75de3fa172
### What this PR does / why we need it? Update doc Signed-off-by: hfadzxy <starmoon_zhang@163.com>
117 lines
8.1 KiB
Markdown
117 lines
8.1 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 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 | `{}` | Configuration options for torchair graph mode |
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| `ascend_scheduler_config` | dict | `{}` | Configuration options for ascend scheduler |
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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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| `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 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. |
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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 effects 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` | When dynamic EPLB is completed, save the current expert load heatmap to the specified path. |
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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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**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. |
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| `mode` | str | `None` | When using reduce-overhead mode for torchair, it needs to be set. |
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| `enable_multistream_mla`| bool | `False` | Whether to put vector operators of MLA to another stream. This option only takes effect on models using MLA (for example, DeepSeek). |
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| `enable_view_optimize` | bool | `True` | Whether to enable torchair view optimization. |
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| `enable_frozen_parameter` | bool | `True` | Whether to fix the memory address of weights during inference to reduce the input address refresh time during graph execution. |
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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 KV Cache NZ layout. This option only takes effect on models using MLA (for example, DeepSeek). |
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| `enable_super_kernel` | bool | `False` | Whether to enable super kernel to fuse operators in deepseek moe layers. This option only takes effects on moe models using dynamic w8a8 quantization.|
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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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| `enable_pd_transfer` | bool | `False` | Whether to enable P-D transfer. When it is enabled, decode is started only when prefill of all requests is done. This option only takes effect on offline inference. |
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| `decode_max_num_seqs` | int | `0` | Whether to change max_num_seqs of decode phase when P-D transfer is enabled. This option only takes effect when enable_pd_transfer is True. |
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| `max_long_partial_prefills` | Union[int, float] | `float('inf')` | The maximum number of prompts longer than long_prefill_token_threshold that will be prefilled concurrently. |
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| `long_prefill_token_threshold` | Union[int, float] | `float('inf')` | a request is considered long if the prompt is longer than this number of tokens. |
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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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**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 weights. |
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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_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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"max_long_partial_prefills": 1,
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"long_prefill_token_threshold": 4096,
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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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"multistream_overlap_shared_expert": True,
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"refresh": False,
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}
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```
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