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import os |
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import pyarrow as pa |
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import pyarrow.parquet as pq |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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@inproceedings{karpukhin-etal-2020-dense, |
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title = "Dense Passage Retrieval for Open-Domain Question Answering", |
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author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau", |
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booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.emnlp-main.550", |
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doi = "10.18653/v1/2020.emnlp-main.550", |
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pages = "6769--6781", |
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} |
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""" |
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_DESCRIPTION = """ |
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This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model. |
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It contains 21M passages from wikipedia along with their DPR embeddings. |
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The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages. |
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""" |
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_LICENSE = """DPR is CC-BY-NC 4.0 licensed.""" |
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_DATA_URL = "https://dl.fbaipublicfiles.com/dpr/wikipedia_split/psgs_w100.tsv.gz" |
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_NQ_VECTORS_URL = "https://dl.fbaipublicfiles.com/dpr/data/wiki_encoded/single/nq/wiki_passages_{i}" |
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_MULTISET_VECTORS_URL = "https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_{i}" |
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_DATA_TO_NUM_SHARDS = { |
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"nq": 157, |
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"multiset": 157, |
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"no_embeddings": 28, |
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"dummy.nq": 1, |
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"dummy.multiset": 1, |
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"dummy.no_embeddings": 1, |
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} |
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class WikiDprConfig(datasets.BuilderConfig): |
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"""BuilderConfig for WikiDprConfig.""" |
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def __init__( |
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self, |
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with_embeddings=True, |
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with_index=True, |
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wiki_split="psgs_w100", |
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embeddings_name="nq", |
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index_name="compressed", |
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index_train_size=262144, |
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dummy=False, |
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**kwargs, |
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): |
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"""BuilderConfig for WikiSnippets. |
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Args: |
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with_embeddings (`bool`, defaults to `True`): Load the 768-dimensional embeddings from DPR. |
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with_index (`bool`, defaults to `True`): Load the faiss index trained on the embeddings. |
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wiki_split (`str`, defaults to `psgs_w100`): name of the splitting method of wiki articles. |
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embeddings_name (`str`, defaults to `nq`): "nq" or "multiset", depending on which dataset DPR was trained on. |
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index_name (`str`, defaults to `compressed`): "compressed" or "exact", the configuration of the faiss index to use. |
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index_train_size (`int`, defaults to `262144`): Size of the subset to use to train the index, if it is trainable. |
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dummy (`bool`, defaults to `False`): Dummy uses only 10 000 examples for testing purposes. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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self.with_embeddings = with_embeddings |
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self.with_index = with_index and index_name != "no_index" |
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self.wiki_split = wiki_split |
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self.embeddings_name = embeddings_name |
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self.index_name = index_name if with_index else "no_index" |
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self.index_train_size = index_train_size |
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self.dummy = dummy |
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name = [self.wiki_split, self.embeddings_name, self.index_name] |
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if not self.with_embeddings: |
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name.append("no_embeddings") |
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if self.dummy: |
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name = ["dummy"] + name |
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assert ( |
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self.index_name != "compressed" or not self.with_index |
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), "Please use `index_name='exact' for dummy wiki_dpr`" |
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assert wiki_split == "psgs_w100" |
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assert embeddings_name in ("nq", "multiset") |
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assert index_name in ("compressed", "exact", "no_index") |
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kwargs["name"] = ".".join(name) |
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super(WikiDprConfig, self).__init__(**kwargs) |
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prefix = f"{wiki_split}.{embeddings_name}." |
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if self.index_name == "exact": |
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self.index_file = prefix + "HNSW128_SQ8-IP-{split}.faiss" |
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else: |
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self.index_file = prefix + "IVF4096_HNSW128_PQ128-IP-{split}.faiss" |
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if self.dummy: |
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self.index_file = "dummy." + self.index_file |
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class WikiDpr(datasets.ArrowBasedBuilder): |
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BUILDER_CONFIG_CLASS = WikiDprConfig |
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BUILDER_CONFIGS = [ |
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WikiDprConfig( |
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embeddings_name=embeddings_name, |
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with_embeddings=with_embeddings, |
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index_name=index_name, |
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version=datasets.Version("0.0.0"), |
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) |
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for with_embeddings in (True, False) |
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for embeddings_name in ("nq", "multiset") |
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for index_name in ("exact", "compressed", "no_index") |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"embeddings": datasets.Sequence(datasets.Value("float32")), |
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} |
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) |
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if self.config.with_embeddings |
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else datasets.Features( |
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{"id": datasets.Value("string"), "text": datasets.Value("string"), "title": datasets.Value("string")} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/facebookresearch/DPR", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = self.config.embeddings_name if self.config.with_embeddings else "no_embeddings" |
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if self.config.dummy: |
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data_dir = "dummy." + data_dir |
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num_shards = _DATA_TO_NUM_SHARDS[data_dir] |
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data_dir = os.path.join("data", self.config.wiki_split, data_dir) |
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files = [os.path.join(data_dir, f"train-{i:05d}-of-{num_shards:05d}.parquet") for i in range(num_shards)] |
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downloaded_files = dl_manager.download_and_extract(files) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": downloaded_files}), |
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] |
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def _generate_tables(self, files): |
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for file in files: |
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with open(file, "rb") as f: |
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f = pq.ParquetFile(f) |
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for batch_idx, batch in enumerate(f.iter_batches(batch_size=1000)): |
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yield batch_idx, pa.Table.from_batches([batch]) |
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batch_idx += 1 |
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def _post_processing_resources(self, split): |
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if self.config.with_index: |
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return {"embeddings_index": self.config.index_file.format(split=split)} |
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else: |
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return {} |
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def _download_post_processing_resources(self, split, resource_name, dl_manager): |
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if resource_name == "embeddings_index": |
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try: |
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embeddings_name = self.config.embeddings_name |
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if self.config.dummy: |
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embeddings_name = "dummy." + embeddings_name |
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index_file = os.path.join( |
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"data", |
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self.config.wiki_split, |
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self.config.embeddings_name, |
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( |
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"HNSW128_SQ8-IP-{split}.faiss" |
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if self.config.index_name == "exaxt" |
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else "IVF4096_HNSW128_PQ128-IP-{split}.faiss" |
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).format(split=split) |
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) |
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downloaded_resources = dl_manager.download_and_extract( |
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{"embeddings_index": index_file} |
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) |
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return downloaded_resources["embeddings_index"] |
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except (FileNotFoundError, ConnectionError): |
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pass |
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def _post_process(self, dataset, resources_paths): |
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if self.config.with_index: |
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index_file = resources_paths["embeddings_index"] |
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if os.path.exists(index_file): |
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dataset.load_faiss_index("embeddings", index_file) |
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else: |
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if "embeddings" not in dataset.column_names: |
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raise ValueError("Couldn't build the index because there are no embeddings.") |
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import faiss |
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d = 768 |
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train_size = self.config.index_train_size |
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logger.info("Building wiki_dpr faiss index") |
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if self.config.index_name == "exact": |
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index = faiss.IndexHNSWSQ(d, faiss.ScalarQuantizer.QT_8bit, 128, faiss.METRIC_INNER_PRODUCT) |
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index.hnsw.efConstruction = 200 |
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index.hnsw.efSearch = 128 |
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dataset.add_faiss_index("embeddings", custom_index=index, train_size=train_size) |
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else: |
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quantizer = faiss.IndexHNSWFlat(d, 128, faiss.METRIC_INNER_PRODUCT) |
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quantizer.hnsw.efConstruction = 200 |
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quantizer.hnsw.efSearch = 128 |
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ivf_index = faiss.IndexIVFPQ(quantizer, d, 4096, 128, 8, faiss.METRIC_INNER_PRODUCT) |
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ivf_index.nprobe = 64 |
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ivf_index.own_fields = True |
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quantizer.this.disown() |
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dataset.add_faiss_index( |
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"embeddings", |
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train_size=train_size, |
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custom_index=ivf_index, |
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) |
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logger.info("Saving wiki_dpr faiss index") |
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dataset.save_faiss_index("embeddings", index_file) |
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return dataset |
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