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| # | |
| # Pyserini: Reproducible IR research with sparse and dense representations | |
| # | |
| # 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 | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # | |
| import argparse | |
| import json | |
| import os | |
| import sys | |
| import numpy as np | |
| import faiss | |
| import torch | |
| from tqdm import tqdm | |
| from transformers import BertTokenizer, BertModel | |
| # We're going to explicitly use a local installation of Pyserini (as opposed to a pip-installed one). | |
| # Comment these lines out to use a pip-installed one instead. | |
| sys.path.insert(0, './') | |
| sys.path.insert(0, '../pyserini/') | |
| def mean_pooling(last_hidden_state, attention_mask): | |
| token_embeddings = last_hidden_state | |
| input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() | |
| sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) | |
| sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) | |
| return sum_embeddings / sum_mask | |
| class TctColBertDocumentEncoder(torch.nn.Module): | |
| def __init__(self, model_name, tokenizer_name=None, device='cuda:0'): | |
| super().__init__() | |
| self.device = device | |
| self.model = BertModel.from_pretrained(model_name) | |
| self.model.to(self.device) | |
| self.tokenizer = BertTokenizer.from_pretrained(tokenizer_name or model_name) | |
| def encode(self, texts, titles=None): | |
| texts = ['[CLS] [D] ' + text for text in texts] | |
| max_length = 512 # hardcode for now | |
| inputs = self.tokenizer( | |
| texts, | |
| max_length=max_length, | |
| padding="longest", | |
| truncation=True, | |
| add_special_tokens=False, | |
| return_tensors='pt' | |
| ) | |
| inputs.to(self.device) | |
| outputs = self.model(**inputs) | |
| embeddings = mean_pooling(outputs["last_hidden_state"][:, 4:, :], inputs['attention_mask'][:, 4:]) | |
| return embeddings.detach().cpu().numpy() | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--encoder', type=str, help='encoder name or path', required=True) | |
| parser.add_argument('--dimension', type=int, help='dimension of passage embeddings', required=False, default=768) | |
| parser.add_argument('--corpus', type=str, | |
| help='collection file to be encoded (format: jsonl)', required=True) | |
| parser.add_argument('--index', type=str, help='directory to store brute force index of corpus', required=True) | |
| parser.add_argument('--batch', type=int, help='batch size', default=8) | |
| parser.add_argument('--device', type=str, help='device cpu or cuda [cuda:0, cuda:1...]', default='cuda:0') | |
| args = parser.parse_args() | |
| # tokenizer = AutoTokenizer.from_pretrained(args.encoder) | |
| # model = AutoModel.from_pretrained(args.encoder) | |
| model = TctColBertDocumentEncoder(model_name=args.encoder, device=args.device) | |
| index = faiss.IndexFlatIP(args.dimension) | |
| if not os.path.exists(args.index): | |
| os.mkdir(args.index) | |
| texts = [] | |
| with open(os.path.join(args.index, 'docid'), 'w') as id_file: | |
| file = os.path.join(args.corpus) | |
| print(f'Loading {file}') | |
| with open(file, 'r') as corpus: | |
| for idx, line in enumerate(tqdm(corpus.readlines())): | |
| info = json.loads(line) | |
| docid = info['id'] | |
| text = info['contents'] | |
| id_file.write(f'{docid}\n') | |
| # docs can have many \n ... | |
| fields = text.split('\n') | |
| title, text = fields[1], fields[2:] | |
| if len(text) > 1: | |
| text = ' '.join(text) | |
| text = f"{title} {text}" | |
| texts.append(text.lower()) | |
| for idx in tqdm(range(0, len(texts), args.batch)): | |
| text_batch = texts[idx: idx+args.batch] | |
| embeddings = model.encode(text_batch) | |
| index.add(np.array(embeddings)) | |
| faiss.write_index(index, os.path.join(args.index, 'index')) | |