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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
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。
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