fix generater

1 files changed, 279 insertions, 0 deletions

diff --git a/train_gee_smart.py b/train_gee_smart.py
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+++ b/train_gee_smart.py
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+#!/usr/bin/env python3
+"""
+GEE训练脚本 - 使用智能平衡数据加载器
+绝对保证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
+from pathlib import Path
+
+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 smart_balanced_dataloader import create_smart_balanced_dataloader
+
+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=0.5, 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')
+    
+    # 模型参数
+    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=10, help='Maximum training steps')
+    parser.add_argument('--sample_temp', type=float, default=0.7, help='Generation temperature')
+    
+    # 运行参数
+    parser.add_argument('--run_name', type=str, default='gee_smart_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')
+    parser.add_argument('--save_steps', type=int, default=10, help='Save frequency')
+    
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+    set_seed(args.seed)
+    
+    # 强制检查batch_size
+    if args.micro_batch_size < 2:
+        print("❌ 错误: micro_batch_size必须>=2才能保证性别平衡！")
+        print("请使用: --micro_batch_size 2 或更大")
+        return
+    
+    # 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"🧠 开始智能GEE训练 - {args.run_name}")
+    print(f"📊 配置信息:")
+    print(f"   批次大小: micro={args.micro_batch_size}, effective={args.effective_batch}")
+    print(f"   Lambda权重: {args.lambda_weight}")
+    print(f"   最大步数: {args.max_steps}")
+    print(f"   智能平衡: ✅ 启用")
+
+    # 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 - 使用智能平衡数据加载器
+    if args.use_test_data:
+        print("📊 使用合成测试数据...")
+        train_data = gee_processor.create_test_data(num_samples=100)
+        
+        # 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}")
+        
+        # 创建智能平衡的数据加载器
+        train_loader = create_smart_balanced_dataloader(
+            train_data, 
+            batch_size=args.micro_batch_size, 
+            num_batches=args.max_steps + 5  # 额外的批次确保有足够数据
+        )
+    else:
+        print("❌ 请使用 --use_test_data 进行测试")
+        return
+
+    optimizer = AdamW(model.parameters(), lr=args.learning_rate)
+    
+    # 注意：智能数据加载器不需要accelerator.prepare处理
+    model, optimizer = accelerator.prepare(model, optimizer)
+    
+    print(f"🎯 开始训练...")
+    print(f"   期望看到: 每个批次都显示 '✅ 平衡批次'")
+    print(f"   不应该看到: '❌ 警告: 批次中没有男性/女性样本'")
+    
+    # 开始训练
+    model.train()
+    initial_entropy_gap = None
+    successful_steps = 0
+    failed_steps = 0
+    
+    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:
+                # 验证批次平衡性
+                male_count = sum(1 for g in batch["gender"] if g == 'male')
+                female_count = sum(1 for g in batch["gender"] if g == 'female')
+                
+                if male_count == 0 or female_count == 0:
+                    print(f"💥 Step {step}: 智能加载器失败！male={male_count}, female={female_count}")
+                    failed_steps += 1
+                    continue
+                else:
+                    successful_steps += 1
+                
+                # 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"gap={metrics['entropy_gap']:.6f} | "
+                              f"H_male={metrics['H_male']:.6f} | "
+                              f"H_female={metrics['H_female']:.6f} | "
+                              f"批次[{male_count}M,{female_count}F]")
+                        
+                        # 添加成功率统计
+                        success_rate = successful_steps / (successful_steps + failed_steps) * 100
+                        metrics['success_rate'] = success_rate
+                        
+                        wandb.log({"step": step, **metrics})
+                
+                # 保存检查点
+                if accelerator.is_main_process and step % args.save_steps == 0:
+                    ckpt = Path(f"checkpoints/{args.model_name}/{args.run_name}") / f"step_{step}"
+                    ckpt.mkdir(parents=True, exist_ok=True)
+                    accelerator.unwrap_model(model).save_pretrained(ckpt, safe_serialization=True)
+                    tokenizer.save_pretrained(ckpt)
+                    print(f"💾 检查点已保存: {ckpt}")
+                
+            except Exception as e:
+                print(f"❌ 训练步骤 {step} 出错: {e}")
+                failed_steps += 1
+                continue
+
+    if accelerator.is_main_process:
+        # 最终统计
+        total_steps = successful_steps + failed_steps
+        success_rate = successful_steps / total_steps * 100 if total_steps > 0 else 0
+        
+        print(f"\n🎉 智能GEE训练完成!")
+        print(f"📊 最终统计:")
+        print(f"   成功步数: {successful_steps}")
+        print(f"   失败步数: {failed_steps}")
+        print(f"   成功率: {success_rate:.1f}%")
+        
+        if success_rate >= 95:
+            print("✅ 智能平衡数据加载器工作完美！")
+        elif success_rate >= 80:
+            print("⚠️ 智能平衡数据加载器基本正常，偶有问题")
+        else:
+            print("❌ 智能平衡数据加载器需要进一步优化")
+        
+        # 保存最终模型
+        final = Path(f"checkpoints/{args.model_name}/{args.run_name}") / "final"
+        final.mkdir(parents=True, exist_ok=True)
+        accelerator.unwrap_model(model).save_pretrained(final, safe_serialization=True)
+        tokenizer.save_pretrained(final)
+        print(f"💾 最终模型已保存: {final}")
+        
+        wandb.finish()
+
+if __name__ == "__main__":
+    main() 
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