def model_parallel_cuda_manual_seed(seed):
    """Initialize model parallel cuda seed.

    This function should be called after the model parallel is
    initialized. Also, no torch.cuda.manual_seed should be called
    after this function. Basically, this is replacement for that
    function.
    Two set of RNG states are tracked:
        default state: This is for data parallelism and is the same among a
                       set of model parallel GPUs but different across
                       different model paralle groups. This is used for
                       example for dropout in the non-tensor-model-parallel regions.
        tensor-model-parallel state: This state is different among a set of model
                              parallel GPUs, but the same across data parallel
                              groups. This is used for example for dropout in
                              model parallel regions.
    """
    # 2718 is just for fun and any POSITIVE value will work.
    offset = seed + 2718
    tensor_model_parallel_seed = offset + get_tensor_model_parallel_rank()
    # Data parallel gets the original seed.
    data_parallel_seed = seed

    _CUDA_RNG_STATE_TRACKER.reset()
    # Set the default state.
    torch.cuda.manual_seed(data_parallel_seed)
    # and model parallel state.
    _CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME, tensor_model_parallel_seed)
Initialize model parallel cuda seed.

    This function should be called after the model parallel is
    initialized. Also, no torch.cuda.manual_seed should be called
    after this function. Basically, this is replacement for that
    function.
    Two set of RNG states are tracked:
        default state: This is for data parallelism and is the same among a
                       set of model parallel GPUs but different across
                       different model paralle groups. This is used for
                       example for dropout in the non-tensor-model-parallel regions.
        tensor-model-parallel state: This state is different among a set of model
                              parallel GPUs, but the same across data parallel
                              groups. This is used for example for dropout in
                              model parallel regions.
    
training/DeepSpeed-Domino/domino/tensor_parallel/random.py