#!/usr/bin/env python3 """ GEE训练脚本 - 使用平衡数据加载器 确保每个批次都包含男女样本 """ import argparse import os import torch import torch.nn.functional as F from torch.optim import AdamW import pandas as pd import numpy as np import wandb from accelerate import Accelerator, DeepSpeedPlugin from accelerate.utils import set_seed from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM # 导入自定义模块 import sys sys.path.append('.') from dataset.gee_processor import GEEProcessor from losses.gee_loss import GEELoss, gender_to_label from balanced_dataloader import create_balanced_dataloader, balanced_collate os.environ.setdefault("NCCL_TIMEOUT", "2700") os.environ.setdefault("TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC", "2700") def parse_args(): parser = argparse.ArgumentParser() # GEE相关参数 parser.add_argument('--lambda_weight', type=float, default=1.0, help='GEE lambda weight') parser.add_argument('--use_l1', action='store_true', help='Use L1 loss instead of L2') parser.add_argument('--auto_anneal', action='store_true', help='Use automatic annealing') # 简化参数 parser.add_argument('--model_name', type=str, default='Qwen2.5-Math-1.5B-Instruct', help='Model name') parser.add_argument('--model_path', type=str, required=True, help='Model path') parser.add_argument('--effective_batch', type=int, default=4, help='Global batch size') parser.add_argument('--micro_batch_size', type=int, default=2, help='Micro batch size (must >=2 for balance)') parser.add_argument('--learning_rate', type=float, default=2e-5, help='Learning rate') parser.add_argument('--max_steps', type=int, default=5, help='Maximum training steps') parser.add_argument('--sample_temp', type=float, default=0.7, help='Generation temperature') parser.add_argument('--run_name', type=str, default='gee_balanced', help='Run name') parser.add_argument('--wandb_project', type=str, default='one-shot-gee', help='W&B project name') parser.add_argument('--use_test_data', action='store_true', help='Use synthetic test data') parser.add_argument('--seed', type=int, default=42, help='Random seed') parser.add_argument('--log_steps', type=int, default=1, help='Logging frequency') return parser.parse_args() def main(): args = parse_args() set_seed(args.seed) # 检查batch_size if args.micro_batch_size < 2: print("⚠️ 警告: micro_batch_size < 2，无法保证性别平衡！") print("建议设置 --micro_batch_size 至少为 2") # DeepSpeed配置 ds_config = { "train_micro_batch_size_per_gpu": args.micro_batch_size, "train_batch_size": args.effective_batch, "gradient_accumulation_steps": max(1, args.effective_batch // args.micro_batch_size), "bf16": {"enabled": True}, "zero_optimization": { "stage": 2, "offload_optimizer": {"device": "cpu"}, "offload_param": {"device": "none"} }, "gradient_clipping": 1.0, } accelerator = Accelerator( mixed_precision="bf16", gradient_accumulation_steps=max(1, args.effective_batch // args.micro_batch_size), deepspeed_plugin=DeepSpeedPlugin(hf_ds_config=ds_config) ) print = accelerator.print print(f"🚀 开始平衡版GEE训练 - {args.run_name}") print(f"📊 批次配置: micro_batch={args.micro_batch_size}, effective_batch={args.effective_batch}") # 加载模型 model_path = args.model_path config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) config.use_cache = False model = AutoModelForCausalLM.from_pretrained(model_path, config=config, trust_remote_code=True) model.gradient_checkpointing_enable() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token # 初始化GEE处理器和损失函数 gee_processor = GEEProcessor(tokenizer) gee_loss_fn = GEELoss(lambda_weight=args.lambda_weight, use_l1=args.use_l1) if accelerator.is_main_process: wandb.init(project=args.wandb_project, name=args.run_name, config=vars(args)) # 准备数据 - 使用平衡数据加载器 if args.use_test_data: print("📊 使用合成测试数据...") train_data = gee_processor.create_test_data(num_samples=50) # 检查数据平衡性 male_count = sum(1 for item in train_data if item['gender'] == 'male') female_count = sum(1 for item in train_data if item['gender'] == 'female') print(f"原始数据: male={male_count}, female={female_count}") # 创建平衡的数据加载器 train_loader = create_balanced_dataloader( train_data, batch_size=args.micro_batch_size, num_batches=args.max_steps * 2 # 确保有足够的批次 ) else: print("❌ 请使用 --use_test_data 进行测试") return optimizer = AdamW(model.parameters(), lr=args.learning_rate) model, optimizer, train_loader = accelerator.prepare(model, optimizer, train_loader) print(f"🎯 训练配置:") print(f" Lambda权重: {args.lambda_weight}") print(f" 最大步数: {args.max_steps}") print(f" 学习率: {args.learning_rate}") # 开始训练 model.train() initial_entropy_gap = None for step, batch in enumerate(train_loader, start=1): if step > args.max_steps: print(f"🛑 达到最大步数 {args.max_steps}，训练结束") break with accelerator.accumulate(model): try: # 准备输入 inputs = tokenizer( batch["input"], return_tensors="pt", padding="longest", truncation=True, max_length=1024 ).to(accelerator.device) # 生成回答 with torch.no_grad(): gen_ids = accelerator.unwrap_model(model).generate( **inputs, max_new_tokens=128, do_sample=True, top_p=0.95, temperature=args.sample_temp, synced_gpus=True, repetition_penalty=1.15, pad_token_id=tokenizer.pad_token_id, use_cache=False ) # 准备完整序列 seq = torch.cat([inputs.input_ids, gen_ids[:, inputs.input_ids.shape[1]:]], dim=1) pad_mask = seq.ne(tokenizer.pad_token_id) prompt_lengths = pad_mask[:, :inputs.input_ids.shape[1]].sum(-1) # 计算logits和熵 logits = model(seq, attention_mask=pad_mask).logits H_tok = gee_loss_fn.compute_token_entropy(logits, pad_mask) H_i = gee_loss_fn.compute_sample_entropy(H_tok, prompt_lengths) # 准备性别标签 gender_labels = torch.tensor([ gender_to_label(g) for g in batch["gender"] ], device=accelerator.device) # 计算GEE损失 loss, metrics = gee_loss_fn.compute_gee_loss(H_i, gender_labels) # 自动退火（可选） if args.auto_anneal and initial_entropy_gap is None: initial_entropy_gap = metrics['entropy_gap'] if args.auto_anneal and initial_entropy_gap > 0: current_gap = metrics['entropy_gap'] anneal_factor = current_gap / initial_entropy_gap new_lambda = args.lambda_weight * anneal_factor gee_loss_fn.update_lambda(new_lambda) metrics['lambda_weight'] = new_lambda # 反向传播 accelerator.backward(loss) accelerator.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() optimizer.zero_grad() # 日志记录 if accelerator.is_main_process: if step % args.log_steps == 0: print(f"📈 Step {step} | loss={loss.item():.6f} | " f"entropy_gap={metrics['entropy_gap']:.6f} | " f"H_male={metrics['H_male']:.6f} | " f"H_female={metrics['H_female']:.6f} | " f"批次性别={batch['gender']}") wandb.log({"step": step, **metrics}) except Exception as e: print(f"❌ 训练步骤 {step} 出错: {e}") continue if accelerator.is_main_process: print("🎉 平衡版GEE训练完成!") wandb.finish() if __name__ == "__main__": main()