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xc-llm-ascend/docs/source/user_guide/configuration/additional_config.md
Mengqing Cao 8cfd257992 [Dist][EP] Remove ETP/EP maintained in vllm-ascend (#1681)
### What this PR does / why we need it?
Remove ETP/EP maintained in branch main. We drop this as there is no
relevant scenarios to use ETP now, and we may subsequently advocate
implementing expert tensor parallelism in vLLM to support scenarios
where the expert is needed to be sliced

This is a part of #1422 backport.

Fixes https://github.com/vllm-project/vllm-ascend/issues/1396
https://github.com/vllm-project/vllm-ascend/issues/1154

### Does this PR introduce _any_ user-facing change?
We'll not maintain etp/ep in vllm-ascend anymore, and use the tp/ep in
vllm instead.

### How was this patch tested?
CI passed with new added and existing test.


- vLLM version: v0.9.2
- vLLM main:
fe8a2c544a

Signed-off-by: MengqingCao <cmq0113@163.com>
2025-07-21 09:08:04 +08:00

3.8 KiB

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.
chunked_prefill_for_mla bool False Whether to enable the fused operator-like chunked_prefill.
kv_cache_dtype str None When using the kv cache quantization method, kv cache dtype needs to be set, currently only int8 is supported.

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
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_multistream_moe bool False Whether to enable multistream shared expert. This option only takes effects on DeepSeek moe models.
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_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

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_multistream_moe": False,
        "enable_kv_nz": False
    },
    "ascend_scheduler_config": {
        "enabled": True,
        "enable_chunked_prefill": True,
    },
    "refresh": False,
}