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@WANDY666 WANDY666 commented Jan 8, 2026

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yeahdongcn and others added 2 commits January 6, 2026 19:29
This PR adds support for Moore Threads (MUSA) GPU platform, expanding
LightLLM's hardware compatibility.

*NOTE:*

1. `_fwd_kernel_token_att1` has been slightly updated to ensure
compatibility with the Triton version.
2. `has_mtlink` will be used in upcoming enhancements to enable
multi-GPU support.
3. `torch` / `torch_musa` need to be upgraded to the latest versions.

### Testing Done

```bash
root@worker3218:/ws# python -m lightllm.server.api_server --model_dir /home/dist/Qwen3-0.6B/ --disable_cudagraph --host 0.0.0.0
WARNING 01-02 12:22:47 [sgl_utils.py:29] sgl_kernel is not installed, or the installed version did not support fa3.         Try to upgrade it.
WARNING 01-02 12:22:47 [light_utils.py:13] lightllm_kernel is not installed, you can't use the api of it.
INFO 01-02 12:22:48 [__init__.py:36] Available plugins for group vllm.platform_plugins:
INFO 01-02 12:22:48 [__init__.py:38] - musa -> vllm_musa:register
INFO 01-02 12:22:48 [__init__.py:41] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load.
INFO 01-02 12:22:48 [__init__.py:232] Platform plugin musa is activated
WARNING 01-02 12:22:48 [vllm_utils.py:18] vllm is not installed, you can't use the api of it.                    You can solve it by running `pip install vllm`.
INFO 01-02 12:22:48 [communication_op.py:57] deep_ep is not installed, you can't use the api of it.
INFO 01-02 12:22:48 [cache_tensor_manager.py:17] USE_GPU_TENSOR_CACHE is On
WARNING 01-02 12:22:48 [grouped_fused_moe_ep.py:28] no deepep or deep_gemm
WARNING 01-02 12:22:48 [nixl_kv_transporter.py:19] nixl is not installed, which is required for pd disagreggation!!!
INFO 01-02 12:22:48 [shm_size_check.py:21] SHM check: Available=500.00 GB,Recommended=2.32 GB.Sufficient: True
INFO 01-02 12:22:48 [api_start.py:94] zmq mode head: ipc:///tmp/_28765_0_
INFO 01-02 12:22:48 [api_start.py:96] use tgi api: False
INFO 01-02 12:22:48 [api_start.py:233] alloced ports: [10105, 10128, 10009, 10002, 10268, 10173, 10255, 10190, 10225, 10305]
INFO 01-02 12:22:48 [api_start.py:284] all start args:Namespace(run_mode='normal', host='0.0.0.0', port=8000, httpserver_workers=1, zmq_mode='ipc:///tmp/_28765_0_', pd_master_ip='0.0.0.0', pd_master_port=1212, pd_decode_rpyc_port=42000, select_p_d_node_strategy='round_robin', config_server_host=None, config_server_port=None, nixl_pd_kv_page_num=16, nixl_pd_kv_page_size=1024, model_name='default_model_name', model_dir='/home/dist/Qwen3-0.6B/', tokenizer_mode='fast', load_way='HF', max_total_token_num=None, mem_fraction=0.9, batch_max_tokens=8448, eos_id=[151645], tool_call_parser=None, reasoning_parser=None, chat_template=None, running_max_req_size=1000, nnodes=1, node_rank=0, multinode_httpmanager_port=12345, multinode_router_gloo_port=20001, tp=1, dp=1, dp_balancer='bs_balancer', max_req_total_len=16384, nccl_host='127.0.0.1', nccl_port=28765, use_config_server_to_init_nccl=False, mode=[], trust_remote_code=False, disable_log_stats=False, log_stats_interval=10, disable_shm_warning=False, router_token_ratio=0.0, router_max_new_token_len=1024, router_max_wait_tokens=1, disable_aggressive_schedule=False, use_dynamic_prompt_cache=False, disable_dynamic_prompt_cache=False, chunked_prefill_size=4096, disable_chunked_prefill=False, diverse_mode=False, token_healing_mode=False, output_constraint_mode='none', first_token_constraint_mode=False, enable_multimodal=False, enable_multimodal_audio=False, enable_mps=False, disable_custom_allreduce=False, enable_custom_allgather=False, enable_tpsp_mix_mode=False, enable_dp_prefill_balance=False, enable_prefill_microbatch_overlap=False, enable_decode_microbatch_overlap=False, enable_flashinfer_prefill=False, enable_flashinfer_decode=False, enable_fa3=False, cache_capacity=200, embed_cache_storage_size=4, data_type='bfloat16', return_all_prompt_logprobs=False, use_reward_model=False, long_truncation_mode=None, use_tgi_api=False, health_monitor=False, metric_gateway=None, job_name='lightllm', grouping_key=[], push_interval=10, visual_infer_batch_size=1, visual_send_batch_size=1, visual_gpu_ids=[0], visual_tp=1, visual_dp=1, visual_nccl_ports=[29500], enable_monitor_auth=False, disable_cudagraph=True, enable_prefill_cudagraph=False, prefll_cudagraph_max_handle_token=512, graph_max_batch_size=256, graph_split_batch_size=32, graph_grow_step_size=16, graph_max_len_in_batch=16384, quant_type='none', quant_cfg=None, vit_quant_type='none', vit_quant_cfg=None, sampling_backend='triton', penalty_counter_mode='gpu_counter', ep_redundancy_expert_config_path=None, auto_update_redundancy_expert=False, enable_fused_shared_experts=False, mtp_mode=None, mtp_draft_model_dir=None, mtp_step=0, kv_quant_calibration_config_path=None, schedule_time_interval=0.03, enable_cpu_cache=False, cpu_cache_storage_size=2, cpu_cache_token_page_size=256, enable_disk_cache=False, disk_cache_storage_size=10, disk_cache_dir=None, enable_dp_prompt_cache_fetch=False, router_port=10105, detokenization_port=10128, http_server_port=10009, visual_port=10002, audio_port=10268, cache_port=10173, metric_port=10255, multi_level_kv_cache_port=10190, pd_node_infer_rpyc_ports=[10305], pd_node_id=294623010895931863621527973304373176200, pd_p_allowed_port_min=20000, pd_p_allowed_port_max=30000)
WARNING 01-02 12:22:55 [sgl_utils.py:29] sgl_kernel is not installed, or the installed version did not support fa3.         Try to upgrade it.
WARNING 01-02 12:22:55 [light_utils.py:13] lightllm_kernel is not installed, you can't use the api of it.
INFO 01-02 12:22:55 [__init__.py:36] Available plugins for group vllm.platform_plugins:
INFO 01-02 12:22:55 [__init__.py:38] - musa -> vllm_musa:register
INFO 01-02 12:22:55 [__init__.py:41] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load.
INFO 01-02 12:22:55 [__init__.py:232] Platform plugin musa is activated
WARNING 01-02 12:22:55 [vllm_utils.py:18] vllm is not installed, you can't use the api of it.                    You can solve it by running `pip install vllm`.
INFO 01-02 12:22:55 [communication_op.py:57] deep_ep is not installed, you can't use the api of it.
2026-01-02 12:22:55 | server | 140684395422848 | INFO : server started on [0.0.0.0]:10255
INFO 01-02 12:22:55 [start_utils.py:37] init func start_metric_manager : init ok
WARNING 01-02 12:23:02 [sgl_utils.py:29] sgl_kernel is not installed, or the installed version did not support fa3.         Try to upgrade it.
WARNING 01-02 12:23:02 [light_utils.py:13] lightllm_kernel is not installed, you can't use the api of it.
WARNING 01-02 12:23:02 [sgl_utils.py:29] sgl_kernel is not installed, or the installed version did not support fa3.         Try to upgrade it.
WARNING 01-02 12:23:02 [light_utils.py:13] lightllm_kernel is not installed, you can't use the api of it.
INFO 01-02 12:23:02 [__init__.py:36] Available plugins for group vllm.platform_plugins:
INFO 01-02 12:23:02 [__init__.py:38] - musa -> vllm_musa:register
INFO 01-02 12:23:02 [__init__.py:41] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load.
INFO 01-02 12:23:02 [__init__.py:232] Platform plugin musa is activated
WARNING 01-02 12:23:02 [vllm_utils.py:18] vllm is not installed, you can't use the api of it.                    You can solve it by running `pip install vllm`.
INFO 01-02 12:23:02 [communication_op.py:57] deep_ep is not installed, you can't use the api of it.
INFO 01-02 12:23:02 [cache_tensor_manager.py:17] USE_GPU_TENSOR_CACHE is On
INFO 01-02 12:23:02 [__init__.py:36] Available plugins for group vllm.platform_plugins:
INFO 01-02 12:23:02 [__init__.py:38] - musa -> vllm_musa:register
INFO 01-02 12:23:02 [__init__.py:41] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load.
INFO 01-02 12:23:02 [__init__.py:232] Platform plugin musa is activated
WARNING 01-02 12:23:02 [vllm_utils.py:18] vllm is not installed, you can't use the api of it.                    You can solve it by running `pip install vllm`.
INFO 01-02 12:23:02 [communication_op.py:57] deep_ep is not installed, you can't use the api of it.
WARNING 01-02 12:23:02 [grouped_fused_moe_ep.py:28] no deepep or deep_gemm
INFO 01-02 12:23:02 [cache_tensor_manager.py:17] USE_GPU_TENSOR_CACHE is On
WARNING 01-02 12:23:03 [grouped_fused_moe_ep.py:28] no deepep or deep_gemm
INFO 01-02 12:23:03 [manager.py:36] pub_to_httpserver sendhwm 1000
WARNING 01-02 12:23:03 [nixl_kv_transporter.py:19] nixl is not installed, which is required for pd disagreggation!!!
2026-01-02 12:23:03 | server | 140684395422848 | INFO : accepted ('127.0.0.1', 36414) with fd 25
2026-01-02 12:23:03 | server | 140653235951168 | INFO : welcome ('127.0.0.1', 36414)
INFO 01-02 12:23:08 [cache_tensor_manager.py:17] USE_GPU_TENSOR_CACHE is On
WARNING 01-02 12:23:09 [sgl_utils.py:29] sgl_kernel is not installed, or the installed version did not support fa3.         Try to upgrade it.
INFO 01-02 12:23:10 [__init__.py:36] Available plugins for group vllm.platform_plugins:
INFO 01-02 12:23:10 [__init__.py:38] - musa -> vllm_musa:register
INFO 01-02 12:23:10 [__init__.py:41] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load.
INFO 01-02 12:23:10 [__init__.py:232] Platform plugin musa is activated
WARNING 01-02 12:23:10 [vllm_utils.py:18] vllm is not installed, you can't use the api of it.                    You can solve it by running `pip install vllm`.
WARNING 01-02 12:23:10 [light_utils.py:13] lightllm_kernel is not installed, you can't use the api of it.
WARNING 01-02 12:23:10 [grouped_fused_moe_ep.py:28] no deepep or deep_gemm
INFO 01-02 12:23:10 [communication_op.py:57] deep_ep is not installed, you can't use the api of it.
WARNING 01-02 12:23:10 [nixl_kv_transporter.py:19] nixl is not installed, which is required for pd disagreggation!!!
INFO 01-02 12:23:10 [model_rpc.py:67] Initialized RPC server for rank 0.
INFO 01-02 12:23:10 [model_rpc.py:168] use ChunkedPrefillBackend
INFO 01-02 12:23:11 [basemodel.py:157] Initial quantization. The default quantization method is none
pid 39235 Loading model weights with 1 workers: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.01it/s]
INFO 01-02 12:23:12 [mem_utils.py:37] mode setting params: []
INFO 01-02 12:23:12 [mem_utils.py:57] Model kv cache using mode normal
INFO 01-02 12:23:12 [mem_manager.py:84] 69.38735313415528 GB space is available after load the model weight
INFO 01-02 12:23:12 [mem_manager.py:84] 0.109375 MB is the size of one token kv cache
INFO 01-02 12:23:12 [mem_manager.py:84] 649624 is the profiled max_total_token_num with the mem_fraction 0.9
INFO 01-02 12:23:12 [mem_manager.py:84] 
warming up:   0%|                                                                                                                                                                  | 0/12 [00:00<?, ?it/s]WARNING 01-02 12:23:23 [autotuner.py:169] No kernel config for silu_and_mul_fwd:v1 in {N=3072,out_dtype=torch.bfloat16}_MTT_S5000.json,the performance may be suboptimal!You can use LIGHTLLM_TRITON_AUTOTUNE_LEVEL=1 to enable autotune.
WARNING 01-02 12:23:23 [kernel_config.py:40] can not find config_path /ws/lightllm/common/all_kernel_configs/moe_silu_and_mul_kernel/{N=3072,out_dtype=torch.bfloat16}_MTT_S5000.json kernel name moe_silu_and_mul_kernel use default kernel setting
warming up: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:15<00:00,  1.29s/it]
INFO 01-02 12:23:30 [basemodel.py:812] begin check max_len infer
INFO 01-02 12:23:30 [basemodel.py:849] check max_len 8448 infer ok
INFO 01-02 12:23:45 [base_backend.py:185] loaded model class <class 'lightllm.models.qwen3.model.Qwen3TpPartModel'>
INFO 01-02 12:23:45 [manager.py:196] use req queue ChunkedPrefillQueue
INFO 01-02 12:23:45 [start_utils.py:37] init func start_router_process : init ok
INFO 01-02 12:23:45 [start_utils.py:37] init func start_detokenization_process : init ok
INFO 01-02 12:23:45 [api_start.py:58] start process pid 30307
INFO 01-02 12:23:45 [api_start.py:59] http server pid 54746
[2026-01-02 12:23:45 +0800] [54746] [INFO] Starting gunicorn 23.0.0
[2026-01-02 12:23:45 +0800] [54746] [INFO] Listening at: http://0.0.0.0:8000 (54746)
[2026-01-02 12:23:45 +0800] [54746] [INFO] Using worker: uvicorn.workers.UvicornWorker
[2026-01-02 12:23:45 +0800] [54966] [INFO] Booting worker with pid: 54966
WARNING 01-02 12:23:51 [sgl_utils.py:29] sgl_kernel is not installed, or the installed version did not support fa3.         Try to upgrade it.
WARNING 01-02 12:23:51 [light_utils.py:13] lightllm_kernel is not installed, you can't use the api of it.
INFO 01-02 12:23:52 [__init__.py:36] Available plugins for group vllm.platform_plugins:
INFO 01-02 12:23:52 [__init__.py:38] - musa -> vllm_musa:register
INFO 01-02 12:23:52 [__init__.py:41] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load.
INFO 01-02 12:23:52 [__init__.py:232] Platform plugin musa is activated
WARNING 01-02 12:23:52 [vllm_utils.py:18] vllm is not installed, you can't use the api of it.                    You can solve it by running `pip install vllm`.
INFO 01-02 12:23:52 [communication_op.py:57] deep_ep is not installed, you can't use the api of it.
INFO 01-02 12:23:52 [cache_tensor_manager.py:17] USE_GPU_TENSOR_CACHE is On
WARNING 01-02 12:23:52 [grouped_fused_moe_ep.py:28] no deepep or deep_gemm
[2026-01-02 12:23:52 +0800] [54966] [INFO] Started server process [54966]
[2026-01-02 12:23:52 +0800] [54966] [INFO] Waiting for application startup.
INFO 01-02 12:23:52 [api_http.py:359] server start up
2026-01-02 12:23:53 | server | 140684395422848 | INFO : accepted ('127.0.0.1', 55128) with fd 26
2026-01-02 12:23:53 | server | 140653227558464 | INFO : welcome ('127.0.0.1', 55128)
2026-01-02 12:23:53 | server | 140684395422848 | INFO : accepted ('127.0.0.1', 55144) with fd 27
2026-01-02 12:23:53 | server | 140653219165760 | INFO : welcome ('127.0.0.1', 55144)
INFO 01-02 12:23:54 [req_id_generator.py:34] ReqIDGenerator init finished
INFO 01-02 12:23:54 [api_http.py:363] server start up ok, loop use is <uvloop.Loop running=True closed=False debug=False>
[2026-01-02 12:23:54 +0800] [54966] [INFO] Application startup complete.
INFO 01-02 12:23:58 [manager.py:417] recieved req X-Request-Id: X-Session-Id: start_time:2026-01-02 12:23:58 lightllm_req_id:8 
INFO 01-02 12:23:58 [manager.py:424] router recive req id 8 cost time 0.05271601676940918 s
DEBUG 01-02 12:23:58 [manager.py:322] Prefill Batch: batch_id=-1, time:1767327838.6764812s req_ids:[8] 
DEBUG 01-02 12:23:58 [manager.py:322] 
INFO 01-02 12:23:58 [manager.py:55] detokenization recv req id 8 cost time 0.0744318962097168 s
INFO 01-02 12:23:59 [manager.py:163] detoken release req id 8
INFO 01-02 12:23:59 [manager.py:611] X-Request-Id: X-Session-Id: start_time:2026-01-02 12:23:58 lightllm_req_id:8 first_token_cost:409.63053703308105ms total_cost_time:907.1474075317383ms,out_token_counter:17 mean_per_token_cost_time: 29.265698264626895ms prompt_token_num:4 gpu cache hit: False gpu_prompt_cache_len:0 gpu_prompt_cache_ratio:0.0 cpu cache hit: False cpu_prompt_cache_len:0 cpu_prompt_cache_ratio:0.0 disk cache hit: False disk_prompt_cache_len:0 disk_prompt_cache_ratio:0.0 mtp_avg_token_per_step:1.0 
127.0.0.1:38158 - "POST /generate HTTP/1.1" 200
DEBUG 01-02 12:23:59 [req_manager.py:78] freed all request size 1008
DEBUG 01-02 12:23:59 [infer_batch.py:172] free a batch state:
DEBUG 01-02 12:23:59 [infer_batch.py:172] radix refed token num 0
DEBUG 01-02 12:23:59 [infer_batch.py:172] radix hold token num 21
DEBUG 01-02 12:23:59 [infer_batch.py:172] mem manager can alloc token num 649603
DEBUG 01-02 12:23:59 [infer_batch.py:172] mem manager total size 649624
INFO 01-02 12:23:59 [batch.py:56] router release req id 8
INFO 01-02 12:23:59 [shm_req_manager.py:111] all shm req has been release ok
```

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
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Summary of Changes

Hello @WANDY666, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request refines the OpenAI API integration by introducing a new CharacterMessage type for character-based interactions and enhancing the ChatCompletionRequest model with Pydantic ConfigDict for improved field handling and aliasing. It also makes message role access more robust and incorporates a system_instruction into the prompt construction, aiming to provide greater flexibility and stability in API interactions.

Highlights

  • New Message Type: Introduced a CharacterMessage Pydantic model to support character-based chat messages, allowing roles to be inferred from names.
  • Pydantic Configuration Enhancements: Updated ChatCompletionRequest to use ConfigDict(populate_by_name=True) for flexible field population and aliased role_settings to role_setting for better compatibility.
  • Robust Role Access: Modified _get_history_tool_calls_cnt to safely access message roles using getattr, preventing errors if the role attribute is missing.
  • Prompt Building Update: Integrated system_instruction into the prompt building logic, allowing for more nuanced control over model behavior.

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Code Review

This pull request updates the OpenAI API compatibility by introducing a CharacterMessage model for character-based chats, adding alias support for role_setting, and making the code more robust when handling different message types. My review focuses on an incomplete feature addition for system_instruction, which is currently non-functional as the corresponding field is missing from the request model.

kwargs = {
"conversation": messages,
# 假设 request 对象里有这个字段,或者你想传空
"system_instruction": getattr(request, "system_instruction", ""),
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medium

The code attempts to access request.system_instruction, but this field is not defined in the ChatCompletionRequest model in api_models.py. This will always result in an empty string "" being used due to getattr, making this new parameter ineffective.

To properly implement this feature, you should add system_instruction as an optional field to the ChatCompletionRequest model in lightllm/server/api_models.py.

For example:

# In lightllm/server/api_models.py
class ChatCompletionRequest(BaseModel):
    # ...
    messages: List[ChatCompletionMessageParam]
    system_instruction: Optional[str] = None
    # ...

Additionally, the Chinese comment # 假设 request 对象里有这个字段,或者你想传空 is informal. It would be better to remove it once the feature is fully implemented, or replace it with a formal English comment explaining the purpose of system_instruction.

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3 participants