def load_ict_checkpoint(model, only_query_model=False, only_block_model=False, from_realm_chkpt=False):
    """selectively load ICT models for indexing/retrieving from ICT or REALM checkpoints"""

    args = get_args()

    if isinstance(model, torchDDP):
        model = model.module

    load_path = args.load if from_realm_chkpt else args.ict_load

    tracker_filename = get_checkpoint_tracker_filename(load_path)
    with open(tracker_filename, 'r') as f:
        iteration = int(f.read().strip())

    # assert iteration > 0
    checkpoint_name = get_checkpoint_name(load_path, iteration, False)
    if mpu.get_data_parallel_rank() == 0:
        print('global rank {} is loading checkpoint {}'.format(
            torch.distributed.get_rank(), checkpoint_name))

    state_dict = torch.load(checkpoint_name, map_location='cpu')
    ict_state_dict = state_dict['model']
    if from_realm_chkpt and mpu.get_data_parallel_rank() == 0:
        print(" loading ICT state dict from REALM", flush=True)
        ict_state_dict = ict_state_dict['retriever']['ict_model']

    if only_query_model:
        ict_state_dict.pop('context_model')
    if only_block_model:
        ict_state_dict.pop('question_model')

    model.load_state_dict(ict_state_dict)
    torch.distributed.barrier()

    if mpu.get_data_parallel_rank() == 0:
        print(' successfully loaded {}'.format(checkpoint_name))

    return model
selectively load ICT models for indexing/retrieving from ICT or REALM checkpoints
megatron_lm/megatron/checkpointing.py