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[Info][main] Corrected the errors in the information (#4055)

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### What this PR does / why we need it?
Corrected the errors in the information

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?
ut

- vLLM version: v0.11.0
- vLLM main:
83f478bb19

Signed-off-by: lilinsiman <lilinsiman@gmail.com>
This commit is contained in:
lilinsiman
2025-11-08 18:48:59 +08:00
committed by GitHub
parent 1d7cb5880a
commit a3ff765c65
20 changed files with 35 additions and 35 deletions
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8
docs/source/user_guide/configuration/additional_config.md
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@@ -1,6 +1,6 @@
# 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.
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
@@ -35,7 +35,7 @@ The following table lists additional configuration options available in vLLM Asc
| `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 effects on MoE models with shared experts. |
| `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. |
@@ -70,14 +70,14 @@ The details of each configuration option are as follows:
| `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](https://docs.vllm.ai/en/stable/api/vllm/config.html#vllm.config.SchedulerConfig). For example, you can add `enable_chunked_prefill: True` to ascend_scheduler_config as well.
ascend_scheduler_config also supports the options from [vllm scheduler config](https://docs.vllm.ai/en/stable/api/vllm/config.html#vllm.config.SchedulerConfig). For example, you can add `enable_chunked_prefill: True` to ascend_scheduler_config as well.
**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 weights. |
| `prefetch_ratio` | dict | `{"attn": {"qkv": 1.0, "o": 1.0}, "moe": {"gate_up": 0.8}}` | Prefetch ratio of each weight. |
### Example