xc-llm-ascend/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 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
torchair_graph_config	dict	{}	Configuration options for torchair graph mode
weight_prefetch_config	dict	{}	Configuration options for weight prefetch
refresh	bool	false	Whether to refresh global Ascend configuration content. This is usually used by rlhf or ut/e2e test case.
expert_map_path	str	None	When using expert load balancing for an MoE model, an expert map path needs to be passed in.
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 expert is shared in DP, it delivers better performance but consumes more memory. Currently only DeepSeek series models are supported.
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 effect 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 begins.
gate_eplb	bool	False	Whether to enable EPLB only once.
num_wait_worker_iterations	int	30	The forward iterations when the EPLB worker will finish CPU tasks. In our test default value 30 can 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.
dump_config	str	None	Configuration file path for msprobe dump(eager mode).

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 or Qwen dense series models 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.

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.
mode	str	None	When using reduce-overhead mode for torchair, it needs to be set.
enable_multistream_mla	bool	False	Whether to put vector operators of MLA to another stream. This option only takes effect on models using MLA (for example, 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 KV Cache NZ layout. This option only takes effect on models using MLA (for example, DeepSeek).
enable_super_kernel	bool	False	Whether to enable super kernel to fuse operators in deepseek moe layers. This option only takes effects on moe models using dynamic w8a8 quantization.

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.

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
    },
    "weight_prefetch_config": {
        "enabled": True,
        "prefetch_ratio": {
            "attn": {
                "qkv": 1.0,
                "o": 1.0,
            },
            "moe": {
                "gate_up": 0.8
            }
        },
    },
    "multistream_overlap_shared_expert": True,
    "refresh": False,
}