twitter_ae_training_for_generated_prompt_multitasks.py

import argparse
import json
import os
from datetime import datetime
from torch import optim
import torch
import torch.multiprocessing as mp
from torch.cuda.amp import GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, ConcatDataset
from torch.utils.data.distributed import DistributedSampler
from torch.utils.tensorboard import SummaryWriter
from transformers import AdamW
import random
from src.data.collation_for_prompt_multitasks import Collator
from src.data.dataset_for_prompt import MVSA_Dataset, Twitter_Dataset
from src.data.tokenization_new_for_generated_prompt_multitasks import ConditionTokenizer
from src.model.config import MultiModalBartConfig
from src.model.MAESC_model_for_generated_aspect_prompt_multitasks import MultiModalBartModel_AESC
from src.model.model import MultiModalBartModelForPretrain
from src.training_multitasks import fine_tune
from src.utils import Logger, save_training_data, load_training_data, setup_process, cleanup_process
from src.model.metrics import AESCSpanMetric, OESpanMetric
from src.model.generater_for_generated_prompt_multitasks import SequenceGeneratorModel
import src.eval_utils_multitasks as eval_utils
import numpy as np
import torch.backends.cudnn as cudnn

def get_parameter_number(model):
    total_num = sum(p.numel() for p in model.parameters())
    trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
    return {'Total': total_num, 'Trainable': trainable_num}

def main(rank, args):
    timestamp = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
    checkpoint_path = os.path.join(args.checkpoint_dir, timestamp)
    tb_writer = None
    add_name = 'epoch_num' + str(args.epochs)
    add_name += 'last'

    if args.is_sample:
        add_name += 'sample_num' + str(args.sample_num)
        add_name += 'start_idx' + str(args.start_idx)
    if args.text_only:
        add_name += ' only text'
    else:
        add_name += ' multi'
    if args.bart_init == 0:
        add_name += '_random_init_'
    if args.checkpoint:
        add_name = add_name + '-pretrain' + args.checkpoint.split('/')[-2]

    add_name = add_name + str(args.lr)
    log_dir = os.path.join(args.log_dir, timestamp + add_name)

    # make log dir and tensorboard writer if log_dir is specified
    if rank == 0 and args.log_dir is not None:
        os.makedirs(log_dir)
        tb_writer = SummaryWriter(log_dir=log_dir)

    logger = Logger(log_dir=os.path.join(log_dir, 'log.txt'),
                    enabled=(rank == 0))
    # prf_logger = Logger(log_dir=os.path.join(log_dir, 'prf_log.txt'),
    #                     enabled=(rank == 0))

    # make checkpoint dir if not exist
    if args.is_check == 1 and not os.path.isdir(checkpoint_path):
        os.makedirs(checkpoint_path)
        logger.info('Made checkpoint directory: "{}"'.format(checkpoint_path))

    logger.info('Initialed with {} GPU(s)'.format(args.gpu_num), pad=True)
    for k, v in vars(args).items():
        logger.info('{}: {}'.format(k, v))

    # =========================== model =============================

    logger.info('Loading model...')

    if args.cpu:
        device = 'cpu'
        map_location = device
    else:
        device = torch.device("cuda:{}".format(rank))
        map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}

    tokenizer = ConditionTokenizer(args)
    label_ids = list(tokenizer.mapping2id.values())
    senti_ids = list(tokenizer.senti2id.values())
    # print(label_ids)
    # print(tokenizer.convert_ids_to_tokens(label_ids))
    if args.model_config is not None:
        bart_config = MultiModalBartConfig.from_dict(
            json.load(open(args.model_config)))
    else:
        bart_config = MultiModalBartConfig.from_pretrained(args.checkpoint)

    if args.dropout is not None:
        bart_config.dropout = args.dropout
    if args.attention_dropout is not None:
        bart_config.attention_dropout = args.attention_dropout
    if args.classif_dropout is not None:
        bart_config.classif_dropout = args.classif_dropout
    if args.activation_dropout is not None:
        bart_config.activation_dropout = args.activation_dropout

    bos_token_id = 0  # 因为是特殊符号
    eos_token_id = 1

    if args.checkpoint:
        pretrain_model = MultiModalBartModelForPretrain.from_pretrained(
            args.checkpoint,
            config=bart_config,
            bart_model=args.bart_model,
            tokenizer=tokenizer,
            label_ids=label_ids,
            senti_ids=senti_ids,
            args=args,
            error_on_mismatch=False)
        seq2seq_model = MultiModalBartModel_AESC(bart_config, args,
                                                 args.bart_model, tokenizer,
                                                 label_ids)
        seq2seq_model.encoder.load_state_dict(
            pretrain_model.encoder.state_dict())
        seq2seq_model.decoder.load_state_dict(
            pretrain_model.span_decoder.state_dict())
        model = SequenceGeneratorModel(seq2seq_model,
                                       bos_token_id=bos_token_id,
                                       eos_token_id=eos_token_id,
                                       max_length=args.max_len,
                                       max_len_a=args.max_len_a,
                                       num_beams=args.num_beams,
                                       do_sample=False,
                                       repetition_penalty=1,
                                       length_penalty=1.0,
                                       pad_token_id=eos_token_id,
                                       restricter=None)
    else:
        seq2seq_model = MultiModalBartModel_AESC(bart_config, args,
                                                 args.bart_model, tokenizer,
                                                 label_ids)
        model = SequenceGeneratorModel(seq2seq_model,
                                       bos_token_id=bos_token_id,
                                       eos_token_id=eos_token_id,
                                       max_length=args.max_len,
                                       max_len_a=args.max_len_a,
                                       num_beams=args.num_beams,
                                       do_sample=False,
                                       repetition_penalty=1,
                                       length_penalty=1.0,
                                       pad_token_id=eos_token_id,
                                       restricter=None)
        # model = MultiModalBartModel_AESC(bart_config, args.bart_model,
        #                                  tokenizer, label_ids)
    model.to(device)
    parameters = get_parameter_number(model) ##{'Total': 169351685, 'Trainable': 169351685}
    print(parameters)

    optimizer = AdamW(model.parameters(), lr=args.lr, betas=(0.9, 0.999))

    scaler = GradScaler() if args.amp else None

    epoch = 0
    logger.info('Loading data...')
    collate_twitter_ae = Collator(
                                  task=args.task,
                                  tokenizer=tokenizer,
                                  mlm_enabled=False,
                                  senti_enabled=False,
                                  ae_enabled=False,
                                  oe_enabled=False,
                                  aesc_enabled=False,
                                  anp_enabled=False,
                                  twitter_ae_enabled=True,
                                  has_prompt=args.has_prompt,
                                  max_img_num=args.num_image_tokens,
                                  text_only=args.text_only,
                                  use_caption=args.use_caption)

    train_dataset = Twitter_Dataset(args.dataset[0][1], split='train')

    dev_dataset = Twitter_Dataset(args.dataset[0][1], split='dev')
    test_dataset = Twitter_Dataset(args.dataset[0][1], split='test')

    train_loader = DataLoader(dataset=train_dataset,
                              batch_size=args.batch_size,
                              shuffle=True,
                              num_workers=args.num_workers,
                              pin_memory=True,
                              collate_fn=collate_twitter_ae)
    dev_loader = DataLoader(dataset=dev_dataset,
                            batch_size=args.batch_size,
                            shuffle=False,
                            num_workers=args.num_workers,
                            pin_memory=True,
                            collate_fn=collate_twitter_ae)
    test_loader = DataLoader(dataset=test_dataset,
                             batch_size=args.batch_size,
                             shuffle=False,
                             num_workers=args.num_workers,
                             pin_memory=True,
                             collate_fn=collate_twitter_ae)

    callback = None
    metric = OESpanMetric(eos_token_id, num_labels=len(label_ids))
    model.train()
    start = datetime.now()
    best_dev_res = None
    best_dev_test_res = None
    best_test_res = None

    while epoch < args.epochs:
        logger.info('Epoch {}'.format(epoch + 1), pad=True)
        fine_tune(epoch=epoch,
                  model=model,
                  train_loader=train_loader,
                  test_loader=test_loader,
                  metric=metric,
                  optimizer=optimizer,
                  args=args,
                  device=device,
                  logger=logger,
                  callback=callback,
                  log_interval=1,
                  tb_writer=tb_writer,
                  tb_interval=1,
                  scaler=scaler)

        if (epoch + 1) % args.eval_every == 0:
            # train_dev = eval_utils.eval(model, train_loader, metric, device)

            
            res_dev, dev_aspects_num_acc = eval_utils.eval(args, model, dev_loader, metric, device)
            res_test, test_aspects_num_acc = eval_utils.eval(args, model, test_loader, metric, device)

        

            logger.info('DEV  ae_p:{} ae_r:{} ae_f:{}, dev_aspects_num_acc: {:.4f}'.format(
                res_dev['oe_pre'], res_dev['oe_rec'], res_dev['oe_f'], dev_aspects_num_acc))
            logger.info('TEST  ae_p:{} ae_r:{} ae_f:{}, test_aspects_num_acc: {:.4f}'.format(
                res_test['oe_pre'], res_test['oe_rec'], res_test['oe_f'], test_aspects_num_acc))
            # logger.info('DEV  ae_p:{} ae_r:{} ae_f:{}'.format(
            #     res_dev['ae_pre'], res_dev['ae_rec'], res_dev['ae_f']))
            save_flag = False
            if best_dev_res is None:
                best_dev_res = res_dev
                best_dev_test_res = res_test

            else:
                if best_dev_res['oe_f'] < res_dev['oe_f']:
                    best_dev_res = res_dev
                    best_dev_test_res = res_test

            if best_test_res is None:
                best_test_res = res_test
                save_flag = True
            else:
                if best_test_res['oe_f'] < res_test['oe_f']:
                    best_test_res = res_test
                    save_flag = True

            if args.is_check == 1 and save_flag:
                current_checkpoint_path = os.path.join(checkpoint_path,
                                                       args.check_info)
                model.seq2seq_model.save_pretrained(current_checkpoint_path)
                print('save model!!!!!!!!!!!')
        epoch += 1
    logger.info("Training complete in: " + str(datetime.now() - start),
                pad=True)
    logger.info('---------------------------')
    logger.info('BEST DEV:-----')
    logger.info('BEST DEV  ae_p:{} ae_r:{} ae_f:{}'.format(
        best_dev_res['oe_pre'], best_dev_res['oe_rec'], best_dev_res['oe_f']))

    logger.info('BEST DEV TEST:-----')
    logger.info('BEST DEV--TEST  ae_p:{} ae_r:{} ae_f:{}'.format(
        best_dev_test_res['oe_pre'], best_dev_test_res['oe_rec'],
        best_dev_test_res['oe_f']))

    logger.info('BEST TEST:-----')
    logger.info('BEST TEST  ae_p:{} ae_r:{} ae_f:{}'.format(
        best_test_res['oe_pre'], best_test_res['oe_rec'],
        best_test_res['oe_f']))

    # if not args.cpu:
    #     cleanup_process()


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset',
                        action='append',
                        nargs=2,
                        metavar=('DATASET_NAME', 'DATASET_PATH'),
                        required=True,
                        help='')
    # required

    parser.add_argument('--checkpoint_dir',
                        required=True,
                        type=str,
                        help='where to save the checkpoint')
    parser.add_argument('--bart_model',
                        default='/home/xiaocui/code/FW-MABSA/data/weights/bart-base',
                        type=str,
                        help='bart pretrain model')
    # path
    parser.add_argument(
        '--log_dir',
        default=None,
        type=str,
        help='path to output log files, not output to file if not specified')
    parser.add_argument('--model_config',
                        default=None,
                        type=str,
                        help='path to load model config')
    parser.add_argument('--text_only',action='store_true', default=False, help='text_only')
    parser.add_argument('--checkpoint',
                        default=None,
                        type=str,
                        help='name or path to load weights')
    parser.add_argument('--lr_decay_every',
                        default=4,
                        type=int,
                        help='lr_decay_every')
    parser.add_argument('--lr_decay_ratio',
                        default=0.8,
                        type=float,
                        help='lr_decay_ratio')
    # training and evaluation
    parser.add_argument('--epochs',
                        default=40,
                        type=int,
                        help='number of training epoch')
    parser.add_argument('--eval_every', default=3, type=int, help='eval_every')
    parser.add_argument('--lr', default=1e-2, type=float, help='learning rate')
    parser.add_argument('--num_beams',
                        default=4,
                        type=int,
                        help='level of beam search on validation')
    parser.add_argument(
        '--continue_training',
        action='store_true',
        help='continue training, load optimizer and epoch from checkpoint')
    parser.add_argument('--warmup', default=0.1, type=float, help='warmup')
    # dropout
    parser.add_argument(
        '--dropout',
        default=None,
        type=float,
        help=
        'dropout rate for the transformer. This overwrites the model config')
    parser.add_argument(
        '--classif_dropout',
        default=None,
        type=float,
        help=
        'dropout rate for the classification layers. This overwrites the model config'
    )
    parser.add_argument(
        '--attention_dropout',
        default=None,
        type=float,
        help=
        'dropout rate for the attention layers. This overwrites the model config'
    )
    parser.add_argument(
        '--activation_dropout',
        default=None,
        type=float,
        help=
        'dropout rate for the activation layers. This overwrites the model config'
    )

    # hardware and performance
    parser.add_argument('--grad_clip', default=5, type=float, help='grad_clip')
    parser.add_argument('--gpu_num',
                        default=1,
                        type=int,
                        help='number of GPUs in total')
    parser.add_argument('--cpu',
                        action='store_true',
                        help='if only use cpu to run the model')
    parser.add_argument('--amp',
                        action='store_true',
                        help='whether or not to use amp')
    parser.add_argument('--master_port',
                        type=str,
                        default='12355',
                        help='master port for DDP')
    parser.add_argument('--batch_size',
                        type=int,
                        default=64,
                        help='training batch size')
    parser.add_argument('--seed', type=int, default=42, help='seed')
    parser.add_argument('--num_workers',
                        type=int,
                        default=0,
                        help='#workers for data loader')
    parser.add_argument('--max_len', type=int, default=10, help='max_len')
    parser.add_argument('--max_len_a',
                        type=float,
                        default=0.6,
                        help='max_len_a')
    parser.add_argument('--ANP_loss_type',
                        type=str,
                        default='KL',
                        help='ANP_loss_type')
    parser.add_argument('--bart_init', type=int, default=1, help='bart_init')
    parser.add_argument('--sample_num',
                        type=int,
                        default=500,
                        help='sample_num')
    parser.add_argument('--is_sample', type=int, default=1, help='is_sample')
    parser.add_argument('--start_idx', type=int, default=0, help='start_idx')
    parser.add_argument('--check_info', type=str, default='', help='start_idx')
    parser.add_argument('--is_check', type=int, default=0, help='start_idx')
    parser.add_argument('--task', type=str, default='twitter_ae', help='task')
    parser.add_argument('--has_prompt',  action='store_true', default=False, help='whether has prompt')
    parser.add_argument('--use_generated_prompt', action='store_true', default=False, help='whether use the generated prompt')
    parser.add_argument('--use_different_senti_prompt', action='store_true', default=False, help='whether use different prompt for different sentiemnt in an instance')
    parser.add_argument('--use_different_aspect_prompt', action='store_true', default=False, help='whether use different prompt for different aspects in an instance')
    parser.add_argument('--num_image_tokens', type=int, default=2, help='the length of image_tokens')
    parser.add_argument('--use_multitasks', action='store_true', default=False, help='whether use multitasks')
    parser.add_argument('--loss_lambda', default=0.1, type=float, help='the weight of aspect_num classification loss')
    parser.add_argument('--use_caption', action='store_true', default=False, help='whether use image caption')

    args = parser.parse_args()

    if args.gpu_num != 1 and args.cpu:
        raise ValueError('--gpu_num are not allowed if --cpu is set to true')

    if args.checkpoint is None and args.model_config is None:
        raise ValueError(
            '--model_config and --checkpoint cannot be empty at the same time')

    return args


if __name__ == '__main__':
    args = parse_args()

    # mp.spawn(main, args=(args, ), nprocs=args.gpu_num, join=True)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed_all(args.seed)
    torch.cuda.manual_seed(args.seed)
    np.random.seed(args.seed)
    random.seed(args.seed)
    cudnn.deterministic = True
    main(0, args)