Source code for transformers.retrieval_rag

# coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# limitations under the License.
"""RAG Retriever model implementation."""

import os
import pickle
import time
from typing import Iterable, List, Optional, Tuple

import numpy as np

from .configuration_rag import RagConfig
from .file_utils import (
from .tokenization_rag import RagTokenizer
from .tokenization_utils_base import BatchEncoding
from .utils import logging

if is_datasets_available():
    from datasets import Dataset, load_dataset, load_from_disk

if is_faiss_available():
    import faiss

logger = logging.get_logger(__name__)


class Index:
    A base class for the Indices encapsulated by the :class:`~transformers.RagRetriever`.

    def get_doc_dicts(self, doc_ids: np.ndarray) -> List[dict]:
        Returns a list of dictionaries, containing titles and text of the retrieved documents.

            doc_ids (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`):
                A tensor of document indices.
        raise NotImplementedError

    def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
        For each query in the batch, retrieves ``n_docs`` documents.

            question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size):
                An array of query vectors.
            n_docs (:obj:`int`):
                The number of docs retrieved per query.

            :obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`: A tensor of indices of retrieved documents.
            :obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`: A tensor of vector representations of
            retrieved documents.
        raise NotImplementedError

    def is_initialized(self):
        Returns :obj:`True` if index is already initialized.
        raise NotImplementedError

    def init_index(self):
        A function responsible for loading the index into memory. Should be called only once per training run of a RAG
        model. E.g. if the model is trained on multiple GPUs in a distributed setup, only one of the workers will load
        the index.
        raise NotImplementedError

class LegacyIndex(Index):
    An index which can be deserialized from the files built using We use
    default faiss index parameters as specified in that repository.

        vector_size (:obj:`int`):
            The dimension of indexed vectors.
        index_path (:obj:`str`):
            A path to a `directory` containing index files compatible with

    INDEX_FILENAME = "hf_bert_base.hnswSQ8_correct_phi_128.c_index"
    PASSAGE_FILENAME = "psgs_w100.tsv.pkl"

    def __init__(self, vector_size, index_path):
        self.index_id_to_db_id = []
        self.index_path = index_path
        self.passages = self._load_passages()
        self.vector_size = vector_size
        self.index = None
        self._index_initialized = False

    def _resolve_path(self, index_path, filename):
        assert os.path.isdir(index_path) or is_remote_url(index_path), "Please specify a valid ``index_path``."
        archive_file = os.path.join(index_path, filename)
            # Load from URL or cache if already cached
            resolved_archive_file = cached_path(archive_file)
        except EnvironmentError:
            msg = (
                f"Can't load '{archive_file}'. Make sure that:\n\n"
                f"- '{index_path}' is a correct remote path to a directory containing a file named {filename}"
                f"- or '{index_path}' is the correct path to a directory containing a file named {filename}.\n\n"
            raise EnvironmentError(msg)
        if resolved_archive_file == archive_file:
  "loading file {}".format(archive_file))
  "loading file {} from cache at {}".format(archive_file, resolved_archive_file))
        return resolved_archive_file

    def _load_passages(self):"Loading passages from {}".format(self.index_path))
        passages_path = self._resolve_path(self.index_path, self.PASSAGE_FILENAME)
        with open(passages_path, "rb") as passages_file:
            passages = pickle.load(passages_file)
        return passages

    def _deserialize_index(self):"Loading index from {}".format(self.index_path))
        resolved_index_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index.dpr")
        self.index = faiss.read_index(resolved_index_path)
        resolved_meta_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index_meta.dpr")
        with open(resolved_meta_path, "rb") as metadata_file:
            self.index_id_to_db_id = pickle.load(metadata_file)
        assert (
            len(self.index_id_to_db_id) == self.index.ntotal
        ), "Deserialized index_id_to_db_id should match faiss index size"

    def is_initialized(self):
        return self._index_initialized

    def init_index(self):
        index = faiss.IndexHNSWFlat(self.vector_size + 1, 512)
        index.hnsw.efSearch = 128
        index.hnsw.efConstruction = 200
        self.index = index
        self._index_initialized = True

    def get_doc_dicts(self, doc_ids: np.array):
        doc_list = []
        for doc_ids_i in doc_ids:
            ids = [str(int(doc_id)) for doc_id in doc_ids_i]
            docs = [self.passages[doc_id] for doc_id in ids]
        doc_dicts = []
        for docs in doc_list:
            doc_dict = {}
            doc_dict["title"] = [doc[1] for doc in docs]
            doc_dict["text"] = [doc[0] for doc in docs]
        return doc_dicts

    def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
        aux_dim = np.zeros(len(question_hidden_states), dtype="float32").reshape(-1, 1)
        query_nhsw_vectors = np.hstack((question_hidden_states, aux_dim))
        _, docs_ids =, n_docs)
        vectors = [[self.index.reconstruct(int(doc_id))[:-1] for doc_id in doc_ids] for doc_ids in docs_ids]
        ids = [[int(self.index_id_to_db_id[doc_id]) for doc_id in doc_ids] for doc_ids in docs_ids]
        return np.array(ids), np.array(vectors)

class HFIndexBase(Index):
    def __init__(self, vector_size, dataset, index_initialized=False):
        self.vector_size = vector_size
        self.dataset = dataset
        self._index_initialized = index_initialized
        dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True)

    def _check_dataset_format(self, with_index: bool):
        if not isinstance(self.dataset, Dataset):
            raise ValueError("Dataset should be a datasets.Dataset object, but got {}".format(type(self.dataset)))
        if len({"title", "text", "embeddings"} - set(self.dataset.column_names)) > 0:
            raise ValueError(
                "Dataset should be a dataset with the following columns: "
                "title (str), text (str) and embeddings (arrays of dimension vector_size), "
                "but got columns {}".format(self.dataset.column_names)
        if with_index and "embeddings" not in self.dataset.list_indexes():
            raise ValueError(
                "Missing faiss index in the dataset. Make sure you called `dataset.add_faiss_index` to compute it "
                "or `dataset.load_faiss_index` to load one from the disk."

    def init_index(self):
        raise NotImplementedError()

    def is_initialized(self):
        return self._index_initialized

    def get_doc_dicts(self, doc_ids: np.ndarray) -> List[dict]:
        return [self.dataset[doc_ids[i].tolist()] for i in range(doc_ids.shape[0])]

    def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
        _, ids = self.dataset.search_batch("embeddings", question_hidden_states, n_docs)
        docs = [self.dataset[[i for i in indices if i >= 0]] for indices in ids]
        vectors = [doc["embeddings"] for doc in docs]
        for i in range(len(vectors)):
            if len(vectors[i]) < n_docs:
                vectors[i] = np.vstack([vectors[i], np.zeros((n_docs - len(vectors[i]), self.vector_size))])
        return np.array(ids), np.array(vectors)  # shapes (batch_size, n_docs) and (batch_size, n_docs, d)

class CanonicalHFIndex(HFIndexBase):
    A wrapper around an instance of :class:`~datasets.Datasets`. If ``index_path`` is set to ``None``, we load the
    pre-computed index available with the :class:`~datasets.arrow_dataset.Dataset`, otherwise, we load the index from
    the indicated path on disk.

        vector_size (:obj:`int`): the dimension of the passages embeddings used by the index
        dataset_name (:obj:`str`, optional, defaults to ``wiki_dpr``):
            A datatset identifier of the indexed dataset on HuggingFace AWS bucket (list all available datasets and ids
            with ``datasets.list_datasets()``).
        dataset_split (:obj:`str`, optional, defaults to ``train``)
            Which split of the ``dataset`` to load.
        index_name (:obj:`str`, optional, defaults to ``train``)
            The index_name of the index associated with the ``dataset``. The index loaded from ``index_path`` will be
            saved under this name.
        index_path (:obj:`str`, optional, defaults to ``None``)
            The path to the serialized faiss index on disk.
        use_dummy_dataset (:obj:`bool`, optional, defaults to ``False``): If True, use the dummy configuration of the dataset for tests.

    def __init__(
        vector_size: int,
        dataset_name: str = "wiki_dpr",
        dataset_split: str = "train",
        index_name: Optional[str] = None,
        index_path: Optional[str] = None,
        if int(index_path is None) + int(index_name is None) != 1:
            raise ValueError("Please provide `index_name` or `index_path`.")
        self.dataset_name = dataset_name
        self.dataset_split = dataset_split
        self.index_name = index_name
        self.index_path = index_path
        self.use_dummy_dataset = use_dummy_dataset"Loading passages from {}".format(self.dataset_name))
        dataset = load_dataset(
            self.dataset_name, with_index=False, split=self.dataset_split, dummy=self.use_dummy_dataset
        super().__init__(vector_size, dataset, index_initialized=False)

    def init_index(self):
        if self.index_path is not None:
  "Loading index from {}".format(self.index_path))
            self.dataset.load_faiss_index("embeddings", file=self.index_path)
  "Loading index from {}".format(self.dataset_name + " with index name " + self.index_name))
            self.dataset = load_dataset(
            self.dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True)
        self._index_initialized = True

class CustomHFIndex(HFIndexBase):
    A wrapper around an instance of :class:`~datasets.Datasets`. The dataset and the index are both loaded from the
    indicated paths on disk.

        vector_size (:obj:`int`): the dimension of the passages embeddings used by the index
        dataset_path (:obj:`str`):
            The path to the serialized dataset on disk. The dataset should have 3 columns: title (str), text (str) and
            embeddings (arrays of dimension vector_size)
        index_path (:obj:`str`)
            The path to the serialized faiss index on disk.

    def __init__(self, vector_size: int, dataset, index_path=None):
        super().__init__(vector_size, dataset, index_initialized=index_path is None)
        self.index_path = index_path

    def load_from_disk(cls, vector_size, dataset_path, index_path):"Loading passages from {}".format(dataset_path))
        if dataset_path is None or index_path is None:
            raise ValueError(
                "Please provide ``dataset_path`` and ``index_path`` after calling ``dataset.save_to_disk(dataset_path)`` "
                "and ``dataset.get_index('embeddings').save(index_path)``."
        dataset = load_from_disk(dataset_path)
        return cls(vector_size=vector_size, dataset=dataset, index_path=index_path)

    def init_index(self):
        if not self.is_initialized():
  "Loading index from {}".format(self.index_path))
            self.dataset.load_faiss_index("embeddings", file=self.index_path)
            self._index_initialized = True

[docs]class RagRetriever: """ 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. Args: config (:class:`~transformers.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 (:class:`~transformers.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 (:class:`~transformers.PreTrainedTokenizer`): The tokenizer used for the generator part of the RagModel. index (:class:`~transformers.retrieval_rag.Index`, optional, defaults to the one defined by the configuration): If specified, use this index instead of the one built using the configuration 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/ >>> from transformers import RagRetriever >>> dataset = ... # dataset must be a datasets.Datasets object with columns "title", "text" and "embeddings", and it must have a faiss index >>> retriever = RagRetriever.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', indexed_dataset=dataset) >>> # To load your own indexed dataset built with the datasets library that was saved on disk. More info in examples/rag/ >>> from transformers import RagRetriever >>> dataset_path = "path/to/my/dataset" # dataset saved via `dataset.save_to_disk(...)` >>> index_path = "path/to/my/index.faiss" # faiss index saved via `dataset.get_index("embeddings").save(...)` >>> retriever = RagRetriever.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', index_name='custom', passages_path=dataset_path, index_path=index_path) >>> # To load the legacy index built originally for Rag's paper >>> from transformers import RagRetriever >>> retriever = RagRetriever.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', index_name='legacy') """ _init_retrieval = True def __init__(self, config, question_encoder_tokenizer, generator_tokenizer, index=None): requires_datasets(self) requires_faiss(self) super().__init__() self.index = index or self._build_index(config) self.generator_tokenizer = generator_tokenizer self.question_encoder_tokenizer = question_encoder_tokenizer self.n_docs = config.n_docs self.batch_size = config.retrieval_batch_size self.config = config if self._init_retrieval: self.init_retrieval() @staticmethod def _build_index(config): if config.index_name == "legacy": return LegacyIndex( config.retrieval_vector_size, config.index_path or LEGACY_INDEX_PATH, ) elif config.index_name == "custom": return CustomHFIndex.load_from_disk( vector_size=config.retrieval_vector_size, dataset_path=config.passages_path, index_path=config.index_path, ) else: return CanonicalHFIndex( vector_size=config.retrieval_vector_size, dataset_name=config.dataset, dataset_split=config.dataset_split, index_name=config.index_name, index_path=config.index_path, use_dummy_dataset=config.use_dummy_dataset, ) @classmethod def from_pretrained(cls, retriever_name_or_path, indexed_dataset=None, **kwargs): requires_datasets(cls) requires_faiss(cls) config = kwargs.pop("config", None) or RagConfig.from_pretrained(retriever_name_or_path, **kwargs) rag_tokenizer = RagTokenizer.from_pretrained(retriever_name_or_path, config=config) question_encoder_tokenizer = rag_tokenizer.question_encoder generator_tokenizer = rag_tokenizer.generator if indexed_dataset is not None: config.index_name = "custom" index = CustomHFIndex(config.retrieval_vector_size, indexed_dataset) else: index = cls._build_index(config) return cls( config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer, index=index, ) def save_pretrained(self, save_directory): if isinstance(self.index, CustomHFIndex): if self.config.index_path is None: index_path = os.path.join(save_directory, "hf_dataset_index.faiss") self.index.dataset.get_index("embeddings").save(index_path) self.config.index_path = index_path if self.config.passages_path is None: passages_path = os.path.join(save_directory, "hf_dataset") # datasets don't support save_to_disk with indexes right now faiss_index = self.index.dataset._indexes.pop("embeddings") self.index.dataset.save_to_disk(passages_path) self.index.dataset._indexes["embeddings"] = faiss_index self.config.passages_path = passages_path self.config.save_pretrained(save_directory) rag_tokenizer = RagTokenizer( question_encoder=self.question_encoder_tokenizer, generator=self.generator_tokenizer, ) rag_tokenizer.save_pretrained(save_directory)
[docs] def init_retrieval(self): """ Retriever initalization function. It loads the index into memory. """"initializing retrieval") self.index.init_index()
[docs] def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None): r""" Postprocessing retrieved ``docs`` and combining them with ``input_strings``. Args: docs (:obj:`dict`): Retrieved documents. input_strings (:obj:`str`): Input strings decoded by ``preprocess_query``. prefix (:obj:`str`): Prefix added at the beginning of each input, typically used with T5-based models. Return: :obj:`tuple(tensors)`: a tuple consisting of two elements: contextualized ``input_ids`` and a compatible ``attention_mask``. """ def cat_input_and_doc(doc_title, doc_text, input_string, prefix): # TODO(Patrick): if we train more RAG models, I want to put the input first to take advantage of effortless truncation # TODO(piktus): better handling of truncation if doc_title.startswith('"'): doc_title = doc_title[1:] if doc_title.endswith('"'): doc_title = doc_title[:-1] if prefix is None: prefix = "" out = (prefix + doc_title + self.config.title_sep + doc_text + self.config.doc_sep + input_string).replace( " ", " " ) return out rag_input_strings = [ cat_input_and_doc( docs[i]["title"][j], docs[i]["text"][j], input_strings[i], prefix, ) for i in range(len(docs)) for j in range(n_docs) ] contextualized_inputs = self.generator_tokenizer.batch_encode_plus( rag_input_strings, max_length=self.config.max_combined_length, return_tensors=return_tensors, padding="max_length", truncation=True, ) return contextualized_inputs["input_ids"], contextualized_inputs["attention_mask"]
def _chunk_tensor(self, t: Iterable, chunk_size: int) -> List[Iterable]: return [t[i : i + chunk_size] for i in range(0, len(t), chunk_size)] def _main_retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, np.ndarray]: question_hidden_states_batched = self._chunk_tensor(question_hidden_states, self.batch_size) ids_batched = [] vectors_batched = [] for question_hidden_states in question_hidden_states_batched: start_time = time.time() ids, vectors = self.index.get_top_docs(question_hidden_states, n_docs) logger.debug( "index search time: {} sec, batch size {}".format( time.time() - start_time, question_hidden_states.shape ) ) ids_batched.extend(ids) vectors_batched.extend(vectors) return ( np.array(ids_batched), np.array(vectors_batched), ) # shapes (batch_size, n_docs) and (batch_size, n_docs, d)
[docs] def retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, List[dict]]: """ Retrieves documents for specified ``question_hidden_states``. Args: question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`): A batch of query vectors to retrieve with. n_docs (:obj:`int`): The number of docs retrieved per query. Return: :obj:`Tuple[np.ndarray, np.ndarray, List[dict]]`: A tuple with the following objects: - **retrieved_doc_embeds** (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs, dim)`) -- The retrieval embeddings of the retrieved docs per query. - **doc_ids** (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`) -- The ids of the documents in the index - **doc_dicts** (:obj:`List[dict]`): The :obj:`retrieved_doc_embeds` examples per query. """ doc_ids, retrieved_doc_embeds = self._main_retrieve(question_hidden_states, n_docs) return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(doc_ids)
def __call__( self, question_input_ids: List[List[int]], question_hidden_states: np.ndarray, prefix=None, n_docs=None, return_tensors=None, ) -> BatchEncoding: """ Retrieves documents for specified :obj:`question_hidden_states`. Args: question_input_ids: (:obj:`List[List[int]]`) batch of input ids question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`: A batch of query vectors to retrieve with. prefix: (:obj:`str`, `optional`): The prefix used by the generator's tokenizer. n_docs (:obj:`int`, `optional`): The number of docs retrieved per query. return_tensors (:obj:`str` or :class:`~transformers.tokenization_utils_base.TensorType`, `optional`, defaults to "pt"): If set, will return tensors instead of list of python integers. Acceptable values are: * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects. * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects. * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects. Returns: :class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields: - **context_input_ids** -- List of token ids to be fed to a model. `What are input IDs? <../glossary.html#input-ids>`__ - **context_attention_mask** -- List of indices specifying which tokens should be attended to by the model (when :obj:`return_attention_mask=True` or if `"attention_mask"` is in :obj:`self.model_input_names`). `What are attention masks? <../glossary.html#attention-mask>`__ - **retrieved_doc_embeds** -- List of embeddings of the retrieved documents - **doc_ids** -- List of ids of the retrieved documents """ n_docs = n_docs if n_docs is not None else self.n_docs prefix = prefix if prefix is not None else self.config.generator.prefix retrieved_doc_embeds, doc_ids, docs = self.retrieve(question_hidden_states, n_docs) input_strings = self.question_encoder_tokenizer.batch_decode(question_input_ids, skip_special_tokens=True) context_input_ids, context_attention_mask = self.postprocess_docs( docs, input_strings, prefix, n_docs, return_tensors=return_tensors ) return BatchEncoding( { "context_input_ids": context_input_ids, "context_attention_mask": context_attention_mask, "retrieved_doc_embeds": retrieved_doc_embeds, "doc_ids": doc_ids, }, tensor_type=return_tensors, )