train_lora.py

# train_lora.py
# Qwen-7B-Chat 微调脚本（真正最终版）
# 核心：添加 labels，让 Trainer 能计算 loss

import os
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TrainingArguments,
    Trainer
)
from peft import get_peft_model, LoraConfig, TaskType
from datasets import load_dataset

# --- 配置 ---
model_path = "/home/yq238/project_pi_aaa247/yq238/qwen_training/models/Qwen-7B-Chat"
data_path = "/home/yq238/project_pi_aaa247/yq238/qwen_training/data/training1.jsonl"
output_dir = "/home/yq238/project_pi_aaa247/yq238/qwen_training/training/test1"

os.makedirs(output_dir, exist_ok=True)

# --- 1. 加载 tokenizer ---
print("🔧 加载 tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

# --- 2. 加载模型 ---
print("🔧 加载模型...")
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    trust_remote_code=True,
    torch_dtype=torch.float16,
)

# ✅ 启用梯度检查点，节省显存
model.gradient_checkpointing_enable()

# --- 3. LoRA 配置 ---
print("🔧 配置 LoRA...")
peft_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=64,
    lora_alpha=128,
    target_modules=["attn.c_proj", "mlp.c_proj"],
    lora_dropout=0.05,
    bias="none",
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()

# --- 4. 数据处理：添加 labels ---
print("🔧 加载并处理数据...")
def tokenize_fn(examples):
    instructions = examples["instruction"]
    inputs = examples["input"]
    outputs = examples["output"]
    texts = [
        f"你是一个自动化助手。\n\n用户：{i}\n{s}\n\n助手：{o}"
        for i, s, o in zip(instructions, inputs, outputs)
    ]
    # ✅ tokenize
    batch = tokenizer(texts, truncation=True, max_length=256, padding=False)
    # ✅ 添加 labels
    batch["labels"] = [
        tokenizer(o, truncation=True, max_length=256, padding=False)["input_ids"]
        for o in outputs
    ]
    return batch

dataset = load_dataset('json', data_files=data_path, split='train')
print(f"✅ 数据集加载完成，共 {len(dataset)} 条样本")

tokenized_dataset = dataset.map(
    tokenize_fn,
    batched=True,
    remove_columns=dataset.column_names,
    num_proc=1
)
print("✅ 数据处理完成")

# --- 5. 训练参数 ---
training_args = TrainingArguments(
    output_dir=output_dir,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=16,
    num_train_epochs=3,
    learning_rate=2e-4,
    logging_steps=1,
    save_steps=10,
    save_total_limit=2,
    fp16=True,
    bf16=False,
    remove_unused_columns=True,
    report_to="none",
    warmup_ratio=0.1,
    weight_decay=0.01,
    dataloader_num_workers=1,
    disable_tqdm=False,
    dataloader_pin_memory=True,
    max_grad_norm=1.0,
)

# --- 6. Trainer ---
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
)

# --- 7. 开始训练 ---
print("🚀 开始训练...")
trainer.train()

# --- 8. 保存 ---
print("💾 保存 LoRA 权重...")
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
print(f"✅ 训练完成！LoRA 权重已保存到: {output_dir}")