Huggingface 变压器训练函数抛出 Device() 收到了无效的参数组合

Question

我试图用 peft qLora 训练来训练模型。 Lora 配置和 peft 训练参数如下所示：

lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=[
        "q_proj",
        "k_proj",
        "v_proj",
        "o_proj",
        "gate_proj",
        "up_proj",
        "down_proj",
        "lm_head",
    ],
    bias="none",
    lora_dropout=0.05,  # Conventional
    task_type="CAUSAL_LM",
)
peft_model = get_peft_model(original_model, 
                            lora_config)

output_dir = f'./peft-bn-mistral-training-{str(int(time.time()))}'

peft_training_args = TrainingArguments(
    output_dir=output_dir,
    auto_find_batch_size=True,
    learning_rate=1e-3, # Higher learning rate than full fine-tuning.
    num_train_epochs=1,
    logging_steps=1,
    max_steps=1    
)
device = torch.device("cuda:0")
peft_trainer = Trainer(
    model=peft_model.to(device),
    args=peft_training_args,
    train_dataset=tokenized_datasets["train"],
)
peft_trainer.train()

代码产生如下错误：

TypeError                                 Traceback (most recent call last)

<ipython-input-46-b47531775ae7> in <cell line: 1>()
----> 1 peft_trainer.train()
      2 
      3 peft_model_path="./peft-bn-mistral-checkpoint-local"
      4 
      5 peft_trainer.model.save_pretrained(peft_model_path)
   1326             current_device_index = current_device.index if isinstance(current_device, torch.device) else current_device
   1327 
-> 1328             if torch.device(current_device_index) != self.device:
   1329                 # if on the first device (GPU 0) we don't care
   1330                 if (self.device.index is not None) or (current_device_index != 0):

TypeError: Device() received an invalid combination of arguments - got (NoneType), but expected one of:
 * (torch.device device)
      didn't match because some of the arguments have invalid types: (!NoneType!)
 * (str type, int index)

我尝试调整差异设置，将设备参数引入模型，但它始终导致上述错误。 请注意，我使用

BitsAndBytesConfig

中的

transformers

模块作为标记器。 TIA

Answer 1

为了解决这个问题，我必须在传递参数之前将模型分配给 GPU。

之前：

peft_trainer = Trainer(
    model=peft_model.to(device),
    args=peft_training_args,
    train_dataset=tokenized_datasets["train"],
)

之后：

peft_model= peft_model.to(device)
peft_trainer = Trainer(
    model=peft_model,
    args=peft_training_args,
    train_dataset=tokenized_datasets["train"],
)