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大模型入门(四)—— 基于peft 微调 LLaMa模型

  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。

参考资料:

https://www.cnblogs.com/jiangxinyang/p/17330352.html

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