a40eee2ba1
### What this PR does / why we need it? This PR adds support for hierarchical communication for `dispatch_v2` and `combine_v2` MoE operations. This is achieved by introducing a new configuration `enable_mc2_hierarchy_comm`. When enabled, the communication algorithm is set to "hierarchy", which support mc2 op comm between two super pod. The changes include: - Adding `enable_mc2_hierarchy_comm` to `AscendConfig`. - Modifying `TokenDispatcherWithMC2` to pass `comm_alg: "hierarchy"` to the underlying `torch_npu` ops when the new config is enabled. - Adding validation to ensure that this feature is only used with compatible PTA/CANN versions and is not used with the conflicting `fused_mc2` op. - Updating `is_hierarchical_communication_enabled` to respect the new configuration flag. ### Does this PR introduce _any_ user-facing change? Yes, this PR introduces a new user-facing configuration option `enable_mc2_hierarchy_comm` in `additional_config` to enable hierarchical communication for MoE. ### How was this patch tested? - vLLM version: v0.18.0 Signed-off-by: zzzzwwjj <1183291235@qq.com>
130 lines
8.1 KiB
Markdown
130 lines
8.1 KiB
Markdown
# Additional Configuration
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Additional configuration is a mechanism provided by vLLM to allow plugins to control internal 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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| `eplb_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 asynchronous exponential overlap. To enable asynchronous 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 multi-stream 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 multi-stream 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 | `True` | Whether to enable CPU binding. Only takes effect on ARM CPUs; A3 uses the global-slicing CPU allocation strategy and other device types use the topo-affinity CPU allocation strategy. |
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| `SLO_limits_for_dynamic_batch` | int | `-1` | SLO limits for dynamic batch. This is new scheduler to support dynamic batch 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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| `enable_kv_nz` | bool | `False` | Whether to enable KV cache NZ layout. This option only takes effects on models using MLA (e.g., DeepSeek). |
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| `layer_sharding` | dict | `{}` | Configuration options for Layer Sharding Linear |
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| `enable_sparse_c8` | bool | `False` | Whether to enable KV cache C8 in DSA models (e.g., DeepSeekV3.2 and GLM5). Not supported on A5 devices now |
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| `enable_mc2_hierarchy_comm` | bool | `False` | Enable dispatch/combine op inter-node communication by ROCE. |
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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}, "mlp": { "gate_up": 1.0, "down": 1.0}}` | 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 lm_head. |
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| `oproj_tensor_parallel_size` | int | `0` | The custom tensor parallel size of o_proj. |
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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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| `enable_npugraph_ex` | bool | `True` | Whether to enable npugraph_ex backend. |
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| `enable_static_kernel` | bool | `False` | Whether to enable static kernel. Suitable for scenarios where shape changes are minimal and some time is available for static kernel compilation. |
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| `fuse_norm_quant` | bool | `True` | Whether to enable fuse_norm_quant pass. |
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| `fuse_qknorm_rope` | bool | `True` | Whether to enable fuse_qknorm_rope pass. If Triton is not in the environment, set it to False. |
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| `fuse_allreduce_rms` | bool | `False` | Whether to enable fuse_allreduce_rms pass. It's set to False because of conflict with SP. |
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| `fuse_muls_add` | bool | `True` | Whether to enable fuse_muls_add pass.|
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**eplb_config**
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| Name | Type | Default | Description |
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| ---- | ---- | ------- | ----------- |
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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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| `expert_heat_collection_interval`| int | `400` | Forward iterations when EPLB begins. |
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| `algorithm_execution_interval` | int | `30` | The forward iterations when the EPLB worker will finish CPU tasks. |
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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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| `num_redundant_experts` | int | `0` | Specify redundant experts during initialization. |
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### Example
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An example of additional configuration is as follows:
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```python
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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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"mlp": {
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"gate_up": 1.0,
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"down": 1.0
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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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"enable_kv_nz": False,
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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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