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