mirror of
https://github.com/jingyaogong/minimind.git
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214 lines
12 KiB
Python
214 lines
12 KiB
Python
import os
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import sys
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__package__ = "trainer"
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import argparse
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import time
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import warnings
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import torch
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import torch.nn.functional as F
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import torch.distributed as dist
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from contextlib import nullcontext
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from torch import optim
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from torch.nn.parallel import DistributedDataParallel
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from torch.utils.data import DataLoader, DistributedSampler
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from model.model_minimind import MiniMindConfig
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from dataset.lm_dataset import DPODataset
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from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler
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warnings.filterwarnings('ignore')
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def logits_to_log_probs(logits, labels):
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# logits shape: (batch_size, seq_len, vocab_size)
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# labels shape: (batch_size, seq_len)
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# log_probs shape: (batch_size, seq_len)
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log_probs = F.log_softmax(logits, dim=2)
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log_probs_per_token = torch.gather(log_probs, dim=2, index=labels.unsqueeze(2)).squeeze(-1)
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return log_probs_per_token
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def dpo_loss(ref_log_probs, policy_log_probs, mask, beta):
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# ref_log_probs 和 policy_log_probs 都是 shape: (batch_size, seq_len)
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# https://github.com/jingyaogong/minimind/issues/298
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seq_lengths = mask.sum(dim=1, keepdim=True).clamp_min(1e-8) # 防止零长度mask导致除零NaN
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ref_log_probs = (ref_log_probs * mask).sum(dim=1) / seq_lengths.squeeze()
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policy_log_probs = (policy_log_probs * mask).sum(dim=1) / seq_lengths.squeeze()
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# 将 chosen 和 rejected 数据分开
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batch_size = ref_log_probs.shape[0]
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chosen_ref_log_probs = ref_log_probs[:batch_size // 2]
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reject_ref_log_probs = ref_log_probs[batch_size // 2:]
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chosen_policy_log_probs = policy_log_probs[:batch_size // 2]
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reject_policy_log_probs = policy_log_probs[batch_size // 2:]
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pi_logratios = chosen_policy_log_probs - reject_policy_log_probs
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ref_logratios = chosen_ref_log_probs - reject_ref_log_probs
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logits = pi_logratios - ref_logratios
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loss = -F.logsigmoid(beta * logits)
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return loss.mean()
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def train_epoch(epoch, loader, iters, ref_model, lm_config, start_step=0, wandb=None, beta=0.1):
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start_time = time.time()
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for step, batch in enumerate(loader, start=start_step + 1):
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x_chosen = batch['x_chosen'].to(args.device)
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x_rejected = batch['x_rejected'].to(args.device)
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y_chosen = batch['y_chosen'].to(args.device)
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y_rejected = batch['y_rejected'].to(args.device)
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mask_chosen = batch['mask_chosen'].to(args.device)
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mask_rejected = batch['mask_rejected'].to(args.device)
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x = torch.cat([x_chosen, x_rejected], dim=0)
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y = torch.cat([y_chosen, y_rejected], dim=0)
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mask = torch.cat([mask_chosen, mask_rejected], dim=0)
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lr = get_lr(epoch * iters + step, args.epochs * iters, args.learning_rate)
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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with autocast_ctx:
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with torch.no_grad():
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ref_outputs = ref_model(x)
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ref_logits = ref_outputs.logits
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ref_log_probs = logits_to_log_probs(ref_logits, y)
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outputs = model(x)
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logits = outputs.logits
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policy_log_probs = logits_to_log_probs(logits, y)
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dpo_loss_val = dpo_loss(ref_log_probs, policy_log_probs, mask, beta=beta)
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loss = dpo_loss_val + outputs.aux_loss
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loss = loss / args.accumulation_steps
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scaler.scale(loss).backward()
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if (step + 1) % args.accumulation_steps == 0:
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad(set_to_none=True)
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if step % args.log_interval == 0 or step == iters - 1:
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spend_time = time.time() - start_time
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current_loss = loss.item() * args.accumulation_steps
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current_dpo_loss = dpo_loss_val.item()
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current_aux_loss = outputs.aux_loss.item()
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current_lr = optimizer.param_groups[-1]['lr']
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eta_min = spend_time / (step + 1) * iters // 60 - spend_time // 60
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Logger(f'Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), loss: {current_loss:.4f}, dpo_loss: {current_dpo_loss:.4f}, aux_loss: {current_aux_loss:.4f}, learning_rate: {current_lr:.8f}, epoch_time: {eta_min:.3f}min')
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if wandb: wandb.log({"loss": current_loss, "dpo_loss": current_dpo_loss, "aux_loss": current_aux_loss, "learning_rate": current_lr, "epoch_time": eta_min})
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if (step % args.save_interval == 0 or step == iters - 1) and is_main_process():
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model.eval()
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moe_suffix = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
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if isinstance(model, torch.nn.parallel.DistributedDataParallel):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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state_dict = {k: v.half().cpu() for k, v in state_dict.items()}
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torch.save(state_dict, ckp)
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lm_checkpoint(lm_config, weight=args.save_weight, model=model, optimizer=optimizer, scaler=scaler, epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints')
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model.train()
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del state_dict
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del x_chosen, x_rejected, y_chosen, y_rejected, mask_chosen, mask_rejected, x, y, mask
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del ref_outputs, ref_logits, ref_log_probs, outputs, logits, policy_log_probs, loss
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind DPO (Direct Preference Optimization)")
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parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录")
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parser.add_argument('--save_weight', default='dpo', type=str, help="保存权重的前缀名")
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parser.add_argument("--epochs", type=int, default=1, help="训练轮数")
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parser.add_argument("--batch_size", type=int, default=4, help="batch size")
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parser.add_argument("--learning_rate", type=float, default=4e-8, help="初始学习率（建议<=5e-8避免遗忘）")
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
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parser.add_argument("--num_workers", type=int, default=8, help="数据加载线程数")
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parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
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parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值")
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parser.add_argument("--log_interval", type=int, default=100, help="日志打印间隔")
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parser.add_argument("--save_interval", type=int, default=100, help="模型保存间隔")
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parser.add_argument('--hidden_size', default=512, type=int, help="隐藏层维度")
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parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量")
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parser.add_argument('--max_seq_len', default=1024, type=int, help="训练的最大截断长度（中文1token≈1.5~1.7字符）")
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parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构（0=否，1=是）")
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parser.add_argument("--data_path", type=str, default="../dataset/dpo.jsonl", help="DPO训练数据路径")
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parser.add_argument('--from_weight', default='full_sft', type=str, help="基于哪个权重训练")
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parser.add_argument('--from_resume', default=0, type=int, choices=[0, 1], help="是否自动检测&续训（0=否，1=是）")
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parser.add_argument('--beta', default=0.1, type=float, help="DPO中的beta参数")
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parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-DPO", help="wandb项目名")
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args = parser.parse_args()
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# ========== 1. 初始化环境和随机种子 ==========
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local_rank = init_distributed_mode()
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if dist.is_initialized(): args.device = f"cuda:{local_rank}"
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setup_seed(42 + (dist.get_rank() if dist.is_initialized() else 0))
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# ========== 2. 配置目录、模型参数、检查ckp ==========
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os.makedirs(args.save_dir, exist_ok=True)
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lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=bool(args.use_moe))
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ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
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# ========== 3. 设置混合精度 ==========
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device_type = "cuda" if "cuda" in args.device else "cpu"
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dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
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autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
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# ========== 4. 配wandb ==========
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wandb = None
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if args.use_wandb and is_main_process():
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import swanlab as wandb
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wandb_id = ckp_data.get('wandb_id') if ckp_data else None
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resume = 'must' if wandb_id else None
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wandb_run_name = f"MiniMind-DPO-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LR-{args.learning_rate}"
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wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume)
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# ========== 5. 定义模型和参考模型 ==========
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model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
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Logger(f'策略模型总参数量：{sum(p.numel() for p in model.parameters()) / 1e6:.3f} M')
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# 初始化参考模型（ref_model冻结）
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ref_model, _ = init_model(lm_config, args.from_weight, device=args.device)
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ref_model.eval()
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ref_model.requires_grad_(False)
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Logger(f'参考模型总参数量：{sum(p.numel() for p in ref_model.parameters()) / 1e6:.3f} M')
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train_ds = DPODataset(args.data_path, tokenizer, max_length=args.max_seq_len)
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train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
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scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
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optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
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# ========== 6. 从ckp恢复状态 ==========
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start_epoch, start_step = 0, 0
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if ckp_data:
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model.load_state_dict(ckp_data['model'])
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optimizer.load_state_dict(ckp_data['optimizer'])
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scaler.load_state_dict(ckp_data['scaler'])
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start_epoch = ckp_data['epoch']
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start_step = ckp_data.get('step', 0)
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# ========== 7. DDP包模型 ==========
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if dist.is_initialized():
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model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
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model = DistributedDataParallel(model, device_ids=[local_rank])
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# ========== 8. 开始训练 ==========
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for epoch in range(start_epoch, args.epochs):
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train_sampler and train_sampler.set_epoch(epoch)
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if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
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batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
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loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
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Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
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train_epoch(epoch, loader, len(loader) + start_step + 1, ref_model, lm_config, start_step, wandb, args.beta)
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else: # 默认从头开始
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loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
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train_epoch(epoch, loader, len(loader), ref_model, lm_config, 0, wandb, args.beta)
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