Additional Configuration

Additional configuration is a mechanism provided by vLLM to allow plugins to control inner behavior by themselves. 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 additional configuration options available in vLLM Ascend:

Name	Type	Default	Description
xlite_graph_config	dict	{}	Configuration options for xlite graph mode
weight_prefetch_config	dict	{}	Configuration options for weight prefetch
finegrained_tp_config	dict	{}	Configuration options for module tensor parallelism
ascend_compilation_config	dict	{}	Configuration options for ascend compilation
eplb_config	dict	{}	Configuration options for ascend compilation
refresh	bool	false	Whether to refresh global Ascend configuration content. This is usually used by rlhf or ut/e2e test case.
dump_config_path	str	None	Configuration file path for msprobe dump(eager mode).
enable_async_exponential	bool	False	Whether to enable async exponential overlap. To enable async exponential, set this config to True.
enable_shared_expert_dp	bool	False	When the expert is shared in DP, it delivers better performance but consumes more memory. Currently only DeepSeek series models are supported.
multistream_overlap_shared_expert	bool	False	Whether to enable multistream shared expert. This option only takes effect on MoE models with shared experts.
multistream_overlap_gate	bool	False	Whether to enable multistream overlap gate. This option only takes effect on MoE models with shared experts.
recompute_scheduler_enable	bool	False	Whether to enable recompute scheduler.
enable_cpu_binding	bool	False	Whether to enable CPU binding.
SLO_limits_for_dynamic_batch	int	-1	SLO limits for dynamic batch. This is new scheduler to support dynamic feature
pa_shape_list	list	[]	The custom shape list of page attention ops.
init_redundancy_expert	int	0	Specify redundant experts during initialization.
enable_kv_nz	bool	False	Whether to enable kvcache NZ layout. This option only takes effects on models using MLA (e.g., DeepSeek).
layer_sharding	dict	{}	Configuration options for layer sharding linear

The details of each configuration option are as follows:

xlite_graph_config

Name	Type	Default	Description
enabled	bool	False	Whether to enable xlite graph mode. Currently only Llama, Qwen dense series models, and Qwen3-vl are supported.
full_mode	bool	False	Whether to enable xlite for both the prefill and decode stages. By default, xlite is only enabled for the decode stage.

weight_prefetch_config

Name	Type	Default	Description
enabled	bool	False	Whether to enable weight prefetch.
prefetch_ratio	dict	{"attn": {"qkv": 1.0, "o": 1.0}, "moe": {"gate_up": 0.8}}	Prefetch ratio of each weight.

finegrained_tp_config

Name	Type	Default	Description
lmhead_tensor_parallel_size	int	0	The custom tensor parallel size of lmhead.
oproj_tensor_parallel_size	int	0	The custom tensor parallel size of oproj.
embedding_tensor_parallel_size	int	0	The custom tensor parallel size of embedding.
mlp_tensor_parallel_size	int	0	The custom tensor parallel size of mlp.

ascend_compilation_config

Name	Type	Default	Description
fuse_norm_quant	bool	True	Whether to enable fuse_norm_quant pass.
fuse_qknorm_rope	bool	False	Whether to enable fuse_qknorm_rope pass. It’s set to True by default when Triton is installed.

eplb_config

Name	Type	Default	Description
dynamic_eplb	bool	False	Whether to enable dynamic EPLB.
expert_map_path	str	None	When using expert load balancing for an MoE model, an expert map path needs to be passed in.
expert_heat_collection_interval	int	400	Forward iterations when EPLB begins.
algorithm_execution_interval	int	30	The forward iterations when the EPLB worker will finish CPU tasks.
expert_map_record_path	str	None	Save the expert load calculation results to a new expert table in the specified directory.
num_redundant_experts	int	0	Specify redundant experts during initialization.

Example

An example of additional configuration is as follows:

{
    "weight_prefetch_config": {
        "enabled": True,
        "prefetch_ratio": {
            "attn": {
                "qkv": 1.0,
                "o": 1.0,
            },
            "moe": {
                "gate_up": 0.8
            }
        },
    },
    "finegrained_tp_config": {
        "lmhead_tensor_parallel_size": 8,
        "oproj_tensor_parallel_size": 8,
        "embedding_tensor_parallel_size": 8,
        "mlp_tensor_parallel_size": 8,
    },
    "enable_kv_nz": False,
    "multistream_overlap_shared_expert": True,
    "refresh": False
}