197 lines
8.7 KiB
Python
197 lines
8.7 KiB
Python
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 |