train_glm4_ner.py

import json
import pandas as pd
import torch
from datasets import Dataset
from modelscope import snapshot_download, AutoTokenizer
from swanlab.integration.huggingface import SwanLabCallback
from peft import LoraConfig, TaskType, get_peft_model
from transformers import AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForSeq2Seq
import os
import swanlab


def dataset_jsonl_transfer(origin_path, new_path):
    """
    将原始数据集转换为大模型微调所需数据格式的新数据集
    """
    messages = []

    # 读取旧的JSONL文件
    with open(origin_path, "r") as file:
        for line in file:
            # 解析每一行的json数据
            data = json.loads(line)
            input_text = data["text"]
            entities = data["entities"]
            match_names = ["地点", "人名", "地理实体", "组织"]
            
            entity_sentence = ""
            for entity in entities:
                entity_json = dict(entity)
                entity_text = entity_json["entity_text"]
                entity_names = entity_json["entity_names"]
                for name in entity_names:
                    if name in match_names:
                        entity_label = name
                        break
                
                entity_sentence += f"""{{"entity_text": "{entity_text}", "entity_label": "{entity_label}"}}"""
            
            if entity_sentence == "":
                entity_sentence = "没有找到任何实体"
            
            message = {
                "instruction": """你是一个文本实体识别领域的专家，你需要从给定的句子中提取 地点; 人名; 地理实体; 组织 实体. 以 json 格式输出, 如 {"entity_text": "南京", "entity_label": "地理实体"} 注意: 1. 输出的每一行都必须是正确的 json 字符串. 2. 找不到任何实体时, 输出"没有找到任何实体". """,
                "input": f"文本:{input_text}",
                "output": entity_sentence,
            }
            
            messages.append(message)

    # 保存重构后的JSONL文件
    with open(new_path, "w", encoding="utf-8") as file:
        for message in messages:
            file.write(json.dumps(message, ensure_ascii=False) + "\n")
            
            
def process_func(example):
    """
    对数据集进行数据预处理，主要用于被dataset.map调用
    """

    MAX_LENGTH = 384 
    input_ids, attention_mask, labels = [], [], []
    system_prompt = """你是一个文本实体识别领域的专家，你需要从给定的句子中提取 地点; 人名; 地理实体; 组织 实体. 以 json 格式输出, 如 {"entity_text": "南京", "entity_label": "地理实体"} 注意: 1. 输出的每一行都必须是正确的 json 字符串. 2. 找不到任何实体时, 输出"没有找到任何实体"."""
    
    instruction = tokenizer(
        f"<|system|>\n{system_prompt}<|endoftext|>\n<|user|>\n{example['input']}<|endoftext|>\n<|assistant|>\n",
        add_special_tokens=False,
    )
    response = tokenizer(f"{example['output']}", add_special_tokens=False)
    input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
    attention_mask = (
        instruction["attention_mask"] + response["attention_mask"] + [1]
    )
    labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
    if len(input_ids) > MAX_LENGTH:  # 做一个截断
        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}   


def predict(messages, model, tokenizer):
    """对测试集进行模型推理，得到预测结果"""
    device = "cuda"
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    model_inputs = tokenizer([text], return_tensors="pt").to(device)

    generated_ids = model.generate(
        model_inputs.input_ids,
        max_new_tokens=512
    )
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    
    print(response)
     
    return response


model_id = "ZhipuAI/glm-4-9b-chat"    
model_dir = "./ZhipuAI/glm-4-9b-chat/"

# 在modelscope上下载GLM4模型到本地目录下
model_dir = snapshot_download(model_id, cache_dir="./", revision="master")

# Transformers加载模型权重
tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
model.enable_input_require_grads()  # 开启梯度检查点时，要执行该方法

# 加载、处理数据集和测试集
train_dataset_path = "ccfbdci.jsonl"
train_jsonl_new_path = "ccf_train.jsonl"

if not os.path.exists(train_jsonl_new_path):
    dataset_jsonl_transfer(train_dataset_path, train_jsonl_new_path)

# 得到训练集
total_df = pd.read_json(train_jsonl_new_path, lines=True)
train_df = total_df[int(len(total_df) * 0.1):]
train_ds = Dataset.from_pandas(train_df)
train_dataset = train_ds.map(process_func, remove_columns=train_ds.column_names)

# 配置LoRA
config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    target_modules=["query_key_value", "dense", "dense_h_to_4h", "activation_func", "dense_4h_to_h"],
    inference_mode=False,  # 训练模式
    r=8,  # Lora 秩
    lora_alpha=32,  # Lora alaph，具体作用参见 Lora 原理
    lora_dropout=0.1,  # Dropout 比例
)

# 得到被peft包装后的模型
model = get_peft_model(model, config)

# 配置Transformers训练参数
args = TrainingArguments(
    output_dir="./output/GLM4-NER",
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    gradient_accumulation_steps=4,
    logging_steps=10,
    num_train_epochs=2,
    save_steps=100,
    learning_rate=1e-4,
    save_on_each_node=True,
    gradient_checkpointing=True,
    report_to="none",
)

# 设置SwanLab与Transformers的回调
swanlab_callback = SwanLabCallback(
    project="GLM4-NER-fintune",
    experiment_name="GLM4-9B-Chat",
    description="使用智谱GLM4-9B-Chat模型在NER数据集上微调，实现关键实体识别任务。",
    config={
        "model": model_id,
        "model_dir": model_dir,
        "dataset": "qgyd2021/chinese_ner_sft",
    },
)

# 设置Transformers Trainer
trainer = Trainer(
    model=model,
    args=args,
    train_dataset=train_dataset,
    data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
    callbacks=[swanlab_callback],
)

# 开始训练
trainer.train()

# 用随机20条数据测试模型
test_df = total_df[:int(len(total_df) * 0.1)].sample(n=20)

test_text_list = []
for index, row in test_df.iterrows():
    instruction = row['instruction']
    input_value = row['input']
    
    messages = [
        {"role": "system", "content": f"{instruction}"},
        {"role": "user", "content": f"{input_value}"}
    ]

    response = predict(messages, model, tokenizer)
    messages.append({"role": "assistant", "content": f"{response}"})
    result_text = f"{messages[0]}\n\n{messages[1]}\n\n{messages[2]}"
    test_text_list.append(swanlab.Text(result_text, caption=response))

# 记录测试结果
swanlab.log({"Prediction": test_text_list})
# 关闭SwanLab记录
swanlab.finish()