embed-bge-m3/FlagEmbedding/research/C_MTEB/MLDR/dense_retrieval/step1-search_results.py

"""
python step1-search_results.py \
--encoder BAAI/bge-m3 \
--languages ar de en es fr hi it ja ko pt ru th zh \
--index_save_dir ./corpus-index \
--result_save_dir ./search_results \
--threads 16 \
--hits 1000 \
--pooling_method cls \
--normalize_embeddings True \
--add_instruction False
"""
import os
import torch
import datasets
from pprint import pprint
from dataclasses import dataclass, field
from transformers import HfArgumentParser, is_torch_npu_available
from pyserini.search.faiss import FaissSearcher, AutoQueryEncoder
from pyserini.output_writer import get_output_writer, OutputFormat


@dataclass
class ModelArgs:
    encoder: str = field(
        default="BAAI/bge-m3",
        metadata={'help': 'Name or path of encoder'}
    )
    add_instruction: bool = field(
        default=False,
        metadata={'help': 'Add instruction?'}
    )
    query_instruction_for_retrieval: str = field(
        default=None,
        metadata={'help': 'query instruction for retrieval'}
    )
    pooling_method: str = field(
        default='cls',
        metadata={'help': "Pooling method. Avaliable methods: 'cls', 'mean'"}
    )
    normalize_embeddings: bool = field(
        default=True,
        metadata={'help': "Normalize embeddings or not"}
    )


@dataclass
class EvalArgs:
    languages: str = field(
        default="en",
        metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', 
                  "nargs": "+"}
    )
    index_save_dir: str = field(
        default='./corpus-index',
        metadata={'help': 'Dir to index and docid. Corpus index path is `index_save_dir/{encoder_name}/{lang}/index`. Corpus ids path is `index_save_dir/{encoder_name}/{lang}/docid` .'}
    )
    result_save_dir: str = field(
        default='./search_results',
        metadata={'help': 'Dir to saving search results. Search results will be saved to `result_save_dir/{encoder_name}/{lang}.txt`'}
    )
    threads: int = field(
        default=1,
        metadata={'help': 'Maximum threads to use during search'}
    )
    hits: int = field(
        default=1000,
        metadata={'help': 'Number of hits'}
    )
    overwrite: bool = field(
        default=False,
        metadata={'help': 'Whether to overwrite embedding'}
    )


def get_query_encoder(model_args: ModelArgs):
    if torch.cuda.is_available():
        device = torch.device("cuda")
    elif is_torch_npu_available():
        device = torch.device("npu")
    else:
        device = torch.device("cpu")
    model = AutoQueryEncoder(
        encoder_dir=model_args.encoder,
        device=device,
        pooling=model_args.pooling_method,
        l2_norm=model_args.normalize_embeddings
    )
    return model


def check_languages(languages):
    if isinstance(languages, str):
        languages = [languages]
    avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']
    for lang in languages:
        if lang not in avaliable_languages:
            raise ValueError(f"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}")
    return languages


def get_queries_and_qids(lang: str, split: str='test', add_instruction: bool=False, query_instruction_for_retrieval: str=None):
    dataset = datasets.load_dataset('Shitao/MLDR', lang, split=split)
    
    queries = []
    qids = []
    for data in dataset:
        qids.append(str(data['query_id']))
        queries.append(str(data['query']))
    if add_instruction and query_instruction_for_retrieval is not None:
        queries = [f"{query_instruction_for_retrieval}{query}" for query in queries]
    return queries, qids


def save_result(search_results, result_save_path: str, qids: list, max_hits: int):
    output_writer = get_output_writer(result_save_path, OutputFormat(OutputFormat.TREC.value), 'w',
                                      max_hits=max_hits, tag='Faiss', topics=qids,
                                      use_max_passage=False,
                                      max_passage_delimiter='#',
                                      max_passage_hits=1000)
    with output_writer:
        for topic, hits in search_results:
            output_writer.write(topic, hits)


def main():
    parser = HfArgumentParser([ModelArgs, EvalArgs])
    model_args, eval_args = parser.parse_args_into_dataclasses()
    model_args: ModelArgs
    eval_args: EvalArgs
    
    languages = check_languages(eval_args.languages)
    
    if model_args.encoder[-1] == '/':
        model_args.encoder = model_args.encoder[:-1]
    
    query_encoder = get_query_encoder(model_args=model_args)
    
    encoder = model_args.encoder
    if os.path.basename(encoder).startswith('checkpoint-'):
        encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)
    
    print("==================================================")
    print("Start generating search results with model:")
    print(model_args.encoder)

    print('Generate search results of following languages: ', languages)
    for lang in languages:
        print("**************************************************")
        print(f"Start searching results of {lang} ...")
        
        result_save_path = os.path.join(eval_args.result_save_dir, os.path.basename(encoder), f"{lang}.txt")
        if not os.path.exists(os.path.dirname(result_save_path)):
            os.makedirs(os.path.dirname(result_save_path))
        
        if os.path.exists(result_save_path) and not eval_args.overwrite:
            print(f'Search results of {lang} already exists. Skip...')
            continue
        
        index_save_dir = os.path.join(eval_args.index_save_dir, os.path.basename(encoder), lang)
        if not os.path.exists(index_save_dir):
            raise FileNotFoundError(f"{index_save_dir} not found")
        searcher = FaissSearcher(
            index_dir=index_save_dir,
            query_encoder=query_encoder
        )
        
        queries, qids = get_queries_and_qids(
            lang=lang, 
            split='test', 
            add_instruction=model_args.add_instruction, 
            query_instruction_for_retrieval=model_args.query_instruction_for_retrieval
        )
        
        search_results = searcher.batch_search(
            queries=queries,
            q_ids=qids,
            k=eval_args.hits,
            threads=eval_args.threads
        )
        search_results = [(_id, search_results[_id]) for _id in qids]
        
        save_result(
            search_results=search_results,
            result_save_path=result_save_path, 
            qids=qids, 
            max_hits=eval_args.hits
        )

    print("==================================================")
    print("Finish generating search results with model:")
    pprint(model_args.encoder)


if __name__ == "__main__":
    main()