Function construct_arguments

bing_bert/deepspeed_train.py:290–327

()

    return args

def construct_arguments():
    args = get_arguments()

    # Prepare Logger
    logger = Logger(cuda=torch.cuda.is_available() and not args.no_cuda)
    args.logger = logger
    config = json.load(open(args.config_file, 'r', encoding='utf-8'))
    args.config = config

    args.job_name = config['name'] if args.job_name is None else args.job_name
    print("Running Config File: ", args.job_name)
    # Setting the distributed variables
    print("Args = {}".format(args))

    # Setting all the seeds so that the task is random but same accross processes
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed_all(args.seed)

    os.makedirs(args.output_dir, exist_ok=True)
    args.saved_model_path = os.path.join(
        args.output_dir, "saved_models/", args.job_name)

    args.n_gpu = 1

    # Loading Tokenizer
    tokenizer = BertTokenizer.from_pretrained(config["bert_token_file"])
    args.tokenizer = tokenizer

    # Issue warning if early exit from epoch is configured
    if args.max_steps < sys.maxsize:
        logging.warning('Early training exit is set after {} global steps'.format(args.max_steps))

    if args.max_steps_per_epoch < sys.maxsize:
        logging.warning('Early epoch exit is set after {} global steps'.format(args.max_steps_per_epoch))

    return args

def prepare_optimizer_parameters(args, model):
    config = args.config