29d2fe653d
1. refresh additional config doc
2. move kv config logic to platform.
3. improve `dump_config` init logic and rename it to `dump_config_path`
this change is user impacted. dump_config is changed from dict to
string.
4. correct `enable_async_exponential` type
5. remove useless `chunked_prefill_for_mla`
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
112 lines
7.0 KiB
Markdown
112 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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| `weight_prefetch_config` | dict | `{}` | Configuration options for weight prefetch |
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| `finegrained_tp_config` | dict | `{}` | Configuration options for module tensor parallelism |
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| `ascend_compilation_config` | dict | `{}` | Configuration options for ascend compilation |
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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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| `dump_config_path` | str | `None` | Configuration file path for msprobe dump(eager mode). |
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| `enable_async_exponential` | bool | `False` | Whether to enable async exponential overlap. To enable async exponential, set this config to True. |
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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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| `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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| `multistream_overlap_gate` | bool | `False` | Whether to enable multistream overlap gate. This option only takes effect on MoE models with shared experts. |
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| `recompute_scheduler_enable` | bool | `False` | Whether to enable recompute scheduler. |
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| `enable_cpu_binding` | bool | `False` | Whether to enable CPU binding. |
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| `SLO_limits_for_dynamic_batch` | int | `-1` | SLO limits for dynamic batch. This is new scheduler to support dynamic feature |
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| `enable_npugraph_ex` | bool | `False` | Whether to enable npugraph ex graph mode. |
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| `pa_shape_list` | list | `[]` | The custom shape list of page attention ops. |
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| `dynamic_eplb` | bool | `False` | Whether to enable dynamic EPLB. |
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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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| `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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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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**ascend_compilation_config**
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| Name | Type | Default | Description |
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| ---- | ---- | ------- | ----------- |
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| `fuse_norm_quant` | bool | `True` | Whether to enable fuse_norm_quant pass. |
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| `fuse_qknorm_rope` | bool | `False` | Whether to enable fuse_qknorm_rope pass. It's set to True by default when Triton is installed. |
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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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