main.py

import argparse
import os
from utils import load_and_cache_examples, load_and_cache_unlabeled_examples, init_logger, load_tokenizer
from trainer import Trainer
import torch 
import numpy as np 
import random 
import torch.nn as nn
from torch.utils.data import Subset
import json
import pickle 
from transformers import AutoTokenizer
from eval import load_pred_data, save_data
import matplotlib 
matplotlib.use("Agg")
import matplotlib.pyplot as plt 
from collections import Counter


def set_seed(args):
    random.seed(args.train_seed)
    np.random.seed(args.train_seed)
    torch.manual_seed(args.train_seed)
    if args.n_gpu > 0  and torch.cuda.is_available():
        # print('yes')
        # assert 0
        torch.cuda.manual_seed_all(args.train_seed)
        torch.cuda.manual_seed(args.train_seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

def main(args):
    init_logger()
    set_seed(args)
    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer, use_fast=True)
    # tokenizer = load_tokenizer(args)
  
    train_dataset, num_labels, train_size  = load_and_cache_examples(args, tokenizer, mode="train")
    dev_dataset, num_labels,  dev_size = load_and_cache_examples(args, tokenizer, mode="dev")
    test_dataset, num_labels, test_size = load_and_cache_examples(args, tokenizer, mode="test")
    unlabeled_dataset, unlabeled_size = load_and_cache_unlabeled_examples(args, tokenizer, mode = 'unlabeled', train_size = train_size)
    contra_datasets = []
  
    print('number of labels:', num_labels)
    print('train_size:', train_size)
    print('dev_size:', dev_size)
    print('test_size:', test_size)
    print('unlabel_size:', unlabeled_size)

    trainer = Trainer(args, train_dataset=train_dataset, dev_dataset=dev_dataset,test_dataset=test_dataset, contra_datasets = contra_datasets, \
            unlabeled = unlabeled_dataset, num_labels = num_labels, data_size = train_size
            )
    if args.load_prev == 1:
        print(f"Load ckpt from {args.prev_ckpt}")
        trainer.load_model(path= args.prev_ckpt)
   

    trainer.init_model()
    if args.semi_method == 'ft':
        print("Begin Fine-Tuning!!")
        trainer.train(n_sample = len(train_dataset))
        train_pred, train_feat, train_label, unlabeled_pred, unlabeled_feat, unlabeled_label, unlabeled_pseudo = trainer.inference()
        save_data(train_pred, train_feat, train_label,  unlabeled_pred, unlabeled_feat, unlabeled_label, unlabeled_pseudo, mutual_info_bald = None, n_iter = args.round, dataset = args.task, ckpt = args.dr_model + f"_{args.dr_N}")
    else:
        print("Begin Self-Training!!")
        trainer.load_model(path= args.prev_ckpt)
        trainer.init_model()
        try:
            train_pred, train_feat, train_label, unlabeled_pred, unlabeled_feat, unlabeled_label, unlabeled_pseudo = load_pred_data(dataset = args.task, ckpt = args.dr_model, n_iter = args.round)
        except:
            train_pred, train_feat, train_label, unlabeled_pred, unlabeled_feat, unlabeled_label, unlabeled_pseudo = trainer.inference()
            save_data(train_pred, train_feat, train_label,  unlabeled_pred, unlabeled_feat, unlabeled_label, unlabeled_pseudo, mutual_info_bald = None, n_iter = args.round, dataset = args.task, ckpt = args.dr_model)
            train_pred, train_feat, train_label, unlabeled_pred, unlabeled_feat, unlabeled_label, unlabeled_pseudo = load_pred_data(dataset = args.task, ckpt = args.dr_model, n_iter = args.round)
            
        current_val = -np.max(unlabeled_pred, axis = -1)
        idx = np.argsort(current_val)
        cnt = Counter()
        labeled_idx = []
        for i in idx[:int(args.num_unlabeled)]:
            labeled_idx.append(i)
            cnt[np.argmax(unlabeled_pred[i])] += 1
        trainer.unlabeled = Subset(trainer.unlabeled, idx[:int(args.num_unlabeled)])
        trainer.selftrain()

    exit()


if __name__ == '__main__':
    
    parser = argparse.ArgumentParser()
    parser.add_argument("--method", default='clean', type=str, help="which method to use")
    parser.add_argument("--gpu", default='0,1,2,3', type=str, help="which gpu to use")
    parser.add_argument("--n_gpu", default=1, type=int, help="which gpu to use")

    parser.add_argument("--seed", default=0, type=int, help="which seed to use")
    parser.add_argument("--train_seed", default=0, type=int, help="which seed to use")
    parser.add_argument("--task", default="agnews", type=str, help="The name of the task to train")
    
    parser.add_argument("--data_dir", default="../datasets", type=str,
                        help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
    parser.add_argument("--model_dir", default="./model", type=str, help="Path to model")
    parser.add_argument("--eval_dir", default="./eval", type=str, help="Evaluation script, result directory")
    parser.add_argument("--train_file", default="train.tsv", type=str, help="Train file")
    parser.add_argument("--dev_file", default="dev.tsv", type=str, help="dev file")
    parser.add_argument("--test_file", default="test.tsv", type=str, help="Test file")
    parser.add_argument("--unlabel_file", default="unlabeled.tsv", type=str, help="Test file")
    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the test set.")

    parser.add_argument("--extra_dataset", default="", type=str, help="Whether to run extra eval on the test set.")

    parser.add_argument("--dev_labels", default=100, type=int, help="number of labels for dev set")
    parser.add_argument("--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from s3",)
    parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.",)

    parser.add_argument('--logging_steps', type=int, default=20, help="Log every X updates steps.")
    parser.add_argument('--self_train_logging_steps', type=int, default=20, help="Log every X updates steps.")
    parser.add_argument('--save_steps', type=int, default=200, help="Save checkpoint every X updates steps.")
    parser.add_argument("--model_type", default="bert-base-uncased", type=str)
    parser.add_argument("--tokenizer", default="bert-base-uncased", type=str)

    parser.add_argument("--auto_load", default=1, type=int, help="Auto loading the model or not")
    parser.add_argument("--add_sep_token", action="store_true", help="Add [SEP] token at the end of the sentence")

    parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.")
    parser.add_argument("--max_steps", default=100, type=int, help="Training steps for initialization.")
    parser.add_argument("--weight_decay", default=1e-4, type=float, help="Weight decay if we apply some.")
    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
    parser.add_argument("--dropout_rate", default=0.1, type=float, help="Dropout for fully-connected layers")
    parser.add_argument("--batch_size", default=32, type=int, help="Batch size for training and evaluation.")
    parser.add_argument("--self_training_batch_size", default=32, type=int, help="Batch size for training and evaluation.")
    parser.add_argument("--eval_batch_size", default=256, type=int, help="Batch size for training and evaluation.")
    parser.add_argument("--learning_rate", default=1e-5, type=float, help="The initial learning rate for Adam.")
    parser.add_argument("--max_seq_len", default=128, type=int, help="The maximum total input sequence length after tokenization.")
    parser.add_argument("--max_seq_len_test", default=128, type=int, help="The maximum total input sequence length after tokenization.")

    parser.add_argument("--gradient_accumulation_steps", default=1, type=int, help="The maximum total input sequence length after tokenization.")
    parser.add_argument("--learning_rate_st", default=1e-5, type=float, help="The initial learning rate for self-training.")
    parser.add_argument('--self_training_max_step', type = int, default = 10000)    
    parser.add_argument('--soft_label', type = int, default = 1)
    parser.add_argument('--self_training_update_period', type = int, default = 100, help = 'update period')
    parser.add_argument("--self_training_eps", default=0.6, type=float)
    parser.add_argument("--self_training_power", default=2, type=float)
    parser.add_argument("--self_training_weight", default=0.5, type=float)
    parser.add_argument("--gce_loss_q", default=0.5, type=float)

    parser.add_argument('--semi_method', type = str, default = 'ft')
    parser.add_argument('--k_cal', type = int, default = 5)

    parser.add_argument('--num_unlabeled', type = int, default = 100, help = 'number of unlabeled data.')

    parser.add_argument('--dr_model', type = str, default = 'arxiv_ckpt', help = 'the name for DR model.')
    parser.add_argument('--dr_N', type = int, default = 10, help = 'the number of retrieved documents.')

    parser.add_argument('--filter', type = int, default = 0, help = 'Whether use filter.')
    parser.add_argument('--template', type = int, default = 0, help = 'Prompt Template.')
    parser.add_argument('--round', type = int, default = 0, help = 'Prompt Template.')
    parser.add_argument('--load_prev', type = int, default = 0, help = 'whether load model from prev_round.')
    parser.add_argument('--prev_ckpt', type = str, default = '', help = 'dir of prev ckpt.')
    parser.add_argument('--train_full_labels', type = int, default = 0, help = 'whether train with full labels or not')

# semi_method
    args = parser.parse_args()
    if 'biobert' in args.model_type:
        args.model_name_or_path = 'dmis-lab/biobert-v1.1'
    elif 'scibert' in args.model_type:
        args.model_name_or_path = 'allenai/scibert_scivocab_uncased'
        args.tokenizer = 'allenai/scibert_scivocab_uncased'
    else:
        args.model_name_or_path = args.model_type
        args.tokenizer=args.model_type
    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
    print(os.environ['CUDA_VISIBLE_DEVICES'])
    main(args)