e46dc142bf
What this PR does / why we need it? Enable kvcache_nz for the decode process in torchair graph mode, which reduces the time consumed by FA in long sequences. Does this PR introduce any user-facing change? If need to enable kvcache_nz, should set the additional_config.torchair_graph_config.enable_kv_nz=True How was this patch tested? 1. Tested in deepseek model: with batchsize 64 and seq_len 1k+3k, 61 layers FA total time improves 20.80ms -> 19.76ms 2. operator precision test: [aclnnFusedInferAttentionScoreV3_result.csv](https://github.com/user-attachments/files/20664138/aclnnFusedInferAttentionScoreV3_result.csv) 3. tpot test from @ttanzhiqiang, and curl one result is normal https://github.com/vllm-project/vllm-ascend/pull/1098#issuecomment-2948542159 https://github.com/vllm-project/vllm-ascend/pull/1098#issuecomment-2954496588 --------- Signed-off-by: chenwaner <861645847@qq.com>
79 lines
3.3 KiB
Markdown
79 lines
3.3 KiB
Markdown
# Additional Configuration
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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.
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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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| `expert_tensor_parallel_size` | str | `0` | Expert tensor parallel size the model to use. |
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| `refresh` | bool | `false` | Whether to refresh global ascend config content. This value is usually used by rlhf 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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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 |
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| `enable_multistream_moe`| bool | `False` | Whether to enable multistream shared expert |
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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 |
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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 `chunked_prefill_enabled: true` to ascend_scheduler_config as well.
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### Example
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A full 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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"chunked_prefill_enabled": true,
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},
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"expert_tensor_parallel_size": 1,
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"refresh": false,
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}
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```
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