191 lines
12 KiB
Python
191 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.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 model.model_minimind_mla import MiniMindMLAConfig
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from dataset.lm_dataset import PretrainDataset
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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, get_model_suffix
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warnings.filterwarnings('ignore')
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def train_epoch(epoch, loader, iters, start_step=0, wandb=None):
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start_time = time.time()
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last_step = start_step
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for step, (input_ids, labels) in enumerate(loader, start=start_step + 1):
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input_ids = input_ids.to(args.device)
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labels = labels.to(args.device)
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last_step = step
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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(input_ids, labels=labels)
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loss = res.loss + res.aux_loss
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loss = loss / args.accumulation_steps
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#将loss放大后再反向传播，防止fp16下梯度下溢
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scaler.scale(loss).backward()
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if step % 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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#step是让优化器更新参数，同时更新优化器内部状态
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scaler.step(optimizer)
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#动态调整scale值，如果scale太大，loss会出现上溢，scale太小会发生下溢
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scaler.update()
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#把梯度全部设置为None，不用保留全0梯度的tensor，可以节省内存和加速下一次反向传播
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optimizer.zero_grad(set_to_none=True)
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if step % args.log_interval == 0 or step == iters:
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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_aux_loss = res.aux_loss.item() if res.aux_loss is not None else 0.0
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current_logits_loss = current_loss - current_aux_loss
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current_lr = optimizer.param_groups[-1]['lr']
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eta_min = spend_time / max(step - start_step, 1) * (iters - step) // 60
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Logger(f'Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), loss: {current_loss:.4f}, logits_loss: {current_logits_loss:.4f}, aux_loss: {current_aux_loss:.4f}, lr: {current_lr:.8f}, epoch_time: {eta_min:.1f}min')
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if wandb: wandb.log({"loss": current_loss, "logits_loss": current_logits_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) and is_main_process():
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model.eval()
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moe_suffix = get_model_suffix(lm_config)
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ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
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raw_model = model.module if isinstance(model, DistributedDataParallel) else model
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raw_model = getattr(raw_model, '_orig_mod', raw_model)
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state_dict = raw_model.state_dict()
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#转成fp16，减小文件大小，转到cpu，避免保存gpu tensor
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torch.save({k: v.half().cpu() for k, v in state_dict.items()}, 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 input_ids, labels, res, loss
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#处理还没更新的梯度
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if last_step > start_step and last_step % 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 __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind Pretraining")
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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='pretrain', type=str, help="保存权重的前缀名")
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parser.add_argument("--epochs", type=int, default=2, help="训练轮数")
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parser.add_argument("--batch_size", type=int, default=128, help="batch size")
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parser.add_argument("--learning_rate", type=float, default=5e-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=8, help="数据加载线程数")
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parser.add_argument("--accumulation_steps", type=int, default=8, 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=1000, help="模型保存间隔")
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parser.add_argument('--hidden_size', default=768, 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=340, 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('--use_mla', default=0, type=int, choices=[0, 1], help="是否使用MLA注意力架构（0=否，1=是）")
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parser.add_argument('--kv_lora_rank', default=128, type=int, help="MLA的KV压缩秩（仅use_mla=1时生效）")
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parser.add_argument("--data_path", type=str, default="../dataset/pretrain_t2t_mini.jsonl", help="预训练数据路径")
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parser.add_argument('--from_weight', default='none', type=str, help="基于哪个权重训练，为none则从头开始")
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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-Pretrain", help="wandb项目名")
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parser.add_argument("--use_compile", default=0, type=int, choices=[0, 1], help="是否使用torch.compile加速（0=否，1=是）")
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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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if args.use_mla:
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lm_config = MiniMindMLAConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=bool(args.use_moe), kv_lora_rank=args.kv_lora_rank)
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else:
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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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#根据设备自动选择是否转半精度。cuda自动混精度加速
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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-Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
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#project是实验的项目名称，id是指定或恢复一个实验运行的唯一标识符，resume是当提供的id存在时是否继续运行（'must'表示必须继续，None表示不继续）。如果提供了wandb_id并且resume设置为'must'，则会尝试恢复之前的运行，否则会创建一个新的运行。
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#id等于none时会认为正在启动一个全新的实验运行
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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、数据、优化器 ==========
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model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
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train_ds = PretrainDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
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#当处于分布式环境时，实例化DistributedSampler会将数据集切分成若干份，确保每个gpu只读取它自己那一部分数据
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train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
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#梯度缩放器，在用fp16计算时，可能会出现梯度下溢现象，GradScaler会将loss乘一个很大的数，从而放大梯度，在优化器更新权重之前，再把放大的梯度恢复到原本的大小
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#当dtype=float16时启动
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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. 编译和分布式包装 ==========
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if args.use_compile == 1:
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#这行代码会对模型进行编译，生成一个优化过的版本，优化过程包括算子融合、生成更高效的底层代码，让模型跑的更快
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model = torch.compile(model)
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Logger('torch.compile enabled')
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if dist.is_initialized():
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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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#set_epoch是在每个epoch重新设置数据集的采样顺序，会根据epoch的索引，重新设置随机数种子
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#使每个epoch产生不同的随机顺序，从而确保每个epoch用不同的样本顺序进行训练
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train_sampler and train_sampler.set_epoch(epoch)
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#代码中的分号；是python中的语法特性，可以用来在同一行中执行多个语句
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#torch.randperm是在生成一个随机打乱后的数据索引列表，会生成从0到len(train_ds)的随机排列，然后再转为list
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setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
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skip = start_step if (epoch == start_epoch and start_step > 0) else 0
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#dataset负责给它一个idx，返回一条数据，Sampler负责决定按上面顺序给dataset提供idx，所以sampler本质上就是一个索引生成器
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batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
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#这里传的是batch_sampler，dataloader不再自己决定batch怎么组成，而是听sampler的
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#根据sampler提供的索引来取数据
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loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
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if skip > 0:
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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) + skip, start_step, wandb)
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else:
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train_epoch(epoch, loader, len(loader), 0, wandb)
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# ========== 9. 清理分布进程 ==========
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if dist.is_initialized(): dist.destroy_process_group() |