base", retriever=retriever)

    >>> inputs = tokenizer("How many people live in Paris?", return_tensors="pt")
    >>> outputs = model(input_ids=inputs["input_ids"])

##
RagSequenceForGeneration

###
class transformers.RagSequenceForGeneration

< source >

( config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None question_encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None generator: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None retriever: typing.Optional[transformers.models.rag.retrieval_rag.RagRetriever] = None **kwargs )

Parameters

- config (RagConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
- question_encoder (PreTrainedModel) — An encoder model compatible with the faiss index encapsulated by the `retriever`.
- generator (PreTrainedModel) — A seq2seq model used as the generator in the RAG architecture.
- retriever (RagRetriever) — A retriever class encapsulating a faiss index queried to obtain context documents for current inputs.

The RagSequenceForGeneration forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

A RAG-sequence model implementation. It performs RAG-sequence specific marginalization in the forward pass.

RAG is a seq2seq model which encapsulates two core components: a question encoder and a generator. During a forward pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator.

The question encoder can be any autoencoding model, preferably DPRQuestionEncoder, and the generator can be any seq2seq model, preferably BartForConditionalGeneration.

The model can be initialized with a RagRetriever for end-to-end generation or used in combination with the outputs of a retriever in multiple steps---see examples for more details. The model is compatible any autoencoding model as the `question_encoder` and any seq2seq model with language model head as the `generator`. It has been tested with DPRQuestionEncoder as the `question_encoder` and BartForConditionalGeneration or T5ForConditionalGeneration as the `generator`.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None output_retrieved: typing.Optional[bool] = None exclude_bos_score: typing.Optional[bool] = None reduce_loss: typing.Optional[bool] = None labels: typing.Optional[torch.LongTensor] = None n_docs: typing.Optional[int] = None **kwargs ) →
transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to obtain the indices.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- encoder_outputs (`tuple(tuple(torch.FloatTensor)`, optional) — Tuple consists of (`generator_enc_last_hidden_state`, optional: `generator_enc_hidden_states`, optional: `generator_enc_attentions`). `generator_enc_last_hidden_state` of shape `(batch_size, n_docs * sequence_length, hidden_size)` is a sequence of hidden-states at the output of the last layer of the generator’s encoder.

  Used by the (RagModel) model during decoding.

- decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional) — Provide for generation tasks. `None` by default, construct as per instructions for the generator model you’re using with your RAG instance.
- decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, optional) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
- past_key_values (`tuple(tuple(torch.FloatTensor))`) — Tuple consists of two elements: `encoder_outputs` of the RAG model (see `encoder_outputs`) and `past_key_values` of the underlying generator. Can be used to speed up decoding. `past_key_values` are used in the (RagTokenForGeneration) model during decoding.
- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever` `doc_scores` has to be provided to the forward pass. `doc_scores` can be computed via `question_encoder_last_hidden_state` and `retrieved_doc_embeds`, see examples for more information.
- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model was not initialized with a `retriever` ``context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`.
- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`,optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model has is not initialized with a `retriever` `context_attention_mask` has to be provided to the forward pass. `context_attention_mask` are returned by `__call__()`.
- use_cache (`bool`, optional, defaults to `True`) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- output_retrieved(`bool`, optional) — Whether or not to return the `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and `context_attention_mask`. See returned tensors for more detail.
- n_docs (`int`, optional, defaults to `config.n_docs“) — Number of documents to retrieve and/or number of documents for which to generate an answer.
- exclude_bos_score (`bool`, optional) — Only relevant if `labels` is passed. If `True`, the score of the BOS token is disregarded when computing the loss.
- reduce_loss (`bool`, optional) — Only relevant if `labels` is passed. If `True`, the NLL loss is reduced using the `torch.Tensor.sum` operation.
- kwargs (`Dict[str, any]`, optional, defaults to `{}`) — Legacy dictionary, which is required so that model can use generate() function.

Returns

transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or `tuple(torch.FloatTensor)`

A transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (RagConfig) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Language modeling loss.

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token.

- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.

- past_key_values (`List[torch.FloatTensor]`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `torch.FloatTensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

  Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.

- retrieved_doc_embeds (`torch.FloatTensor` of shape `(batch_size, config.n_docs, hidden_size)`, optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`.

- retrieved_doc_ids (`torch.LongTensor` of shape `(batch_size, config.n_docs)`, optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.

- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.

- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

- question_encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model.

- question_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.

- question_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_enc_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.

- generator_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.

- generator_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_dec_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.

- generator_dec_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_cross_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Cross-attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the cross-attention heads.

The RagSequenceForGeneration forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, RagRetriever, RagSequenceForGeneration
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
    >>> retriever = RagRetriever.from_pretrained(
    ...     "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
    ... )
    >>> # initialize with RagRetriever to do everything in one forward call
    >>> model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)

    >>> inputs = tokenizer("How many people live in Paris?", return_tensors="pt")
    >>> targets = tokenizer(text_target="In Paris, there are 10 million people.", return_tensors="pt")
    >>> input_ids = inputs["input_ids"]
    >>> labels = targets["input_ids"]
    >>> outputs = model(input_ids=input_ids, labels=labels)

    >>> # or use retriever separately
    >>> model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", use_dummy_dataset=True)
    >>> # 1. Encode
    >>> question_hidden_states = model.question_encoder(input_ids)[0]
    >>> # 2. Retrieve
    >>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.detach().numpy(), return_tensors="pt")
    >>> doc_scores = torch.bmm(
    ...     question_hidden_states.unsqueeze(1), docs_dict["retrieved_doc_embeds"].float().transpose(1, 2)
    ... ).squeeze(1)
    >>> # 3. Forward to generator
    >>> outputs = model(
    ...     context_input_ids=docs_dict["context_input_ids"],
    ...     context_attention_mask=docs_dict["context_attention_mask"],
    ...     doc_scores=doc_scores,
    ...     decoder_input_ids=labels,
    ... )

####
generate

< source >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.LongTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None do_deduplication: typing.Optional[bool] = None num_return_sequences: typing.Optional[int] = None num_beams: typing.Optional[int] = None n_docs: typing.Optional[int] = None **model_kwargs ) →
`torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — The sequence used as a prompt for the generation. If `input_ids` is not passed, then `context_input_ids` has to be provided.
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder input_ids by the retriever.
- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model is not initialized with a `retriever` or `input_ids` is not given, `context_input_ids` and `context_attention_mask` have to be provided to the forward pass. They are returned by `__call__()`.

- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.

  If the model is not initialized with a `retriever` or `input_ids` is not given, `doc_scores` has to be provided to the forward pass. `doc_scores` are returned by `__call__()`.

- do_deduplication (`bool`, optional) — 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.
- num_return_sequences(`int`, optional, defaults to 1) — The number of independently computed returned sequences for each element in the batch. Note that this is not the value we pass to the `generator`’s `[generate()](/docs/transformers/pr_22105/en/main_classes/text_generation#transformers.GenerationMixin.generate)` function, where we set `num_return_sequences` to `num_beams`.
- num_beams (`int`, optional, defaults to 1) — Number of beams for beam search. 1 means no beam search.
- n_docs (`int`, optional, defaults to `config.n_docs`) — Number of documents to retrieve and/or number of documents for which to generate an answer.
- kwargs (`Dict[str, Any]`, optional) — Additional kwargs will be passed to generate().

Returns

`torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`

The generated sequences. The second dimension (sequence length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.

Implements RAG sequence “thorough” decoding. Read the generate()` documentation for more information on how to set other generate input parameters.

##
RagTokenForGeneration

###
class transformers.RagTokenForGeneration

< source >

( config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None question_encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None generator: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None retriever: typing.Optional[transformers.models.rag.retrieval_rag.RagRetriever] = None **kwargs )

Parameters

- config (RagConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
- question_encoder (PreTrainedModel) — An encoder model compatible with the faiss index encapsulated by the `retriever`.
- generator (PreTrainedModel) — A seq2seq model used as the generator in the RAG architecture.
- retriever (RagRetriever) — A retriever class encapsulating a faiss index queried to obtain context documents for current inputs.

The RagTokenForGeneration forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

A RAG-token model implementation. It performs RAG-token specific marginalization in the forward pass.

RAG is a seq2seq model which encapsulates two core components: a question encoder and a generator. During a forward pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator.

The question encoder can be any autoencoding model, preferably DPRQuestionEncoder, and the generator can be any seq2seq model, preferably BartForConditionalGeneration.

The model can be initialized with a RagRetriever for end-to-end generation or used in combination with the outputs of a retriever in multiple steps---see examples for more details. The model is compatible any autoencoding model as the `question_encoder` and any seq2seq model with language model head as the `generator`. It has been tested with DPRQuestionEncoder as the `question_encoder` and BartForConditionalGeneration or T5ForConditionalGeneration as the `generator`.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None output_retrieved: typing.Optional[bool] = None do_marginalize: typing.Optional[bool] = None reduce_loss: typing.Optional[bool] = None labels: typing.Optional[torch.LongTensor] = None n_docs: typing.Optional[int] = None **kwargs ) →
transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to obtain the indices.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- encoder_outputs (`tuple(tuple(torch.FloatTensor)`, optional) — Tuple consists of (`generator_enc_last_hidden_state`, optional: `generator_enc_hidden_states`, optional: `generator_enc_attentions`). `generator_enc_last_hidden_state` of shape `(batch_size, n_docs * sequence_length, hidden_size)` is a sequence of hidden-states at the output of the last layer of the generator’s encoder.

  Used by the (RagModel) model during decoding.

- decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional) — Provide for generation tasks. `None` by default, construct as per instructions for the generator model you’re using with your RAG instance.
- decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, optional) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
- past_key_values (`tuple(tuple(torch.FloatTensor))`) — Tuple consists of two elements: `encoder_outputs` of the RAG model (see `encoder_outputs`) and `past_key_values` of the underlying generator. Can be used to speed up decoding. `past_key_values` are used in the (RagTokenForGeneration) model during decoding.
- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever` `doc_scores` has to be provided to the forward pass. `doc_scores` can be computed via `question_encoder_last_hidden_state` and `retrieved_doc_embeds`, see examples for more information.
- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model was not initialized with a `retriever` ``context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`.
- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`,optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model has is not initialized with a `retriever` `context_attention_mask` has to be provided to the forward pass. `context_attention_mask` are returned by `__call__()`.
- use_cache (`bool`, optional, defaults to `True`) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- output_retrieved(`bool`, optional) — Whether or not to return the `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and `context_attention_mask`. See returned tensors for more detail.
- n_docs (`int`, optional, defaults to `config.n_docs“) — Number of documents to retrieve and/or number of documents for which to generate an answer.
- do_marginalize (`bool`, optional) — If `True`, the logits are marginalized over all documents by making use of `torch.nn.functional.log_softmax`.
- reduce_loss (`bool`, optional) — Only relevant if `labels` is passed. If `True`, the NLL loss is reduced using the `torch.Tensor.sum` operation.
- kwargs (`Dict[str, any]`, optional, defaults to `{}`) — Legacy dictionary, which is required so that model can use generate() function.

Returns

transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or `tuple(torch.FloatTensor)`

A transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (RagConfig) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Language modeling loss.

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token.

- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.

- past_key_values (`List[torch.FloatTensor]`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `torch.FloatTensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

  Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.

- retrieved_doc_embeds (`torch.FloatTensor` of shape `(batch_size, config.n_docs, hidden_size)`, optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`.

- retrieved_doc_ids (`torch.LongTensor` of shape `(batch_size, config.n_docs)`, optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.

- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.

- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

- question_encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model.

- question_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.

- question_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_enc_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.

- generator_enc_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.

- generator_enc_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_dec_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.

- generator_dec_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_cross_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Cross-attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the cross-attention heads.

The RagTokenForGeneration forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, RagRetriever, RagTokenForGeneration
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-nq")
    >>> retriever = RagRetriever.from_pretrained(
    ...     "facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True
    ... )
    >>> # initialize with RagRetriever to do everything in one forward call
    >>> model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)

    >>> inputs = tokenizer("How many people live in Paris?", return_tensors="pt")
    >>> targets = tokenizer(text_target="In Paris, there are 10 million people.", return_tensors="pt")
    >>> input_ids = inputs["input_ids"]
    >>> labels = targets["input_ids"]
    >>> outputs = model(input_ids=input_ids, labels=labels)

    >>> # or use retriever separately
    >>> model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", use_dummy_dataset=True)
    >>> # 1. Encode
    >>> question_hidden_states = model.question_encoder(input_ids)[0]
    >>> # 2. Retrieve
    >>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.detach().numpy(), return_tensors="pt")
    >>> doc_scores = torch.bmm(
    ...     question_hidden_states.unsqueeze(1), docs_dict["retrieved_doc_embeds"].float().transpose(1, 2)
    ... ).squeeze(1)
    >>> # 3. Forward to generator
    >>> outputs = model(
    ...     context_input_ids=docs_dict["context_input_ids"],
    ...     context_attention_mask=docs_dict["context_attention_mask"],
    ...     doc_scores=doc_scores,
    ...     decoder_input_ids=labels,
    ... )

    >>> # or directly generate
    >>> generated = model.generate(
    ...     context_input_ids=docs_dict["context_input_ids"],
    ...     context_attention_mask=docs_dict["context_attention_mask"],
    ...     doc_scores=doc_scores,
    ... )
    >>> generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)

####
generate

< source >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.LongTensor] = None context_input_ids: typing.Optional[torch.LongTensor] = None context_attention_mask: typing.Optional[torch.LongTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None n_docs: typing.Optional[int] = None generation_config: typing.Optional[transformers.generation.configuration_utils.GenerationConfig] = None prefix_allowed_tokens_fn: typing.Callable[[int, torch.Tensor], typing.List[int]] = None logits_processor: typing.Optional[transformers.generation.logits_process.LogitsProcessorList] = [] stopping_criteria: typing.Optional[transformers.generation.stopping_criteria.StoppingCriteriaList] = [] **kwargs ) →
`torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — The sequence used as a prompt for the generation. If `input_ids` is not passed, then `context_input_ids` has to be provided.
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- context_input_ids (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever`, `context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`.

- context_attention_mask (`torch.LongTensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever`, `context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`.

- doc_scores (`torch.FloatTensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.

  If the model has is not initialized with a `retriever`, `context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`.

- n_docs (`int`, optional, defaults to `config.n_docs`) — Number of documents to retrieve and/or number of documents for which to generate an answer.
- generation_config (`~generation.GenerationConfig`, optional) — The generation configuration to be used as base parametrization for the generation call. `**kwargs` passed to generate matching the attributes of `generation_config` will override them. If `generation_config` is not provided, the default will be used, which has the following loading priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit GenerationConfig’s default values, whose documentation should be checked to parameterize generation.
- prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, optional) — If provided, this function constraints the beam search to allowed tokens only at each step. If not provided no constraint is applied. This function takes 2 arguments `inputs_ids` and the batch ID `batch_id`. It has to return a list with the allowed tokens for the next generation step conditioned on the previously generated tokens `inputs_ids` and the batch ID `batch_id`. This argument is useful for constrained generation conditioned on the prefix, as described in Autoregressive Entity Retrieval.
- logits_processor (`LogitsProcessorList`, optional) — Custom logits processors that complement the default logits processors built from arguments and a model’s config. If a logit processor is passed that is already created with the arguments or a model’s config an error is thrown.
- stopping_criteria (`StoppingCriteriaList`, optional) — Custom stopping criteria that complement the default stopping criteria built from arguments and a model’s config. If a stopping criteria is passed that is already created with the arguments or a model’s config an error is thrown.
- kwargs (`Dict[str, Any]`, optional) — Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model.

Returns

`torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`

The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.

Implements RAG token decoding.

TensorFlow
Hide TensorFlow content

##
TFRagModel

###
class transformers.TFRagModel

< source >

( config: Optional[PretrainedConfig] = None question_encoder: Optional[TFPreTrainedModel] = None generator: Optional[TFPreTrainedModel] = None retriever: Optional[RagRetriever] = None load_weight_prefix: Optional[str] = None **kwargs )

Parameters

- config (RagConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
- question_encoder (TFPreTrainedModel) — An encoder model compatible with the faiss index encapsulated by the `retriever`.
- generator (TFPreTrainedModel) — A seq2seq model used as the generator in the RAG architecture.
- retriever (RagRetriever) — A retriever class encapsulating a faiss index queried to obtain context documents for current inputs.

The TFRagModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

RAG is a sequence-to-sequence model which encapsulates two core components: a question encoder and a generator. During a forward pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator.

The question encoder can be any autoencoding model, preferably TFDPRQuestionEncoder, and the generator can be any seq2seq model, preferably TFBartForConditionalGeneration.

The model can be initialized with a RagRetriever for end-to-end generation or used in combination with the outputs of a retriever in multiple steps---see examples for more details. The model is compatible any autoencoding model as the `question_encoder` and any seq2seq model with language model head as the `generator`. It has been tested with TFDPRQuestionEncoder as the `question_encoder` and TFBartForConditionalGeneration as the `generator`.

This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a Tensorflow keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

The model is in a developing state as it is now fully supports in eager-mode only, and may not be exported in SavedModel format.

####
call

< source >

( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None encoder_outputs: np.ndarray | tf.Tensor | None = None decoder_input_ids: np.ndarray | tf.Tensor | None = None decoder_attention_mask: np.ndarray | tf.Tensor | None = None past_key_values: Tuple[Tuple[Union[np.ndarray, tf.Tensor]]] | None = None doc_scores: np.ndarray | tf.Tensor | None = None context_input_ids: np.ndarray | tf.Tensor | None = None context_attention_mask: np.ndarray | tf.Tensor | None = None use_cache: bool | None = None output_attentions: bool | None = None output_hidden_states: bool | None = None output_retrieved: bool | None = None n_docs: int | None = None return_dict: bool | None = None training: bool = False **kwargs ) →
`transformers.models.rag.modeling_tf_rag.TFRetrievAugLMOutput` or `tuple(tf.Tensor)`

Parameters

- input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to obtain the indices.
- attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- encoder_outputs (`tuple(tuple(tf.Tensor)`, optional) — Tuple consists of (`generator_enc_last_hidden_state`, optional: `generator_enc_hidden_states`, optional: `generator_enc_attentions`). `generator_enc_last_hidden_state` of shape `(batch_size, n_docs * sequence_length, hidden_size)` is a sequence of hidden-states at the output of the last layer of the generator’s encoder.

  Used by the (TFRagModel) model during decoding.

- decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, optional) — Provide for generation tasks. `None` by default, construct as per instructions for the generator model you’re using with your RAG instance.
- decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, optional) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
- past_key_values (`tuple(tuple(tf.Tensor))`) — Tuple consists of two elements: `encoder_outputs` of the RAG model (see `encoder_outputs`) and `past_key_values` of the underlying generator. Can be used to speed up decoding. `past_key_values` are used in the (RagTokenForGeneration) model during decoding.
- doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever` `doc_scores` has to be provided to the forward pass. `doc_scores` can be computed via `question_encoder_last_hidden_state` and `retrieved_doc_embeds`, see examples for more information.
- context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever` ``context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`. context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True): Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever` `context_attention_mask` has to be provided to the forward pass. `context_attention_mask` are returned by `__call__()`.

- use_cache (`bool`, optional, defaults to `True`) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- output_retrieved(`bool`, optional) — Whether or not to return the `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and `context_attention_mask`. See returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a `TFRetrievAugLMOutput` instead of a plain tuple.
- n_docs (`int`, optional, defaults to `config.n_docs“) — Number of documents to retrieve and/or number of documents for which to generate an answer.

Returns

`transformers.models.rag.modeling_tf_rag.TFRetrievAugLMOutput` or `tuple(tf.Tensor)`

A `transformers.models.rag.modeling_tf_rag.TFRetrievAugLMOutput` or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (RagConfig) and inputs.

- logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token.

- past_key_values (`List[tf.Tensor]`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

  Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.

- doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.

- retrieved_doc_embeds (`tf.Tensor` of shape `(batch_size, config.n_docs, hidden_size)`, optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`.

- retrieved_doc_ids (`tf.Tensor` of shape `(batch_size, config.n_docs)`, optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.

- context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.

- context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

- question_encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model.

- question_enc_hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.

- question_enc_attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_enc_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.

- generator_enc_hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.

- generator_enc_attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_dec_hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.

- generator_dec_attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFRagModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, RagRetriever, TFRagModel
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-base")
    >>> retriever = RagRetriever.from_pretrained(
    ...     "facebook/rag-token-base", index_name="exact", use_dummy_dataset=True
    ... )
    >>> # initialize with RagRetriever to do everything in one forward call
    >>> model = TFRagModel.from_pretrained("facebook/rag-token-base", retriever=retriever, from_pt=True)

    >>> input_dict = tokenizer.prepare_seq2seq_batch(
    ...     "How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="tf"
    ... )
    >>> input_ids = input_dict["input_ids"]
    >>> outputs = model(input_ids)

##
TFRagSequenceForGeneration

###
class transformers.TFRagSequenceForGeneration

< source >

( config: Optional[PretrainedConfig] = None question_encoder: Optional[TFPreTrainedModel] = None generator: Optional[TFPreTrainedModel] = None retriever: Optional[RagRetriever] = None **kwargs )

Parameters

- config (RagConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
- question_encoder (TFPreTrainedModel) — An encoder model compatible with the faiss index encapsulated by the `retriever`.
- generator (TFPreTrainedModel) — A seq2seq model used as the generator in the RAG architecture.
- retriever (RagRetriever) — A retriever class encapsulating a faiss index queried to obtain context documents for current inputs.

The TFRagSequenceForGeneration forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

A TF RAG-sequence model implementation. It performs RAG-sequence specific marginalization in the forward pass.

RAG is a sequence-to-sequence model which encapsulates two core components: a question encoder and a generator. During a forward pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator.

The question encoder can be any autoencoding model, preferably TFDPRQuestionEncoder, and the generator can be any seq2seq model, preferably TFBartForConditionalGeneration.

The model can be initialized with a RagRetriever for end-to-end generation or used in combination with the outputs of a retriever in multiple steps---see examples for more details. The model is compatible any autoencoding model as the `question_encoder` and any seq2seq model with language model head as the `generator`. It has been tested with TFDPRQuestionEncoder as the `question_encoder` and TFBartForConditionalGeneration as the `generator`.

This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a Tensorflow keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

The model is in a developing state as it is now fully supports in eager-mode only, and may not be exported in SavedModel format.

####
call

< source >

( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None decoder_input_ids: np.ndarray | tf.Tensor | None = None decoder_attention_mask: np.ndarray | tf.Tensor | None = None encoder_outputs: np.ndarray | tf.Tensor | None = None past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None doc_scores: np.ndarray | tf.Tensor | None = None context_input_ids: np.ndarray | tf.Tensor | None = None context_attention_mask: np.ndarray | tf.Tensor | None = None use_cache: Optional[bool] = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None output_retrieved: Optional[bool] = None n_docs: Optional[int] = None exclude_bos_score: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None reduce_loss: Optional[bool] = None return_dict: Optional[bool] = None training: bool = False **kwargs ) →
`transformers.models.rag.modeling_tf_rag.TFRetrievAugLMMarginOutput` or `tuple(tf.Tensor)`

Parameters

- input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to obtain the indices.
- attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- encoder_outputs (`tuple(tuple(tf.Tensor)`, optional) — Tuple consists of (`generator_enc_last_hidden_state`, optional: `generator_enc_hidden_states`, optional: `generator_enc_attentions`). `generator_enc_last_hidden_state` of shape `(batch_size, n_docs * sequence_length, hidden_size)` is a sequence of hidden-states at the output of the last layer of the generator’s encoder.

  Used by the (TFRagModel) model during decoding.

- decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, optional) — Provide for generation tasks. `None` by default, construct as per instructions for the generator model you’re using with your RAG instance.
- decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, optional) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
- past_key_values (`tuple(tuple(tf.Tensor))`) — Tuple consists of two elements: `encoder_outputs` of the RAG model (see `encoder_outputs`) and `past_key_values` of the underlying generator. Can be used to speed up decoding. `past_key_values` are used in the (RagTokenForGeneration) model during decoding.
- doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever` `doc_scores` has to be provided to the forward pass. `doc_scores` can be computed via `question_encoder_last_hidden_state` and `retrieved_doc_embeds`, see examples for more information.
- context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever` ``context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`. context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True): Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever` `context_attention_mask` has to be provided to the forward pass. `context_attention_mask` are returned by `__call__()`.

- use_cache (`bool`, optional, defaults to `True`) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- output_retrieved(`bool`, optional) — Whether or not to return the `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and `context_attention_mask`. See returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a `TFRetrievAugLMOutput` instead of a plain tuple.
- n_docs (`int`, optional, defaults to `config.n_docs“) — Number of documents to retrieve and/or number of documents for which to generate an answer.
- exclude_bos_score (`bool`, optional) — Only relevant if `labels` is passed. If `True`, the score of the BOS token is disregarded when computing the loss.
- labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the cross entropy classification loss according to Rag-Sequence model formulation See https://arxiv.org/pdf/2005.11401.pdf Section 2.1 for details about Rag-Sequence formulation. Indices should be in `[0, ..., config.vocab_size - 1]`.
- reduce_loss (`bool`, optional) — Only relevant if `labels` is passed. If `True`, the NLL loss is reduced using the `tf.Tensor.sum` operation.
- kwargs (`Dict[str, any]`, optional, defaults to `{}`) — Legacy dictionary, which is required so that model can use generate() function.

Returns

`transformers.models.rag.modeling_tf_rag.TFRetrievAugLMMarginOutput` or `tuple(tf.Tensor)`

A `transformers.models.rag.modeling_tf_rag.TFRetrievAugLMMarginOutput` or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (RagConfig) and inputs.

- loss (`tf.Tensor` of shape `(1,)`, optional, returned when `labels` is provided) — Language modeling loss.

- logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token.

- past_key_values (`List[tf.Tensor]`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

  Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.

- doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.

- retrieved_doc_embeds (`tf.Tensor` of shape `(batch_size, config.n_docs, hidden_size)`, optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`.

- retrieved_doc_ids (`tf.Tensor` (int32) of shape `(batch_size, config.n_docs)`, optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.

- context_input_ids (`tf.Tensor`(int32) of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.

- context_attention_mask (`tf.Tensor` (int32) of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

- question_encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model.

- question_enc_hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.

- question_enc_attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_enc_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.

- generator_enc_hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.

- generator_enc_attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_dec_hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.

- generator_dec_attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFRagSequenceForGeneration forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, RagRetriever, TFRagSequenceForGeneration

    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
    >>> retriever = RagRetriever.from_pretrained(
    ...     "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
    ... )
    >>> # initialize with RagRetriever to do everything in one forward call
    >>> model = TFRagSequenceForGeneration.from_pretrained(
    ...     "facebook/rag-sequence-nq", retriever=retriever, from_pt=True
    ... )

    >>> input_dict = tokenizer.prepare_seq2seq_batch(
    ...     "How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="tf"
    ... )
    >>> outputs = model(input_dict, output_retrieved=True)

    >>> # or use retriever separately
    >>> # 1. Encode
    >>> input_ids = input_dict["input_ids"]
    >>> question_hidden_states = model.question_encoder(input_ids)[0]
    >>> # 2. Retrieve
    >>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.numpy(), return_tensors="tf")
    >>> doc_scores = tf.squeeze(
    ...     tf.matmul(
    ...         tf.expand_dims(question_hidden_states, axis=1), docs_dict["retrieved_doc_embeds"], transpose_b=True
    ...     ),
    ...     axis=1,
    ... )
    >>> # 3. Forward to generator
    >>> outputs = model(
    ...     inputs=None,
    ...     context_input_ids=docs_dict["context_input_ids"],
    ...     context_attention_mask=docs_dict["context_attention_mask"],
    ...     doc_scores=doc_scores,
    ...     decoder_input_ids=input_dict["labels"],
    ... )

    >>> # or directly generate
    >>> generated = model.generate(
    ...     context_input_ids=docs_dict["context_input_ids"],
    ...     context_attention_mask=docs_dict["context_attention_mask"],
    ...     doc_scores=doc_scores,
    ... )
    >>> generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)

####
generate

< source >

( input_ids: TFModelInputType | None = None attention_mask: tf.Tensor | None = None context_input_ids = None context_attention_mask = None doc_scores = None do_deduplication = None num_return_sequences = None num_beams = None n_docs = None **model_kwargs ) →
`tf.Tensor` of shape `(batch_size * num_return_sequences, sequence_length)`

Parameters

- input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — The sequence used as a prompt for the generation. If `input_ids` is not passed, then `context_input_ids` has to be provided.
- attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are not masked, - 0 for tokens that are masked. What are attention masks?
- context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder input_ids by the retriever.
- context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever. If the model has is not initialized with a `retriever` or `input_ids` is not given, `context_input_ids` and `context_attention_mask` have to be provided to the forward pass. They are returned by `__call__()`.
- doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever` or `input_ids` is not given, `doc_scores` has to be provided to the forward pass. `doc_scores` are returned by `__call__()`.
- do_deduplication (`bool`, optional) — 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.
- num_return_sequences(`int`, optional, defaults to 1) — The number of independently computed returned sequences for each element in the batch. Note that this is not the value we pass to the `generator`’s `[generate()](/docs/transformers/pr_22105/en/main_classes/text_generation#transformers.GenerationMixin.generate)` function, where we set `num_return_sequences` to `num_beams`.
- num_beams (`int`, optional, defaults to 1) — Number of beams for beam search. 1 means no beam search.
- n_docs (`int`, optional, defaults to `config.n_docs`) — Number of documents to retrieve and/or number of documents for which to generate an answer.
- kwargs (`Dict[str, Any]`, optional) — Additional kwargs will be passed to generate()

Returns

`tf.Tensor` of shape `(batch_size * num_return_sequences, sequence_length)`

The generated sequences. The second dimension (sequence length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.

Implements RAG sequence “thorough” decoding. Read the generate()` documentation for more information on how to set other generate input parameters

##
TFRagTokenForGeneration

###
class transformers.TFRagTokenForGeneration

< source >

( config: Optional[PretrainedConfig] = None question_encoder: Optional[TFPreTrainedModel] = None generator: Optional[TFPreTrainedModel] = None retriever: Optional[RagRetriever] = None **kwargs )

Parameters

- config (RagConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
- question_encoder (TFPreTrainedModel) — An encoder model compatible with the faiss index encapsulated by the `retriever`.
- generator (TFPreTrainedModel) — A seq2seq model used as the generator in the RAG architecture.
- retriever (RagRetriever) — A retriever class encapsulating a faiss index queried to obtain context documents for current inputs.

The TFRagTokenForGeneration forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

A TF RAG-token model implementation. It performs RAG-token specific marginalization in the forward pass.

RAG is a sequence-to-sequence model which encapsulates two core components: a question encoder and a generator. During a forward pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context documents. The documents are then prepended to the input. Such contextualized inputs is passed to the generator.

The question encoder can be any autoencoding model, preferably TFDPRQuestionEncoder, and the generator can be any seq2seq model, preferably TFBartForConditionalGeneration.

The model can be initialized with a RagRetriever for end-to-end generation or used in combination with the outputs of a retriever in multiple steps---see examples for more details. The model is compatible any autoencoding model as the `question_encoder` and any seq2seq model with language model head as the `generator`. It has been tested with TFDPRQuestionEncoder as the `question_encoder` and TFBartForConditionalGeneration as the `generator`.

This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a Tensorflow keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

The model is in a developing state as it is now fully supports in eager-mode only, and may not be exported in SavedModel format.

####
call

< source >

( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None decoder_input_ids: np.ndarray | tf.Tensor | None = None decoder_attention_mask: np.ndarray | tf.Tensor | None = None encoder_outputs: np.ndarray | tf.Tensor | None = None past_key_values: Tuple[Tuple[Union[np.ndarray, tf.Tensor]]] | None = None doc_scores: np.ndarray | tf.Tensor | None = None context_input_ids: np.ndarray | tf.Tensor | None = None context_attention_mask: np.ndarray | tf.Tensor | None = None use_cache: bool | None = None output_attentions: bool | None = None output_hidden_states: bool | None = None output_retrieved: bool | None = None n_docs: int | None = None do_marginalize: bool | None = None labels: np.ndarray | tf.Tensor | None = None reduce_loss: bool | None = None return_dict: bool | None = None training: bool = False **kwargs ) →
`transformers.models.rag.modeling_tf_rag.TFRetrievAugLMMarginOutput` or `tuple(tf.Tensor)`

Parameters

- input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. RagConfig, used to initialize the model, specifies which generator to use, it also specifies a compatible generator tokenizer. Use that tokenizer class to obtain the indices.
- attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- encoder_outputs (`tuple(tuple(tf.Tensor)`, optional) — Tuple consists of (`generator_enc_last_hidden_state`, optional: `generator_enc_hidden_states`, optional: `generator_enc_attentions`). `generator_enc_last_hidden_state` of shape `(batch_size, n_docs * sequence_length, hidden_size)` is a sequence of hidden-states at the output of the last layer of the generator’s encoder.

  Used by the (TFRagModel) model during decoding.

- decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, optional) — Provide for generation tasks. `None` by default, construct as per instructions for the generator model you’re using with your RAG instance.
- decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, optional) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
- past_key_values (`tuple(tuple(tf.Tensor))`) — Tuple consists of two elements: `encoder_outputs` of the RAG model (see `encoder_outputs`) and `past_key_values` of the underlying generator. Can be used to speed up decoding. `past_key_values` are used in the (RagTokenForGeneration) model during decoding.
- doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`. If the model has is not initialized with a `retriever` `doc_scores` has to be provided to the forward pass. `doc_scores` can be computed via `question_encoder_last_hidden_state` and `retrieved_doc_embeds`, see examples for more information.
- context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever` ``context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`. context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True): Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever` `context_attention_mask` has to be provided to the forward pass. `context_attention_mask` are returned by `__call__()`.

- use_cache (`bool`, optional, defaults to `True`) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- output_retrieved(`bool`, optional) — Whether or not to return the `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and `context_attention_mask`. See returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a `TFRetrievAugLMOutput` instead of a plain tuple.
- n_docs (`int`, optional, defaults to `config.n_docs“) — Number of documents to retrieve and/or number of documents for which to generate an answer.
- do_marginalize (`bool`, optional) — If `True`, the logits are marginalized over all documents by making use of `torch.nn.functional.log_softmax`.
- labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the cross entropy classification loss according to Rag-Token model formulation See https://arxiv.org/pdf/2005.11401.pdf Section 2.1 for details about Rag-Token formulation. Indices should be in `[0, ..., config.vocab_size - 1]`.
- reduce_loss (`bool`, optional) — Only relevant if `labels` is passed. If `True`, the NLL loss is reduced using the `tf.Tensor.sum` operation.
- kwargs (`Dict[str, any]`, optional, defaults to `{}`) — Legacy dictionary, which is required so that model can use generate() function.

Returns

`transformers.models.rag.modeling_tf_rag.TFRetrievAugLMMarginOutput` or `tuple(tf.Tensor)`

A `transformers.models.rag.modeling_tf_rag.TFRetrievAugLMMarginOutput` or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (RagConfig) and inputs.

- loss (`tf.Tensor` of shape `(1,)`, optional, returned when `labels` is provided) — Language modeling loss.

- logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head. The score is possibly marginalized over all documents for each vocabulary token.

- past_key_values (`List[tf.Tensor]`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

  Contains precomputed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.

- doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.

- retrieved_doc_embeds (`tf.Tensor` of shape `(batch_size, config.n_docs, hidden_size)`, optional, returned when output_retrieved=True) — Embedded documents retrieved by the retriever. Is used with `question_encoder_last_hidden_state` to compute the `doc_scores`.

- retrieved_doc_ids (`tf.Tensor` (int32) of shape `(batch_size, config.n_docs)`, optional, returned when output_retrieved=True) — The indexes of the embedded documents retrieved by the retriever.

- context_input_ids (`tf.Tensor`(int32) of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input ids post-processed from the retrieved documents and the question encoder input_ids by the retriever.

- context_attention_mask (`tf.Tensor` (int32) of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

- question_encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden states at the output of the last layer of the question encoder pooled output of the model.

- question_enc_hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the question encoder at the output of each layer plus the initial embedding outputs.

- question_enc_attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the question encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_enc_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the generator encoder of the model.

- generator_enc_hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator encoder at the output of each layer plus the initial embedding outputs.

- generator_enc_attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- generator_dec_hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden states of the generator decoder at the output of each layer plus the initial embedding outputs.

- generator_dec_attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the generator decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFRagTokenForGeneration forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> import tensorflow as tf
    >>> from transformers import AutoTokenizer, RagRetriever, TFRagTokenForGeneration

    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/rag-token-nq")
    >>> retriever = RagRetriever.from_pretrained(
    ...     "facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True
    ... )
    >>> # initialize with RagRetriever to do everything in one forward call
    >>> model = TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever, from_pt=True)

    >>> input_dict = tokenizer.prepare_seq2seq_batch(
    ...     "How many people live in Paris?", "In Paris, there are 10 million people.", return_tensors="tf"
    ... )
    >>> outputs = model(input_dict, output_retrieved=True)

    >>> # or use retriever separately
    >>> # 1. Encode
    >>> input_ids = input_dict["input_ids"]
    >>> question_hidden_states = model.question_encoder(input_ids)[0]
    >>> # 2. Retrieve
    >>> docs_dict = retriever(input_ids.numpy(), question_hidden_states.numpy(), return_tensors="tf")
    >>> doc_scores = tf.squeeze(
    ...     tf.matmul(
    ...         tf.expand_dims(question_hidden_states, axis=1), docs_dict["retrieved_doc_embeds"], transpose_b=True
    ...     ),
    ...     axis=1,
    ... )
    >>> # 3. Forward to generator
    >>> outputs = model(
    ...     inputs=None,
    ...     context_input_ids=docs_dict["context_input_ids"],
    ...     context_attention_mask=docs_dict["context_attention_mask"],
    ...     doc_scores=doc_scores,
    ...     decoder_input_ids=input_dict["labels"],
    ... )

    >>> # or directly generate
    >>> generated = model.generate(
    ...     context_input_ids=docs_dict["context_input_ids"],
    ...     context_attention_mask=docs_dict["context_attention_mask"],
    ...     doc_scores=doc_scores,
    ... )
    >>> generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)

####
generate

< source >

( input_ids: TFModelInputType | None = None attention_mask: tf.Tensor | None = None context_input_ids = None context_attention_mask = None doc_scores = None n_docs = None generation_config = None logits_processor = [] **kwargs ) →
`tf.Tensor` of shape `(batch_size * num_return_sequences, sequence_length)`

Parameters

- input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — The sequence used as a prompt for the generation. If `input_ids` is not passed, then `context_input_ids` has to be provided.
- attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- context_input_ids (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Input IDs post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever`, `context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`.

- context_attention_mask (`tf.Tensor` of shape `(batch_size * config.n_docs, config.max_combined_length)`, optional, returned when output_retrieved=True) — Attention mask post-processed from the retrieved documents and the question encoder `input_ids` by the retriever.

  If the model has is not initialized with a `retriever`, `context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`.

- doc_scores (`tf.Tensor` of shape `(batch_size, config.n_docs)`) — Score between each retrieved document embeddings (see `retrieved_doc_embeds`) and `question_encoder_last_hidden_state`.

  If the model has is not initialized with a `retriever`, `context_input_ids` has to be provided to the forward pass. `context_input_ids` are returned by `__call__()`.

- n_docs (`int`, optional, defaults to `config.n_docs`) — Number of documents to retrieve and/or number of documents for which to generate an answer.
- generation_config (`~generation.GenerationConfig`, optional) — The generation configuration to be used as base parametrization for the generation call. `**kwargs` passed to generate matching the attributes of `generation_config` will override them. If `generation_config` is not provided, the default will be used, which had the following loading priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit GenerationConfig’s default values, whose documentation should be checked to parameterize generation.
- logits_processor (`TFLogitsProcessorList`, optional) — Custom logits processors that complement the default logits processors built from arguments and a model’s config. If a logit processor is passed that is already created with the arguments or a model’s config an error is thrown.
- kwargs (`Dict[str, Any]`, optional) — Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model.

Returns

`tf.Tensor` of shape `(batch_size * num_return_sequences, sequence_length)`

The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.

Implements TFRAG token decoding.

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