wehub-resource-sync
120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
import os
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import tqdm
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import torch
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from contextlib import nullcontext
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from torch.utils.data import DataLoader
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from functools import partial
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from datasets import load_dataset
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from typing import Dict, List
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from transformers.file_utils import PaddingStrategy
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from transformers import (
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AutoTokenizer,
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PreTrainedTokenizerFast,
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DataCollatorWithPadding,
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HfArgumentParser,
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BatchEncoding
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)
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from config import Arguments
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from logger_config import logger
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from utils import move_to_cuda
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from models import BiencoderModelForInference, BiencoderOutput
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parser = HfArgumentParser((Arguments,))
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args: Arguments = parser.parse_args_into_dataclasses()[0]
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def _psg_transform_func(tokenizer: PreTrainedTokenizerFast,
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examples: Dict[str, List]) -> BatchEncoding:
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batch_dict = tokenizer(examples['title'],
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text_pair=examples['contents'],
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max_length=args.p_max_len,
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padding=PaddingStrategy.DO_NOT_PAD,
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truncation=True)
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# for co-Condenser reproduction purpose only
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if args.model_name_or_path.startswith('Luyu/'):
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del batch_dict['token_type_ids']
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return batch_dict
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@torch.no_grad()
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def _worker_encode_passages(gpu_idx: int):
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def _get_out_path(shard_idx: int = 0) -> str:
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return '{}/shard_{}_{}'.format(args.encode_save_dir, gpu_idx, shard_idx)
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if os.path.exists(_get_out_path(0)):
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logger.error('{} already exists, will skip encoding'.format(_get_out_path(0)))
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return
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dataset = load_dataset('json', data_files=args.encode_in_path)['train']
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if args.dry_run:
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dataset = dataset.select(range(4096))
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dataset = dataset.shard(num_shards=torch.cuda.device_count(),
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index=gpu_idx,
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contiguous=True)
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logger.info('GPU {} needs to process {} examples'.format(gpu_idx, len(dataset)))
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torch.cuda.set_device(gpu_idx)
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tokenizer: PreTrainedTokenizerFast = AutoTokenizer.from_pretrained(args.model_name_or_path)
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model: BiencoderModelForInference = BiencoderModelForInference.build(args)
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model.eval()
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model.cuda()
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dataset.set_transform(partial(_psg_transform_func, tokenizer))
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data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8 if args.fp16 else None)
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data_loader = DataLoader(
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dataset,
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batch_size=args.encode_batch_size,
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shuffle=False,
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drop_last=False,
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num_workers=args.dataloader_num_workers,
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collate_fn=data_collator,
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pin_memory=True)
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num_encoded_docs, encoded_embeds, cur_shard_idx = 0, [], 0
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for batch_dict in tqdm.tqdm(data_loader, desc='passage encoding', mininterval=8):
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batch_dict = move_to_cuda(batch_dict)
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with torch.cuda.amp.autocast() if args.fp16 else nullcontext():
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outputs: BiencoderOutput = model(query=None, passage=batch_dict)
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encoded_embeds.append(outputs.p_reps.cpu())
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num_encoded_docs += outputs.p_reps.shape[0]
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if num_encoded_docs >= args.encode_shard_size:
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out_path = _get_out_path(cur_shard_idx)
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concat_embeds = torch.cat(encoded_embeds, dim=0)
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logger.info('GPU {} save {} embeds to {}'.format(gpu_idx, concat_embeds.shape[0], out_path))
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torch.save(concat_embeds, out_path)
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cur_shard_idx += 1
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num_encoded_docs = 0
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encoded_embeds.clear()
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if num_encoded_docs > 0:
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out_path = _get_out_path(cur_shard_idx)
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concat_embeds = torch.cat(encoded_embeds, dim=0)
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logger.info('GPU {} save {} embeds to {}'.format(gpu_idx, concat_embeds.shape[0], out_path))
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torch.save(concat_embeds, out_path)
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logger.info('Done computing score for worker {}'.format(gpu_idx))
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def _batch_encode_passages():
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logger.info('Args={}'.format(str(args)))
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gpu_count = torch.cuda.device_count()
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if gpu_count == 0:
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logger.error('No gpu available')
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return
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logger.info('Use {} gpus'.format(gpu_count))
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torch.multiprocessing.spawn(_worker_encode_passages, args=(), nprocs=gpu_count)
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logger.info('Done batch encode passages')
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if __name__ == '__main__':
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_batch_encode_passages()
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