import os import platform import time import math import warnings import torch import torch.distributed as dist from torch import optim from torch.nn.parallel import DistributedDataParallel from torch.optim.lr_scheduler import CosineAnnealingLR from torch.utils.data import DataLoader, DistributedSampler from contextlib import nullcontext from model.model import Transformer from model.LMConfig import LMConfig from model.dataset import PretrainDataset # 忽略警告信息 warnings.filterwarnings('ignore') # 定义日志打印函数,仅在主进程(rank 0)打印日志信息 def Logger(content): if not ddp or dist.get_rank() == 0: print(content) # 定义学习率调度函数,根据当前迭代次数计算学习率,采用余弦退火策略 def get_lr(it, all): warmup_iters = 0 # 预热迭代次数 lr_decay_iters = all # 学习率衰减的总迭代次数 min_lr = learning_rate / 10 # 最小学习率 # 如果当前迭代次数小于预热迭代次数,使用线性预热策略 if it < warmup_iters: return learning_rate * it / warmup_iters # 如果当前迭代次数大于衰减迭代次数,返回最小学习率 if it > lr_decay_iters: return min_lr # 计算衰减系数,使用余弦退火策略 decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters) assert 0 <= decay_ratio <= 1 coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) return min_lr + coeff * (learning_rate - min_lr) # 定义训练 epoch 的函数 def train_epoch(epoch, accumulation_steps=8): start_time = time.time() # 记录开始时间 for step, (X, Y) in enumerate(train_loader): # 遍历数据加载器 X = X.to(device) # 将输入数据移动到设备上 Y = Y.to(device) # 将目标数据移动到设备上 lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch) # 计算当前学习率 for param_group in optimizer.param_groups: param_group['lr'] = lr # 设置优化器的学习率 with ctx: # 使用混合精度训练(如果设备是 GPU) out = model(X, Y) # 前向传播,计算输出 loss = out.last_loss / accumulation_steps # 计算损失,并进行梯度累积 scaler.scale(loss).backward() # 反向传播,计算梯度 # 每 accumulation_steps 步进行一次梯度更新 if (step + 1) % accumulation_steps == 0: scaler.unscale_(optimizer) # 反缩放梯度 torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # 梯度裁剪 scaler.step(optimizer) # 更新模型参数 scaler.update() # 更新缩放器 optimizer.zero_grad(set_to_none=True) # 清空梯度 # 每 100 步打印一次训练信息 if step % 100 == 0: spend_time = time.time() - start_time # 计算已用时间 Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format( epoch, epochs, step, iter_per_epoch, loss.item() * accumulation_steps, optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) # 每 1000 步保存一次模型 if (step + 1) % 1000 == 0 and (not ddp or dist.get_rank() == 0): model.eval() # 切换到评估模式 # torch.save(model.state_dict(), '{}/iter_{}.pth'.format(save_dir, int(step + epoch * iter_per_epoch))) moe_path = '_moe' if lm_config.use_moe else '' # 根据是否使用 MoE 设置保存路径 ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() # 获取模型状态字典 else: state_dict = model.state_dict() torch.save(state_dict, ckp) # 保存模型 model.train() # 切换回训练模式 # 定义初始化模型的函数 def init_model(): def count_parameters(model): return sum(p.numel() for p in model.parameters() if p.requires_grad) # 计算模型可训练参数的数量 # 初始化模型 model = Transformer(lm_config).to(device) moe_path = '_moe' if lm_config.use_moe else '' # ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth' # # state_dict = torch.load(ckp, map_location=device) # unwanted_prefix = '_orig_mod.' # for k, v in list(state_dict.items()): # if k.startswith(unwanted_prefix): # state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) # model.load_state_dict(state_dict, strict=False) Logger(f'LLM总参数量:{count_parameters(model) / 1e6:.3f} 百万') # 打印模型总参数量 return model # 定义初始化分布式训练环境的函数 def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") # 初始化分布式进程组,使用 NCCL 后端 ddp_rank = int(os.environ["RANK"]) # 获取当前进程的 rank ddp_local_rank = int(os.environ["LOCAL_RANK"]) # 获取当前进程的本地 rank ddp_world_size = int(os.environ["WORLD_SIZE"]) # 获取分布式训练的总进程数 DEVICE = f"cuda:{ddp_local_rank}" # 设置当前设备的 CUDA 设备 torch.cuda.set_device(DEVICE) # 设置当前设备的 CUDA 设备 # torchrun --nproc_per_node 2 1-pretrain.py # I/O if __name__ == "__main__": # ----------------------------------------------------------------------------- lm_config = LMConfig() # 加载配置文件 max_seq_len = lm_config.max_seq_len # 获取最大序列长度 out_dir = 'out' # 设置输出目录 epochs = 20 # 设置训练 epoch 数 batch_size = 64 # 设置批量大小 learning_rate = 2e-4 # 设置初始学习率 device = 'cuda:0' # 设置设备为 CUDA:0 dtype = 'bfloat16' # 设置数据类型为 bfloat16 save_dir = os.path.join(out_dir) # 设置模型保存目录 os.makedirs(save_dir, exist_ok=True) # 创建模型保存目录 os.makedirs(out_dir, exist_ok=True) # 创建输出目录 tokens_per_iter = batch_size * max_seq_len # 计算每个迭代处理的 token 数量 torch.manual_seed(1337) # 设置随机种子 device_type = device if "cuda" in device else "cpu" # 设置设备类型 ctx = ( nullcontext() # 如果设备是 CPU,使用 nullcontext if device_type == "cpu" else torch.cuda.amp.autocast() # 如果设备是 GPU,使用混合精度训练 ) ddp = int(os.environ.get("RANK", -1)) != -1 # 判断是否为分布式训练 ddp_local_rank, DEVICE = 0, "cuda:0" # 初始化分布式训练的本地 rank 和设备 if ddp: init_distributed_mode() # 初始化分布式训练环境 device = torch.device(DEVICE) # 设置设备 # ----------------------------------------------------------------------------- # -----init dataloader------ data_path_list = ['./dataset/pretrain_data.bin'] # 设置数据路径 train_ds = PretrainDataset(data_path_list, max_length=max_seq_len, memmap=True) # 初始化数据集 train_sampler = DistributedSampler(train_ds) if ddp else None # 如果是分布式训练,使用分布式采样器 num_workers = 8 # 设置数据加载器的 num_workers train_loader = DataLoader( train_ds, batch_size=batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=num_workers, sampler=train_sampler ) # 初始化数据加载器 # init model model = init_model() # 初始化模型 scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype)) # 初始化梯度缩放器 # optimizer optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 初始化优化器 # compile the model if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2: Logger("compiling the model... (takes a ~minute)") unoptimized_model = model model = torch.compile(model) # 编译模型(如果条件满足) if ddp: # Ignore the freqs_cis buffer so that DDP does not broadcast it at # construction time since NCCL does not support ComplexFloat model._ddp_params_and_buffers_to_ignore = {"pos_cis"} # 设置 DDP 忽略的参数和缓冲区 model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) # 使用 DDP 包装模型 # training loop iter_per_epoch = len(train_loader) # 计算每个 epoch 的迭代次数 for epoch in range(epochs): # 遍历每个 epoch train_epoch(epoch) # 训练一个 epoch