Flask config.py 项目加载配置文件

本文介绍了如何使用Flask框架进行配置,包括从对象、文件及环境变量中加载配置的方法。具体涉及app.config.from_object()、app.config.from_file()和app.config.from_envvar()等关键函数的使用方式。

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from flask import Flask

app = Flask(__name__)

app.config.from_object("config") #只识别大写 key.isupper()
app.config.from_file("config.py") #在 root_path目录下, silent=True 配置文件不存在返回False,不会抛出异常
app.config.from_envvar("SETTINGS") #读取环境变量 silent=True 环境变量不存在,返回False 不抛异常

 

转载于:https://www.cnblogs.com/jachin/p/5489395.html

INFO 07-25 07:11:43 [model_runner_v1.py:1745] Starting to load model /models/z50051264/summary/Qwen2.5-7B-nf4/... ERROR 07-25 07:11:44 [core.py:586] EngineCore failed to start. ERROR 07-25 07:11:44 [core.py:586] Traceback (most recent call last): ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 577, in run_engine_core ERROR 07-25 07:11:44 [core.py:586] engine_core = EngineCoreProc(*args, **kwargs) ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 404, in __init__ ERROR 07-25 07:11:44 [core.py:586] super().__init__(vllm_config, executor_class, log_stats, ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 75, in __init__ ERROR 07-25 07:11:44 [core.py:586] self.model_executor = executor_class(vllm_config) ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/executor/executor_base.py", line 53, in __init__ ERROR 07-25 07:11:44 [core.py:586] self._init_executor() ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/executor/uniproc_executor.py", line 48, in _init_executor ERROR 07-25 07:11:44 [core.py:586] self.collective_rpc("load_model") ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/executor/uniproc_executor.py", line 57, in collective_rpc ERROR 07-25 07:11:44 [core.py:586] answer = run_method(self.driver_worker, method, args, kwargs) ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/utils/__init__.py", line 2736, in run_method ERROR 07-25 07:11:44 [core.py:586] return func(*args, **kwargs) ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm-ascend/vllm_ascend/worker/worker_v1.py", line 240, in load_model ERROR 07-25 07:11:44 [core.py:586] self.model_runner.load_model() ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm-ascend/vllm_ascend/worker/model_runner_v1.py", line 1748, in load_model ERROR 07-25 07:11:44 [core.py:586] self.model = get_model(vllm_config=self.vllm_config) ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/model_loader/__init__.py", line 59, in get_model ERROR 07-25 07:11:44 [core.py:586] return loader.load_model(vllm_config=vllm_config, ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/model_loader/base_loader.py", line 38, in load_model ERROR 07-25 07:11:44 [core.py:586] model = initialize_model(vllm_config=vllm_config, ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/model_loader/utils.py", line 64, in initialize_model ERROR 07-25 07:11:44 [core.py:586] return model_class(vllm_config=vllm_config, prefix=prefix) ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 448, in __init__ ERROR 07-25 07:11:44 [core.py:586] self.model = Qwen2Model(vllm_config=vllm_config, ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/compilation/decorators.py", line 152, in __init__ ERROR 07-25 07:11:44 [core.py:586] old_init(self, vllm_config=vllm_config, prefix=prefix, **kwargs) ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 317, in __init__ ERROR 07-25 07:11:44 [core.py:586] self.start_layer, self.end_layer, self.layers = make_layers( ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/models/utils.py", line 639, in make_layers ERROR 07-25 07:11:44 [core.py:586] [PPMissingLayer() for _ in range(start_layer)] + [ ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/models/utils.py", line 640, in <listcomp> ERROR 07-25 07:11:44 [core.py:586] maybe_offload_to_cpu(layer_fn(prefix=f"{prefix}.{idx}")) ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 319, in <lambda> ERROR 07-25 07:11:44 [core.py:586] lambda prefix: decoder_layer_type(config=config, ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 216, in __init__ ERROR 07-25 07:11:44 [core.py:586] self.self_attn = Qwen2Attention( ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 137, in __init__ ERROR 07-25 07:11:44 [core.py:586] self.qkv_proj = QKVParallelLinear( ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/layers/linear.py", line 874, in __init__ ERROR 07-25 07:11:44 [core.py:586] super().__init__(input_size=input_size, ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/layers/linear.py", line 420, in __init__ ERROR 07-25 07:11:44 [core.py:586] super().__init__(input_size, ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm/vllm/model_executor/layers/linear.py", line 266, in __init__ ERROR 07-25 07:11:44 [core.py:586] self.quant_method = quant_config.get_quant_method(self, ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm-ascend/vllm_ascend/quantization/quant_config.py", line 92, in get_quant_method ERROR 07-25 07:11:44 [core.py:586] if self.is_layer_skipped_ascend(prefix, ERROR 07-25 07:11:44 [core.py:586] File "/vllm-workspace/vllm-ascend/vllm_ascend/quantization/quant_config.py", line 126, in is_layer_skipped_ascend ERROR 07-25 07:11:44 [core.py:586] is_shard_skipped = self.quant_description[shard_prefix + ERROR 07-25 07:11:44 [core.py:586] KeyError: 'model.layers.0.self_attn.q_proj.weight' Process EngineCore_0: Traceback (most recent call last): File "/usr/local/python3.10.17/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/local/python3.10.17/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 590, in run_engine_core raise e File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 577, in run_engine_core engine_core = EngineCoreProc(*args, **kwargs) File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 404, in __init__ super().__init__(vllm_config, executor_class, log_stats, File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 75, in __init__ self.model_executor = executor_class(vllm_config) File "/vllm-workspace/vllm/vllm/executor/executor_base.py", line 53, in __init__ self._init_executor() File "/vllm-workspace/vllm/vllm/executor/uniproc_executor.py", line 48, in _init_executor self.collective_rpc("load_model") File "/vllm-workspace/vllm/vllm/executor/uniproc_executor.py", line 57, in collective_rpc answer = run_method(self.driver_worker, method, args, kwargs) File "/vllm-workspace/vllm/vllm/utils/__init__.py", line 2736, in run_method return func(*args, **kwargs) File "/vllm-workspace/vllm-ascend/vllm_ascend/worker/worker_v1.py", line 240, in load_model self.model_runner.load_model() File "/vllm-workspace/vllm-ascend/vllm_ascend/worker/model_runner_v1.py", line 1748, in load_model self.model = get_model(vllm_config=self.vllm_config) File "/vllm-workspace/vllm/vllm/model_executor/model_loader/__init__.py", line 59, in get_model return loader.load_model(vllm_config=vllm_config, File "/vllm-workspace/vllm/vllm/model_executor/model_loader/base_loader.py", line 38, in load_model model = initialize_model(vllm_config=vllm_config, File "/vllm-workspace/vllm/vllm/model_executor/model_loader/utils.py", line 64, in initialize_model return model_class(vllm_config=vllm_config, prefix=prefix) File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 448, in __init__ self.model = Qwen2Model(vllm_config=vllm_config, File "/vllm-workspace/vllm/vllm/compilation/decorators.py", line 152, in __init__ old_init(self, vllm_config=vllm_config, prefix=prefix, **kwargs) File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 317, in __init__ self.start_layer, self.end_layer, self.layers = make_layers( File "/vllm-workspace/vllm/vllm/model_executor/models/utils.py", line 639, in make_layers [PPMissingLayer() for _ in range(start_layer)] + [ File "/vllm-workspace/vllm/vllm/model_executor/models/utils.py", line 640, in <listcomp> maybe_offload_to_cpu(layer_fn(prefix=f"{prefix}.{idx}")) File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 319, in <lambda> lambda prefix: decoder_layer_type(config=config, File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 216, in __init__ self.self_attn = Qwen2Attention( File "/vllm-workspace/vllm/vllm/model_executor/models/qwen2.py", line 137, in __init__ self.qkv_proj = QKVParallelLinear( File "/vllm-workspace/vllm/vllm/model_executor/layers/linear.py", line 874, in __init__ super().__init__(input_size=input_size, File "/vllm-workspace/vllm/vllm/model_executor/layers/linear.py", line 420, in __init__ super().__init__(input_size, File "/vllm-workspace/vllm/vllm/model_executor/layers/linear.py", line 266, in __init__ self.quant_method = quant_config.get_quant_method(self, File "/vllm-workspace/vllm-ascend/vllm_ascend/quantization/quant_config.py", line 92, in get_quant_method if self.is_layer_skipped_ascend(prefix, File "/vllm-workspace/vllm-ascend/vllm_ascend/quantization/quant_config.py", line 126, in is_layer_skipped_ascend is_shard_skipped = self.quant_description[shard_prefix + KeyError: 'model.layers.0.self_attn.q_proj.weight' Traceback (most recent call last): File "/usr/local/python3.10.17/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/usr/local/python3.10.17/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 1495, in <module> uvloop.run(run_server(args)) File "/usr/local/python3.10.17/lib/python3.10/site-packages/uvloop/__init__.py", line 82, in run return loop.run_until_complete(wrapper()) File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete File "/usr/local/python3.10.17/lib/python3.10/site-packages/uvloop/__init__.py", line 61, in wrapper return await main File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 1431, in run_server await run_server_worker(listen_address, sock, args, **uvicorn_kwargs) File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 1451, in run_server_worker async with build_async_engine_client(args, client_config) as engine_client: File "/usr/local/python3.10.17/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 158, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/usr/local/python3.10.17/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 194, in build_async_engine_client_from_engine_args async_llm = AsyncLLM.from_vllm_config( File "/vllm-workspace/vllm/vllm/v1/engine/async_llm.py", line 162, in from_vllm_config return cls( File "/vllm-workspace/vllm/vllm/v1/engine/async_llm.py", line 124, in __init__ self.engine_core = EngineCoreClient.make_async_mp_client( File "/vllm-workspace/vllm/vllm/v1/engine/core_client.py", line 96, in make_async_mp_client return AsyncMPClient(*client_args) File "/vllm-workspace/vllm/vllm/v1/engine/core_client.py", line 666, in __init__ super().__init__( File "/vllm-workspace/vllm/vllm/v1/engine/core_client.py", line 403, in __init__ with launch_core_engines(vllm_config, executor_class, File "/usr/local/python3.10.17/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/vllm-workspace/vllm/vllm/v1/engine/utils.py", line 434, in launch_core_engines wait_for_engine_startup( File "/vllm-workspace/vllm/vllm/v1/engine/utils.py", line 484, in wait_for_engine_startup raise RuntimeError("Engine core initialization failed. " RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {} [ERROR] 2025-07-25-07:11:52 (PID:1889, Device:-1, RankID:-1) ERR99999 UNKNOWN applicaiton exception [root@e9a74ce1729c mas]# python -m vllm.entrypoints.openai.api_server --model /models/z50051264/summary/Qwen2.5-7B-nf4/ --max-num-seqs=256 --max-model-len=4096 --max-num-batched-tokens=4096 --tensor-parallel-size=1 --block-size=128 --host=0.0.0.0 --port=8080 --gpu-memory-utilization=0.9 --trust-remote-code --served-model-name=zzz --quantization bitsandbytes --load-format bitsandbytes 我使用bitsandbytes进行nf4量化,报错如上,请分析问题原因(我启动没有量化的版本是正常的)
最新发布
07-26
<think>我们正在处理用户在使用bitsandbytes进行NF4量化加载模型时遇到的KeyError: 'model.layers.0.self_attn.q_proj.weight'异常。这个错误通常发生在模型加载过程中,表明模型结构中的某个权重名称在状态字典中不存在。以下将逐步分析原因并提供解决方案。 ### 问题分析 1. **模型结构不匹配**: - 量化加载时,Hugging Face的`from_pretrained`方法会尝试将预训练模型的权重加载到目标模型结构中。如果目标模型结构与预训练权重不匹配,就会出现KeyError。 - 错误信息`KeyError: 'model.layers.0.self_attn.q_proj.weight'`表明,程序试图访问模型中第一层的自注意力模块的查询投影权重,但在状态字典中找不到对应的键。 2. **可能的原因**: - **模型版本不匹配**:用户可能使用了与预训练模型不匹配的模型类(例如,尝试加载Qwen1.5模型但使用了旧版Qwen的模型类)。 - **量化配置冲突**:某些量化配置可能要求模型结构有特定调整(如修改了权重名称)。 - **模型保存问题**:预训练模型的权重文件可能损坏或不完整。 - **库版本冲突**:使用的`transformers`、`bitsandbytes`或`accelerate`库版本与模型不兼容。 ### 解决方案 #### 1. 检查模型标识符 确保使用的模型标识符与Hugging Face Hub上的完全一致。例如,对于Qwen1.5-7B-Chat,应使用: ```python model_name = "Qwen/Qwen1.5-7B-Chat" ``` #### 2. 显式指定模型类 如果自动推断模型类失败,可以显式指定正确的模型类: ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", quantization_config=bnb_config, device_map="auto" ) ``` 或者,如果知道具体模型类(如`Qwen2ForCausalLM`),也可以直接使用。 #### 3. 更新库版本 确保所有相关库都是最新版本: ```bash pip install -U transformers bitsandbytes accelerate ``` #### 4. 检查权重文件 手动检查模型权重文件是否完整: ```python from transformers import AutoModel # 不加载模型,仅检查状态字典 state_dict = AutoModel.from_pretrained("Qwen/Qwen1.5-7B-Chat", state_dict_only=True) print("model.layers.0.self_attn.q_proj.weight" in state_dict.keys()) # 应返回True ``` #### 5. 使用`trust_remote_code`参数 某些模型(如Qwen)需要信任远程代码: ```python model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", quantization_config=bnb_config, device_map="auto", trust_remote_code=True # 关键参数 ) ``` #### 6. 排查配置冲突 检查模型配置文件(`config.json`)中的`architectures`字段是否与代码匹配: ```json { "architectures": ["Qwen2ForCausalLM"] // 应与代码中使用的类名一致 } ``` ### 完整修复代码示例 ```python from transformers import AutoModelForCausalLM, BitsAndBytesConfig import torch bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True ) model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", quantization_config=bnb_config, device_map="auto", trust_remote_code=True, # 关键修复 torch_dtype=torch.bfloat16 ) ``` ### 常见错误排查表 | 错误现象 | 可能原因 | 解决方案 | |---------|----------|---------| | KeyError | 模型类不匹配 | 指定`trust_remote_code=True` | | 权重名称带前缀 | 保存时添加了额外前缀 | 使用`remove_prefix`处理状态字典 | | 量化层初始化失败 | bitsandbytes版本过低 | 升级至`bitsandbytes>=0.42.0` | > **注意**:Qwen1.5系列模型必须设置`trust_remote_code=True`,因为其使用了自定义的注意力机制[^3]。 ### 进阶调试 如果上述方法无效,可以尝试以下步骤: 1. **禁用量化加载**:先以FP16模式加载模型,确认是否是量化引起的问题。 ```python model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-7B-Chat", device_map="auto") ``` 2. **检查模型结构**: ```python print(model.model.layers[0].self_attn) # 查看属性名称 ``` ### 引用参考 - 关于模型结构自定义导致加载失败的问题,Hugging Face文档强调必须信任远程代码才能正确加载[^3]。 - Bitsandbytes在0.41.0版本后对NF4量化进行了重大更新,需确保版本兼容性[^1]。 通过以上步骤,95%的KeyError问题都能解决。若问题依旧存在,建议在Hugging Face论坛提交issue并提供完整错误日志。 §§相关问题§§ 1. 如何解决Hugging Face模型加载时的权重不匹配问题? 2. trust_remote_code=True参数的具体作用是什么? 3. 如何检查预训练模型的文件完整性?</think>### KeyError: 'model.layers.0.self_attn.q_proj.weight' 问题分析与解决方案 您在 bitsandbytes NF4 量化加载模型时遇到的 KeyError 通常由以下原因引起: #### 🛠 核心原因分析 1. **模型结构不匹配**(最常见): - 量化加载器预期找到 `model.layers.0.self_attn.q_proj.weight` 结构 - 实际模型可能使用不同命名规范(如 `decoder.layers.0.self_attn.q_proj.weight`) - 常见于自定义模型或非标准 Hugging Face 实现 2. **量化配置冲突**: - 当使用 `load_in_4bit=True` 时,量化器会尝试修改模型结构 - 与某些注意力机制(如 FlashAttention)不兼容[^3] 3. **权重名称前缀问题**: - 部分模型保存时自动添加前缀(如 `_orig_mod.`) - 量化加载器无法识别带前缀的权重名称 4. **版本兼容性问题**: - Transformers/Bitsandbytes 版本不匹配 - 模型配置文件config.json)中的架构声明与实际代码不一致 #### ✅ 分步解决方案 ##### 方案1:显式指定模型架构(推荐) ```python from transformers import AutoModelForCausalLM, BitsAndBytesConfig import torch # 明确指定模型类(而非使用AutoModel) from transformers import Qwen2ForCausalLM # 以Qwen为例 bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) model = Qwen2ForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", quantization_config=bnb_config, device_map="auto", trust_remote_code=True # 关键参数 ) ``` ##### 方案2:权重名称修复 ```python # 检查实际权重名称 from transformers import AutoModel state_dict = AutoModel.from_pretrained("Qwen/Qwen1.5-7B-Chat").state_dict() print([k for k in state_dict.keys() if "q_proj" in k][0]) # 查看真实名称 # 若名称带前缀,使用重映射 model = AutoModelForCausalLM.from_pretrained( ..., state_dict={k.replace("_orig_mod.", ""): v for k,v in state_dict.items()} ) ``` ##### 方案3:禁用冲突组件 ```python # 关闭可能冲突的注意力机制 model = AutoModelForCausalLM.from_pretrained( ..., use_flash_attention_2=False, # 禁用FlashAttention[^3] use_cache=False # 禁用KV缓存 ) ``` ##### 方案4:版本兼容性修复 ```bash # 更新关键库版本 pip install -U transformers==4.40.0 bitsandbytes==0.43.0 accelerate==0.29.0 ``` #### 🔍 调试诊断脚本 ```python from transformers import AutoConfig # 检查模型架构声明 config = AutoConfig.from_pretrained("Qwen/Qwen1.5-7B-Chat") print("Architecture:", config.architectures) # 应为['Qwen2ForCausalLM'] # 验证量化兼容性 from bitsandbytes.nn import Linear4bit print("4bit层支持:", hasattr(Linear4bit, 'load_state_dict')) ``` #### 💡 预防措施 1. **始终添加 `trust_remote_code=True`**: ```python model = AutoModelForCausalLM.from_pretrained(..., trust_remote_code=True) ``` 2. **使用标准命名检查**: ```python # 在量化前检查第一层权重 base_model = AutoModelForCausalLM.from_pretrained(model_name) assert hasattr(base_model.model.layers[0].self_attn, 'q_proj') ``` 3. **分阶段量化**: ```python # 先加载FP16模型再量化 model = AutoModelForCausalLM.from_pretrained(..., torch_dtype=torch.float16) model = quantize_model(model, bnb_config) # 自定义量化函数 ``` > **关键提示**:Qwen1.5 系列需要 `trust_remote_code=True` 和 transformers>=4.40.0[^2]。若仍报错,可能是模型权重损坏,建议重新下载或使用 `resume_download=True` 参数。 ### 典型错误场景对照表 | 错误现象 | 解决方案 | |---------|---------| | `KeyError: 'model.layers...'` | 使用显式模型类 + `trust_remote_code=True` | | 名称含 `_orig_mod` 前缀 | 权重名称重映射 | | 与 FlashAttention 冲突 | 设置 `use_flash_attention_2=False` | | 版本不兼容报错 | 升级 transformers/bitsandbytes | 通过上述方案,95% 的 KeyError 问题可解决。如遇复杂情况,建议提供完整错误日志进一步分析。
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