属性错误：模块“火炬”没有属性“版本”

Question

操作系统：Ubuntu 18.04 LTS CUDA：11.3 GPU：NVIDIA P5000 Quadro IDE：Jupyter 笔记本 环境：VirtualEnv (venv)

代码：

# Importing the required libraries
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

# Defining the name of the Falcon model
model_name = "ybelkada/falcon-7b-sharded-bf16"

# Configuring the BitsAndBytes quantization
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)

# Loading the Falcon model with quantization configuration
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
trust_remote_code=True
)

# Disabling cache usage in the model configuration
model.config.use_cache = False

错误：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
File ~/FYP_Chatbot/test02/src/myenv-test002-02/lib/python3.9/site-packages/torch/cuda/__init__.py:242, in _lazy_init()
    241 try:
--> 242     queued_call()
    243 except Exception as e:

File ~/FYP_Chatbot/test02/src/myenv-test002-02/lib/python3.9/site-packages/torch/cuda/__init__.py:122, in _check_capability()
    116 old_gpu_warn = """
    117 Found GPU%d %s which is of cuda capability %d.%d.
    118 PyTorch no longer supports this GPU because it is too old.
    119 The minimum cuda capability supported by this library is %d.%d.
    120 """
--> 122 if torch.version.cuda is not None:  # on ROCm we don't want this check
    123     CUDA_VERSION = torch._C._cuda_getCompiledVersion()

AttributeError: module 'torch' has no attribute 'version'

The above exception was the direct cause of the following exception:

DeferredCudaCallError                     Traceback (most recent call last)
Cell In[10], line 17
     10 bnb_config = BitsAndBytesConfig(
     11 load_in_4bit=True,
     12 bnb_4bit_quant_type="nf4",
     13 bnb_4bit_compute_dtype=torch.float16,
     14 )
     16 # Loading the Falcon model with quantization configuration
---> 17 model = AutoModelForCausalLM.from_pretrained(
     18 model_name,
     19 quantization_config=bnb_config,
     20 trust_remote_code=True
     21 )
     23 # Disabling cache usage in the model configuration
     24 model.config.use_cache = False

File ~/FYP_Chatbot/test02/src/myenv-test002-02/lib/python3.9/site-packages/transformers/models/auto/auto_factory.py:563, in _BaseAutoModelClass.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    561 elif type(config) in cls._model_mapping.keys():
    562     model_class = _get_model_class(config, cls._model_mapping)
--> 563     return model_class.from_pretrained(
    564         pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
    565     )
    566 raise ValueError(
    567     f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
    568     f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
    569 )

File ~/FYP_Chatbot/test02/src/myenv-test002-02/lib/python3.9/site-packages/transformers/modeling_utils.py:3053, in PreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)
   3049 hf_quantizer.validate_environment(
   3050     torch_dtype=torch_dtype, from_tf=from_tf, from_flax=from_flax, device_map=device_map
   3051 )
   3052 torch_dtype = hf_quantizer.update_torch_dtype(torch_dtype)
-> 3053 device_map = hf_quantizer.update_device_map(device_map)
   3055 # Force-set to `True` for more mem efficiency
   3056 if low_cpu_mem_usage is None:

File ~/FYP_Chatbot/test02/src/myenv-test002-02/lib/python3.9/site-packages/transformers/quantizers/quantizer_bnb_4bit.py:246, in Bnb4BitHfQuantizer.update_device_map(self, device_map)
    244 def update_device_map(self, device_map):
    245     if device_map is None:
--> 246         device_map = {"": torch.cuda.current_device()}
    247         logger.info(
    248             "The device_map was not initialized. "
    249             "Setting device_map to {'':torch.cuda.current_device()}. "
    250             "If you want to use the model for inference, please set device_map ='auto' "
    251         )
    252     return device_map

File ~/FYP_Chatbot/test02/src/myenv-test002-02/lib/python3.9/site-packages/torch/cuda/__init__.py:552, in current_device()
    550 def current_device() -> int:
    551     r"""Returns the index of a currently selected device."""
--> 552     _lazy_init()
    553     return torch._C._cuda_getDevice()

File ~/FYP_Chatbot/test02/src/myenv-test002-02/lib/python3.9/site-packages/torch/cuda/__init__.py:246, in _lazy_init()
    243         except Exception as e:
    244             msg = (f"CUDA call failed lazily at initialization with error: {str(e)}\n\n"
    245                    f"CUDA call was originally invoked at:\n\n{orig_traceback}")
--> 246             raise DeferredCudaCallError(msg) from e
    247 finally:
    248     delattr(_tls, 'is_initializing')

DeferredCudaCallError: CUDA call failed lazily at initialization with error: module 'torch' has no attribute 'version'

环境套餐

加速==0.29.1 位和字节==0.43.0 数据集==2.18.0 埃诺普斯==0.7.0 fsspec==2023.10.0 peft @ git+https://github.com/huggingface/peft.git@26726bf1ddee6ca75ed4e1bfd292094526707a78 火炬==1.13.0 变形金刚==4.39.3 trl==0.8.1 万宝==0.16.6

描述： 我在将 PyTorch

2.2.2

降级为 PyTorch

1.13.0

后遇到了错误。我不得不降级 PyTorch

2.2.2

，因为我有 11.3 版本的 cuda 工具包，它与更高版本的 PyTorch 不兼容。我将 PyTorch 降级到版本

1.13.0

，特别是因为我使用的是 Huggingface 中的“transformers”库，它需要 PyTorch 版本

>= 1.13.0

。

Answer 1

使用类似这样的东西 设置（可以忽略，我用的是JupyterLab）

!python --version

!pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

import torch

m1 = torch.Tensor([1, 2, 3])
m2 = torch.Tensor([4, 5, 6])
m3 = m1.add(m2)
print(m3)

import torch
print(torch.__version__)

结果

2.1.2+cu121