e1ab6d318e
More and more config options are added to additional_config. This PR provide a new AscendConfig to manage these config options by an easier way to make code cleaner and readable. This PR also added the `additional_config` doc for users. Added the test_ascend_config.py to make sure the new AscendConfig works as expect. TODO: Add e2e test with torchair and deepseek once the CI resource is available. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
71 lines
2.3 KiB
Markdown
71 lines
2.3 KiB
Markdown
# Additional Configuration
|
|
|
|
addintional 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**:
|
|
|
|
```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 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 |
|
|
| `expert_tensor_parallel_size` | str | `1` | Expert tensor parallel size the model to use. |
|
|
|
|
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 |
|
|
| `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 |
|
|
|
|
**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](https://docs.vllm.ai/en/stable/api/vllm/config.html#vllm.config.SchedulerConfig). For example, you can add `chunked_prefill_enabled: true` to ascend_scheduler_config as well.
|
|
|
|
### Example
|
|
|
|
A full 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": true
|
|
},
|
|
"ascend_scheduler_config": {
|
|
"enabled": true,
|
|
"chunked_prefill_enabled": true,
|
|
},
|
|
"expert_tensor_parallel_size": 1
|
|
}
|
|
```
|