/home/dc/.local/lib/python3.10/site-packages/torch/nn/
时间: 2023-09-26 19:13:06 浏览: 83
这个路径下是 PyTorch 框架中的 nn 模块,包含了深度学习中常用的神经网络层、损失函数等模块的实现。其中,nn 模块是 PyTorch 中最重要的模块之一,它提供了构建神经网络所需的基础组件,可以大大简化神经网络的搭建过程。如果你是使用 PyTorch 进行深度学习开发的话,这个路径下的内容会是你经常使用的。
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Traceback (most recent call last): File "/home/nano/Downloads/chatglm6B-int4/web_demo.py", line 6, in <module> model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda() File "/home/nano/.local/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 441, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/home/nano/.local/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py", line 915, in from_pretrained return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs) File "/home/nano/.local/lib/python3.10/site-packages/transformers/configuration_utils.py", line 553, in from_pretrained return cls.from_dict(config_dict, **kwargs) File "/home/nano/.local/lib/python3.10/site-packages/transformers/configuration_utils.py", line 696, in from_dict config = cls(**config_dict) File "/home/nano/.cache/huggingface/modules/transformers_modules/THUDM/chatglm-6b/619e736c6d4cd139840579c5482063b75bed5666/configuration_chatglm.py", line 98, in __init__ super().__init__( File "/home/nano/.local/lib/python3.10/site-packages/transformers/configuration_utils.py", line 336, in __init__ import torch File "/home/nano/.local/lib/python3.10/site-packages/torch/__init__.py", line 229, in <module> from torch._C import * # noqa: F403 ImportError: /home/nano/.local/lib/python3.10/site-packages/torch/lib/../../torch.libs/libgomp-d22c30c5.so.1.0.0: cannot allocate memory in static TLS block
这个错误提示表明在运行代码时,系统无法在静态TLS(Thread-Local Storage)块中分配内存。这可能是由于系统内存不足导致的。
你可以尝试以下方法解决这个问题:
1. 重新启动机器:有时候,系统内存不足可能是由于其他进程或应用程序占用了大量内存。通过重新启动机器可以释放内存,并解决该问题。
2. 关闭其他应用程序:关闭其他正在运行的应用程序,以释放内存空间。
3. 检查内存使用:你可以使用系统监视工具来检查内存使用情况,并确保有足够的可用内存来运行你的代码。
4. 增加虚拟内存:如果你的系统内存不足,你可以尝试增加虚拟内存大小。虚拟内存是硬盘上的一块预留空间,当物理内存不足时,操作系统会将部分数据转移到虚拟内存中。
5. 检查torch版本:确保你正在使用最新版本的torch库,并尝试更新或重新安装torch库。
如果上述方法都不起作用,那可能是由于系统硬件或操作系统本身的限制导致的。在这种情况下,你可能需要升级硬件或调整操作系统配置来处理更大的内存需求。
>> %Run first2.py Traceback (most recent call last): File "/home/pi/Desktop/first2.py", line 74, in <module> model.load_state_dict(torch.load(r"/home/pi/Desktop/epoch50.pkl"))#模型位置 File "/home/pi/.local/lib/python3.9/site-packages/torch/serialization.py", line 592, in load return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args) File "/home/pi/.local/lib/python3.9/site-packages/torch/serialization.py", line 851, in _load result = unpickler.load() File "/home/pi/.local/lib/python3.9/site-packages/torch/serialization.py", line 843, in persistent_load load_tensor(data_type, size, key, _maybe_decode_ascii(location)) File "/home/pi/.local/lib/python3.9/site-packages/torch/serialization.py", line 832, in load_tensor loaded_storages[key] = restore_location(storage, location) File "/home/pi/.local/lib/python3.9/site-packages/torch/serialization.py", line 175, in default_restore_location result = fn(storage, location) File "/home/pi/.local/lib/python3.9/site-packages/torch/serialization.py", line 151, in _cuda_deserialize device = validate_cuda_device(location) File "/home/pi/.local/lib/python3.9/site-packages/torch/serialization.py", line 135, in validate_cuda_device raise RuntimeError('Attempting to deserialize object on a CUDA ' RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
这个错误是因为你的代码尝试在CUDA设备上加载模型参数,但是检测到没有可用的CUDA设备。如果你在一个只有CPU的机器上运行代码,请使用`torch.load`函数的`map_location`参数将模型参数映射到CPU上。你可以尝试修改代码如下:
```python
model.load_state_dict(torch.load(r"/home/pi/Desktop/epoch50.pkl", map_location=torch.device('cpu')))
```
这样就能够将模型参数加载到CPU上了。希望对你有帮助!如果还有其他问题,请随时提问。
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