import argparse from torch.utils.data import DataLoader import torch from bert import BERT from pretrainer import BERTTrainer, BERTFineTuneTrainer from dataset import PretrainerDataset, TokenizerDataset from vocab import Vocab import time def train(): parser = argparse.ArgumentParser() parser.add_argument('-workspace_name', type=str, default=None) parser.add_argument("-p", "--pretrain_dataset", type=str, default="pretraining/pretrain.txt", help="pretraining dataset for bert") parser.add_argument("-pv", "--pretrain_val_dataset", type=str, default="pretraining/test.txt", help="pretraining validation dataset for bert") # default="finetuning/test.txt", parser.add_argument("-f", "--train_dataset", type=str, default="finetuning/test_in.txt", help="fine tune train dataset for progress classifier") parser.add_argument("-t", "--test_dataset", type=str, default="finetuning/train_in.txt", help="test set for evaluate fine tune train set") parser.add_argument("-flabel", "--train_label", type=str, default="finetuning/test_in_label.txt", help="fine tune train dataset for progress classifier") parser.add_argument("-tlabel", "--test_label", type=str, default="finetuning/train_in_label.txt", help="test set for evaluate fine tune train set") ##### change Checkpoint parser.add_argument("-c", "--pretrained_bert_checkpoint", type=str, default="output_feb09/bert_trained.model.ep40", help="checkpoint of saved pretrained bert model") # output_1: output_1/bert_trained.model.ep3 parser.add_argument("-v", "--vocab_path", type=str, default="pretraining/vocab.txt", help="built vocab model path with bert-vocab") parser.add_argument("-hs", "--hidden", type=int, default=64, help="hidden size of transformer model") parser.add_argument("-l", "--layers", type=int, default=4, help="number of layers") parser.add_argument("-a", "--attn_heads", type=int, default=8, help="number of attention heads") parser.add_argument("-s", "--seq_len", type=int, default=100, help="maximum sequence length") parser.add_argument("-b", "--batch_size", type=int, default=32, help="number of batch_size") parser.add_argument("-e", "--epochs", type=int, default=301, help="number of epochs") # Use 50 for pretrain, and 10 for fine tune parser.add_argument("-w", "--num_workers", type=int, default=4, help="dataloader worker size") # Later run with cuda parser.add_argument("--with_cuda", type=bool, default=True, help="training with CUDA: true, or false") parser.add_argument("--log_freq", type=int, default=10, help="printing loss every n iter: setting n") parser.add_argument("--corpus_lines", type=int, default=None, help="total number of lines in corpus") parser.add_argument("--cuda_devices", type=int, nargs='+', default=None, help="CUDA device ids") parser.add_argument("--on_memory", type=bool, default=True, help="Loading on memory: true or false") parser.add_argument("--dropout", type=float, default=0.1, help="dropout of network") parser.add_argument("--lr", type=float, default=1e-3, help="learning rate of adam") parser.add_argument("--adam_weight_decay", type=float, default=0.01, help="weight_decay of adam") parser.add_argument("--adam_beta1", type=float, default=0.9, help="adam first beta value") parser.add_argument("--adam_beta2", type=float, default=0.999, help="adam first beta value") # These two need to be changed for fine tuning # parser.add_argument("--pretrain", type=bool, default=True, help="pretraining: true, or false") # parser.add_argument("-o", "--output_path", type=str, default="output/bert_trained.seq_encoder.model", help="ex)output/bert.model") # parser.add_argument("--same_student_prediction", type=bool, default=False, help="predict sequences by same student: true, or false") #clear;python3 src/main.py --output_path output/masked/bert_trained.model #clear;python3 src/main.py --output_path output/masked_prediction/bert_trained.model --same_student_prediction True parser.add_argument("--pretrain", type=bool, default=False, help="pretraining: true, or false") parser.add_argument("-o", "--output_path", type=str, default="output/bert_fine_tuned.FS.model", help="ex)output/bert.model") # python3 src/main.py args = parser.parse_args() for k,v in vars(args).items(): if ('dataset' in k) or ('path' in k) or ('label' in k): if v: setattr(args, f"{k}", args.workspace_name+"/"+v) print(f"args.{k} : {getattr(args, f'{k}')}") print("Loading Vocab", args.vocab_path) vocab_obj = Vocab(args.vocab_path) vocab_obj.load_vocab() print("Vocab Size: ", len(vocab_obj.vocab)) if args.pretrain: print("Pre-training......") print("Loading Pretraining Dataset", args.pretrain_dataset) print(f"Workspace: {args.workspace_name}") pretrain_dataset = PretrainerDataset(args.pretrain_dataset, vocab_obj, seq_len=args.seq_len, select_next_seq=args.same_student_prediction) print("Loading Pretraining validation Dataset", args.pretrain_val_dataset) pretrain_valid_dataset = PretrainerDataset(args.pretrain_val_dataset, vocab_obj, seq_len=args.seq_len, select_next_seq=args.same_student_prediction) \ if args.pretrain_val_dataset is not None else None print("Creating Dataloader") pretrain_data_loader = DataLoader(pretrain_dataset, batch_size=args.batch_size, num_workers=args.num_workers) pretrain_val_data_loader = DataLoader(pretrain_valid_dataset, batch_size=args.batch_size, num_workers=args.num_workers)\ if pretrain_valid_dataset is not None else None print("Building BERT model") # a = 5/0 # hidden = pretrain_dataset.seq_len if pretrain_dataset.seq_len > args.hidden else args.hidden # print("hidden: ", hidden) bert = BERT(len(vocab_obj.vocab), hidden=args.hidden, n_layers=args.layers, attn_heads=args.attn_heads, dropout=args.dropout) print(f"Creating BERT Trainer .... masking: True, prediction: {args.same_student_prediction}") trainer = BERTTrainer(bert, len(vocab_obj.vocab), train_dataloader=pretrain_data_loader, test_dataloader=pretrain_val_data_loader, lr=args.lr, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, with_cuda=args.with_cuda, cuda_devices=args.cuda_devices, log_freq=args.log_freq, same_student_prediction = args.same_student_prediction, workspace_name = args.workspace_name) print("Training Start") start_time = time.time() for epoch in range(args.epochs): trainer.train(epoch) if pretrain_val_data_loader is not None: trainer.test(epoch) if epoch > 19 and trainer.save_model: # or epoch%10 == 0 trainer.save(epoch, args.output_path) end_time = time.time() print("Time Taken to pretrain dataset = ", end_time - start_time) else: print("Fine Tuning......") print("Loading Train Dataset", args.train_dataset) train_dataset = TokenizerDataset(args.train_dataset, args.train_label, vocab_obj, seq_len=args.seq_len, train=True) print("Loading Test Dataset", args.test_dataset) test_dataset = TokenizerDataset(args.test_dataset, args.test_label, vocab_obj, seq_len=args.seq_len, train=False) \ if args.test_dataset is not None else None print("Creating Dataloader") train_data_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.num_workers) test_data_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.num_workers) \ if test_dataset is not None else None print("Load Pre-trained BERT model") # bert = BERT(len(vocab_obj.vocab), hidden=args.hidden, n_layers=args.layers, attn_heads=args.attn_heads) cuda_condition = torch.cuda.is_available() and args.with_cuda device = torch.device("cuda:0" if cuda_condition else "cpu") bert = torch.load(args.pretrained_bert_checkpoint, map_location=device) if args.workspace_name == "ratio_proportion_change4": num_labels = 7 elif args.workspace_name == "ratio_proportion_change3": num_labels = 7 elif args.workspace_name == "scale_drawings_3": num_labels = 7 elif args.workspace_name == "sales_tax_discounts_two_rates": num_labels = 3 # num_labels = 1 print(f"Number of Labels : {num_labels}") print("Creating BERT Fine Tune Trainer") trainer = BERTFineTuneTrainer(bert, len(vocab_obj.vocab), train_dataloader=train_data_loader, test_dataloader=test_data_loader, lr=args.lr, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, with_cuda=args.with_cuda, cuda_devices=args.cuda_devices, log_freq=args.log_freq, workspace_name = args.workspace_name, num_labels=num_labels) print("Training Start....") start_time = time.time() for epoch in range(args.epochs): trainer.train(epoch) if epoch > 4 and trainer.save_model: trainer.save(epoch, args.output_path) if test_data_loader is not None: trainer.test(epoch) end_time = time.time() print("Time Taken to fine tune dataset = ", end_time - start_time) if __name__ == "__main__": train()