Source code for transformers.models.rag.modeling_tf_rag

# coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
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"""TFRAG model implementation."""

from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple

import numpy as np
import tensorflow as tf

from ...configuration_utils import PretrainedConfig
from ...file_utils import ModelOutput, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...modeling_tf_outputs import TFBaseModelOutput
from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, input_processing, shape_list
from ...utils import logging
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "RagConfig"


@dataclass
class TFRetrievAugLMMarginOutput(ModelOutput):
    """
    Base class for retriever augmented marginalized models outputs.

    Args:
        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
            Language modeling loss.
        logits (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
            List of :obj:`tf.Tensor` of length :obj:`config.n_layers`, with each tensor of shape :obj:`(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 :obj:`past_key_values` input) to speed up sequential decoding.
        doc_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.n_docs)`):
            Score between each retrieved document embeddings (see :obj:`retrieved_doc_embeds`) and
            :obj:`question_encoder_last_hidden_state`.
        retrieved_doc_embeds (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`tf.Tensor` (int32) of shape :obj:`(batch_size, config.n_docs)`, `optional`, returned when `output_retrieved=True`):
            The indexes of the embedded documents retrieved by the retriever.
        context_input_ids (:obj:`tf.Tensor`(int32) of shape :obj:`(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 (:obj:`tf.Tensor` (int32) of shape :obj:`(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 :obj:`input_ids` by the
            retriever.
        question_encoder_last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of
            shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(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 (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of
            shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of
            shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(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.
    """

    loss: Optional[tf.Tensor] = None
    logits: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    doc_scores: Optional[tf.Tensor] = None
    retrieved_doc_embeds: Optional[tf.Tensor] = None
    retrieved_doc_ids: Optional[tf.Tensor] = None
    context_input_ids: Optional[tf.Tensor] = None
    context_attention_mask: Optional[tf.Tensor] = None
    question_encoder_last_hidden_state: Optional[tf.Tensor] = None
    question_enc_hidden_states: Optional[Tuple[tf.Tensor]] = None
    question_enc_attentions: Optional[Tuple[tf.Tensor]] = None
    generator_enc_last_hidden_state: Optional[tf.Tensor] = None
    generator_enc_hidden_states: Optional[Tuple[tf.Tensor]] = None
    generator_enc_attentions: Optional[Tuple[tf.Tensor]] = None
    generator_dec_hidden_states: Optional[Tuple[tf.Tensor]] = None
    generator_dec_attentions: Optional[Tuple[tf.Tensor]] = None


@dataclass
class TFRetrievAugLMOutput(ModelOutput):
    """
    Args:
        logits (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
            List of :obj:`tf.Tensor` of length :obj:`config.n_layers`, with each tensor of shape :obj:`(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 :obj:`past_key_values` input) to speed up sequential decoding.
        doc_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.n_docs)`):
            Score between each retrieved document embeddings (see :obj:`retrieved_doc_embeds`) and
            :obj:`question_encoder_last_hidden_state`.
        retrieved_doc_embeds (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.n_docs)`, `optional`, returned when `output_retrieved=True`):
            The indexes of the embedded documents retrieved by the retriever.
        context_input_ids (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`tf.Tensor` of shape :obj:`(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 :obj:`input_ids` by the
            retriever.
        question_encoder_last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of
            shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(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 (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of
            shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`tf.Tensor` (one for the output of the embeddings and one for the output of each layer) of
            shape :obj:`(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 (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(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.
    """

    logits: tf.Tensor = None
    past_key_values: Optional[List[tf.Tensor]] = None
    doc_scores: Optional[tf.Tensor] = None
    retrieved_doc_embeds: Optional[tf.Tensor] = None
    retrieved_doc_ids: Optional[tf.Tensor] = None
    context_input_ids: Optional[tf.Tensor] = None
    context_attention_mask: Optional[tf.Tensor] = None
    question_encoder_last_hidden_state: Optional[tf.Tensor] = None
    question_enc_hidden_states: Optional[Tuple[tf.Tensor]] = None
    question_enc_attentions: Optional[Tuple[tf.Tensor]] = None
    generator_enc_last_hidden_state: Optional[tf.Tensor] = None
    generator_enc_hidden_states: Optional[Tuple[tf.Tensor]] = None
    generator_enc_attentions: Optional[Tuple[tf.Tensor]] = None
    generator_dec_hidden_states: Optional[Tuple[tf.Tensor]] = None
    generator_dec_attentions: Optional[Tuple[tf.Tensor]] = None


class TFRagPreTrainedModel(TFPreTrainedModel):
    r"""
    RAG models were released with the paper `Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
    <https://arxiv.org/abs/2005.11401>`__ by Patrick Lewis, Ethan Perez, Aleksandra Piktus et al.

    RAG is a retriever augmented model and encapsulate three components: a question encoder, a dataset retriever and a
    generator, the encoder and generator are trainable while the retriever is just an indexed dataset.

    """
    config_class = RagConfig
    base_model_prefix = "rag"
    _keys_to_ignore_on_load_missing = [r"position_ids"]

    @classmethod
    def from_pretrained_question_encoder_generator(
        cls,
        question_encoder_pretrained_model_name_or_path: str = None,
        generator_pretrained_model_name_or_path: str = None,
        retriever: RagRetriever = None,
        *model_args,
        **kwargs
    ) -> TFPreTrainedModel:
        r"""
        Instantiates an question encoder and a generator from one or two base classes of the library from pretrained
        model checkpoints.

        Params:
            question_encoder_pretrained_model_name_or_path (:obj: `str`, `optional`):
                Information necessary to initiate the question encoder. Can be either:

                    - A string with the `shortcut name` of a pretrained model to load from cache or download, e.g.,
                      ``bert-base-uncased``.
                    - A string with the `identifier name` of a pretrained model that was user-uploaded to our S3, e.g.,
                      ``dbmdz/bert-base-german-cased``.
                    - A path to a `directory` containing model weights saved using
                      :func:`~transformers.TFPreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
                    - A path or url to a `pytorch index checkpoint file` (e.g, ``./pt_model/``). In this case,
                      ``question_encoder_from_pt`` should be set to :obj:`True`.

            generator_pretrained_model_name_or_path (:obj: `str`, `optional`, defaults to `None`):
                Information necessary to initiate the generator. Can be either:

                    - A string with the `shortcut name` of a pretrained model to load from cache or download, e.g.,
                      ``t5-small``.
                    - A string with the `identifier name` of a pretrained model that was user-uploaded to our S3, e.g.,
                      ``facebook/bart-base``.
                    - A path to a `directory` containing model weights saved using
                      :func:`~transformers.TFPreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
                    - A path or url to a `pytorch checkpoint file` (e.g, ``./pt_model/``). In this case,
                      ``generator_from_pt`` should be set to :obj:`True`.

            model_args (remaining positional arguments, `optional`):
                All remaning positional arguments will be passed to the underlying model's ``__init__`` method.
            retriever (:class:`~transformers.RagRetriever`, `optional`):
                The retriever to use.
            kwargs (remaining dictionary of keyword arguments, `optional`):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                ``output_attentions=True``).

                - To update the question_encoder configuration, use the prefix `question_encoder_` for each
                  configuration parameter.
                - To update the generator configuration, use the prefix `generator_` for each configuration parameter.
                - To update the parent model configuration, do not use a prefix for each configuration parameter.

                Behaves differently depending on whether a :obj:`config` is provided or automatically loaded.

        Example::

            >>> from transformers import RagRetriever, TFRagModel
            >>> # initialize a RAG from two pretrained models.
            >>> model = TFRagModel.from_pretrained_question_encoder_generator('facebook/dpr-question_encoder-single-nq-base', 't5-small')
            >>> # alternatively, initialize from pytorch pretrained models can also be done
            >>> model = TFRagModel.from_pretrained_question_encoder_generator('facebook/dpr-question_encoder-single-nq-base', "facebook/bart-base", generator_from_pt=True, question_encoder_from_pt=True)

            >>> # saving model after fine-tuning
            >>> model.save_pretrained("./rag")

            >>> # load retriever
            >>> retriever = RagRetriever.from_pretrained(PATH, index_name="exact", use_dummy_dataset=True)
            >>> # load fine-tuned model with retriever
            >>> model = TFRagModel.from_pretrained("./rag", retriever=retriever)
        """

        kwargs_question_encoder = {
            argument[len("question_encoder_") :]: value
            for argument, value in kwargs.items()
            if argument.startswith("question_encoder_")
        }

        kwargs_generator = {
            argument[len("generator_") :]: value
            for argument, value in kwargs.items()
            if argument.startswith("generator_")
        }

        # remove question_encoder, generator kwargs from kwargs
        for key in kwargs_question_encoder.keys():
            del kwargs["question_encoder_" + key]
        for key in kwargs_generator.keys():
            del kwargs["generator_" + key]

        # Load and initialize the question_encoder and generator
        # The distinction between question_encoder and generator at the model level is made
        # by the value of the flag `is_generator` that we need to set correctly.
        question_encoder = kwargs_question_encoder.pop("model", None)
        if question_encoder is None:
            assert (
                question_encoder_pretrained_model_name_or_path is not None
            ), "If `model` is not defined as an argument, a `question_encoder_pretrained_model_name_or_path` has to be defined"

            from ..auto.modeling_tf_auto import TFAutoModel

            if "config" not in kwargs_question_encoder:
                from ..auto.configuration_auto import AutoConfig

                question_encoder_config = AutoConfig.from_pretrained(question_encoder_pretrained_model_name_or_path)
                kwargs_question_encoder["config"] = question_encoder_config

            question_encoder = TFAutoModel.from_pretrained(
                question_encoder_pretrained_model_name_or_path,
                name="question_encoder",
                load_weight_prefix=cls.load_weight_prefix,
                *model_args,
                **kwargs_question_encoder,
            )

        generator = kwargs_generator.pop("generator", None)
        if generator is None:
            assert (
                generator_pretrained_model_name_or_path is not None
            ), "If `generator_model` is not defined as an argument, a `generator_pretrained_model_name_or_path` has to be defined"

            from ..auto.modeling_tf_auto import TFAutoModelForSeq2SeqLM

            if "config" not in kwargs_generator:
                from ..auto.configuration_auto import AutoConfig

                generator_config = AutoConfig.from_pretrained(generator_pretrained_model_name_or_path)
                kwargs_generator["config"] = generator_config

            generator = TFAutoModelForSeq2SeqLM.from_pretrained(
                generator_pretrained_model_name_or_path,
                name="generator",
                load_weight_prefix=cls.load_weight_prefix,
                **kwargs_generator,
            )

        # instantiate config with corresponding kwargs
        config = kwargs.get("config", None)
        if config is None:
            config = RagConfig.from_question_encoder_generator_configs(
                question_encoder.config, generator.config, **kwargs
            )

        return cls(question_encoder=question_encoder, generator=generator, config=config, retriever=retriever)


RAG_START_DOCSTRING = r"""

    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 :class:`~transformers.TFDPRQuestionEncoder`, and
    the generator can be any `seq2seq` model, preferably :class:`~transformers.TFBartForConditionalGeneration`.

    The model can be initialized with a :class:`~transformers.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 :class:`~transformers.TFDPRQuestionEncoder` as the ``question_encoder``
    and :class:`~transformers.TFBartForConditionalGeneration` as the ``generator``.

    This model inherits from :class:`~transformers.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 `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/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.

    Args:
        config (:class:`~transformers.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
            :meth:`~transformers.TFPreTrainedModel.from_pretrained` method to load the model weights.
        question_encoder (:class:`transformers.TFPreTrainedModel`):
            An encoder model compatible with the faiss index encapsulated by the ``retriever``.
        generator (:class:`transformers.TFPreTrainedModel`):
            A seq2seq model used as the generator in the RAG architecture.
        retriever (:class:`~transformers.RagRetriever`):
            A retriever class encapsulating a faiss index queried to obtain context documents for current inputs.
"""


RAG_FORWARD_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. :class:`~transformers.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 (:obj:`tf.Tensor` of shape :obj:`(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? <../glossary.html#attention-mask>`__
        encoder_outputs (:obj:`tuple(tuple(tf.Tensor)`, `optional`)
            Tuple consists of (:obj:`generator_enc_last_hidden_state`, `optional`: :obj:`generator_enc_hidden_states`,
            `optional`: :obj:`generator_enc_attentions`). :obj:`generator_enc_last_hidden_state` of shape
            :obj:`(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 (:class:`~transformers.TFRagModel`) model during decoding.
        decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`torch.BoolTensor` of shape :obj:`(batch_size,  target_sequence_length)`, `optional`):
            Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
            also be used by default.
        past_key_values (:obj:`tuple(tuple(tf.Tensor))`):
            Tuple consists of two elements: :obj:`encoder_outputs` of the RAG model (see :obj:`encoder_outputs`) and
            :obj:`past_key_values` of the underlying generator. Can be used to speed up decoding.
            :obj:`past_key_values` are used in the (:class:`~transformers.RagTokenForGeneration`) model during
            decoding.
        doc_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.n_docs)`):
            Score between each retrieved document embeddings (see :obj:`retrieved_doc_embeds`) and
            :obj:`question_encoder_last_hidden_state`. If the model has is not initialized with a ``retriever``
            :obj:`doc_scores` has to be provided to the forward pass. :obj:`doc_scores` can be computed via
            :obj:`question_encoder_last_hidden_state` and :obj:`retrieved_doc_embeds`, see examples for more
            information.
        context_input_ids (:obj:`tf.Tensor` of shape :obj:`(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 :obj:`input_ids` by the
            retriever.

            If the model has is not initialized with a ``retriever`` :obj:`context_input_ids` has to be provided to the
            forward pass. :obj:`context_input_ids` are returned by :meth:`~transformers.RagRetriever.__call__`.
        context_attention_mask (:obj:`tf.Tensor` of shape :obj:`(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 :obj:`input_ids` by the
            retriever.

            If the model has is not initialized with a ``retriever`` :obj:`context_attention_mask` has to be provided
            to the forward pass. :obj:`context_attention_mask` are returned by
            :meth:`~transformers.RagRetriever.__call__`.
        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        output_attentions (:obj:`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 (:obj:`bool`, `optional`):
            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
            more detail.
        output_retrieved(:obj:`bool`, `optional`):
            Whether or not to return the :obj:`retrieved_doc_embeds`, :obj:`retrieved_doc_ids`,
            :obj:`context_input_ids` and :obj:`context_attention_mask`. See returned tensors for more detail.
        return_dict (:obj:`bool`, `optional`):
            Whether or not to return a :class:`~TFRetrievAugLMOutput` instead of a plain tuple.
        n_docs (:obj:`int`, `optional`, defaults to :obj:`config.n_docs`)
            Number of documents to retrieve and/or number of documents for which to generate an answer.
"""


[docs]@add_start_docstrings_to_model_forward(RAG_START_DOCSTRING) class TFRagModel(TFRagPreTrainedModel): load_weight_prefix = "tf_rag_model_1" def __init__( self, config: Optional[PretrainedConfig] = None, question_encoder: Optional[TFPreTrainedModel] = None, generator: Optional[TFPreTrainedModel] = None, retriever: Optional = None, load_weight_prefix: Optional[str] = None, **kwargs, ): assert config is not None or ( question_encoder is not None and generator is not None ), "Either a configuration or an question_encoder and a generator has to be provided." if config is None: config = RagConfig.from_question_encoder_generator_configs( question_encoder.config, generator.config, **kwargs ) else: assert isinstance(config, self.config_class), f"config: {config} has to be of type {self.config_class}" super().__init__(config, **kwargs) if question_encoder is None: from ..auto.modeling_tf_auto import TFAutoModel question_encoder = TFAutoModel.from_config(config.question_encoder, name="question_encoder") if generator is None: from ..auto.modeling_tf_auto import TFAutoModelForSeq2SeqLM load_weight_prefix = load_weight_prefix if load_weight_prefix is not None else self.load_weight_prefix generator = TFAutoModelForSeq2SeqLM.from_config( config.generator, name="generator", load_weight_prefix=load_weight_prefix + "/generator" ) self.retriever = retriever if self.retriever is not None: assert isinstance( retriever, RagRetriever ), f"`self.retriever` is of type {type(self.retriever)}, but should be of type `RagRetriever`" self.retriever = retriever self.question_encoder = question_encoder self.generator = generator def set_retriever(self, retriever: RagRetriever): self.retriever = retriever
[docs] @add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFRetrievAugLMOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids=None, attention_mask=None, encoder_outputs=None, decoder_input_ids=None, decoder_attention_mask=None, past_key_values=None, doc_scores=None, context_input_ids=None, context_attention_mask=None, use_cache=None, output_attentions=None, output_hidden_states=None, output_retrieved=None, n_docs=None, return_dict=None, training=False, **kwargs ): r""" Returns: Example:: >>> from transformers import RagTokenizer, RagRetriever, RagModel >>> import torch >>> tokenizer = RagTokenizer.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) """ assert ( "decoder_cached_states" not in kwargs ), "Please use past_key_values to cache intermediate outputs" # from modeling_tf_bart.py inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, doc_scores=doc_scores, context_input_ids=context_input_ids, context_attention_mask=context_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_retrieved=output_retrieved, return_dict=return_dict, n_docs=n_docs, training=training, kwargs_call=kwargs, ) # aliasing to minimize code changing input_ids = inputs["input_ids"] attention_mask = inputs["attention_mask"] decoder_input_ids = inputs["decoder_input_ids"] decoder_attention_mask = inputs["decoder_attention_mask"] encoder_outputs = inputs["encoder_outputs"] past_key_values = inputs["past_key_values"] doc_scores = inputs["doc_scores"] context_input_ids = inputs["context_input_ids"] context_attention_mask = inputs["context_attention_mask"] use_cache = inputs["use_cache"] output_attentions = inputs["output_attentions"] output_hidden_states = inputs["output_hidden_states"] return_dict = inputs["return_dict"] n_docs = inputs["n_docs"] if inputs["n_docs"] is not None else self.config.n_docs output_retrieved = inputs["output_retrieved"] training = inputs["training"] # whether retriever has to be used has_to_retrieve = ( self.retriever is not None and (context_input_ids is None or context_attention_mask is None or doc_scores is None) and encoder_outputs is None ) # encoder_outputs are pre-computed during RAG-token generation if encoder_outputs is None: if has_to_retrieve: question_enc_outputs = self.question_encoder( input_ids, attention_mask=attention_mask, return_dict=True, training=training ) # see https://github.com/huggingface/transformers/blob/master/src/transformers/models/dpr/modeling_tf_dpr.py#L91 question_encoder_last_hidden_state = question_enc_outputs[ 0 ] # hidden states of question encoder => pooler_output retriever_outputs = self.retriever( input_ids, question_encoder_last_hidden_state.numpy(), prefix=self.generator.config.prefix, n_docs=n_docs, return_tensors="tf", ) context_input_ids, context_attention_mask, retrieved_doc_embeds, retrieved_doc_ids = ( retriever_outputs["context_input_ids"], retriever_outputs["context_attention_mask"], retriever_outputs["retrieved_doc_embeds"], retriever_outputs["doc_ids"], ) context_input_ids = tf.cast(context_input_ids, tf.int32) context_attention_mask = tf.cast(context_attention_mask, tf.int32) retrieved_doc_embeds = tf.cast(retrieved_doc_embeds, tf.float32) retrieved_doc_ids = tf.cast(retrieved_doc_ids, tf.int32) # compute doc_scores doc_scores = tf.squeeze( tf.matmul( tf.expand_dims(question_encoder_last_hidden_state, axis=1), retrieved_doc_embeds, transpose_b=True, ), axis=1, ) else: assert ( context_input_ids is not None ), "Make sure that `context_input_ids` are passed, if no `retriever` is set. Alternatively, you can set a retriever using the `set_retriever(...)` function." assert ( context_attention_mask is not None ), "Make sure that `context_attention_mask` are passed, if no `retriever` is set. Alternatively, you can set a retriever using the `set_retriever(...)` function." assert ( doc_scores is not None ), "Make sure that `doc_scores` are passed, if no `retriever` is set. Alternatively, you can set a retriever using the `set_retriever(...)` function." assert ( doc_scores is not None ), "Make sure that `doc_scores` are passed when passing `encoder_outputs` to the forward function." assert ( doc_scores.shape[1] % n_docs ) == 0, f" The first dimension of `context_input_ids` should be a multiple of `n_docs`={n_docs}, but is {context_input_ids.shape[0]}." # Decoder input without context documents if decoder_input_ids is not None: decoder_input_ids = tf.repeat(decoder_input_ids, n_docs, axis=0) if decoder_attention_mask is not None: decoder_attention_mask = tf.repeat(decoder_attention_mask, n_docs, axis=0) gen_outputs = self.generator( context_input_ids, attention_mask=context_attention_mask, encoder_outputs=encoder_outputs, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, return_dict=True, training=training, ) if not has_to_retrieve: question_encoder_last_hidden_state = None question_enc_hidden_states = None question_enc_attentions = None retrieved_doc_embeds = None retrieved_doc_ids = None else: question_enc_hidden_states = question_enc_outputs.hidden_states question_enc_attentions = question_enc_outputs.attentions if not has_to_retrieve or not output_retrieved: # don't output retrieved docs context_input_ids = (None,) context_attention_mask = None retrieved_doc_embeds = None retrieved_doc_ids = None return TFRetrievAugLMOutput( logits=gen_outputs.logits, doc_scores=doc_scores, past_key_values=gen_outputs.past_key_values, context_input_ids=context_input_ids, context_attention_mask=context_attention_mask, retrieved_doc_embeds=retrieved_doc_embeds, retrieved_doc_ids=retrieved_doc_ids, question_encoder_last_hidden_state=question_encoder_last_hidden_state, question_enc_hidden_states=question_enc_hidden_states, question_enc_attentions=question_enc_attentions, generator_enc_last_hidden_state=gen_outputs.encoder_last_hidden_state, generator_enc_hidden_states=gen_outputs.encoder_hidden_states, generator_enc_attentions=gen_outputs.encoder_attentions, generator_dec_hidden_states=gen_outputs.decoder_hidden_states, generator_dec_attentions=gen_outputs.decoder_attentions, )
[docs]@add_start_docstrings_to_model_forward( """ A TF RAG-token model implementation. It performs RAG-token specific marginalization in the forward pass. """, RAG_START_DOCSTRING, ) class TFRagTokenForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingLoss): load_weight_prefix = "tf_rag_token_for_generation_1/rag" def __init__( self, config: Optional[PretrainedConfig] = None, question_encoder: Optional[TFPreTrainedModel] = None, generator: Optional[TFPreTrainedModel] = None, retriever: Optional = None, **kwargs, ): assert config is not None or ( question_encoder is not None and generator is not None ), "Either a configuration or an encoder and a generator has to be provided." if config is None: config = RagConfig.from_question_encoder_generator_configs( question_encoder.config, generator.config, **kwargs ) super().__init__(config) # instantiate model self.rag = TFRagModel( config=config, question_encoder=question_encoder, generator=generator, retriever=retriever, load_weight_prefix=self.load_weight_prefix, name="rag", ) def set_retriever(self, retriever: RagRetriever): self.rag.retriever = retriever # Adapted from https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_tf_bart.py def prepare_inputs_for_generation( self, decoder_input_ids, past, attention_mask, use_cache, doc_scores, n_docs=None, **kwargs ) -> Dict: assert past is not None and len(past) in {1, 2}, f"past has to be an iterable of length 1,2 got {past}" if len(past) == 1: assert isinstance(past[0], tf.Tensor) encoder_outputs = TFBaseModelOutput(last_hidden_state=past[0]) decoder_cached_states = None else: assert len(past) == 2 # Note: encoder_outputs is never changed by Bart as a generator encoder_outputs, decoder_cached_states = past if isinstance(encoder_outputs, tuple): assert isinstance(encoder_outputs[0], tf.Tensor) encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_outputs[0]) elif isinstance(encoder_outputs, tf.Tensor): encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_outputs) assert ( decoder_cached_states ), f"decoder cached states must be truthy. got {decoder_cached_states} from the 2nd element of past" # if past is defined cut decoder_input_ids to last token decoder_input_ids = decoder_input_ids[:, -1:] assert isinstance( encoder_outputs, TFBaseModelOutput ), f"encoder_outputs should be a TFBaseModelOutput, Instead got {type(encoder_outputs)}." return { "input_ids": None, # encoder_outputs is defined. input_ids not needed "encoder_outputs": encoder_outputs, "doc_scores": doc_scores, "context_attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "past_key_values": decoder_cached_states, "use_cache": use_cache, # change this to avoid caching (presumably for debugging) "do_marginalize": True, "n_docs": n_docs, } @property def retriever(self): return self.rag.retriever @property def generator(self): return self.rag.generator @property def question_encoder(self): return self.rag.question_encoder @staticmethod def _reorder_cache(past, beam_idx): """Reorders cache for generation. BART-inspired but we need to take care of the extra dimension for docs""" def tf_index_select(input_, dim, indices): """ Input: input_(tensor): input tensor dim(int): dimension indices(list): selected indices list Output: mimic of torch_tensor.index_select(dim, indices) credit: https://stackoverflow.com/questions/58464790/is-there-an-equivalent-function-of-pytorch-named-index-select-in-tensorflow """ shape = shape_list(input_) if dim == -1: dim = len(shape) - 1 shape[dim] = 1 tmp = [] for idx in indices: begin = [0] * len(shape) begin[dim] = idx tmp.append(tf.slice(input_, begin, shape)) res = tf.concat(tmp, axis=dim) return res def _reorder_stacked(hidden_states, new_order=beam_idx): n_docs = hidden_states.shape[0] // new_order.shape[0] hidden_states = tf.reshape(hidden_states, (-1, n_docs, *hidden_states.shape[1:])) hidden_states = tf_index_select(hidden_states, 0, new_order) return tf.reshape(hidden_states, (-1, *hidden_states.shape[2:])) if len(past) == 1: return past past_key_values = past[1] reordered_past = () for layer_past in past_key_values: reordered_past += (tuple(_reorder_stacked(past_state, beam_idx) for past_state in layer_past),) return (past[0], reordered_past) def marginalize(self, seq_logits, doc_scores, n_docs=None): n_docs = n_docs if n_docs is not None else self.config.n_docs # RAG-token marginalization seq_logprobs = tf.nn.log_softmax(seq_logits, axis=-1) seq_logprobs = tf.reshape(seq_logprobs, [seq_logits.shape[0] // n_docs, n_docs, -1, seq_logits.shape[-1]]) doc_logprobs = tf.nn.log_softmax(doc_scores, axis=1) doc_logprobs = tf.expand_dims(doc_logprobs, axis=-1) doc_logprobs = tf.expand_dims(doc_logprobs, axis=-1) # twice log_prob_sum = seq_logprobs + doc_logprobs return tf.reduce_logsumexp(log_prob_sum, axis=1)
[docs] @add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFRetrievAugLMMarginOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_outputs=None, past_key_values=None, doc_scores=None, context_input_ids=None, context_attention_mask=None, use_cache=None, output_attentions=None, output_hidden_states=None, output_retrieved=None, n_docs=None, do_marginalize=None, labels=None, reduce_loss=None, return_dict=None, training=False, **kwargs # needs kwargs for generation ): r""" do_marginalize (:obj:`bool`, `optional`): If :obj:`True`, the logits are marginalized over all documents by making use of ``torch.nn.functional.log_softmax``. labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(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 (:obj:`bool`, `optional`): Only relevant if ``labels`` is passed. If :obj:`True`, the NLL loss is reduced using the ``tf.Tensor.sum`` operation. kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`): Legacy dictionary, which is required so that model can use `generate()` function. Returns: Example:: >>> from transformers import RagTokenizer, RagRetriever, TFRagTokenForGeneration >>> tokenizer = RagTokenizer.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) """ assert ( "decoder_cached_states" not in kwargs ), "Please use past_key_values to cache intermediate outputs" # from modeling_tf_bart.py inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, doc_scores=doc_scores, context_input_ids=context_input_ids, context_attention_mask=context_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_retrieved=output_retrieved, n_docs=n_docs, do_marginalize=do_marginalize, labels=labels, reduce_loss=reduce_loss, return_dict=return_dict, training=training, kwargs_call=kwargs, ) inputs["do_marginalize"] = inputs["do_marginalize"] if inputs["do_marginalize"] else self.config.do_marginalize inputs["reduce_loss"] = inputs["reduce_loss"] if inputs["reduce_loss"] else self.config.reduce_loss if inputs["labels"] is not None: if inputs["decoder_input_ids"] is None: inputs["decoder_input_ids"] = inputs["labels"] inputs["use_cache"] = False outputs = self.rag( inputs["input_ids"], attention_mask=inputs["attention_mask"], encoder_outputs=inputs["encoder_outputs"], decoder_input_ids=inputs["decoder_input_ids"], decoder_attention_mask=inputs["decoder_attention_mask"], context_input_ids=inputs["context_input_ids"], context_attention_mask=inputs["context_attention_mask"], doc_scores=inputs["doc_scores"], past_key_values=inputs["past_key_values"], use_cache=inputs["use_cache"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], output_retrieved=inputs["output_retrieved"], n_docs=inputs["n_docs"], training=inputs["training"], ) loss = None logits = outputs.logits if inputs["labels"] is not None: assert inputs["decoder_input_ids"] is not None loss = self.get_nll( outputs.logits, outputs.doc_scores, inputs["labels"], reduce_loss=inputs["reduce_loss"], epsilon=self.config.label_smoothing, n_docs=inputs["n_docs"], ) if inputs["do_marginalize"]: logits = self.marginalize(logits, outputs.doc_scores, inputs["n_docs"]) return TFRetrievAugLMMarginOutput( loss=loss, logits=logits, past_key_values=outputs.past_key_values, doc_scores=outputs.doc_scores, context_input_ids=outputs.context_input_ids, context_attention_mask=outputs.context_attention_mask, retrieved_doc_embeds=outputs.retrieved_doc_embeds, retrieved_doc_ids=outputs.retrieved_doc_ids, question_encoder_last_hidden_state=outputs.question_encoder_last_hidden_state, question_enc_hidden_states=outputs.question_enc_hidden_states, question_enc_attentions=outputs.question_enc_attentions, generator_enc_last_hidden_state=outputs.generator_enc_last_hidden_state, generator_enc_hidden_states=outputs.generator_enc_hidden_states, generator_enc_attentions=outputs.generator_enc_attentions, generator_dec_hidden_states=outputs.generator_dec_hidden_states, generator_dec_attentions=outputs.generator_dec_attentions, )
[docs] def generate( self, input_ids: Optional[tf.Tensor] = None, attention_mask: Optional[tf.Tensor] = None, context_input_ids=None, context_attention_mask=None, doc_scores=None, max_length=None, min_length=None, early_stopping=None, use_cache=None, num_beams=None, bos_token_id=None, pad_token_id=None, eos_token_id=None, length_penalty=None, no_repeat_ngram_size=None, bad_words_ids=None, num_return_sequences=None, decoder_start_token_id=None, n_docs=None, output_scores=None, output_attentions=None, output_hidden_states=None, return_dict_in_generate=None, **model_kwargs ): """ Implements TFRAG token decoding. Args: input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): The sequence used as a prompt for the generation. If :obj:`input_ids` is not passed, then :obj:`context_input_ids` has to be provided. attention_mask (:obj:`tf.Tensor` of shape :obj:`(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? <../glossary.html#attention-mask>`__ context_input_ids (:obj:`tf.Tensor` of shape :obj:`(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 :obj:`input_ids` by the retriever. If the model has is not initialized with a ``retriever``, :obj:`context_input_ids` has to be provided to the forward pass. :obj:`context_input_ids` are returned by :meth:`~transformers.RagRetriever.__call__`. context_attention_mask (:obj:`tf.Tensor` of shape :obj:`(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 :obj:`input_ids` by the retriever. If the model has is not initialized with a ``retriever``, :obj:`context_input_ids` has to be provided to the forward pass. :obj:`context_input_ids` are returned by :meth:`~transformers.RagRetriever.__call__`. doc_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.n_docs)`): Score between each retrieved document embeddings (see :obj:`retrieved_doc_embeds`) and :obj:`question_encoder_last_hidden_state`. If the model has is not initialized with a ``retriever``, :obj:`context_input_ids` has to be provided to the forward pass. :obj:`context_input_ids` are returned by :meth:`~transformers.RagRetriever.__call__`. max_length (:obj:`int`, `optional`, defaults to 20): The maximum length of the sequence to be generated. min_length (:obj:`int`, `optional`, defaults to 10): The minimum length of the sequence to be generated. early_stopping (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to stop the beam search when at least ``num_beams`` sentences are finished per batch or not. use_cache: (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should use the past last key/values attentions (if applicable to the model) to speed up decoding. pad_token_id (:obj:`int`, `optional`): The id of the `padding` token. bos_token_id (:obj:`int`, `optional`): The id of the `beginning-of-sequence` token. eos_token_id (:obj:`int`, `optional`): The id of the `end-of-sequence` token. length_penalty (:obj:`float`, `optional`, defaults to 1.0): Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer sequences. no_repeat_ngram_size (:obj:`int`, `optional`, defaults to 0): If set to int > 0, all ngrams of that size can only occur once. bad_words_ids(:obj:`List[int]`, `optional`): List of token ids that are not allowed to be generated. In order to get the tokens of the words that should not appear in the generated text, use :obj:`tokenizer.encode(bad_word, add_prefix_space=True)`. num_beams (:obj:`int`, `optional`, defaults to 1): Number of beams for beam search. 1 means no beam search. num_return_sequences(:obj:`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 `:func:`~transformers.generation_utils.GenerationMixin.generate` function, where we set ``num_return_sequences`` to :obj:`num_beams`. decoder_start_token_id (:obj:`int`, `optional`): If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token. n_docs (:obj:`int`, `optional`, defaults to :obj:`config.n_docs`) Number of documents to retrieve and/or number of documents for which to generate an answer. output_attentions (:obj:`bool`, `optional`, defaults to `False`): Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned tensors for more details. output_hidden_states (:obj:`bool`, `optional`, defaults to `False`): Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for more details. output_scores (:obj:`bool`, `optional`, defaults to `False`): Whether or not to return the prediction scores. See ``scores`` under returned tensors for more details. return_dict_in_generate (:obj:`bool`, `optional`, defaults to `False`): Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. model_specific_kwargs: Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. Return: :obj:`tf.Tensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or shorter if all batches finished early due to the :obj:`eos_token_id`. """ # set default parameters n_docs = n_docs if n_docs is not None else self.config.n_docs max_length = max_length if max_length is not None else self.config.max_length min_length = min_length if min_length is not None else self.config.min_length early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping use_cache = use_cache if use_cache is not None else self.config.use_cache num_beams = num_beams if num_beams is not None else self.config.num_beams bos_token_id = bos_token_id if bos_token_id is not None else self.config.generator.bos_token_id pad_token_id = pad_token_id if pad_token_id is not None else self.config.generator.pad_token_id eos_token_id = eos_token_id if eos_token_id is not None else self.config.generator.eos_token_id length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty no_repeat_ngram_size = ( no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size ) bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids num_return_sequences = ( num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences ) decoder_start_token_id = ( decoder_start_token_id if decoder_start_token_id is not None else self.config.generator.decoder_start_token_id ) output_scores = output_scores if output_scores is not None else self.config.output_scores output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict_in_generate = ( return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate ) model_kwargs["output_scores"] = output_scores model_kwargs["output_attentions"] = output_attentions model_kwargs["output_hidden_states"] = output_hidden_states model_kwargs["encoder_attentions"] = None model_kwargs["encoder_hidden_states"] = None # retrieve docs if self.retriever is not None and context_input_ids is None: question_hidden_states = self.question_encoder(input_ids, attention_mask=attention_mask)[0] out = self.retriever( input_ids, question_hidden_states.numpy().astype(np.float32), prefix=self.generator.config.prefix, n_docs=n_docs, return_tensors="tf", ) context_input_ids, context_attention_mask, retrieved_doc_embeds = ( out["context_input_ids"], out["context_attention_mask"], out["retrieved_doc_embeds"], ) context_input_ids = tf.cast(context_input_ids, tf.int32) context_attention_mask = tf.cast(context_attention_mask, tf.int32) retrieved_doc_embeds = tf.cast(retrieved_doc_embeds, tf.float32) # compute doc_scores doc_scores = tf.matmul( tf.expand_dims(question_hidden_states, axis=1), retrieved_doc_embeds, transpose_b=True ) doc_scores = tf.squeeze(doc_scores, axis=1) assert ( context_input_ids.shape[0] % n_docs ) == 0, f" The first dimension of `context_input_ids` should be a multiple of `n_docs`={n_docs}, but is {context_input_ids.shape[0]}." batch_size = context_input_ids.shape[0] // n_docs encoder = self.rag.generator.get_encoder() encoder_outputs = encoder( input_ids=context_input_ids, attention_mask=context_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, ) if return_dict_in_generate: if output_attentions: model_kwargs["encoder_attentions"] = encoder_outputs.attentions if output_hidden_states: model_kwargs["encoder_hidden_states"] = encoder_outputs.hidden_states decoder_input_ids = tf.fill( (batch_size * num_beams, 1), tf.cast(decoder_start_token_id, tf.int32), ) last_hidden_state = encoder_outputs["last_hidden_state"] def extend_enc_output(tensor, num_beams=None): """ Broadcast tensor with `num_beams` replica, with correct order Input: tensor of shape (batch_size*n_docs , d) Output: tensor of shape (batch_size*num_beams*n_docs , d) """ # expand batch_size & num_beam dimensions d_shape_list = tensor.shape[1:] # split n_docs dimensions new_shape = (batch_size, 1, n_docs) + d_shape_list tensor = tf.reshape(tensor, new_shape) # repeat same last hidden states over `num_beams` dimension new_shape = (batch_size, num_beams, n_docs) + d_shape_list tensor = tf.broadcast_to(tensor, new_shape) # merge `batch_size`, `num_beams`, `num_docs` dims again new_shape = (batch_size * num_beams * n_docs,) + d_shape_list return tf.reshape(tensor, new_shape) # correctly extend last_hidden_state and attention mask context_attention_mask = extend_enc_output(context_attention_mask, num_beams=num_beams) encoder_outputs["last_hidden_state"] = extend_enc_output(last_hidden_state, num_beams=num_beams) doc_scores = tf.repeat(doc_scores, num_beams, axis=0) # define start_len & additional parameters cur_len = 1 vocab_size = self.config.generator.vocab_size model_kwargs["doc_scores"] = doc_scores model_kwargs["encoder_outputs"] = encoder_outputs model_kwargs["n_docs"] = n_docs # not needed. TODO(PVP): change after generate refactor do_sample = False temperature = self.config.temperature top_k = self.config.top_k top_p = self.config.top_p repetition_penalty = self.config.repetition_penalty if num_beams > 1: return self._generate_beam_search( decoder_input_ids, cur_len=cur_len, max_length=max_length, min_length=min_length, do_sample=do_sample, early_stopping=early_stopping, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, no_repeat_ngram_size=no_repeat_ngram_size, bad_words_ids=bad_words_ids, pad_token_id=pad_token_id, eos_token_id=eos_token_id, batch_size=batch_size, num_return_sequences=num_return_sequences, length_penalty=length_penalty, num_beams=num_beams, vocab_size=vocab_size, attention_mask=context_attention_mask, use_cache=use_cache, forced_bos_token_id=None, forced_eos_token_id=None, return_dict_in_generate=return_dict_in_generate, **model_kwargs, # encoder_outputs is here as in Pytorch's version ) else: return self._generate_no_beam_search( decoder_input_ids, cur_len=cur_len, max_length=max_length, min_length=min_length, do_sample=do_sample, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, no_repeat_ngram_size=no_repeat_ngram_size, bad_words_ids=bad_words_ids, pad_token_id=pad_token_id, eos_token_id=eos_token_id, batch_size=batch_size, vocab_size=vocab_size, attention_mask=context_attention_mask, use_cache=use_cache, forced_bos_token_id=None, forced_eos_token_id=None, return_dict_in_generate=return_dict_in_generate, **model_kwargs, # encoder_outputs is here as in Pytorch's version )
def get_input_embeddings(self): return self.rag.generator.get_input_embeddings() def get_output_embeddings(self): return self.rag.generator.get_output_embeddings() # Adapted from tf_t5's & tf_bart's _shift_right def shift_tokens_right(self, input_ids, start_token_id=None): """Shift input ids one token to the right, and pad with start_token_id""" if start_token_id is None: start_token_id = self.generator.config.decoder_start_token_id assert ( start_token_id is not None ), "self.generator.config.decoder_start_token_id has to be defined. In Rag we commonly use Bart as generator, see Bart docs for more information" pad_token_id = self.generator.config.pad_token_id assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined." shifted_input_ids = tf.cast(input_ids, tf.int32) start_tokens = tf.fill((shape_list(shifted_input_ids)[0], 1), start_token_id) shifted_input_ids = tf.concat([start_tokens, shifted_input_ids[:, :-1]], -1) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = tf.where( shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids ) # "Verify that `labels` has only positive values and -100" assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.cast(0, tf.int32)) # Make sure the assertion op is called by wrapping the result in an identity no-op with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids) return shifted_input_ids # nll stands for 'negative log likelihood' def get_nll(self, seq_logits, doc_scores, target, reduce_loss=False, epsilon=0.0, n_docs=None): n_docs = n_docs if n_docs is not None else self.config.n_docs # shift tokens left (from original Pytorch's version) target = tf.concat([target[:, 1:], tf.fill([target.shape[0], 1], self.config.generator.pad_token_id)], axis=1) rag_logprobs = self.marginalize(seq_logits, doc_scores, n_docs) loss = self.compute_loss(target, rag_logprobs, from_logits=True, reduce_loss=reduce_loss) return loss # Adopted modeling_tf_bart + add smooth_loss to match with pytorch version def compute_loss(self, labels, y_pred, smooth_epsilon=0.0, from_logits=True, reduce_loss=False): """CrossEntropyLoss that ignores pad tokens""" loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( from_logits=True, reduction=tf.keras.losses.Reduction.SUM, ) if from_logits is False: # convert to logits eps = 1e-9 y_pred = tf.clip_by_value(y_pred, clip_value_min=eps, clip_value_max=1 - eps) y_pred = tf.math.log(y_pred) logits = y_pred melted_labels = tf.reshape(labels, (-1,)) active_loss = tf.not_equal(melted_labels, self.config.generator.pad_token_id) reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, logits.shape[2])), active_loss) labels = tf.boolean_mask(melted_labels, active_loss) nll_loss = loss_fn(labels, reduced_logits) smooth_loss = -tf.reduce_sum(reduced_logits, axis=-1) smooth_loss = tf.reduce_sum(smooth_loss) # sum and squeeze like torch eps_i = smooth_epsilon / reduced_logits.shape[-1] loss = (1.0 - smooth_epsilon) * nll_loss + eps_i * smooth_loss return loss
[docs]@add_start_docstrings_to_model_forward( """ A TF RAG-sequence model implementation. It performs RAG-sequence specific marginalization in the forward pass. """, RAG_START_DOCSTRING, ) class TFRagSequenceForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingLoss): load_weight_prefix = "tf_rag_sequence_for_generation_1/rag" def __init__( self, config: Optional[PretrainedConfig] = None, question_encoder: Optional[TFPreTrainedModel] = None, generator: Optional[TFPreTrainedModel] = None, retriever: Optional = None, **kwargs, ): assert config is not None or ( question_encoder is not None and generator is not None ), "Either a configuration or an encoder and a generator has to be provided." if config is None: config = RagConfig.from_question_encoder_generator_configs( question_encoder.config, generator.config, **kwargs ) super().__init__(config) # instantiate model self.rag = TFRagModel( config=config, question_encoder=question_encoder, generator=generator, retriever=retriever, load_weight_prefix=self.load_weight_prefix, name="rag", ) def set_retriever(self, retriever: RagRetriever): self.rag.retriever = retriever @property def retriever(self): return self.rag.retriever @property def generator(self): return self.rag.generator @property def question_encoder(self): return self.rag.question_encoder
[docs] @add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFRetrievAugLMMarginOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_outputs=None, past_key_values=None, doc_scores=None, context_input_ids=None, context_attention_mask=None, use_cache=None, output_attentions=None, output_hidden_states=None, output_retrieved=None, n_docs=None, exclude_bos_score=None, labels=None, reduce_loss=None, return_dict=None, training=False, **kwargs # needs kwargs for generation ): r""" exclude_bos_score (:obj:`bool`, `optional`): Only relevant if ``labels`` is passed. If :obj:`True`, the score of the BOS token is disregarded when computing the loss. labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(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 (:obj:`bool`, `optional`): Only relevant if ``labels`` is passed. If :obj:`True`, the NLL loss is reduced using the ``tf.Tensor.sum`` operation. kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`): Legacy dictionary, which is required so that model can use `generate()` function. Returns: Example:: >>> from transformers import RagTokenizer, RagRetriever, TFRagSequenceForGeneration >>> tokenizer = RagTokenizer.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 = TFRagRagSequenceForGeneration.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) """ assert ( "decoder_cached_states" not in kwargs ), "Please use past_key_values to cache intermediate outputs" # from modeling_tf_bart.py inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, past_key_values=past_key_values, doc_scores=doc_scores, context_input_ids=context_input_ids, context_attention_mask=context_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_retrieved=output_retrieved, n_docs=n_docs, exclude_bos_score=exclude_bos_score, labels=labels, reduce_loss=reduce_loss, training=training, return_dict=return_dict, kwargs_call=kwargs, ) inputs["exclude_bos_score"] = ( inputs["exclude_bos_score"] if inputs["exclude_bos_score"] else self.config.exclude_bos_score ) inputs["reduce_loss"] = inputs["reduce_loss"] if inputs["reduce_loss"] else self.config.reduce_loss if inputs["labels"] is not None: if inputs["decoder_input_ids"] is None: inputs["decoder_input_ids"] = inputs["labels"] inputs["use_cache"] = False outputs = self.rag( inputs["input_ids"], attention_mask=inputs["attention_mask"], encoder_outputs=inputs["encoder_outputs"], decoder_input_ids=inputs["decoder_input_ids"], decoder_attention_mask=inputs["decoder_attention_mask"], context_input_ids=inputs["context_input_ids"], context_attention_mask=inputs["context_attention_mask"], doc_scores=inputs["doc_scores"], past_key_values=inputs["past_key_values"], use_cache=inputs["use_cache"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], output_retrieved=inputs["output_retrieved"], n_docs=inputs["n_docs"], training=inputs["training"], ) loss = None if inputs["labels"] is not None: loss = self.get_nll( outputs.logits, outputs.doc_scores, inputs["labels"], reduce_loss=inputs["reduce_loss"], epsilon=self.config.label_smoothing, n_docs=inputs["n_docs"], ) return TFRetrievAugLMMarginOutput( loss=loss, logits=outputs.logits, doc_scores=outputs.doc_scores, past_key_values=outputs.past_key_values, context_input_ids=outputs.context_input_ids, context_attention_mask=outputs.context_attention_mask, retrieved_doc_embeds=outputs.retrieved_doc_embeds, retrieved_doc_ids=outputs.retrieved_doc_ids, question_encoder_last_hidden_state=outputs.question_encoder_last_hidden_state, question_enc_hidden_states=outputs.question_enc_hidden_states, question_enc_attentions=outputs.question_enc_attentions, generator_enc_last_hidden_state=outputs.generator_enc_last_hidden_state, generator_enc_hidden_states=outputs.generator_enc_hidden_states, generator_enc_attentions=outputs.generator_enc_attentions, generator_dec_hidden_states=outputs.generator_dec_hidden_states, generator_dec_attentions=outputs.generator_dec_attentions, )
def get_nll( self, seq_logits, doc_scores, target, reduce_loss=False, epsilon=0.0, exclude_bos_score=False, n_docs=None ): # shift tokens left target = tf.concat([target[:, 1:], tf.fill([target.shape[0], 1], self.config.generator.pad_token_id)], axis=1) # bos_token_id is None for T5 bos_token_id = self.config.bos_token_id or self.config.generator.bos_token_id n_docs = n_docs if n_docs is not None else self.config.n_docs equal_bos_token_id_all = tf.reduce_all(tf.equal(target[:, 0], bos_token_id)) use_bos = bos_token_id is not None and equal_bos_token_id_all def _mask_pads(ll, smooth_obj): pad_mask = tf.equal(target, self.config.generator.pad_token_id) if tf.reduce_any(pad_mask): ll = tf.where(pad_mask, 0.0, ll) smooth_obj = tf.where(pad_mask, 0.0, smooth_obj) return tf.squeeze(ll, axis=-1), tf.squeeze(smooth_obj, axis=-1) # seq_logits.shape = (batch*n_docs, tgt_len , vocabs) seq_logprobs = tf.nn.log_softmax(seq_logits, axis=-1) seq_logprobs = tf.reshape( seq_logprobs, (seq_logits.shape[0] // n_docs, n_docs, -1, seq_logits.shape[-1]) ) # (batch_size, n_docs, tgt_len, vocabs) doc_logprobs = tf.nn.log_softmax(doc_scores, axis=1) doc_logprobs = tf.expand_dims(doc_logprobs, axis=-1) doc_logprobs = tf.expand_dims(doc_logprobs, axis=-1) # done twice to get 4-D # RAG-sequence marginalization first_token_scores = seq_logprobs[:, :, :1, :] second_token_scores = seq_logprobs[:, :, 1:2, :] remainder = seq_logprobs[:, :, 2:, :] rag_logprobs = tf.concat([first_token_scores, second_token_scores + doc_logprobs, remainder], axis=2) # calculate loss target = tf.expand_dims(target, axis=1) # n_docs dimension target = tf.expand_dims(target, axis=-1) # logits dimension target = tf.repeat(target, n_docs, axis=1) assert len(target.shape) == len(rag_logprobs.shape) # last-axis gathering only - use 2D-reshape-trick for Torch's style nD gathering def torch_gather(param, id_tensor): # 2d-gather torch equivalent: https://stackoverflow.com/questions/52129909/tensorflow-equivalent-of-torch-gather def gather2d(target, id_tensor): idx = tf.stack([tf.range(tf.shape(id_tensor)[0]), id_tensor[:, 0]], axis=-1) result = tf.gather_nd(target, idx) return tf.expand_dims(result, axis=-1) target = tf.reshape(param, (-1, param.shape[-1])) # reshape 2D target_shape = id_tensor.shape id_tensor = tf.reshape(id_tensor, (-1, 1)) # also 2D-index result = gather2d(target, id_tensor) return tf.reshape(result, target_shape) ll = torch_gather(rag_logprobs, id_tensor=target) smooth_obj = tf.reduce_sum(rag_logprobs, axis=-1, keepdims=True) # total sum of all (normalised) logits ll, smooth_obj = _mask_pads(ll, smooth_obj) # sum over tokens, exclude bos while scoring if exclude_bos_score and use_bos: ll = tf.reduce_sum(ll[:, :, 1:], axis=2) else: ll = tf.reduce_sum(ll, axis=2) smooth_obj = tf.reduce_sum(smooth_obj, axis=2) ll = tf.math.reduce_logsumexp(ll, axis=1) # logsumexp over docs smooth_obj = tf.math.reduce_logsumexp(smooth_obj, axis=1) nll_loss = -ll smooth_loss = -smooth_obj if reduce_loss: nll_loss = tf.reduce_sum(nll_loss) smooth_loss = tf.reduce_sum(smooth_loss) eps_i = epsilon / rag_logprobs.shape[-1] loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss return loss
[docs] def generate( self, input_ids: Optional[tf.Tensor] = None, attention_mask: Optional[tf.Tensor] = None, context_input_ids=None, context_attention_mask=None, doc_scores=None, do_deduplication=None, # defaults to True num_return_sequences=None, # defaults to 1 num_beams=None, # defaults to 1 n_docs=None, **model_kwargs ): """ Implements RAG sequence "thorough" decoding. Read the :meth:`~transformers.generation_utils.GenerationMixin.generate`` documentation for more information on how to set other generate input parameters Args: input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): The sequence used as a prompt for the generation. If :obj:`input_ids` is not passed, then :obj:`context_input_ids` has to be provided. attention_mask (:obj:`tf.Tensor` of shape :obj:`(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? <../glossary.html#attention-mask>`__ context_input_ids (:obj:`tf.Tensor` of shape :obj:`(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 (:obj:`tf.Tensor` of shape :obj:`(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 :obj:`input_ids` by the retriever. If the model has is not initialized with a ``retriever`` or ``input_ids`` is not given, :obj:`context_input_ids` and :obj:`context_attention_mask` have to be provided to the forward pass. They are returned by :meth:`~transformers.RagRetriever.__call__`. doc_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.n_docs)`): Score between each retrieved document embeddings (see :obj:`retrieved_doc_embeds`) and :obj:`question_encoder_last_hidden_state`. If the model has is not initialized with a ``retriever`` or ``input_ids`` is not given, :obj:`doc_scores` has to be provided to the forward pass. :obj:`doc_scores` are returned by :meth:`~transformers.RagRetriever.__call__`. do_deduplication (:obj:`bool`, `optional`): Whether or not to deduplicate the generations from different context documents for a given input. Has to be set to :obj:`False` if used while training with distributed backend. num_return_sequences(:obj:`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 `:func:`~transformers.generation_utils.GenerationMixin.generate`` function, where we set ``num_return_sequences`` to :obj:`num_beams`. num_beams (:obj:`int`, `optional`, defaults to 1): Number of beams for beam search. 1 means no beam search. n_docs (:obj:`int`, `optional`, defaults to :obj:`config.n_docs`) Number of documents to retrieve and/or number of documents for which to generate an answer. kwargs: Additional kwargs will be passed to :meth:`~transformers.generation_utils.GenerationMixin.generate` Return: :obj:`tf.Tensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated sequences. The second dimension (sequence length) is either equal to :obj:`max_length` or shorter if all batches finished early due to the :obj:`eos_token_id`. """ n_docs = n_docs if n_docs is not None else self.config.n_docs do_deduplication = do_deduplication if do_deduplication is not None else self.config.do_deduplication num_doc_return_sequences = ( num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences ) num_beams = num_beams if num_beams is not None else self.config.num_beams assert ( input_ids is not None or context_input_ids is not None ), " At least one of input_ids or context_input_ids must be given" if self.retriever is not None and context_input_ids is None: question_hidden_states = self.question_encoder(input_ids, attention_mask=attention_mask)[0] context_input_ids = self.retriever( input_ids, question_hidden_states.numpy(), prefix=self.generator.config.prefix, n_docs=n_docs, return_tensors="tf", )["context_input_ids"] hypos = [] model_kwargs["num_beams"] = num_beams model_kwargs["num_return_sequences"] = num_beams # put here so that not confused with num_doc_return_sequences model_kwargs["attention_mask"] = None batch_size = input_ids.shape[0] if input_ids is not None else context_input_ids.shape[0] // n_docs for index in range(batch_size): # first, generate beams from documents: generator_input_ids = context_input_ids[index * n_docs : (index + 1) * n_docs] # (n_docs, max_len) output_sequences = self.generator.generate( generator_input_ids, **model_kwargs, ) # n_docs * n_beam, tgt_len if do_deduplication: # do_deduplication -- for TF, work on Eager mode only! output_sequences = tf.stack(list({str(k.numpy().tolist()): k for k in output_sequences}.values())) num_candidates = output_sequences.shape[ 0 ] # after deduplication, this number can be less than n_docs*n_beam # then, run model forwards to get nll scores: if input_ids is not None: new_input_ids = tf.tile(input_ids[index : index + 1], (num_candidates, 1)) outputs = self(new_input_ids, labels=output_sequences, exclude_bos_score=True) else: # input_ids is None, need context_input_ids/mask and doc_scores assert ( context_attention_mask is not None ), "Make sure that `context_attention_mask` are passed, if no `input_ids` is set. Alternatively, you can set a retriever using the `set_retriever(...)` function." assert ( doc_scores is not None ), "Make sure that `doc_scores` are passed, if no `input_ids` is set. Alternatively, you can set a retriever using the `set_retriever(...)` function." individual_input_ids = tf.tile( generator_input_ids, (num_candidates, 1) ) # (num_candidates*n_docs, max_len) individual_attention_mask = context_attention_mask[index * n_docs : (index + 1) * n_docs] individual_attention_mask = tf.tile(individual_attention_mask, (num_candidates, 1)) individual_doc_scores = doc_scores[index : (index + 1), :] # doc_scores.shape = [batch, n_docs] individual_doc_scores = tf.tile(individual_doc_scores, (num_candidates, 1)) # [num_candidates, n_docs] outputs = self( input_ids=None, context_input_ids=individual_input_ids, context_attention_mask=individual_attention_mask, doc_scores=individual_doc_scores, labels=output_sequences, exclude_bos_score=True, ) top_cand_inds = tf.math.top_k((-outputs["loss"]), k=num_doc_return_sequences)[1] # add hypothesis hypos.append(tf.gather(output_sequences, top_cand_inds)) return self._cat_and_pad(hypos, pad_token_id=self.config.generator.pad_token_id)
@staticmethod def _cat_and_pad(tensors, pad_token_id): # used by generate(): tensors is a (batched) list of (candidates, len); len is varied across batch # Initialize padded tensor with shape ( all_candidates , max_candidate_length ), # where all_candidates counted from all inputs new_shape = sum([t.shape[0] for t in tensors]), max([t.shape[1] for t in tensors]) output = tf.fill(new_shape, pad_token_id) # Normal tensor doesn't support slice assignment, so we need tf.Variable output = tf.Variable(output) # Assign, and then convert back to tensor ind = 0 for t in tensors: output[ind : ind + t.shape[0], : t.shape[1]].assign(t) ind += t.shape[0] output = tf.convert_to_tensor(output) return tf.cast(output, tensors[0][0][0].dtype)