fc818f1509
### What this PR does / why we need it?
Correct mistakes in doc
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
99 lines
6.9 KiB
Markdown
99 lines
6.9 KiB
Markdown
# 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 |
|
|
| `finegrained_tp_config` | dict | `{}` | Configuration options for module tensor parallelism |
|
|
| `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. Restriction: Can only be used when tensor_parallel=1 |
|
|
| `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` | Save the expert load calculation results to a new expert table in the specified directory. |
|
|
| `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. |
|
|
|
|
**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. |
|
|
|
|
### 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,
|
|
},
|
|
"multistream_overlap_shared_expert": True,
|
|
"refresh": False,
|
|
}
|
|
```
|