调用p-tuning报错

[linguoqi]

`import os os.environ['CUDA_VISIBLE_DEVICES'] = '3' from modelscope import Model, AutoModelForSequenceClassification, AutoTokenizer, MsDataset from swift import Swift, LoRAConfig, AdapterConfig, Trainer, TrainingArguments, PromptConfig import torch from transformers import default_data_collator

model = Model.from_pretrained('/tf/model/Llama-2-7b-chat-ms/', torch_dtype=torch.bfloat16) adapter_config = AdapterConfig( dim=model.config.hidden_size, target_modules=['mlp'], method_name='forward', hidden_pos=0, adapter_length=32, )

prompt_config = PromptConfig( dim=768, # hidden states的维度 target_modules=r'.*layer.\d+$', # 要使用正则表达式替换的模块 embedding_pos=0, # embedding张量的位置 prompt_length=10, # 提示符token的长度 attach_front=False # 是否将提示符附加在embedding前面 )

print(adapter_config) model = Swift.prepare_model(model, prompt_config) tokenizer = AutoTokenizer.from_pretrained('/tf/model/Llama-2-7b-chat-ms/')

train_dataset = MsDataset.load(dataset_name = "/tf/user/lgq/data",data_files={"train": '/tf/user/lgq/data/black_start_case14_versionB.jsonl'}).to_hf_dataset().select(range(4)) val_dataset = MsDataset.load(dataset_name = "/tf/user/lgq/data",data_files={"val": '/tf/user/lgq/data/black_start_case14_versionB.jsonl'}).to_hf_dataset().select(range(4))

print(train_dataset) def tokenize_function(examples): return tokenizer(examples["instruction"], examples["input"],examples["output"], padding="max_length", truncation=True, max_length=128)

train_dataset = train_dataset.map(tokenize_function, batched=True) val_dataset = val_dataset.map(tokenize_function, batched=True)

arguments = TrainingArguments( output_dir='./outputs', per_device_train_batch_size=4, )

trainer = Trainer(model, arguments, train_dataset=train_dataset, eval_dataset=val_dataset, data_collator=default_data_collator,)

trainer.train()`