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