260 lines
8.1 KiB
Python
260 lines
8.1 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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基于评分数据的LoRA增量训练脚本 - 简化版
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只保留核心的数据训练模型功能
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"""
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import json
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import torch
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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TrainingArguments,
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Trainer,
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DataCollatorForLanguageModeling
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)
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from peft import LoraConfig, get_peft_model, TaskType
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import os
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from typing import List, Dict
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import numpy as np
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from torch.utils.data import Dataset
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import swanlab
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from swanlab.integration.transformers import SwanLabCallback
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class DialogueDataset(Dataset):
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"""对话数据集类"""
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def __init__(self, samples: List[Dict], tokenizer, max_length: int = 512):
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self.samples = samples
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self.tokenizer = tokenizer
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self.max_length = max_length
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, idx):
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sample = self.samples[idx]
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text = f"角色: {sample['speaker']}\n对话内容: {sample['content']}"
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encoding = self.tokenizer(
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text,
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truncation=True,
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padding='max_length',
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max_length=self.max_length,
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return_tensors='pt'
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)
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return {
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'input_ids': encoding['input_ids'].flatten(),
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'attention_mask': encoding['attention_mask'].flatten(),
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'labels': encoding['input_ids'].flatten().clone()
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}
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class IterativeLoRATrainer:
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def __init__(self, config: Dict):
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self.config = config
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self.base_model_path = config['base_model_path']
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self.training_data_path = config['training_data_path']
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self.output_path = config['output_path']
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# 训练配置
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self.training_args = TrainingArguments(
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output_dir=self.output_path,
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overwrite_output_dir=True,
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num_train_epochs=config.get('epochs', 3),
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per_device_train_batch_size=config.get('batch_size', 2),
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gradient_accumulation_steps=config.get('gradient_accumulation_steps', 8),
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learning_rate=config.get('learning_rate', 5e-4),
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weight_decay=config.get('weight_decay', 0.01),
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warmup_steps=config.get('warmup_steps', 100),
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logging_steps=config.get('logging_steps', 10),
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save_steps=config.get('save_steps', 500),
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bf16=config.get('bf16', True),
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remove_unused_columns=False,
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report_to="none"
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)
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# LoRA配置
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self.lora_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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inference_mode=False,
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r=config.get('lora_r', 32),
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lora_alpha=config.get('lora_alpha', 64),
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lora_dropout=config.get('lora_dropout', 0.05),
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target_modules=config.get('target_modules', ["q_proj", "v_proj", "k_proj", "o_proj"]),
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bias="none"
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)
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torch.manual_seed(config.get('seed', 42))
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np.random.seed(config.get('seed', 42))
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def load_training_data(self):
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"""加载训练数据"""
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with open(self.training_data_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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return data['data']
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def prepare_training_samples(self, data):
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"""准备训练样本"""
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samples = []
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quality_threshold = self.config.get('quality_threshold', 7.0)
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for item in data:
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overall_score = self._calculate_overall_score(item.get('scores', {}))
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if overall_score >= quality_threshold:
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sample = {
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'speaker': item['speaker'],
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'content': item['content'],
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'quality_score': overall_score
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}
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samples.append(sample)
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print(f"筛选出高质量样本: {len(samples)}个")
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return samples
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def _calculate_overall_score(self, scores: Dict) -> float:
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"""计算综合评分"""
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if not scores:
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return 0.0
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weights = {
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'coherence': 0.3,
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'character_consistency': 0.25,
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'naturalness': 0.25,
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'information_density': 0.1,
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'creativity': 0.1
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}
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total_score = 0.0
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total_weight = 0.0
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for key, weight in weights.items():
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if key in scores:
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total_score += scores[key] * weight
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total_weight += weight
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return total_score / total_weight if total_weight > 0 else 0.0
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def train(self):
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"""执行训练"""
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print("开始LoRA增量训练...")
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# 创建输出目录
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os.makedirs(self.output_path, exist_ok=True)
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# 加载模型和分词器
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print(f"加载基础模型: {self.base_model_path}")
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tokenizer = AutoTokenizer.from_pretrained(
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self.base_model_path,
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trust_remote_code=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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self.base_model_path,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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)
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# 设置pad_token
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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model.config.pad_token_id = tokenizer.eos_token_id
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# 应用LoRA
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print("应用LoRA配置...")
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model = get_peft_model(model, self.lora_config)
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model.print_trainable_parameters()
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# 加载和准备训练数据
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training_data = self.load_training_data()
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training_samples = self.prepare_training_samples(training_data)
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if len(training_samples) == 0:
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raise ValueError("没有找到符合条件的训练样本")
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print(f"训练样本数量: {len(training_samples)}")
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# 创建数据集
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train_dataset = DialogueDataset(
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training_samples,
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tokenizer,
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self.config.get('max_length', 512)
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)
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# 创建数据整理器
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data_collator = DataCollatorForLanguageModeling(
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tokenizer=tokenizer,
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mlm=False
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)
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#添加swan监测
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swanlab_callback = SwanLabCallback(
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project = "QwenLora_Learn",
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experiment_name="Qwen3-8B-LoRA-experiment"
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)
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swanlab.login(api_key="pAxFTROvv3aspmEijax46")
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# 创建训练器
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trainer = Trainer(
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model=model,
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args=self.training_args,
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train_dataset=train_dataset,
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data_collator=data_collator,
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tokenizer=tokenizer,
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callbacks = [swanlab_callback]
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)
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# 开始训练
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print("开始训练...")
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trainer.train()
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# 保存模型
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print("保存训练完成的模型...")
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final_output_dir = os.path.join(self.output_path, "final_model")
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trainer.save_model(final_output_dir)
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tokenizer.save_pretrained(final_output_dir)
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print(f"训练完成，模型保存到: {self.output_path}")
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def main():
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config = {
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'base_model_path': '/mnt/g/Project02/AITrain/Qwen/Qwen3-4B',
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'training_data_path': './training_data/high_quality_dialogues_20250823_1819.json',
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'output_path': './output/iterative_lora_simple',
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# 训练参数
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'epochs': 3,
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'batch_size': 1,
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'gradient_accumulation_steps': 16,
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'learning_rate': 5e-6,
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'weight_decay': 0.01,
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'warmup_steps': 100,
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'max_length': 1024,
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'bf16': True,
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# LoRA参数
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'lora_r': 8,
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'lora_alpha': 8,
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'lora_dropout': 0.05,
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'target_modules': ["q_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
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# 数据参数
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'quality_threshold': 7.0,
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'logging_steps': 5,
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'save_steps': 100,
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'seed': 42
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}
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trainer = IterativeLoRATrainer(config)
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trainer.train()
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if __name__ == '__main__':
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main() |