GLM4大模型微调入门实战（完整代码）

swanlab
modelscope
transformers
datasets
peft
accelerate
pandas
tiktoken

pip install swanlab modelscope transformers datasets peft pandas accelerate tiktoken

"""
[PROMPT]Text: 第四届全国大企业足球赛复赛结束新华社郑州５月３日电（实习生田兆运）上海大隆机器厂队昨天在洛阳进行的第四届牡丹杯全国大企业足球赛复赛中，以５：４力克成都冶金实验厂队，进入前四名。沪蓉之战，双方势均力敌，９０分钟不分胜负。最后，双方互射点球，沪队才以一球优势取胜。复赛的其它３场比赛，青海山川机床铸造厂队３：０击败东道主洛阳矿山机器厂队，青岛铸造机械厂队３：１战胜石家庄第一印染厂队，武汉肉联厂队１：０险胜天津市第二冶金机械厂队。在今天进行的决定九至十二名的两场比赛中，包钢无缝钢管厂队和河南平顶山矿务局一矿队分别击败河南平顶山锦纶帘子布厂队和江苏盐城无线电总厂队。４日将进行两场半决赛，由青海山川机床铸造厂队和青岛铸造机械厂队分别与武汉肉联厂队和上海大隆机器厂队交锋。本届比赛将于６日结束。（完）
Category: Sports, Politics
Output:[OUTPUT]Sports
"""

from modelscope import snapshot_download, AutoTokenizer
from transformers import AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForSeq2Seq

# 在modelscope上下载GLM模型到本地目录下
model_dir = snapshot_download("ZhipuAI/glm-4-9b-chat", cache_dir="./", revision="master")

# Transformers加载模型权重
tokenizer = AutoTokenizer.from_pretrained("./ZhipuAI/glm-4-9b-chat/", use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("./ZhipuAI/glm-4-9b-chat/", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)

from swanlab.integration.huggingface import SwanLabCallback

swanlab_callback = SwanLabCallback(...)

trainer = Trainer(
    ...
    callbacks=[swanlab_callback],
)

|--- train.py
|--- train.jsonl
|--- test.jsonl

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)
            context = data["text"]
            catagory = data["category"]
            label = data["output"]
            message = {
                "instruction": "你是一个文本分类领域的专家，你会接收到一段文本和几个潜在的分类选项，请输出文本内容的正确类型",
                "input": f"文本:{context},类型选型:{catagory}",
                "output": label,
            }
            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):
    """
    将数据集进行预处理
    """
    MAX_LENGTH = 384 
    input_ids, attention_mask, labels = [], [], []
    instruction = tokenizer(
        f"<|system|>\n你是一个文本分类领域的专家，你会接收到一段文本和几个潜在的分类选项，请输出文本内容的正确类型<|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):
    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
    
# 在modelscope上下载GLM模型到本地目录下
model_dir = snapshot_download("ZhipuAI/glm-4-9b-chat", cache_dir="./", revision="master")

# Transformers加载模型权重
tokenizer = AutoTokenizer.from_pretrained("./ZhipuAI/glm-4-9b-chat/", use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("./ZhipuAI/glm-4-9b-chat/", 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 = "train.jsonl"
test_dataset_path = "test.jsonl"

train_jsonl_new_path = "new_train.jsonl"
test_jsonl_new_path = "new_test.jsonl"

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

# 得到训练集
train_df = pd.read_json(train_jsonl_new_path, lines=True)
train_ds = Dataset.from_pandas(train_df)
train_dataset = train_ds.map(process_func, remove_columns=train_ds.column_names)

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
)

model = get_peft_model(model, config)

args = TrainingArguments(
    output_dir="./output/GLM4-9b",
    per_device_train_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",
)

swanlab_callback = SwanLabCallback(
    project="GLM4-fintune",
    experiment_name="GLM4-9B-Chat",
    description="使用智谱GLM4-9B-Chat模型在zh_cls_fudan-news数据集上微调。",
    config={
        "model": "ZhipuAI/glm-4-9b-chat",
        "dataset": "huangjintao/zh_cls_fudan-news",
    },
)

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

trainer.train()

# 用测试集的前10条，测试模型
test_df = pd.read_json(test_jsonl_new_path, lines=True)[:10]

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))
    
swanlab.log({"Prediction": test_text_list})
swanlab.finish()