pretrain.py

import argparse
import torch
import uer.trainer as trainer
from uer.utils.config import load_hyperparam
from uer.opts import *


def main():
    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)

    # Path options.
    parser.add_argument("--dataset_path", type=str, default="dataset.pt",
                        help="Path of the preprocessed dataset.")
    parser.add_argument("--pretrained_model_path", type=str, default=None,
                        help="Path of the pretrained model.")
    parser.add_argument("--output_model_path", type=str, required=True,
                        help="Path of the output model.")
    parser.add_argument("--config_path", type=str, default="models/bert/base_config.json",
                        help="Config file of model hyper-parameters.")

    # Training and saving options.
    parser.add_argument("--total_steps", type=int, default=100000,
                        help="Total training steps.")
    parser.add_argument("--save_checkpoint_steps", type=int, default=10000,
                        help="Specific steps to save model checkpoint.")
    parser.add_argument("--report_steps", type=int, default=100,
                        help="Specific steps to print prompt.")
    parser.add_argument("--accumulation_steps", type=int, default=1,
                        help="Specific steps to accumulate gradient.")
    parser.add_argument("--batch_size", type=int, default=32,
                        help="Training batch size. The actual batch_size is [batch_size x world_size x accumulation_steps].")
    parser.add_argument("--instances_buffer_size", type=int, default=25600,
                        help="The buffer size of instances in memory.")
    parser.add_argument("--labels_num", type=int, required=False,
                        help="Number of prediction labels.")
    parser.add_argument("--dropout", type=float, default=0.1, help="Dropout value.")
    parser.add_argument("--seed", type=int, default=7, help="Random seed.")

    # Preprocess options.
    tokenizer_opts(parser)
    tgt_tokenizer_opts(parser)

    # Model options.
    model_opts(parser)
    parser.add_argument("--data_processor", choices=["bert", "lm", "mlm", "bilm", "albert", "mt", "t5", "cls", "prefixlm", "gsg", "bart", "cls_mlm"], default="bert",
                        help="The data processor of the pretraining model.")
    parser.add_argument("--deep_init", action="store_true",
                        help="Scaling initialization of projection layers by a "
                             "factor of 1/sqrt(2N). Necessary to large models.")

    # Masking options.
    parser.add_argument("--whole_word_masking", action="store_true", help="Whole word masking.")
    parser.add_argument("--span_masking", action="store_true", help="Span masking.")
    parser.add_argument("--span_geo_prob", type=float, default=0.2,
                        help="Hyperparameter of geometric distribution for span masking.")
    parser.add_argument("--span_max_length", type=int, default=10,
                        help="Max length for span masking.")

    # Optimizer options.
    optimization_opts(parser)

    # GPU options.
    parser.add_argument("--world_size", type=int, default=1, help="Total number of processes (GPUs) for training.")
    parser.add_argument("--gpu_ranks", default=[], nargs='+', type=int, help="List of ranks of each process."
                        " Each process has a unique integer rank whose value is in the interval [0, world_size), and runs in a single GPU.")
    parser.add_argument("--master_ip", default="tcp://localhost:12345", type=str, help="IP-Port of master for training.")
    parser.add_argument("--backend", choices=["nccl", "gloo"], default="nccl", type=str, help="Distributed backend.")

    # Log options.
    log_opts(parser)

    args = parser.parse_args()

    if "cls" in args.target:
        assert args.labels_num is not None, "Cls target needs the denotation of the number of labels."

    # Load hyper-parameters from config file.
    if args.config_path:
        args = load_hyperparam(args)

    ranks_num = len(args.gpu_ranks)

    if args.world_size > 1:
        # Multiprocessing distributed mode.
        assert torch.cuda.is_available(), "No available GPUs."
        assert ranks_num <= args.world_size, "Started processes exceed `world_size` upper limit."
        assert ranks_num <= torch.cuda.device_count(), "Started processes exceeds the available GPUs."
        args.dist_train = True
        args.ranks_num = ranks_num
        print("Using distributed mode for training.")
    elif args.world_size == 1 and ranks_num == 1:
        # Single GPU mode.
        assert torch.cuda.is_available(), "No available GPUs."
        args.local_rank = args.gpu_ranks[0]
        assert args.local_rank < torch.cuda.device_count(), "Invalid specified GPU device."
        args.dist_train = False
        args.single_gpu = True
        print("Using GPU %d for training." % args.local_rank)
    else:
        # CPU mode.
        assert ranks_num == 0, "GPUs are specified, please check the arguments."
        args.dist_train = False
        args.single_gpu = False
        print("Using CPU mode for training.")

    trainer.train_and_validate(args)


if __name__ == "__main__":
    main()