xc-llm-ascend/docs/source/user_guide/additional_config.md

Additional Configuration

addintional configuration is a mechanism provided by vLLM to allow plugins to control inner behavior by their own. vLLM Ascend uses this mechanism to make the project more flexible.

How to use

With either online mode or offline mode, users can use additional configuration. Take Qwen3 as an example:

Online mode:

vllm serve Qwen/Qwen3-8B --additional-config='{"config_key":"config_value"}'

Offline mode:

from vllm import LLM

LLM(model="Qwen/Qwen3-8B", additional_config={"config_key":"config_value"})

Configuration options

The following table lists the additional configuration options available in vLLM Ascend:

Name	Type	Default	Description
torchair_graph_config	dict	{}	The config options for torchair graph mode
ascend_scheduler_config	dict	{}	The config options for ascend scheduler
expert_tensor_parallel_size	str	0	Expert tensor parallel size the model to use.
refresh	bool	false	Whether to refresh global ascend config content. This value is usually used by rlhf case.
expert_map_path	str	None	When using expert load balancing for the MOE model, an expert map path needs to be passed in.

The details of each config option are as follows:

torchair_graph_config

Name	Type	Default	Description
enabled	bool	False	Whether to enable torchair graph mode
enable_view_optimize	bool	True	Whether to enable torchair view optimization
use_cached_graph	bool	False	Whether to use cached graph
graph_batch_sizes	list[int]	[]	The batch size for torchair graph cache
graph_batch_sizes_init	bool	False	Init graph batch size dynamically if graph_batch_sizes is empty
enable_multistream_shared_expert	bool	False	Whether to enable multistream shared expert

ascend_scheduler_config

Name	Type	Default	Description
enabled	bool	False	Whether to enable ascend scheduler for V1 engine

ascend_scheduler_config also support the options from vllm scheduler config. For example, you can add chunked_prefill_enabled: true to ascend_scheduler_config as well.

Example

A full example of additional configuration is as follows:

{
    "torchair_graph_config": {
        "enabled": true,
        "use_cached_graph": true,
        "graph_batch_sizes": [1, 2, 4, 8],
        "graph_batch_sizes_init": false,
        "enable_multistream_shared_expert": false
    },
    "ascend_scheduler_config": {
        "enabled": true,
        "chunked_prefill_enabled": true,
    },
    "expert_tensor_parallel_size": 1,
    "refresh": false,
}