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[Feat] Add custom Embedding tensor model parallel (#2616)

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Similar to #2309 , this PR introduces Embedding tensor model parallel to
achieve decreasing of memory consumption. It support both eager mode and
graph mode.

And this PR refactor module tensor parallel configurations supported in
#2309, #2167, #2120, merge all config into `finegrained_tp_config` in
`additional_config`, including:
`lmhead_tensor_parallel_size`
`oproj_tensor_parallel_size`
`embedding_tensor_parallel_size`
`mlp_tensor_parallel_size`

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Signed-off-by: zzhxx <zhangzihang23@mails.ucas.ac.cn>
Co-authored-by: zzhx1 <zzh_201018@outlook.com>
Co-authored-by: chenxiao <Jaychou1620@Gmail.com>
Co-authored-by: zzhxx <zhangzihang23@mails.ucas.ac.cn>
Co-authored-by: Jade Zheng <zheng.shoujian@outlook.com>
This commit is contained in:
lidenghui1110
2025-12-12 14:41:20 +08:00
committed by GitHub
parent b8a317caac
commit d65fb194d9
9 changed files with 301 additions and 162 deletions
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20
docs/source/user_guide/configuration/additional_config.md
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@@ -27,14 +27,13 @@ The following table lists additional configuration options available in vLLM Asc
| Name | Type | Default | Description |
|-------------------------------------|------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------|
| `xlite_graph_config` | dict | `{}` | Configuration options for xlite graph mode |
| `finegrained_tp_config` | dict | `{}` | Configuration options for module tensor parallelism |
| `weight_prefetch_config` | dict | `{}` | Configuration options for weight prefetch |
| `refresh` | bool | `false` | Whether to refresh global Ascend configuration content. This is usually used by rlhf or ut/e2e test case. |
| `expert_map_path` | str | `None` | When using expert load balancing for an MoE model, an expert map path needs to be passed in. |
| `kv_cache_dtype` | str | `None` | When using the KV cache quantization method, KV cache dtype needs to be set, currently only int8 is supported. |
| `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. |
| `lmhead_tensor_parallel_size` | int | `None` | The custom tensor parallel size of lmhead. |
| `oproj_tensor_parallel_size` | int | `None` | The custom tensor parallel size of oproj. |
| `multistream_overlap_shared_expert` | bool | `False` | Whether to enable multistream shared expert. This option only takes effect on MoE models with shared experts. |
| `multistream_overlap_shared_expert` | bool | `False` | Whether to enable multistream shared expert. This option only takes effects on MoE models with shared experts. |
| `dynamic_eplb` | bool | `False` | Whether to enable dynamic EPLB. |
| `num_iterations_eplb_update` | int | `400` | Forward iterations when EPLB begins. |
| `gate_eplb` | bool | `False` | Whether to enable EPLB only once. |
@@ -58,6 +57,15 @@ The details of each configuration option are as follows:
| `enabled` | bool | `False` | Whether to enable weight prefetch. |
| `prefetch_ratio` | dict | `{"attn": {"qkv": 1.0, "o": 1.0}, "moe": {"gate_up": 0.8}}` | Prefetch ratio of each weight. |
**finegrained_tp_config**
| Name | Type | Default | Description |
| ---- | ---- | ------- | ----------- |
| `lmhead_tensor_parallel_size` | int | `0` | The custom tensor parallel size of lmhead. |
| `oproj_tensor_parallel_size` | int | `0` | The custom tensor parallel size of oproj. |
| `embedding_tensor_parallel_size` | int | `0` | The custom tensor parallel size of embedding. |
| `mlp_tensor_parallel_size` | int | `0` | The custom tensor parallel size of mlp. |
### Example
An example of additional configuration is as follows:
@@ -76,6 +84,12 @@ An example of additional configuration is as follows:
}
},
},
"finegrained_tp_config": {
"lmhead_tensor_parallel_size": 8,
"oproj_tensor_parallel_size": 8,
"embedding_tensor_parallel_size": 8,
"mlp_tensor_parallel_size": 8,
},
"multistream_overlap_shared_expert": True,
"refresh": False,
}