llama-7b模型大小大约27G,本文在单张/两张 16G V100上基于hugging face的peft库实现了llama-7b的微调。
1、模型和数据准备
使用的大模型:https://huggingface.co/decapoda-research/llama-7b-hf,已经是float16的模型。
微调数据集:https://github.com/LC1332/Chinese-alpaca-lora/blob/main/data/trans_chinese_alpaca_data.json
微调的代码已上传到github:https://github.com/jiangxinyang227/LLM-tuning/tree/master/llama_tuning
2、微调技巧
1)lora微调。float16的模型刚刚好存放在16G的GPU上,没有太多显存用于存放梯度、优化器等参数,因此在这里使用lora微调部分参数。
2)混合精度训练,因为llama-7b有27g,想在单张V100上加载就需要转换成float16才行,而lora参数用的是float32,需要使用混合精度训练。同时混合精度训练也会有所加速。
3)梯度累积,单张gpu在存放完模型参数,lora参数、梯度、优化器等参数之后只剩下很少的显存给到输入输出等中间变量,经测试单张V100的极限大致是batch size=1,sequence length=200,只能使用梯度累积实现mini-batch训练。
4)当有多张卡时,可以使用数据并行、模型并行等方法微调,数据并行只是将模型复制到每张GPU上,因此单张GPU的batch size仍然只能是1,模型并行会将模型均分到每个GPU上,可以增大每张GPU上的batch size,在2张V100上测试了ddp(数据并行)和 基于zero-3 + cpu offload(数据并行+模型并行+CPU)。
3、要注意的代码讲解
3.1 data_helper.py
data_helper.py中主要注意下tokenizer()函数,一是padding是在左边padding,和我们通常的右边padding不太一样;二是labels中的pad_id=-100,因为pytorch中label=-100时不参与loss的计算。
def tokenize(self, prompt, add\_eos\_token=True):
# there's probably a way to do this with the tokenizer settings
# but again, gotta move fast
result = self.tokenizer(
prompt,
truncation\=True,
max\_length\=self.sequence\_len,
padding\=False,
return\_tensors\=None
)
input\_ids, attention\_mask, labels \= \[\], \[\], \[\]
if (
result\["input\_ids"\]\[-1\] != self.eos\_token\_id
and len(result\["input\_ids"\]) < self.sequence\_len
and add\_eos\_token
):
result\["input\_ids"\].append(self.eos\_token\_id)
result\["attention\_mask"\].append(1)
pad\_len \= self.sequence\_len - len(result\["input\_ids"\])
if pad\_len <= 0:
input\_ids \= result\["input\_ids"\]\[:self.sequence\_len\]
attention\_mask \= result\["attention\_mask"\]\[:self.sequence\_len\]
labels \= input\_ids.copy()
else:
input\_ids \= \[self.pad\_token\_id\] \* pad\_len + result\["input\_ids"\]
attention\_mask \= \[0\] \* pad\_len + result\["attention\_mask"\]
labels \= \[self.label\_pad\_token\_id\] \* pad\_len + result\["input\_ids"\]
return input\_ids, attention\_mask, labels
3.2 metric.py
在指标计算中只实现了准确率,在这里要注意的是生成任务是前n-1个token生成第n个token,因此这里的预测结果和label要做一次不同的移位,即
pred_y = pred_y[:-1]
true_y = true_y[1:]
只要注意这里就好了,剩下的你需要计算什么指标都可以。
def accuracy(pred\_ys, true\_ys, masks):
total \= 0
corr \= 0
for pred\_y, true\_y, mask in zip(pred\_ys, true\_ys, masks):
# 做一层转换,让生成的结果对应上预测的结果,即前n-1个token预测第n个token
pred\_y = pred\_y\[:-1\]
true\_y \= true\_y\[1:\]
mask \= mask\[:-1\]
for p, t, m in zip(pred\_y, true\_y, mask):
if m == 1:
total += 1
if p == t:
corr += 1
return corr / total if total > 0 else 0
4、训练方式
4.1 单GPU训练
单GPU训练很好理解,训练的时候只要注意下面的一段代码即可,混合精度训练+梯度累积
with autocast():
loss, predictions \= self.model(input\_ids, attention\_mask, labels)
# 梯度累积训练
loss /= self.accu\_steps
# loss.backward()
# 放大loss,并求梯度
scaled\_loss = self.scaler.scale(loss)
scaled\_loss.backward()
if current\_step % self.accu\_steps == 0:
# 先将梯度缩放回去,再执行梯度裁剪
self.scaler.unscale\_(self.optimizer)
clip\_grad\_norm\_(self.model.parameters(), 1.0)
self.scaler.step(self.optimizer)
self.scheduler.step()
self.scaler.update()
self.optimizer.zero\_grad()
4.2 多GPU + DDP训练
DDP训练也是大家最常用的方法,尤其是在模型没那么大的情况下,DDP训练就是主流,就不多赘述,在这里值得注意的是,每个GPU会分担一部分数据,在验证的时候如果需要拿到全部数据的验证结果并输出时,需要通过dist.all_gather 或者 dist.gather的方法将验证集的结果聚合到一块。详细代码见https://github.com/jiangxinyang227/LLM-tuning/blob/master/llama_tuning/lora_ddp/trainer.py
def eval(self):
self.model.eval()
with torch.no\_grad():
eval\_losses \= \[\]
eval\_word\_preds \= \[\]
eval\_word\_labels \= \[\]
eval\_masks \= \[\]
for batch\_data in self.valid\_data\_loader:
input\_ids \= batch\_data\[0\].cuda()
attention\_mask \= batch\_data\[1\].cuda()
labels \= batch\_data\[2\].cuda()
with autocast():
loss, predictions \= self.model(input\_ids, attention\_mask, labels)
# 获取所有gpu上输出的数据
avg\_loss\_multi\_gpu = reduce\_value(loss, average=True)
gather\_preds \= \[torch.zeros\_like(predictions, dtype=predictions.dtype) for \_ in range(Config.world\_size)\]
gather\_labels \= \[torch.zeros\_like(labels, dtype=labels.dtype) for \_ in range(Config.world\_size)\]
gather\_masks \= \[torch.zeros\_like(attention\_mask, dtype=attention\_mask.dtype) for \_ in range(Config.world\_size)\]
gather\_value(predictions, gather\_preds)
gather\_value(labels, gather\_labels)
gather\_value(attention\_mask, gather\_masks)
eval\_losses.append(float(avg\_loss\_multi\_gpu))
for pred, label, mask in zip(gather\_preds, gather\_labels, gather\_masks):
eval\_word\_preds.extend(pred.tolist())
eval\_word\_labels.extend(label.tolist())
eval\_masks.extend(mask.tolist())
if is\_main\_process():
acc \= accuracy(pred\_ys=eval\_word\_preds, true\_ys=eval\_word\_labels, masks=eval\_masks)
logger.info("\\n")
logger.info("eval: num: {}, loss: {}, acc: {}".format(
len(eval\_word\_preds), mean(eval\_losses), acc))
logger.info("\\n")
4.3 deepspeed的zero-3 + cpu offload
在这里使用的是hugging face的accelerate库中的deepspeed方法,zero-3会将模型、梯度、优化器参数都分割到不同的GPU,并且使用cpu offload将一些中间变量放到cpu上,经实测使用两张GPU时,每张GPU的使用大概5个G多一点,单张卡的batch size可以设置到8,但是在实际训练过程中速度比DDP还要慢一点,这里的原因还是因为模型并行、CPU offload等带来了大量的通信工作,所以单张gpu能存放一整个模型时还是首推DDP。
使用accelerate中的deepspeed时,首先要通过accelerate config这个命令互动式配置训练参数,以下是我在配置时选择的参数
![](https://img-blog.csdnimg.cn/img_convert/6569ad1e175665b879aee2d468e77aac.png)
在使用deepspeed时可以通过json文件去配置其他参数,accelerate config只配置一些通用参数。zero-3 + cpu offload的json文件如下,配置的时候有几个参数(如allgather_bucket_size 和 reduce_bucket_size)要设小一点,不然显存会爆掉,默认的值会比较大,主要是V100太小了。
{
"fp16": {
"enabled": true,
"loss\_scale": 0,
"loss\_scale\_window": 1000,
"initial\_scale\_power": 16,
"hysteresis": 2,
"min\_loss\_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": 3e-4,
"weight\_decay": 0.0
}
},
"scheduler": {
"type": "WarmupDecayLR",
"params": {
"warmup\_min\_lr": "auto",
"warmup\_max\_lr": "auto",
"warmup\_num\_steps": "auto",
"total\_num\_steps": "auto"
}
},
"zero\_optimization": {
"stage": 3,
"offload\_optimizer": {
"device": "cpu",
"pin\_memory": true
},
"offload\_param": {
"device": "cpu",
"pin\_memory": true
},
"overlap\_comm": true,
"contiguous\_gradients": true,
"allgather\_bucket\_size": 1e6, # 参数要小,不然容易内存爆掉
"reduce\_bucket\_size": 1e6, # 参数要小,不然容易内存爆掉
"stage3\_prefetch\_bucket\_size": 1e6, # 参数要小,不然容易内存爆掉
"stage3\_param\_persistence\_threshold": 1e6, # 参数要小,不然容易内存爆掉
"sub\_group\_size": 1e9,
"stage3\_max\_live\_parameters": 1e9,
"stage3\_max\_reuse\_distance": 1e9,
"stage3\_gather\_16bit\_weights\_on\_model\_save": true
},
"gradient\_accumulation\_steps": 1,
"gradient\_clipping": 1.0,
"steps\_per\_print": 2000,
"train\_batch\_size": "auto",
"train\_micro\_batch\_size\_per\_gpu": "auto",
"wall\_clock\_breakdown": false
}
在使用的时候有一个问题一直没有解决,保存模型时,保存完之后会出现GPU1掉线的情况,所以在这里将保存模型放在整个训练结束后保存,这个问题还没找到解决的办法,有知道怎么解的还麻烦指导下。
如果在运行时报这样的错误的话:
Traceback (most recent call last):
File "/mnt/workspace/project/llm/local\_proj/chatglm\_tune/lora\_deepspeed/trainer.py", line 271, in <module>
main()
File "/mnt/workspace/project/llm/local\_proj/chatglm\_tune/lora\_deepspeed/trainer.py", line 265, in main
trainer \= Trainer()
File "/mnt/workspace/project/llm/local\_proj/chatglm\_tune/lora\_deepspeed/trainer.py", line 93, in \_\_init\_\_
self.model, self.optimizer, self.train\_data\_loader, self.valid\_data\_loader, self.scheduler \= self.accelerator.prepare(
File "/home/pai/lib/python3.9/site-packages/accelerate/accelerator.py", line 1118, in prepare
result \= self.\_prepare\_deepspeed(\*args)
File "/home/pai/lib/python3.9/site-packages/accelerate/accelerator.py", line 1415, in \_prepare\_deepspeed
engine, optimizer, \_, lr\_scheduler \= deepspeed.initialize(\*\*kwargs)
File "/home/pai/lib/python3.9/site-packages/deepspeed/\_\_init\_\_.py", line 165, in initialize
engine \= DeepSpeedEngine(args=args,
File "/home/pai/lib/python3.9/site-packages/deepspeed/runtime/engine.py", line 308, in \_\_init\_\_
self.\_configure\_optimizer(optimizer, model\_parameters)
File "/home/pai/lib/python3.9/site-packages/deepspeed/runtime/engine.py", line 1173, in \_configure\_optimizer
self.optimizer \= self.\_configure\_zero\_optimizer(basic\_optimizer)
File "/home/pai/lib/python3.9/site-packages/deepspeed/runtime/engine.py", line 1463, in \_configure\_zero\_optimizer
optimizer \= DeepSpeedZeroOptimizer\_Stage3(
File "/home/pai/lib/python3.9/site-packages/deepspeed/runtime/zero/stage3.py", line 298, in \_\_init\_\_
largest\_partitioned\_param\_numel \= max(\[
File "/home/pai/lib/python3.9/site-packages/deepspeed/runtime/zero/stage3.py", line 299, in <listcomp>
max(\[max(tensor.numel(), tensor.ds\_numel) for tensor in fp16\_partitioned\_group\])
ValueError: max() arg is an empty sequence
具体原因不知道为什么会导致这样,可以进入到/home/pai/lib/python3.9/site-packages/deepspeed/runtime/zero/stage3.py(具体的路径看报错的日志)文件中,将
largest\_partitioned\_param\_numel = max(\[
max(\[max(tensor.numel(), tensor.ds\_numel) for tensor in fp16\_partitioned\_group\])
for fp16\_partitioned\_group in self.fp16\_partitioned\_groups
\])
改成
largest\_partitioned\_param\_numel = max(\[
max(\[max(tensor.numel(), tensor.ds\_numel) for tensor in fp16\_partitioned\_group\])
for fp16\_partitioned\_group in self.fp16\_partitioned\_groups if len (fp16\_partitioned\_group) > 0
\])
即可运行。