55beac9c91
### What this PR does / why we need it?
This patch adds support for the Qwen3-VL model in Xlite. For more
details about Xlite, please refer to the following
link:https://atomgit.com/openeuler/GVirt/blob/master/xlite/README.md.
The latest performance comparison data between xlite and the default
aclgraph mode is as follows:
### Does this PR introduce _any_ user-facing change?
XLite graph mode supports the Qwen3-VL model.
### How was this patch tested?
vLLM version: v0.12.0
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: lvjunqi <lvjunqi1@huawei.com>
Co-authored-by: lvjunqi <lvjunqi1@huawei.com>
99 lines
7.0 KiB
Markdown
99 lines
7.0 KiB
Markdown
# Additional Configuration
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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.
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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 additional configuration options available in vLLM Ascend:
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| Name | Type | Default | Description |
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|-------------------------------------|------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------|
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| `xlite_graph_config` | dict | `{}` | Configuration options for xlite graph mode |
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| `finegrained_tp_config` | dict | `{}` | Configuration options for module tensor parallelism |
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| `weight_prefetch_config` | dict | `{}` | Configuration options for weight prefetch |
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| `refresh` | bool | `false` | Whether to refresh global Ascend configuration content. This 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 an MoE model, an expert map path needs to be passed in. | |
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| `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. |
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| `lmhead_tensor_parallel_size` | int | `None` | The custom tensor parallel size of lmhead. Restriction: Can only be used when tensor_parallel=1 |
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| `oproj_tensor_parallel_size` | int | `None` | The custom tensor parallel size of oproj. |
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| `multistream_overlap_shared_expert` | bool | `False` | Whether to enable multistream shared expert. This option only takes effect on MoE models with shared experts. |
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| `dynamic_eplb` | bool | `False` | Whether to enable dynamic EPLB. |
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| `num_iterations_eplb_update` | int | `400` | Forward iterations when EPLB begins. |
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| `gate_eplb` | bool | `False` | Whether to enable EPLB only once. |
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| `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. |
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| `expert_map_record_path` | str | `None` | Save the expert load calculation results to a new expert table in the specified directory. |
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| `init_redundancy_expert` | int | `0` | Specify redundant experts during initialization. |
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| `dump_config` | str | `None` | Configuration file path for msprobe dump(eager mode). |
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| `enable_async_exponential` | int | `0` | Whether to enable async exponential overlap. To enable async exponential, set this config to 1. |
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The details of each configuration option are as follows:
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**xlite_graph_config**
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| Name | Type | Default | Description |
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| ---- | ---- | ------- | ----------- |
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| `enabled` | bool | `False` | Whether to enable xlite graph mode. Currently only Llama, Qwen dense series models, and Qwen3-vl are supported. |
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| `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. |
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**weight_prefetch_config**
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| Name | Type | Default | Description |
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|------------------|------|-------------------------------------------------------------|------------------------------------|
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| `enabled` | bool | `False` | Whether to enable weight prefetch. |
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| `prefetch_ratio` | dict | `{"attn": {"qkv": 1.0, "o": 1.0}, "moe": {"gate_up": 0.8}}` | Prefetch ratio of each weight. |
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**finegrained_tp_config**
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| Name | Type | Default | Description |
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| ---- | ---- | ------- | ----------- |
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| `lmhead_tensor_parallel_size` | int | `0` | The custom tensor parallel size of lmhead. |
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| `oproj_tensor_parallel_size` | int | `0` | The custom tensor parallel size of oproj. |
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| `embedding_tensor_parallel_size` | int | `0` | The custom tensor parallel size of embedding. |
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| `mlp_tensor_parallel_size` | int | `0` | The custom tensor parallel size of mlp. |
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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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"weight_prefetch_config": {
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"enabled": True,
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"prefetch_ratio": {
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"attn": {
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"qkv": 1.0,
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"o": 1.0,
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},
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"moe": {
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"gate_up": 0.8
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}
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},
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},
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"finegrained_tp_config": {
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"lmhead_tensor_parallel_size": 8,
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"oproj_tensor_parallel_size": 8,
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"embedding_tensor_parallel_size": 8,
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"mlp_tensor_parallel_size": 8,
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},
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"multistream_overlap_shared_expert": True,
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"refresh": False,
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}
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```
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