def broadcast_object(
    obj: Any,
    src_rank: int,
    group: object,
    dist_device: Optional[torch.device] = None,
) -> Any:
    """Broadcast an arbitrary Python object to other workers."""
    if dist_device is None:
        if torch.distributed.get_backend(group) == "nccl":
            dist_device = torch.device("cuda")
        else:
            dist_device = torch.device("cpu")

    if get_rank(group) == src_rank:
        # split the tensors from the non-tensors so we can broadcast them
        # directly, avoiding unnecessary serialization/deserialization
        tensors = []
        obj = _split_tensors_from_obj(obj, tensors)
        obj = _broadcast_object_slow(obj, src_rank, group, dist_device)
        tensors = broadcast_tensors(tensors, src_rank, group, dist_device)
    else:
        obj = _broadcast_object_slow(None, src_rank, group, dist_device)
        tensors = broadcast_tensors(None, src_rank, group, dist_device)
    return _put_tensors_in_obj(obj, tensors)
Broadcast an arbitrary Python object to other workers.
fairseq/distributed/utils.py