Chapter 24
Prompt Tuning 实战
Notebooktransformers33 cells
Prompt Tuning 实战
Step1 导入相关包
In [ ]python · cell 3
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from datasets import Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, TrainerStep2 加载数据集
In [ ]python · cell 5
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ds = Dataset.load_from_disk("../data/alpaca_data_zh/")
dsIn [ ]python · cell 6
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ds[:3]Step3 数据集预处理
In [ ]python · cell 8
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tokenizer = AutoTokenizer.from_pretrained("Langboat/bloom-1b4-zh")
tokenizerIn [ ]python · cell 9
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def process_func(example):
MAX_LENGTH = 256
input_ids, attention_mask, labels = [], [], []
instruction = tokenizer("\n".join(["Human: " + example["instruction"], example["input"]]).strip() + "\n\nAssistant: ")
response = tokenizer(example["output"] + tokenizer.eos_token)
input_ids = instruction["input_ids"] + response["input_ids"]
attention_mask = instruction["attention_mask"] + response["attention_mask"]
labels = [-100] * len(instruction["input_ids"]) + response["input_ids"]
if len(input_ids) > MAX_LENGTH:
input_ids = input_ids[:MAX_LENGTH]
attention_mask = attention_mask[:MAX_LENGTH]
labels = labels[:MAX_LENGTH]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": labels
}In [ ]python · cell 10
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tokenized_ds = ds.map(process_func, remove_columns=ds.column_names)
tokenized_dsIn [ ]python · cell 11
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tokenizer.decode(tokenized_ds[1]["input_ids"])In [ ]python · cell 12
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tokenizer.decode(list(filter(lambda x: x != -100, tokenized_ds[1]["labels"])))Step4 创建模型
In [ ]python · cell 14
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model = AutoModelForCausalLM.from_pretrained("Langboat/bloom-1b4-zh")Prompt tuning
PEFT Step1 配置文件
In [ ]python · cell 17
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from peft import PromptTuningConfig, get_peft_model, TaskType, PromptTuningInit
# Soft Prompt
# config = PromptTuningConfig(task_type=TaskType.CAUSAL_LM, num_virtual_tokens=10)
# config
# Hard Prompt
config = PromptTuningConfig(task_type=TaskType.CAUSAL_LM,
prompt_tuning_init=PromptTuningInit.TEXT,
prompt_tuning_init_text="下面是一段人与机器人的对话。",
num_virtual_tokens=len(tokenizer("下面是一段人与机器人的对话。")["input_ids"]),
tokenizer_name_or_path="Langboat/bloom-1b4-zh")
configPEFT Step2 创建模型
In [ ]python · cell 19
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model = get_peft_model(model, config)In [ ]python · cell 20
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modelIn [ ]python · cell 21
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model.print_trainable_parameters()Step5 配置训练参数
In [ ]python · cell 23
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args = TrainingArguments(
output_dir="./chatbot",
per_device_train_batch_size=1,
gradient_accumulation_steps=8,
logging_steps=10,
num_train_epochs=1,
save_steps=20,
)Step6 创建训练器
In [ ]python · cell 25
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trainer = Trainer(
model=model,
args=args,
tokenizer=tokenizer,
train_dataset=tokenized_ds,
data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
)Step7 模型训练
In [ ]python · cell 27
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trainer.train()加载训练好的PEFT模型
In [ ]python · cell 29
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from peft import PeftModelIn [ ]python · cell 30
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# 在一个jupyter文件中，如果前面已经加载了模型，并对模型做了一定修改，则需要重新加载原始模型
model = AutoModelForCausalLM.from_pretrained("Langboat/bloom-1b4-zh")
peft_model = PeftModel.from_pretrained(model=model, model_id="./chatbot/checkpoint-20/")Step8 模型推理
In [ ]python · cell 32
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peft_model = peft_model.cuda()
ipt = tokenizer("Human: {}\n{}".format("考试有哪些技巧？", "").strip() + "\n\nAssistant: ", return_tensors="pt").to(peft_model.device)
print(tokenizer.decode(peft_model.generate(**ipt, max_length=128, do_sample=True)[0], skip_special_tokens=True))In [ ]python · cell 33
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