def create_colossalai_plugin(
    plugin: str,
    dtype: str,
    grad_clip: float,
    **kwargs,
) -> LowLevelZeroPlugin | HybridParallelPlugin:
    """
    Create a ColossalAI plugin.

    Args:
        plugin (str): The plugin name.
        dtype (str): The data type.
        grad_clip (float): The gradient clip value.

    Returns:
        LowLevelZeroPlugin |  HybridParallelPlugin: The plugin.
    """
    plugin_kwargs = dict(
        precision=dtype,
        initial_scale=2**16,
        max_norm=grad_clip,
        overlap_allgather=True,
        cast_inputs=False,
        reduce_bucket_size_in_m=20,
    )
    plugin_kwargs.update(kwargs)
    sp_size = plugin_kwargs.get("sp_size", 1)
    if plugin == "zero1" or plugin == "zero2":
        assert sp_size == 1, "Zero plugin does not support sequence parallelism"
        stage = 1 if plugin == "zero1" else 2
        plugin = LowLevelZeroPlugin(
            stage=stage,
            **plugin_kwargs,
        )
        set_data_parallel_group(dist.group.WORLD)
    elif plugin == "hybrid":
        plugin_kwargs["find_unused_parameters"] = True
        reduce_bucket_size_in_m = plugin_kwargs.pop("reduce_bucket_size_in_m")
        if "zero_bucket_size_in_m" not in plugin_kwargs:
            plugin_kwargs["zero_bucket_size_in_m"] = reduce_bucket_size_in_m
        plugin_kwargs.pop("cast_inputs")
        plugin_kwargs["enable_metadata_cache"] = False

        custom_policy = plugin_kwargs.pop("custom_policy", None)
        if custom_policy is not None:
            custom_policy = custom_policy()
        plugin = HybridParallelPlugin(
            custom_policy=custom_policy,
            **plugin_kwargs,
        )
        set_tensor_parallel_group(plugin.tp_group)
        set_sequence_parallel_group(plugin.sp_group)
        set_data_parallel_group(plugin.dp_group)
    else:
        raise ValueError(f"Unknown plugin {plugin}")
    return plugin

    Create a ColossalAI plugin.

    Args:
        plugin (str): The plugin name.
        dtype (str): The data type.
        grad_clip (float): The gradient clip value.

    Returns:
        LowLevelZeroPlugin |  HybridParallelPlugin: The plugin.
    
opensora/utils/train.py