enginex-ascend-910-vllm/docs/source/user_guide/configuration/additional_config.md

Additional Configuration

additional 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
refresh	bool	false	Whether to refresh global ascend config content. This value is usually used by rlhf or ut/e2e test case.
expert_map_path	str	None	When using expert load balancing for the MOE model, an expert map path needs to be passed in.
enable_prefetch	bool	False	Whether to enable weight prefetch.
kv_cache_dtype	str	None	When using the kv cache quantization method, kv cache dtype needs to be set, currently only int8 is supported.
enable_shared_expert_dp	bool	False	When the shared expert in DP, it has better performance but consumes more memory. Currently only DeepSeek series models are supported to use.
lmhead_tensor_parallel_size	int	None	The custom tensor parallel size of lmhead.
oproj_tensor_parallel_size	int	None	The custom tensor parallel size of oproj.
multistream_overlap_shared_expert	bool	False	Whether to enable multistream shared expert. This option only takes effects on moe models with shared experts.
dynamic_eplb	bool	False	Whether to enable dynamic eplb
num_iterations_eplb_update	int	400	Forward iterations when eplb would begin
gate_eplb	bool	False	Whether to enale eplb only once.
num_wait_worker_iterations	int	30	The forward iterations when eplb worker will finish cpu task. In our test default value 30 would cover most cases.
expert_map_record_path	str	None	When dynamic eplb is completed, save the current expert load heatmap to the specified path.
init_redundancy_expert	int	0	Specify redundant experts during initialization.

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. Currently only DeepSeek series models and PanguProMoE are supported to use torchair graph mode
mode	str	None	When using reduce-overhead mode for torchair, mode needs to be set
enable_multistream_mla	bool	False	Whether to put vector ops of MLA to another stream. This option only takes effects on models using MLA (e.g., DeepSeek).
enable_view_optimize	bool	True	Whether to enable torchair view optimization
enable_frozen_parameter	bool	True	Whether to fix the memory address of weights during inference to reduce the input address refresh time during graph execution.
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_kv_nz	bool	False	Whether to enable kvcache NZ layout. This option only takes effects on models using MLA (e.g., DeepSeek).

ascend_scheduler_config

Name	Type	Default	Description
enabled	bool	False	Whether to enable ascend scheduler for V1 engine
enable_pd_transfer	bool	False	Whether to enable pd transfer. When using it, decode is started only when prefill of all requests is done. This option only takes effects on offline inference.
decode_max_num_seqs	int	0	Whether to change max_num_seqs of decode phase when enable pd transfer. This option only takes effects when enable_pd_transfer is True.
max_long_partial_prefills	Union[int, float]	float('inf')	the maximum number of prompts longer than long_prefill_token_threshold that will be prefilled concurrently.
long_prefill_token_threshold	Union[int, float]	float('inf')	a request is considered long if the prompt is longer than this number of tokens.

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

Example

An 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_kv_nz": False
    },
    "ascend_scheduler_config": {
        "enabled": True,
        "enable_chunked_prefill": True,
        "max_long_partial_prefills": 1,
        "long_prefill_token_threshold": 4096,
    },
    "multistream_overlap_shared_expert": True,
    "refresh": False,
}