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RAG

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This model was released on 2020-05-22 and added to Hugging Face Transformers on 2020-11-16.

RAG

PyTorch FlashAttention

Retrieval-Augmented Generation (RAG) combines a pretrained language model (parametric memory) with access to an external data source (non-parametric memory) by means of a pretrained neural retriever. RAG fetches relevant passages and conditions its generation on them during inference. This often makes the answers more factual and lets you update knowledge by changing the index instead of retraining the whole model.

You can find all the original RAG checkpoints under the AI at Meta organization.

This model was contributed by ola13.

Click on the RAG models in the right sidebar for more examples of how to apply RAG to different language tasks.

The examples below demonstrates how to generate text with AutoModel.

AutoModel 
import torch
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration

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

model = RagSequenceForGeneration.from_pretrained(
    "facebook/rag-token-nq",
    retriever=retriever,
    dtype="auto",
    attn_implementation="flash_attention_2",
)
input_dict = tokenizer.prepare_seq2seq_batch("How many people live in Paris?", return_tensors="pt")
generated = model.generate(input_ids=input_dict["input_ids"])
print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0])

Quantization reduces memory by storing weights in lower precision. See the Quantization overview for supported backends. The example below uses bitsandbytes to quantize the weights to 4-bits.

import torch
from transformers import BitsAndBytesConfig, RagTokenizer, RagRetriever, RagSequenceForGeneration

bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)

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

model = RagSequenceForGeneration.from_pretrained(
    "facebook/rag-token-nq",
    retriever=retriever,
    quantization_config=bnb,   # quantizes generator weights
    device_map="auto",
)
input_dict = tokenizer.prepare_seq2seq_batch("How many people live in Paris?", return_tensors="pt")
generated = model.generate(input_ids=input_dict["input_ids"])
print(tokenizer.batch_decode(generated, skip_special_tokens=True)[0])

RagConfig

class transformers.RagConfig

< >

( 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

< >

( 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

< >

( question_encoder generator )

Rag specific outputs

class transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput

< >

( loss: typing.Optional[torch.FloatTensor] = None logits: typing.Optional[torch.FloatTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None past_key_values: typing.Optional[transformers.cache_utils.Cache] = 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[tuple[torch.FloatTensor, ...]] = None question_enc_attentions: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_enc_last_hidden_state: typing.Optional[torch.FloatTensor] = None generator_enc_hidden_states: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_enc_attentions: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_dec_hidden_states: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_dec_attentions: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_cross_attentions: typing.Optional[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 (Cache, 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

< >

( logits: typing.Optional[torch.FloatTensor] = None doc_scores: typing.Optional[torch.FloatTensor] = None past_key_values: typing.Optional[transformers.cache_utils.Cache] = 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[tuple[torch.FloatTensor, ...]] = None question_enc_attentions: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_enc_last_hidden_state: typing.Optional[torch.FloatTensor] = None generator_enc_hidden_states: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_enc_attentions: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_dec_hidden_states: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_dec_attentions: typing.Optional[tuple[torch.FloatTensor, ...]] = None generator_cross_attentions: typing.Optional[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 (Cache, 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

< >

( 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 supported index (e.g., Faiss or other index types depending on your setup)
>>> 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"  # 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

< >

( )

Retriever initialization function. It loads the index into memory.

postprocess_docs

< >

( 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

< >

( 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.

RagModel

class transformers.RagModel

< >

( 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 (PretrainedConfig, optional) — 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, optional) — The model responsible for encoding the question into hidden states for retrieval.
  • generator (PreTrainedModel, optional) — The model responsible for generating text based on retrieved documents.
  • retriever (RagRetriever, optional) — The component responsible for retrieving documents from a knowledge base given the encoded question.

The bare Rag Model outputting raw hidden-states without any specific head on top.

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

< >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[tuple[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[transformers.cache_utils.Cache] = 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_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are decoder input IDs?

  • 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 (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • 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) — 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) — The number of documents to retrieve.

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 (Cache, 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

< >

( 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 (PretrainedConfig, optional) — 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, optional) — The model responsible for encoding the question into hidden states for retrieval.
  • generator (PreTrainedModel, optional) — The model responsible for generating text based on retrieved documents.
  • retriever (RagRetriever, optional) — The component responsible for retrieving documents from a knowledge base given the encoded question.

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

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

< >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[tuple[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[transformers.cache_utils.Cache] = 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_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are decoder input IDs?

  • 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 (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • 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__().
  • 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.
  • use_cache (bool, optional) — 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.
  • 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.
  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].
  • n_docs (int, optional) — The number of documents to retrieve.

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 (Cache, 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

< >

( 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/main/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

< >

( 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 (PretrainedConfig, optional) — 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, optional) — The model responsible for encoding the question into hidden states for retrieval.
  • generator (PreTrainedModel, optional) — The model responsible for generating text based on retrieved documents.
  • retriever (RagRetriever, optional) — The component responsible for retrieving documents from a knowledge base given the encoded question.

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

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

< >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None encoder_outputs: typing.Optional[tuple[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[transformers.cache_utils.Cache] = 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.FloatTensor 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_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are decoder input IDs?

  • 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 (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • 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__().
  • 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.
  • use_cache (bool, optional) — 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.
  • 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.
  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].
  • n_docs (int, optional) — The number of documents to retrieve.

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 (Cache, 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

< >

( 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.Optional[typing.Callable[[int, torch.Tensor], 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.

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