Delete train_pretrain.py
紫苏
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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 math
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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 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 contextlib import nullcontext
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from transformers import AutoTokenizer
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from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
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from dataset.lm_dataset import PretrainDataset
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warnings.filterwarnings('ignore')
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def Logger(content):
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if not ddp or dist.get_rank() == 0:
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print(content)
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def get_lr(current_step, total_steps, lr):
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return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps))
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loss_fct = nn.CrossEntropyLoss(reduction='none')
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def train_epoch(epoch, start_step, wandb):
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start_time = time.time()
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if epoch == start_epoch:
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train_loader_iter = iter(train_loader)
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for _ in range(start_step + 1):
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next(train_loader_iter)
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for step, (X, Y, loss_mask) in enumerate(train_loader_iter, start=start_step + 1):
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train_step(epoch, step, X, Y, loss_mask, wandb, start_time)
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else:
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for step, (X, Y, loss_mask) in enumerate(train_loader):
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train_step(epoch, step, X, Y, loss_mask, wandb, start_time)
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def train_step(epoch, step, X, Y, loss_mask, wandb, start_time):
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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 * iter_per_epoch + step, args.epochs * iter_per_epoch, 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 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_(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:
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spend_time = time.time() - start_time
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Logger(
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'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format(
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epoch + 1,
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args.epochs,
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step,
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iter_per_epoch,
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loss.item() * args.accumulation_steps,
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optimizer.param_groups[-1]['lr'],
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spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
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if (wandb is not None) and (not ddp or dist.get_rank() == 0):
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wandb.log({"loss": loss.item() * args.accumulation_steps,
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"lr": optimizer.param_groups[-1]['lr'],
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"epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
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if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
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model.eval()
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/pretrain_{lm_config.hidden_size}{moe_path}.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() for k, v in state_dict.items()} # 半精度保存
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torch.save(state_dict, ckp)
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# 保存训练状态
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training_state = {
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'epoch': epoch,
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'step': step,
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'optimizer_state_dict': optimizer.state_dict(),
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'scaler_state_dict': scaler.state_dict() if scaler is not None else None
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}
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training_state_path = f'{args.save_dir}/training_state.pth'
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torch.save(training_state, training_state_path)
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model.train()
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def init_model(lm_config):
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tokenizer = AutoTokenizer.from_pretrained('../model/')
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model = MiniMindForCausalLM(lm_config).to(args.device)
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Logger(f'LLM可训练总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
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return model, tokenizer
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def init_distributed_mode():
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if not ddp: return
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global ddp_local_rank, DEVICE
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dist.init_process_group(backend="nccl")
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ddp_rank = int(os.environ["RANK"])
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ddp_local_rank = int(os.environ["LOCAL_RANK"])
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ddp_world_size = int(os.environ["WORLD_SIZE"])
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DEVICE = f"cuda:{ddp_local_rank}"
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torch.cuda.set_device(DEVICE)
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# torchrun --nproc_per_node 2 1-pretrain.py
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if __name__ == "__main__":
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# 命令行参数解析
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parser = argparse.ArgumentParser(description="MiniMind Pretraining")
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parser.add_argument("--out_dir", type=str, default="../out") # 输出目录
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parser.add_argument("--epochs", type=int, default=8) # 训练轮次
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parser.add_argument("--batch_size", type=int, default=8) # 物理批次大小
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parser.add_argument("--learning_rate", type=float, default=5e-4)
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
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parser.add_argument("--dtype", type=str, default="bfloat16") # 混合精度类型
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parser.add_argument("--use_wandb", action="store_true") # 是否使用WandB
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parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain")
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parser.add_argument("--num_workers", type=int, default=1) # 数据加载线程数
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parser.add_argument("--ddp", action="store_true") # 是否启用DDP
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parser.add_argument("--accumulation_steps", type=int, default=4) # 梯度累积步数
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parser.add_argument("--grad_clip", type=float, default=1.0) # 梯度裁剪阈值
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parser.add_argument("--log_interval", type=int, default=100) # 日志间隔（步）
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parser.add_argument("--save_interval", type=int, default=100) # 保存间隔（步）
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parser.add_argument('--local_rank', type=int, default=-1) # DDP自动传入参数
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# 模型架构参数
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parser.add_argument('--hidden_size', default=512, type=int) # 隐藏层维度
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parser.add_argument('--num_hidden_layers', default=8, type=int) # 层数
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parser.add_argument('--max_seq_len', default=4096, type=int) # 序列长度
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parser.add_argument('--use_moe', default=False, type=bool) # 是否使用MoE
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parser.add_argument("--data_path", type=str, default="../dataset/pretrain_hq.jsonl")
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args = parser.parse_args()
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lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe)
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args.save_dir = os.path.join(args.out_dir)
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os.makedirs(args.save_dir, exist_ok=True)
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os.makedirs(args.out_dir, exist_ok=True)
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tokens_per_iter = args.batch_size * args.max_seq_len
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device_type = "cuda" if "cuda" in args.device else "cpu"
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args.wandb_run_name = f"MiniMind-Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
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ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
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ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run?
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ddp_local_rank, DEVICE = 0, "cuda:0"
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base_seed = 1337
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torch.manual_seed(base_seed)
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torch.cuda.manual_seed(base_seed)
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if ddp:
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init_distributed_mode()
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args.device = torch.device(DEVICE)
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rank = dist.get_rank()
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torch.manual_seed(base_seed + rank)
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# 同时设置 CUDA 的随机种子
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torch.cuda.manual_seed(base_seed + rank)
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if args.use_wandb and (not ddp or ddp_local_rank == 0):
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import wandb
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wandb.init(project=args.wandb_project, name=args.wandb_run_name)
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else:
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wandb = None
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model, tokenizer = init_model(lm_config)
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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 ddp else None
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train_loader = DataLoader(
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train_ds,
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batch_size=args.batch_size,
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pin_memory=True,
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drop_last=False,
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shuffle=False,
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num_workers=args.num_workers,
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sampler=train_sampler
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)
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scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
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optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
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if ddp:
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model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
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model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
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iter_per_epoch = len(train_loader)
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# 检查是否存在保存的训练状态文件
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training_state_path = f'{args.save_dir}/training_state.pth'
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if os.path.exists(training_state_path):
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training_state = torch.load(training_state_path)
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start_epoch = training_state['epoch']
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start_step = training_state['step']
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optimizer.load_state_dict(training_state['optimizer_state_dict'])
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if scaler is not None and training_state['scaler_state_dict'] is not None:
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scaler.load_state_dict(training_state['scaler_state_dict'])
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else:
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start_epoch = 0
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start_step = 0
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for epoch in range(start_epoch, args.epochs):
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train_epoch(epoch, start_step if epoch == start_epoch else 0, wandb)
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