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**:

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

**Offline mode**:

```python
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                                                                                                        |
| `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. |

**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:

```
{
    "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,
}
```