210 lines
12 KiB
Python
210 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 datasets # noqa: F401 # Windows pyarrow/torch DLL conflict workaround (issue #771)
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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 PretrainDataset
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from trainer.trainer_utils import (
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get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode,
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setup_seed, init_model, SkipBatchSampler,
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detect_gpu_peak_tflops_bf16, compute_model_flops_per_token,
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)
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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_log_time, last_log_step = start_time, start_step
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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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# torch.compile(mode="max-autotune") 会启用 CUDA graph capture 复用输出 buffer；
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# 训练循环里每步都是新 batch，必须显式 mark step 边界告诉 CUDAGraph "上一步的输出
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# 现在不能再复用了"。否则 backward 拿到被覆盖的 tensor 报 "accessing tensor output
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# of CUDAGraphs that has been overwritten" 错。torch 2.6+ 严格执行此检查。
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if args.use_compile == 1:
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torch.compiler.cudagraph_mark_step_begin()
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# non_blocking=True overlaps host->device copy with compute (pin_memory=True 已在 DataLoader)
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input_ids = input_ids.to(args.device, non_blocking=True)
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labels = labels.to(args.device, non_blocking=True)
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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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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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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:
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now = time.time()
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spend_time = now - start_time
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window_dt = max(now - last_log_time, 1e-6)
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window_steps = step - last_log_step
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window_its = window_steps / window_dt
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# MFU = 实际 FLOPs/s / GPU 峰值；按 window 内的 token 吞吐算（避开 warmup / ckpt save 污染）
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# tokens 用 per-rank batch × seq_len × world_size × window_steps（DDP 每卡同时处理 batch）
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tokens_window = window_steps * args.batch_size * args.max_seq_len * world_size
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mfu = (flops_per_token * tokens_window / window_dt) / (gpu_peak_tflops * 1e12) * 100
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last_log_time, last_log_step = now, step
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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'[{time.strftime("%H:%M:%S")}] Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), it/s: {window_its:.2f}, MFU: {mfu:.1f}%, 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 = '_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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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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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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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=32, 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("--data_path", type=str, default="../dataset/pretrain_t2t.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=1, type=int, choices=[0, 1], help="是否使用torch.compile加速（0=否，1=是，A100 上推荐默认 1）")
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parser.add_argument("--gpu_peak_tflops", type=float, default=0,
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help="GPU bf16 理论峰值 TFLOPS，用于 MFU 计算；默认 0 表示按 GPU 名自动检测（A100=312）")
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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-Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{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、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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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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# fused=True 把所有 param 的 Adam 更新融成一个 CUDA kernel（A100/3090 + CUDA tensor 支持）
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optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate, fused=True)
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# ========== 5b. MFU 测量准备 ==========
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flops_per_token = compute_model_flops_per_token(model, lm_config)
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gpu_peak_tflops = detect_gpu_peak_tflops_bf16(override=args.gpu_peak_tflops)
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world_size = dist.get_world_size() if dist.is_initialized() else 1
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Logger(f'[MFU] params_flops/token={flops_per_token/1e9:.3f} GFLOPS, '
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f'GPU peak={gpu_peak_tflops:.0f} TFLOPS bf16, world_size={world_size}')
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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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# 新 CUDA 13 stack 上 max-autotune 会为每个 GEMM shape 试 21 种 triton kernel（分钟级
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# autotune 循环，且实测 cuBLAS aten mm 几乎每次都赢过 triton 变体，autotune 白花时间）。
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# 用 default 更实惠——inductor 融合仍在，只是不额外扫 triton kernel。
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# (max-autotune 还需要 model 无 graph break，否则 CUDA graph 跨 step 覆盖崩溃；
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# 我们已消除 RoPE graph break，若将来单独 GEMM shape 少可再试 max-autotune)
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model = torch.compile(model)
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Logger('torch.compile enabled (mode=default)')
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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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train_sampler and train_sampler.set_epoch(epoch)
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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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batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
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loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers,
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pin_memory=True, persistent_workers=args.num_workers > 0)
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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():
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dist.barrier()
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dist.destroy_process_group()
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