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wwwwk123 opened this issue May 13, 2025 · 7 comments · Fixed by #3575
Closed
3 tasks done

[Bug] RuntimeError: yaml-cpp: error at line 13, column 30: bad conversion #3544

wwwwk123 opened this issue May 13, 2025 · 7 comments · Fixed by #3575
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@lvhan028
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@wwwwk123
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wwwwk123 commented May 13, 2025 •
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Checklist

  • 1. I have searched related issues but cannot get the expected help.
  • 2. The bug has not been fixed in the latest version.
  • 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.

Describe the bug

使用0.8.0运行qwen3-32b时报错，此问题在相同启动命令的0.7.3中没有遇到过。
RuntimeError: yaml-cpp: error at line 13, column 30: bad conversion

经过确认，--rope-scaling-factor 无论输入多少都会报错，尝试了1.0 1.5 2.0 4.0 4，都获得了上方相同的报错，去除该参数能正常启动。

尝试了pyyaml==6.0.1与6.0.2，无效果
尝试了conda install conda-forge::yaml-cpp，无效果

2025-05-13 08:05:36,413 - lmdeploy - �[37mINFO�[0m - async_engine.py:259 - input backend=turbomind, backend_config=TurbomindEngineConfig(dtype='auto', model_format=None, tp=2, dp=1, device_num=None, attn_tp_size=None, attn_dp_size=None, mlp_tp_size=None, mlp_dp_size=None, outer_dp_size=None, session_len=131072, max_batch_size=512, cache_max_entry_count=0.85, cache_chunk_size=-1, cache_block_seq_len=128, enable_prefix_caching=True, quant_policy=0, rope_scaling_factor=4.0, use_logn_attn=False, download_dir=None, revision=None, max_prefill_token_num=32768, num_tokens_per_iter=0, max_prefill_iters=1, communicator='nccl')
2025-05-13 08:05:36,413 - lmdeploy - �[37mINFO�[0m - async_engine.py:260 - input chat_template_config=ChatTemplateConfig(model_name='qwen2d5', system=None, meta_instruction=None, eosys=None, user=None, eoh=None, assistant=None, eoa=None, tool=None, eotool=None, separator=None, capability=None, stop_words=None)
2025-05-13 08:05:36,418 - lmdeploy - �[37mINFO�[0m - async_engine.py:269 - updated chat_template_onfig=ChatTemplateConfig(model_name='qwen2d5', system=None, meta_instruction=None, eosys=None, user=None, eoh=None, assistant=None, eoa=None, tool=None, eotool=None, separator=None, capability=None, stop_words=None)
2025-05-13 08:05:36,939 - lmdeploy - �[37mINFO�[0m - turbomind.py:312 - model_source: ModelSource.HF_MODEL
2025-05-13 08:05:36,977 - lmdeploy - �[37mINFO�[0m - turbomind.py:226 - turbomind model config:

{
"model_config": {
"model_name": "",
"chat_template": "",
"model_arch": "Qwen3ForCausalLM",
"head_num": 64,
"kv_head_num": 8,
"hidden_units": 5120,
"vocab_size": 151936,
"embedding_size": 151936,
"num_layer": 64,
"inter_size": [
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600,
25600
],
"norm_eps": 1e-06,
"attn_bias": 0,
"qk_norm": true,
"size_per_head": 128,
"group_size": 64,
"weight_type": "bfloat16",
"session_len": 131072,
"attn_tp_size": 2,
"mlp_tp_size": 2,
"model_format": "hf",
"expert_num": [],
"expert_inter_size": 0,
"experts_per_token": 0,
"moe_shared_gate": false,
"norm_topk_prob": false,
"routed_scale": 1.0,
"topk_group": 1,
"topk_method": "greedy",
"moe_group_num": 1,
"q_lora_rank": 0,
"kv_lora_rank": 0,
"qk_rope_dim": 0,
"v_head_dim": 0,
"tune_layer_num": 1
},
"attention_config": {
"softmax_scale": 0.0,
"cache_block_seq_len": 128,
"use_logn_attn": 0,
"max_position_embeddings": 40960,
"rope_param": {
"type": "dynamic",
"base": 1000000.0,
"dim": 128,
"factor": 4.0,
"max_position_embeddings": null,
"attention_factor": 1.0,
"beta_fast": 32,
"beta_slow": 1,
"low_freq_factor": null,
"high_freq_factor": null,
"original_max_position_embeddings": null
}
},
"lora_config": {
"lora_policy": "",
"lora_r": 0,
"lora_scale": 0.0,
"lora_max_wo_r": 0,
"lora_rank_pattern": "",
"lora_scale_pattern": ""
},
"engine_config": {
"dtype": "auto",
"model_format": null,
"tp": 2,
"dp": 1,
"device_num": 2,
"attn_tp_size": 2,
"attn_dp_size": 1,
"mlp_tp_size": 2,
"mlp_dp_size": 1,
"outer_dp_size": 1,
"session_len": 131072,
"max_batch_size": 512,
"cache_max_entry_count": 0.85,
"cache_chunk_size": -1,
"cache_block_seq_len": 128,
"enable_prefix_caching": true,
"quant_policy": 0,
"rope_scaling_factor": 4.0,
"use_logn_attn": false,
"download_dir": null,
"revision": null,
"max_prefill_token_num": 32768,
"num_tokens_per_iter": 32768,
"max_prefill_iters": 4,
"communicator": "nccl"
}
}
Traceback (most recent call last):
File "/home/wk/miniconda3/envs/lmdeploy/bin/lmdeploy", line 8, in
sys.exit(run())
^^^^^
File "/home/wk/miniconda3/envs/lmdeploy/lib/python3.12/site-packages/lmdeploy/cli/entrypoint.py", line 39, in run
args.run(args)
File "/home/wk/miniconda3/envs/lmdeploy/lib/python3.12/site-packages/lmdeploy/cli/serve.py", line 333, in api_server
run_api_server(args.model_path,
File "/home/wk/miniconda3/envs/lmdeploy/lib/python3.12/site-packages/lmdeploy/serve/openai/api_server.py", line 1121, in serve
VariableInterface.async_engine = pipeline_class(model_path=model_path,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wk/miniconda3/envs/lmdeploy/lib/python3.12/site-packages/lmdeploy/serve/async_engine.py", line 277, in init
self._build_turbomind(model_path=model_path, backend_config=backend_config, **kwargs)
File "/home/wk/miniconda3/envs/lmdeploy/lib/python3.12/site-packages/lmdeploy/serve/async_engine.py", line 328, in _build_turbomind
self.engine = tm.TurboMind.from_pretrained(model_path,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wk/miniconda3/envs/lmdeploy/lib/python3.12/site-packages/lmdeploy/turbomind/turbomind.py", line 313, in from_pretrained
return cls(model_path=pretrained_model_name_or_path,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wk/miniconda3/envs/lmdeploy/lib/python3.12/site-packages/lmdeploy/turbomind/turbomind.py", line 149, in init
self.model_comm = self._from_hf(model_source=model_source,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wk/miniconda3/envs/lmdeploy/lib/python3.12/site-packages/lmdeploy/turbomind/turbomind.py", line 242, in _from_hf
model_comm = _tm.AbstractTransformerModel.create_llama_model(model_dir='',
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: yaml-cpp: error at line 13, column 30: bad conversion

Reproduction

lmdeploy serve api_server "Qwen3-32B"
--model-name "Qwen3-32B"
--server-name 0.0.0.0
--server-port $PORT
--tp 2
--cache-max-entry-count 0.85
--tool-call-parser "qwen"
--chat-template qwen2d5

--max-prefill-token-num 32768
--rope-scaling-factor 4.0
--session-len 131072

--cache-block-seq-len 128
--enable-prefix-caching

--log-level "INFO"
>

Environment

lmdeploy check_env
sys.platform: linux
Python: 3.12.9 | packaged by Anaconda, Inc. | (main, Feb  6 2025, 18:56:27) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1,2,3,4,5: NVIDIA H100 80GB HBM3
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.8, V12.8.93
GCC: gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
PyTorch: 2.6.0+cu124
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 12.4
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
  - CuDNN 90.1
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=2236df1770800ffea5697b11b0bb0d910b2e59e1, CUDA_VERSION=12.4, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.6.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 

TorchVision: 0.21.0+cu124
LMDeploy: 0.8.0+8ce41ec
transformers: 4.51.3
gradio: Not Found
fastapi: 0.115.12
pydantic: 2.11.3
triton: 3.2.0
NVIDIA Topology: 
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    NIC0    NIC1    NIC2    NIC3    NIC4  NIC5     NIC6    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    PIX     NODE    NODE    SYS     SYS   SYS      SYS     0-23,96-119     0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NODE    PIX     NODE    SYS     SYS   SYS      SYS     0-23,96-119     0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NODE    NODE    PIX     SYS     SYS   SYS      SYS     0-23,96-119     0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    SYS     SYS     SYS     PIX     SYS   SYS      NODE    24-47,120-143   1               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    SYS     SYS     SYS     SYS     PIX   SYS      SYS     48-71,144-167   2               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      SYS     SYS     SYS     SYS     SYS   PIX      SYS     72-95,168-191   3               N/A
NIC0    PIX     NODE    NODE    SYS     SYS     SYS      X      NODE    NODE    SYS     SYS   SYS      SYS
NIC1    NODE    PIX     NODE    SYS     SYS     SYS     NODE     X      NODE    SYS     SYS   SYS      SYS
NIC2    NODE    NODE    PIX     SYS     SYS     SYS     NODE    NODE     X      SYS     SYS   SYS      SYS
NIC3    SYS     SYS     SYS     PIX     SYS     SYS     SYS     SYS     SYS      X      SYS   SYS      NODE
NIC4    SYS     SYS     SYS     SYS     PIX     SYS     SYS     SYS     SYS     SYS      X    SYS      SYS
NIC5    SYS     SYS     SYS     SYS     SYS     PIX     SYS     SYS     SYS     SYS     SYS    X       SYS
NIC6    SYS     SYS     SYS     NODE    SYS     SYS     SYS     SYS     SYS     NODE    SYS   SYS       X 

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_5
  NIC4: mlx5_6
  NIC5: mlx5_7
  NIC6: mlx5_bond_0

Error traceback


      		
    

      
  





        



            
            

              
            

        

      


      
    








      

    



      

  
      



  

  @lvhan028





  


    

      

  

    

        
    
    

  


      
          
  
      
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没能复现这个问题

      		
    

  





        


            

            
            

              
            

        

      


      

            
  
  

    
  
    
    

  

  

  


    












      

  
      



  

  @lvhan028





  


    

      

  

    

        
    
    

  


      
          
  
      
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yaml-cpp 是以 csrc 源码方式编译到 turbomind中的。


FetchContent_Declare(
  yaml-cpp
  GIT_REPOSITORY https://github.com/jbeder/yaml-cpp.git
  GIT_TAG 0.8.0
)



0.7.3 也是用的 yaml-cpp 0.8.0，这个一直没有变过

      		
    

  





        


            

            
            

              
            

        

      


      

            
  
  

    
  
    
    

  

  

  


    












      

  
      



  

  @lvhan028





  


    

      

  

    

        
    
    

  


      
          
  
      
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建议在 LlamaTritonModel::LlamaTritonModel 中增加下关于 yaml config的日志，试试debug源码

      		
    

  





        


            

            
            

              
            

        

      


      

            
  
  

    
  
    
    

  

  

  


    












      

  
      



  

  @nstl-zyb





  


    

      

  

    

        
    
    

  


      
          
  
      
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加载qwen3-30b-A3B，遇到同样的问题，当设置rope_scaling_factor时，会出现  ：RuntimeError: yaml-cpp: error at line 13, column 30: bad conversion。当rope_scaling_factor默认为0时，可以正常加载


报错如下：

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr | Traceback (most recent call last):

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |   File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |     return _run_code(code, main_globals, None,

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |   File "/usr/lib/python3.10/runpy.py", line 86, in _run_code

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |     exec(code, run_globals)

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |   File "/opt/py3/lib/python3.10/site-packages/fastchat/serve/lmdeploy_worker.py", line 560, in 

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |     lm_engine = async_engine.AsyncEngine(model_path=args.model_path,

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |   File "/opt/py3/lib/python3.10/site-packages/lmdeploy/serve/async_engine.py", line 277, in init

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |     self._build_turbomind(model_path=model_path, backend_config=backend_config, **kwargs)

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |   File "/opt/py3/lib/python3.10/site-packages/lmdeploy/serve/async_engine.py", line 328, in _build_turbomind

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |     self.engine = tm.TurboMind.from_pretrained(model_path,

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |   File "/opt/py3/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 313, in from_pretrained

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |     return cls(model_path=pretrained_model_name_or_path,

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |   File "/opt/py3/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 149, in init

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |     self.model_comm = self._from_hf(model_source=model_source,

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |   File "/opt/py3/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 242, in _from_hf

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr |     model_comm = _tm.AbstractTransformerModel.create_llama_model(model_dir='',

[2025-05-19 10:39:10] 2025-05-19 02:39:10 | ERROR | stderr | RuntimeError: yaml-cpp: error at line 13, column 30: bad conversion

[2025-05-19 10:39:11] time="2025-05-19T02:39:11Z" level=info msg="program stopped with status:exit status 1" program=/bin/sh


调用代码如下：

backend_config = TurbomindEngineConfig(

tp=args.tp_size,

cache_max_entry_count=args.cache_max_entry_count,

cache_chunk_size=args.cache_chunk_size,

enable_prefix_caching=args.enable_prefix_caching,

session_len=args.session_len,

# max_batch_size=args.max_batch_size,

quant_policy=args.quant_policy,

rope_scaling_factor=args.rope_scaling_factor,

use_logn_attn=args.use_logn_attn,

max_prefill_token_num = args.max_prefill_token_num,

num_tokens_per_iter=args.num_tokens_per_iter,

max_prefill_iters=args.max_prefill_iters,

cache_block_seq_len=args.cache_block_seq_len,

)


lm_engine = async_engine.AsyncEngine(model_path=args.model_path,

backend="turbomind",

backend_config=backend_config,

# log_level=args.log_level,

chat_template_config=ChatTemplateConfig(args.conv_template))

      		
    

  





        


            

            
            

              
            

        

      


      

            
  
  

    
  
    
    

  

  

  


    












      

  
      



  

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抱歉，我没有留意到输入参数 --rope-scaling-factor

      		
    

  





        


            

            
            

              
            

        

      


      

            
  
  

    
  
    
    

  

  

  


    












      

  
          

  

    
  


  

      

    @lvhan028
lvhan028



    mentioned this issue
    
      May 19, 2025
    





  


  

      
   Merged

  







  









      

  
      



  

  @lvhan028





  


    

      

  

    

        
    
    

  


      
          
  
      
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我在下面的 PR 中修复了，可以验证看看。

#3575

      		
    

  





        


            

            
            

              
            

        

      


      

            
  
  

    
  
    
    

  

  

  


    












      

  
            

    

      
    

    


      

          @lvhan028
lvhan028




              self-assigned this


      May 19, 2025

    

  



    







      

  
      



  

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      fix parsing dynamic rope param failed

    
      lvhan028/lmdeploy
    





      
    

  




      

    

  
      

  

    

      3 participants
    

    

        
          @lvhan028 
        
          @nstl-zyb 
        
          @wwwwk123