c8d1df3a3f
### What this PR does / why we need it?
In order to support fused kernels, multi-stream, communication
optimization etc, it's better to aggregate all opreations in Attention
layer togather. This PR tries to refactor mla_v1 by moving all MLA
preprocessing ops into mla_v1 attention impl.
Note that new mla_v1 doesn't take torchair into consideration. So this
PR can only be merged after torchair related mla_v1 is isolated into a
new file.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
### Features Test
<img width="506" height="141" alt="image"
src="https://github.com/user-attachments/assets/f1ab2906-a1ac-4450-8433-94811cd89466"
/>
### Performance After Refact
<img width="648" height="486" alt="image"
src="https://github.com/user-attachments/assets/e33e038c-c5d9-4ba7-a8e9-1ac22f9833eb"
/>
### Performance Before Refact
<img width="618" height="494" alt="image"
src="https://github.com/user-attachments/assets/83861dc2-dc51-4af3-9310-90ab10c43bb1"
/>
- vLLM version: v0.10.1.1
- vLLM main:
e03940762b
---------
Signed-off-by: lwq <liwenquan5@huawei.com>
Signed-off-by: whx-sjtu <2952154980@qq.com>
Signed-off-by: SunnyLee219 <3294305115@qq.com>
Co-authored-by: lwq <liwenquan5@huawei.com>
Co-authored-by: whx-sjtu <2952154980@qq.com>
82 lines
4.1 KiB
Markdown
82 lines
4.1 KiB
Markdown
# Additional Configuration
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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.
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## How to use
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With either online mode or offline mode, users can use additional configuration. Take Qwen3 as an example:
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**Online mode**:
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```bash
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vllm serve Qwen/Qwen3-8B --additional-config='{"config_key":"config_value"}'
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```
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**Offline mode**:
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```python
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from vllm import LLM
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LLM(model="Qwen/Qwen3-8B", additional_config={"config_key":"config_value"})
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```
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### Configuration options
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The following table lists the additional configuration options available in vLLM Ascend:
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| Name | Type | Default | Description |
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|-------------------------------| ---- |------|-----------------------------------------------------------------------------------------------|
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| `torchair_graph_config` | dict | `{}` | The config options for torchair graph mode |
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| `ascend_scheduler_config` | dict | `{}` | The config options for ascend scheduler |
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| `refresh` | bool | `false` | Whether to refresh global ascend config content. This value is usually used by rlhf or ut/e2e test case. |
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| `expert_map_path` | str | `None` | When using expert load balancing for the MOE model, an expert map path needs to be passed in. |
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| `chunked_prefill_for_mla` | bool | `False` | Whether to enable the fused operator-like chunked_prefill. |
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| `enable_prefetch` | bool | `False` | Whether to enable weight prefetch. |
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| `kv_cache_dtype` | str | `None` | When using the kv cache quantization method, kv cache dtype needs to be set, currently only int8 is supported. |
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| `enable_shared_expert_dp` | bool | `False` | When the shared expert in DP, it has better performance but consumes more memory. Currently only DeepSeek series models are supported to use. |
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The details of each config option are as follows:
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**torchair_graph_config**
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| Name | Type | Default | Description |
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| ---- | ---- | ------- | ----------- |
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| `enabled` | bool | `False` | Whether to enable torchair graph mode. Currently only DeepSeek series models and PanguProMoE are supported to use torchair graph mode |
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| `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). |
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| `enable_multistream_moe`| bool | `False` | Whether to enable multistream shared expert. This option only takes effects on DeepSeek moe models. |
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| `enable_view_optimize` | bool | `True` | Whether to enable torchair view optimization |
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| `use_cached_graph` | bool | `False` | Whether to use cached graph |
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| `graph_batch_sizes` | list[int] | `[]` | The batch size for torchair graph cache |
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| `graph_batch_sizes_init` | bool | `False` | Init graph batch size dynamically if `graph_batch_sizes` is empty |
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| `enable_kv_nz`| bool | `False` | Whether to enable kvcache NZ layout. This option only takes effects on models using MLA (e.g., DeepSeek). |
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**ascend_scheduler_config**
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| Name | Type | Default | Description |
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| ---- | ---- | ------- | ----------- |
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| `enabled` | bool | `False` | Whether to enable ascend scheduler for V1 engine|
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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.
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### Example
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An example of additional configuration is as follows:
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```
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{
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"torchair_graph_config": {
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"enabled": True,
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"use_cached_graph": True,
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"graph_batch_sizes": [1, 2, 4, 8],
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"graph_batch_sizes_init": False,
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"enable_multistream_moe": False,
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"enable_kv_nz": False
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},
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"ascend_scheduler_config": {
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"enabled": True,
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"enable_chunked_prefill": True,
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},
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"refresh": False,
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}
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```
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