main_dense.py

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import json
import sys

from tqdm import tqdm
import logging
import torch
import numpy as np
from colorama import init
from termcolor import colored

import blink.ner as NER
from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
from blink.biencoder.biencoder import BiEncoderRanker, load_biencoder
from blink.crossencoder.crossencoder import CrossEncoderRanker, load_crossencoder
from blink.biencoder.data_process import (
    process_mention_data,
    get_candidate_representation,
)
import blink.candidate_ranking.utils as utils
from blink.crossencoder.train_cross import modify, evaluate
from blink.crossencoder.data_process import prepare_crossencoder_data
from blink.indexer.faiss_indexer import DenseFlatIndexer, DenseHNSWFlatIndexer


HIGHLIGHTS = [
    "on_red",
    "on_green",
    "on_yellow",
    "on_blue",
    "on_magenta",
    "on_cyan",
]


def _print_colorful_text(input_sentence, samples):
    init()  # colorful output
    msg = ""
    if samples and (len(samples) > 0):
        msg += input_sentence[0 : int(samples[0]["start_pos"])]
        for idx, sample in enumerate(samples):
            msg += colored(
                input_sentence[int(sample["start_pos"]) : int(sample["end_pos"])],
                "grey",
                HIGHLIGHTS[idx % len(HIGHLIGHTS)],
            )
            if idx < len(samples) - 1:
                msg += input_sentence[
                    int(sample["end_pos"]) : int(samples[idx + 1]["start_pos"])
                ]
            else:
                msg += input_sentence[int(sample["end_pos"]) :]
    else:
        msg = input_sentence
        print("Failed to identify entity from text:")
    print("\n" + str(msg) + "\n")


def _print_colorful_prediction(
    idx, sample, e_id, e_title, e_text, e_url, show_url=False
):
    print(colored(sample["mention"], "grey", HIGHLIGHTS[idx % len(HIGHLIGHTS)]))
    to_print = "id:{}\ntitle:{}\ntext:{}\n".format(e_id, e_title, e_text[:256])
    if show_url:
        to_print += "url:{}\n".format(e_url)
    print(to_print)


def _annotate(ner_model, input_sentences):
    ner_output_data = ner_model.predict(input_sentences)
    sentences = ner_output_data["sentences"]
    mentions = ner_output_data["mentions"]
    samples = []
    for mention in mentions:
        record = {}
        record["label"] = "unknown"
        record["label_id"] = -1
        # LOWERCASE EVERYTHING !
        record["context_left"] = sentences[mention["sent_idx"]][
            : mention["start_pos"]
        ].lower()
        record["context_right"] = sentences[mention["sent_idx"]][
            mention["end_pos"] :
        ].lower()
        record["mention"] = mention["text"].lower()
        record["start_pos"] = int(mention["start_pos"])
        record["end_pos"] = int(mention["end_pos"])
        record["sent_idx"] = mention["sent_idx"]
        samples.append(record)
    return samples


def _load_candidates(
    entity_catalogue, entity_encoding, faiss_index=None, index_path=None, logger=None
):
    # only load candidate encoding if not using faiss index
    if faiss_index is None:
        candidate_encoding = torch.load(entity_encoding)
        indexer = None
    else:
        if logger:
            logger.info("Using faiss index to retrieve entities.")
        candidate_encoding = None
        assert index_path is not None, "Error! Empty indexer path."
        if faiss_index == "flat":
            indexer = DenseFlatIndexer(1)
        elif faiss_index == "hnsw":
            indexer = DenseHNSWFlatIndexer(1)
        else:
            raise ValueError("Error! Unsupported indexer type! Choose from flat,hnsw.")
        indexer.deserialize_from(index_path)

    # load all the 5903527 entities
    title2id = {}
    id2title = {}
    id2text = {}
    wikipedia_id2local_id = {}
    local_idx = 0
    with open(entity_catalogue, "r") as fin:
        lines = fin.readlines()
        for line in lines:
            entity = json.loads(line)

            if "idx" in entity:
                split = entity["idx"].split("curid=")
                if len(split) > 1:
                    wikipedia_id = int(split[-1].strip())
                else:
                    wikipedia_id = entity["idx"].strip()

                assert wikipedia_id not in wikipedia_id2local_id
                wikipedia_id2local_id[wikipedia_id] = local_idx

            title2id[entity["title"]] = local_idx
            id2title[local_idx] = entity["title"]
            id2text[local_idx] = entity["text"]
            local_idx += 1
    return (
        candidate_encoding,
        title2id,
        id2title,
        id2text,
        wikipedia_id2local_id,
        indexer,
    )


def __map_test_entities(test_entities_path, title2id, logger):
    # load the 732859 tac_kbp_ref_know_base entities
    kb2id = {}
    missing_pages = 0
    n = 0
    with open(test_entities_path, "r") as fin:
        lines = fin.readlines()
        for line in lines:
            entity = json.loads(line)
            if entity["title"] not in title2id:
                missing_pages += 1
            else:
                kb2id[entity["entity_id"]] = title2id[entity["title"]]
            n += 1
    if logger:
        logger.info("missing {}/{} pages".format(missing_pages, n))
    return kb2id


def __load_test(test_filename, kb2id, wikipedia_id2local_id, logger):
    test_samples = []
    with open(test_filename, "r") as fin:
        lines = fin.readlines()
        for line in lines:
            record = json.loads(line)
            record["label"] = str(record["label_id"])

            # for tac kbp we should use a separate knowledge source to get the entity id (label_id)
            if kb2id and len(kb2id) > 0:
                if record["label"] in kb2id:
                    record["label_id"] = kb2id[record["label"]]
                else:
                    continue

            # check that each entity id (label_id) is in the entity collection
            elif wikipedia_id2local_id and len(wikipedia_id2local_id) > 0:
                try:
                    key = int(record["label"].strip())
                    if key in wikipedia_id2local_id:
                        record["label_id"] = wikipedia_id2local_id[key]
                    else:
                        continue
                except:
                    continue

            # LOWERCASE EVERYTHING !
            record["context_left"] = record["context_left"].lower()
            record["context_right"] = record["context_right"].lower()
            record["mention"] = record["mention"].lower()
            test_samples.append(record)

    if logger:
        logger.info("{}/{} samples considered".format(len(test_samples), len(lines)))
    return test_samples


def _get_test_samples(
    test_filename, test_entities_path, title2id, wikipedia_id2local_id, logger
):
    kb2id = None
    if test_entities_path:
        kb2id = __map_test_entities(test_entities_path, title2id, logger)
    test_samples = __load_test(test_filename, kb2id, wikipedia_id2local_id, logger)
    return test_samples


def _process_biencoder_dataloader(samples, tokenizer, biencoder_params):
    _, tensor_data = process_mention_data(
        samples,
        tokenizer,
        biencoder_params["max_context_length"],
        biencoder_params["max_cand_length"],
        silent=True,
        logger=None,
        debug=biencoder_params["debug"],
    )
    sampler = SequentialSampler(tensor_data)
    dataloader = DataLoader(
        tensor_data, sampler=sampler, batch_size=biencoder_params["eval_batch_size"]
    )
    return dataloader


def _run_biencoder(biencoder, dataloader, candidate_encoding, top_k=100, indexer=None):
    biencoder.model.eval()
    labels = []
    nns = []
    all_scores = []
    for batch in tqdm(dataloader):
        context_input, _, label_ids = batch
        with torch.no_grad():
            if indexer is not None:
                context_encoding = biencoder.encode_context(context_input).numpy()
                context_encoding = np.ascontiguousarray(context_encoding)
                scores, indicies = indexer.search_knn(context_encoding, top_k)
            else:
                scores = biencoder.score_candidate(
                    context_input, None, cand_encs=candidate_encoding  # .to(device)
                )
                scores, indicies = scores.topk(top_k)
                scores = scores.data.numpy()
                indicies = indicies.data.numpy()

        labels.extend(label_ids.data.numpy())
        nns.extend(indicies)
        all_scores.extend(scores)
    return labels, nns, all_scores


def _process_crossencoder_dataloader(context_input, label_input, crossencoder_params):
    tensor_data = TensorDataset(context_input, label_input)
    sampler = SequentialSampler(tensor_data)
    dataloader = DataLoader(
        tensor_data, sampler=sampler, batch_size=crossencoder_params["eval_batch_size"]
    )
    return dataloader


def _run_crossencoder(crossencoder, dataloader, logger, context_len, device="cuda"):
    crossencoder.model.eval()
    accuracy = 0.0
    crossencoder.to(device)

    res = evaluate(crossencoder, dataloader, device, logger, context_len, zeshel=False, silent=False)
    accuracy = res["normalized_accuracy"]
    logits = res["logits"]

    if accuracy > -1:
        predictions = np.argsort(logits, axis=1)
    else:
        predictions = []

    return accuracy, predictions, logits


def load_models(args, logger=None):

    # load biencoder model
    if logger:
        logger.info("loading biencoder model")
    with open(args.biencoder_config) as json_file:
        biencoder_params = json.load(json_file)
        biencoder_params["path_to_model"] = args.biencoder_model
    biencoder = load_biencoder(biencoder_params)

    crossencoder = None
    crossencoder_params = None
    if not args.fast:
        # load crossencoder model
        if logger:
            logger.info("loading crossencoder model")
        with open(args.crossencoder_config) as json_file:
            crossencoder_params = json.load(json_file)
            crossencoder_params["path_to_model"] = args.crossencoder_model
        crossencoder = load_crossencoder(crossencoder_params)

    # load candidate entities
    if logger:
        logger.info("loading candidate entities")
    (
        candidate_encoding,
        title2id,
        id2title,
        id2text,
        wikipedia_id2local_id,
        faiss_indexer,
    ) = _load_candidates(
        args.entity_catalogue, 
        args.entity_encoding, 
        faiss_index=getattr(args, 'faiss_index', None), 
        index_path=getattr(args, 'index_path' , None),
        logger=logger,
    )

    return (
        biencoder,
        biencoder_params,
        crossencoder,
        crossencoder_params,
        candidate_encoding,
        title2id,
        id2title,
        id2text,
        wikipedia_id2local_id,
        faiss_indexer,
    )


def run(
    args,
    logger,
    biencoder,
    biencoder_params,
    crossencoder,
    crossencoder_params,
    candidate_encoding,
    title2id,
    id2title,
    id2text,
    wikipedia_id2local_id,
    faiss_indexer=None,
    test_data=None,
):

    if not test_data and not args.test_mentions and not args.interactive:
        msg = (
            "ERROR: either you start BLINK with the "
            "interactive option (-i) or you pass in input test mentions (--test_mentions)"
            "and test entitied (--test_entities)"
        )
        raise ValueError(msg)

    id2url = {
        v: "https://en.wikipedia.org/wiki?curid=%s" % k
        for k, v in wikipedia_id2local_id.items()
    }

    stopping_condition = False
    while not stopping_condition:

        samples = None

        if args.interactive:
            logger.info("interactive mode")

            # biencoder_params["eval_batch_size"] = 1

            # Load NER model
            ner_model = NER.get_model()

            # Interactive
            text = input("insert text:")

            # Identify mentions
            samples = _annotate(ner_model, [text])

            _print_colorful_text(text, samples)

        else:
            if logger:
                logger.info("test dataset mode")

            if test_data:
                samples = test_data
            else:
                # Load test mentions
                samples = _get_test_samples(
                    args.test_mentions,
                    args.test_entities,
                    title2id,
                    wikipedia_id2local_id,
                    logger,
                )

            stopping_condition = True

        # don't look at labels
        keep_all = (
            args.interactive
            or samples[0]["label"] == "unknown"
            or samples[0]["label_id"] < 0
        )

        # prepare the data for biencoder
        if logger:
            logger.info("preparing data for biencoder")
        dataloader = _process_biencoder_dataloader(
            samples, biencoder.tokenizer, biencoder_params
        )

        # run biencoder
        if logger:
            logger.info("run biencoder")
        top_k = args.top_k
        labels, nns, scores = _run_biencoder(
            biencoder, dataloader, candidate_encoding, top_k, faiss_indexer
        )

        if args.interactive:

            print("\nfast (biencoder) predictions:")

            _print_colorful_text(text, samples)

            # print biencoder prediction
            idx = 0
            for entity_list, sample in zip(nns, samples):
                e_id = entity_list[0]
                e_title = id2title[e_id]
                e_text = id2text[e_id]
                e_url = id2url[e_id]
                _print_colorful_prediction(
                    idx, sample, e_id, e_title, e_text, e_url, args.show_url
                )
                idx += 1
            print()

            if args.fast:
                # use only biencoder
                continue

        else:

            biencoder_accuracy = -1
            recall_at = -1
            if not keep_all:
                # get recall values
                top_k = args.top_k
                x = []
                y = []
                for i in range(1, top_k):
                    temp_y = 0.0
                    for label, top in zip(labels, nns):
                        if label in top[:i]:
                            temp_y += 1
                    if len(labels) > 0:
                        temp_y /= len(labels)
                    x.append(i)
                    y.append(temp_y)
                # plt.plot(x, y)
                biencoder_accuracy = y[0]
                recall_at = y[-1]
                print("biencoder accuracy: %.4f" % biencoder_accuracy)
                print("biencoder recall@%d: %.4f" % (top_k, y[-1]))

            if args.fast:

                predictions = []
                for entity_list in nns:
                    sample_prediction = []
                    for e_id in entity_list:
                        e_title = id2title[e_id]
                        sample_prediction.append(e_title)
                    predictions.append(sample_prediction)

                # use only biencoder
                return (
                    biencoder_accuracy,
                    recall_at,
                    -1,
                    -1,
                    len(samples),
                    predictions,
                    scores,
                )

        # prepare crossencoder data
        context_input, candidate_input, label_input = prepare_crossencoder_data(
            crossencoder.tokenizer, samples, labels, nns, id2title, id2text, keep_all,
        )

        context_input = modify(
            context_input, candidate_input, crossencoder_params["max_seq_length"]
        )

        dataloader = _process_crossencoder_dataloader(
            context_input, label_input, crossencoder_params
        )

        # run crossencoder and get accuracy
        accuracy, index_array, unsorted_scores = _run_crossencoder(
            crossencoder,
            dataloader,
            logger,
            context_len=biencoder_params["max_context_length"],
        )

        if args.interactive:

            print("\naccurate (crossencoder) predictions:")

            _print_colorful_text(text, samples)

            # print crossencoder prediction
            idx = 0
            for entity_list, index_list, sample in zip(nns, index_array, samples):
                e_id = entity_list[index_list[-1]]
                e_title = id2title[e_id]
                e_text = id2text[e_id]
                e_url = id2url[e_id]
                _print_colorful_prediction(
                    idx, sample, e_id, e_title, e_text, e_url, args.show_url
                )
                idx += 1
            print()
        else:

            scores = []
            predictions = []
            for entity_list, index_list, scores_list in zip(
                nns, index_array, unsorted_scores
            ):

                index_list = index_list.tolist()

                # descending order
                index_list.reverse()

                sample_prediction = []
                sample_scores = []
                for index in index_list:
                    e_id = entity_list[index]
                    e_title = id2title[e_id]
                    sample_prediction.append(e_title)
                    sample_scores.append(scores_list[index])
                predictions.append(sample_prediction)
                scores.append(sample_scores)

            crossencoder_normalized_accuracy = -1
            overall_unormalized_accuracy = -1
            if not keep_all:
                crossencoder_normalized_accuracy = accuracy
                print(
                    "crossencoder normalized accuracy: %.4f"
                    % crossencoder_normalized_accuracy
                )

                if len(samples) > 0:
                    overall_unormalized_accuracy = (
                        crossencoder_normalized_accuracy * len(label_input) / len(samples)
                    )
                print(
                    "overall unnormalized accuracy: %.4f" % overall_unormalized_accuracy
                )
            return (
                biencoder_accuracy,
                recall_at,
                crossencoder_normalized_accuracy,
                overall_unormalized_accuracy,
                len(samples),
                predictions,
                scores,
            )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--interactive", "-i", action="store_true", help="Interactive mode."
    )

    # test_data
    parser.add_argument(
        "--test_mentions", dest="test_mentions", type=str, help="Test Dataset."
    )
    parser.add_argument(
        "--test_entities", dest="test_entities", type=str, help="Test Entities."
    )

    # biencoder
    parser.add_argument(
        "--biencoder_model",
        dest="biencoder_model",
        type=str,
        default="models/biencoder_wiki_large.bin",
        help="Path to the biencoder model.",
    )
    parser.add_argument(
        "--biencoder_config",
        dest="biencoder_config",
        type=str,
        default="models/biencoder_wiki_large.json",
        help="Path to the biencoder configuration.",
    )
    parser.add_argument(
        "--entity_catalogue",
        dest="entity_catalogue",
        type=str,
        # default="models/tac_entity.jsonl",  # TAC-KBP
        default="models/entity.jsonl",  # ALL WIKIPEDIA!
        help="Path to the entity catalogue.",
    )
    parser.add_argument(
        "--entity_encoding",
        dest="entity_encoding",
        type=str,
        # default="models/tac_candidate_encode_large.t7",  # TAC-KBP
        default="models/all_entities_large.t7",  # ALL WIKIPEDIA!
        help="Path to the entity catalogue.",
    )

    # crossencoder
    parser.add_argument(
        "--crossencoder_model",
        dest="crossencoder_model",
        type=str,
        default="models/crossencoder_wiki_large.bin",
        help="Path to the crossencoder model.",
    )
    parser.add_argument(
        "--crossencoder_config",
        dest="crossencoder_config",
        type=str,
        default="models/crossencoder_wiki_large.json",
        help="Path to the crossencoder configuration.",
    )

    parser.add_argument(
        "--top_k",
        dest="top_k",
        type=int,
        default=10,
        help="Number of candidates retrieved by biencoder.",
    )

    # output folder
    parser.add_argument(
        "--output_path",
        dest="output_path",
        type=str,
        default="output",
        help="Path to the output.",
    )

    parser.add_argument(
        "--fast", dest="fast", action="store_true", help="only biencoder mode"
    )

    parser.add_argument(
        "--show_url",
        dest="show_url",
        action="store_true",
        help="whether to show entity url in interactive mode",
    )

    parser.add_argument(
        "--faiss_index", type=str, default=None, help="whether to use faiss index",
    )

    parser.add_argument(
        "--index_path", type=str, default=None, help="path to load indexer",
    )

    args = parser.parse_args()

    logger = utils.get_logger(args.output_path)

    models = load_models(args, logger)
    run(args, logger, *models)