mirror of
https://github.com/jingyaogong/minimind.git
synced 2026-01-13 19:57:20 +08:00
177 lines
9.2 KiB
Python
177 lines
9.2 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.distributed as dist
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from contextlib import nullcontext
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from torch import optim, nn
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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 SFTDataset
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from model.model_lora import save_lora, apply_lora
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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 train_epoch(epoch, loader, iters, lora_params, start_step=0, wandb=None):
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loss_fct = nn.CrossEntropyLoss(reduction='none')
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start_time = time.time()
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for step, (X, Y, loss_mask) in enumerate(loader, start=start_step + 1):
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X = X.to(args.device)
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Y = Y.to(args.device)
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loss_mask = loss_mask.to(args.device)
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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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res = model(X)
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loss = loss_fct(
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res.logits.view(-1, res.logits.size(-1)),
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Y.view(-1)
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).view(Y.size())
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loss = (loss * loss_mask).sum() / loss_mask.sum()
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loss += res.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_(lora_params, 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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torch.cuda.empty_cache()
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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_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:.6f} lr:{current_lr:.12f} epoch_Time:{eta_min}min:')
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if wandb: wandb.log({"loss": current_loss, "lr": 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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lora_save_path = f'{args.save_dir}/{args.lora_name}_{lm_config.hidden_size}.pth'
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# LoRA只保存LoRA权重
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save_lora(model, lora_save_path)
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lm_checkpoint(lm_config, weight=args.lora_name, 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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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind LoRA Fine-tuning")
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parser.add_argument("--save_dir", type=str, default="../out/lora", help="模型保存目录")
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parser.add_argument("--lora_name", type=str, default="lora_identity", help="LoRA权重名称(如lora_identity/lora_medical等)")
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parser.add_argument("--epochs", type=int, default=50, help="训练轮数")
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parser.add_argument("--batch_size", type=int, default=32, help="batch size")
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parser.add_argument("--learning_rate", type=float, default=1e-4, help="初始学习率")
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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=1, 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=10, help="日志打印间隔")
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parser.add_argument("--save_interval", type=int, default=1, 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=512, type=int, help="训练的最大截断长度")
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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/lora_identity.jsonl", help="LoRA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，默认full_sft")
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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("--use_wandb", action="store_true", help="是否使用wandb")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-LoRA", 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.lora_name, 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-LoRA-{args.lora_name}-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、应用LoRA、冻结非LoRA参数 ==========
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model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
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apply_lora(model)
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# 统计参数
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total_params = sum(p.numel() for p in model.parameters())
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lora_params_count = sum(p.numel() for name, p in model.named_parameters() if 'lora' in name)
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Logger(f"LLM 总参数量: {total_params / 1e6:.3f} M")
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Logger(f"LoRA 参数量: {lora_params_count / 1e6:.3f} M")
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Logger(f"LoRA 参数占比: {lora_params_count / total_params * 100:.2f}%")
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# 冻结非LoRA参数，收集LoRA参数
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lora_params = []
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for name, param in model.named_parameters():
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if 'lora' in name:
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param.requires_grad = True
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lora_params.append(param)
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else:
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param.requires_grad = False
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# ========== 6. 定义数据和优化器 ==========
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train_ds = SFTDataset(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(lora_params, lr=args.learning_rate)
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# ========== 7. 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'], strict=False)
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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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# ========== 8. 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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# ========== 9. 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, lora_params, start_step, wandb)
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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), lora_params, 0, wandb)
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