Launches translation (inference).
    Inference is executed on a single GPU, implementation supports beam search
    with length normalization and coverage penalty.

def main():
    """
    Launches translation (inference).
    Inference is executed on a single GPU, implementation supports beam search
    with length normalization and coverage penalty.
    """
    args = parse_args()
    utils.set_device(args.cuda, args.local_rank)
    utils.init_distributed(args.cuda)
    setup_logging()

    if args.env:
        utils.log_env_info()

    logging.info(f'Run arguments: {args}')

    if not args.cuda and torch.cuda.is_available():
        warnings.warn('cuda is available but not enabled')
    if not args.cudnn:
        torch.backends.cudnn.enabled = False

    # load checkpoint and deserialize to CPU (to save GPU memory)
    checkpoint = torch.load(args.model, map_location={'cuda:0': 'cpu'})

    # build GNMT model
    tokenizer = Tokenizer()
    tokenizer.set_state(checkpoint['tokenizer'])
    vocab_size = tokenizer.vocab_size
    model_config = checkpoint['model_config']
    model_config['batch_first'] = args.batch_first
    model = GNMT(vocab_size=vocab_size, **model_config)
    model.load_state_dict(checkpoint['state_dict'])

    for (math, batch_size, beam_size) in product(args.math, args.batch_size,
                                                 args.beam_size):
        logging.info(f'math: {math}, batch size: {batch_size}, '
                     f'beam size: {beam_size}')
        if math == 'fp32':
            dtype = torch.FloatTensor
        if math == 'fp16':
            dtype = torch.HalfTensor
        model.type(dtype)

        if args.cuda:
            model = model.cuda()
        model.eval()

        # construct the dataset
        test_data = TextDataset(src_fname=args.input,
                                tokenizer=tokenizer,
                                sort=args.sort)

        # build the data loader
        test_loader = test_data.get_loader(batch_size=batch_size,
                                           batch_first=args.batch_first,
                                           shuffle=False,
                                           pad=True,
                                           num_workers=0)

        # build the translator object
        translator = Translator(model=model,
                                tokenizer=tokenizer,
                                loader=test_loader,
                                beam_size=beam_size,
                                max_seq_len=args.max_seq_len,
                                len_norm_factor=args.len_norm_factor,
                                len_norm_const=args.len_norm_const,
                                cov_penalty_factor=args.cov_penalty_factor,
                                cuda=args.cuda,
                                print_freq=args.print_freq,
                                dataset_dir=args.dataset_dir)

        # execute the inference
        translator.run(calc_bleu=args.bleu, eval_path=args.output,
                       reference_path=args.reference, summary=True)