Transformer从菜鸟到新手(五)

引言

上篇文章我们在单卡上完成了完整的训练过程。

从本文开始介绍模型训练/推理上的一些优化技巧，本文主要介绍多卡并行训练。

下篇文章将介绍大模型推理常用的缓存技术。

多卡训练

第一个要介绍的是利用多GPU优化，因为在单卡上训练实在是太慢。这里使用的是PyTorch提供的DistributedDataParallel。

还有一种简单的方法是DataParallel，但效率没有DistributedDataParallel高。

DistributedDataParallel is proven to be significantly faster than torch.nn.DataParallel for single-node multi-GPU data parallel training.

分布式数据并行训练(Distributed Data Parallel Training, DDP)是一种广泛采用的单程序多数据训练范式。使用DDP，模型在每个进程上都被复制，每个模型副本将被提供不同的输入数据样本。DDP负责梯度通信，以保持模型副本同步，并将其与梯度计算重叠，以加快训练速度。

如果想让你的单GPU训练代码可并行化，而且只想做最少的改动，那么你可以选择DataParallel，但正如上面所说，它的效率不高。因此我们使用DistributedDataParallel来进一步加速训练。

由于只有单机资源，因此本文不会涉及多机训练，只关注单机多GPU。

我们先了解下将涉及到的几个术语：

• 主节点(master node)：负责同步、复制以及加载模型和记录日志的主GPU；
• 进程组(process group)：要并行训练的N个GPU组成一个组，由nccl后端支持；
• 排名(rank)：在进程组内，每个进程通过其排名进行标识，从0到N-1。rank=0为主节点；
• 世界大小(world size)：进程组内的进程数量，即GPU数量N；

DistributedDataParallel通过在每个模型副本之间同步梯度来提供数据并行，要同步的设备由输入process_group指定，默认情况下是所有设备(entire world)。注意DistributedDataParallel需要由用户指定如何对参与的GPU进行分片，比如通过使用DistributedSampler对数据进行分片。也就是说假设有N个GPU，我们可以对数据切分成N部分，每个GPU只需要处理原来$\frac{1}{N}$大小的数量，但批大小可以保持不变，从而加速训练过程。

假设在一个包含N个GPU的设备上。

多GPU示意图， 图片来自https://pytorch.org/tutorials/beginner/ddp_series_theory.html

首先通过torch.distributed.init_process_group来创建进程组；

我们接着需要创建(spawn)N个进程，并且要确保每个进程独占从0到N-1的单个GPU，可以通过为每个进程设置torch.cuda.set_device(i)来实现。要创建进程可以通过torch.multiprocessing.spawn来实现；

torch.distributed.init_process_group(
    backend='nccl', world_size=N, init_method='...'
)
model = DistributedDataParallel(model, device_ids=[i], output_device=i)

DistributedDataParallel可以与 torch.distributed.optim.ZeroRedundancyOptimizer 结合使用，以减少每个rank上优化器状态的内存占用。

nccl 后端目前是使用 GPU 时最快且最受推荐的后端，适用于单节点和多节点分布式训练。

当模型在M个节点上以 batch=N进行训练时，如果损失在一个批次中的样本之间进行求和（而不是常用的平均），梯那度将比在单个节点上以 batch=M*N 进行训练的相同模型小 M 倍（因为不同节点之间的梯度是平均的）。

当想要获得与本地训练对应的数学等价训练过程时，你应该考虑这一点。但在大多数情况下，可以将一个 DistributedDataParallel 包装的模型和一个普通的单 GPU 上的模型视为相同的（例如，可以为同样的批大小使用同样的学习率）。

参数永远不会在进程之间广播。该模块(DistributedDataParallel)对梯度执行全局归约(all-reduce)步骤，并假定它们将以相同的方式被优化器在所有进程中修改。缓冲(如BatchNorm统计信息)从rank为 0 的进程开始，在每次迭代中对系统中的所有副本进行广播。

总结一下，我们要做的事情是：

1. 设置进程组；
2. 拆分进程组内的数据加载器；
3. 通过DDP封装我们的模型；
4. 训练/测试模型，与单GPU相同；
5. 最后清理进程组，释放内存；

核心流程如下：

from argparse import ArgumentParser

import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.distributed import DistributedSampler

SEED = 42
BATCH_SIZE = 8
NUM_EPOCHS = 3

class YourDataset(Dataset):

    def __init__(self):
        pass


def main():
    parser = ArgumentParser('DDP usage example')
    parser.add_argument('--local_rank', type=int, default=-1, metavar='N', help='Local process rank.')  # you need this argument in your scripts for DDP to work
    args = parser.parse_args()

    # 记录当前进程是否为主节点
    args.is_master = args.local_rank == 0

    # 获取当期设备
    args.device = torch.cuda.device(args.local_rank)

    # 初始化进程组
    dist.init_process_group(backend='nccl', init_method='env://', world_size=N)
    # 设置GPU设备
    torch.cuda.set_device(args.local_rank)

    # 设置所有GPU的随机种子
    torch.cuda.manual_seed_all(SEED)

    # 初始化模型
    model = YourModel()

    # 将模型设置到GPU
    model = model.to(device)

    # 初始化DDP
    model = DDP(
        model,
        device_ids=[args.local_rank],
        output_device=args.local_rank
    )

    # 初始化数据集
    dataset = YourDataset()

    # 初始化分布式采样器
    sampler = DistributedSampler(dataset)

    # 基于分布式采样器初始化数据加载器
    dataloader = DataLoader(
        dataset=dataset,
        sampler=sampler,
        batch_size=BATCH_SIZE
    )

    # 开始训练
    for epoch in range(NUM_EPOCHS):
    
        model.train()

        # 在开始新epoch之前，让所有进程保持同步
        dist.barrier()

        for step, batch in enumerate(dataloader):
            # 将数据发送到对应的设备
            batch = tuple(t.to(args.device) for t in batch)
            
            # 正常的前向传播
            outputs = model(*batch)
            
            # 计算损失 假设是基于Transformers的模型，它会在第一个变量中返回损失
            loss = outputs[0]

if __name__ == '__main__':
    main()

下面来对单GPU训练代码进行改造。

首先额外引入三个包：

from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler
import torch.multiprocessing as mp

接着，定义一个函数用于初始化进程组：

def setup(rank: int, world_size: int) -> None:
    """

    Args:
        rank (int): within the process group, each process is identified by its rank, from 0 to world_size - 1
        world_size (int): the number of processes in the group
    """

    # Initialize the process group
    # world_size process forms a group which is supported by a backend(nccl)
    # rank 0 as master node
    # master node: the main gpu responsible for synchronizations, making copies, loading models, writing logs.
    dist.init_process_group("nccl", rank=rank, world_size=world_size)

同时定义清理函数：

def cleanup():
    "Cleans up the distributed environment"
    dist.destroy_process_group()

然后修改脚本入口代码：

if __name__ == "__main__":
    os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, train_args.gpus))

    # Sets up the process group and configuration for PyTorch Distributed Data Parallelism
    os.environ["MASTER_ADDR"] = "localhost"
    os.environ["MASTER_PORT"] = "12355"
    world_size = min(torch.cuda.device_count(), len(train_args.gpus))

    print(f"Number of GPUs used: {world_size}")

    mp.spawn(main, args=(world_size,), nprocs=world_size)

通过CUDA_VISIBLE_DEVICES环境变量设置可见的GPU；设置Master地址和端口；

调用spawn方法来创建进行，它需要传入要使用的GPU总数量，假设为N，它会依次创建rank=0到N-1的进程。

那么我们就看这个main函数是如何定义的。

def main(rank, world_size):
    print(f"Running  DDP on rank {rank}.")
	# 设置GPU设备
    torch.cuda.set_device(rank)
	
    setup(rank, world_size)
	# 加载分词器
    source_tokenizer, target_tokenizer = load_tokenizer(rank)
	# 设置随机种子
    set_random_seed(train_args.seed)
	# 获取训练集
    train_dataset = get_dataset(rank, source_tokenizer, target_tokenizer, "train")
    valid_dataset = get_dataset(rank, source_tokenizer, target_tokenizer, "dev")
	# 准备数据加载器
    train_dataloader = prepare_dataloader(
        train_dataset, rank, world_size, train_args.batch_size
    )
    valid_dataloader = prepare_dataloader(
        valid_dataset, rank, world_size, train_args.batch_size
    )
	# 定义模型并发送到设备rank上
    model = TranslationHead(
        model_args,
        target_tokenizer.pad_id(),
        target_tokenizer.bos_id(),
        target_tokenizer.eos_id(),
    ).to(rank)
	# 是否为master
    is_main_process = rank == 0
	# master负责打印
    if is_main_process:
        print(f"The model has {count_parameters(model)} trainable parameters")
	# 通过DDP封装model
    model = DDP(model, device_ids=[rank])
    # 获取封装的model
    module = model.module  # the wrapped model

    args = asdict(model_args)
    args.update(asdict(train_args))

    if train_args.use_wandb and is_main_process:
        import wandb

        # start a new wandb run to track this script
        wandb.init(
            # set the wandb project where this run will be logged
            project="transformer",
            config=args,
        )


    train_criterion = LabelSmoothingLoss(train_args.label_smoothing, model_args.pad_idx)
    
    valid_criterion = LabelSmoothingLoss(pad_idx=model_args.pad_idx)

    optimizer = torch.optim.Adam(
        model.parameters(), betas=train_args.betas, eps=train_args.eps
    )

    scheduler = WarmupScheduler(
        optimizer,
        warmup_steps=train_args.warmup_steps,
        d_model=model_args.d_model,
        factor=train_args.warmup_factor,
    )

    if train_args.calc_bleu_during_train:
        # bleu score
        early_stopper = EarlyStopper(mode="max", patience=train_args.patient)
        best_score = 0.0
    else:
        # dev loss
        early_stopper = EarlyStopper(mode="min", patience=train_args.patient)
        best_score = 1000

    if is_main_process:
        print(f"begin train with arguments: {args}")

        print(f"total train steps: {len(train_dataloader) * train_args.num_epochs}")

    for epoch in range(train_args.num_epochs):
        # 记录训练时长
        start = time.time()
        # 每个数据加载器的sampler需要指定当前的epoch
        train_dataloader.sampler.set_epoch(epoch)
        valid_dataloader.sampler.set_epoch(epoch)
		# 调用训练函数
        train_loss = train(
            model,
            train_dataloader,
            train_criterion,
            optimizer,
            train_args.grad_clipping,
            train_args.gradient_accumulation_steps,
            scheduler,
            rank,
        )

        if is_main_process:
            print()
            # 显示GPU利用率
            GPUtil.showUtilization()
		# 清除GPU缓存
        torch.cuda.empty_cache()
        if is_main_process:
            print("begin evaluate")
        valid_loss = evaluate(model, valid_dataloader, valid_criterion, rank)
        torch.cuda.empty_cache()

        if train_args.calc_bleu_during_train:
            if is_main_process:
                print("calculate bleu score for dev dataset")
            # 计算bleu得分
            valid_bleu_score = calculate_bleu(
                model.module,
                target_tokenizer,
                valid_dataloader,
                train_args.max_gen_len,
                rank,
                save_result=True,
                save_path="result-dev.txt",
            )
            torch.cuda.empty_cache()
            metric_score = valid_bleu_score
        else:
            valid_bleu_score = 0
            metric_score = valid_loss

        elapsed = time.time() - start
		# 每个GPU都打印信息
        print(
            f"[GPU{rank}] end of epoch {epoch+1:3d} [{elapsed:4.0f}s]| train loss: {train_loss:.4f} | valid loss: {valid_loss:.4f} |  valid bleu_score {valid_bleu_score:.2f}"
        )

        if is_main_process:
            if train_args.use_wandb:
                wandb.log(
                    {
                        "train_loss": train_loss,
                        "valid_bleu_score": valid_bleu_score,
                        "valid_loss": valid_loss,
                    }
                )
                wandb.save(f"result-dev.txt")
			
            if train_args.calc_bleu_during_train:
                if metric_score > best_score:
                    best_score = metric_score

                    print(f"Save model with best bleu score :{metric_score:.2f}")
                    # 保存验证集上bleu得分最好的模型
                    torch.save(module.state_dict(), train_args.model_save_path)
            else:
                if metric_score < best_score:
                    best_score = metric_score
                    print(f"Save model with best valid loss :{metric_score:.4f}")
                    torch.save(module.state_dict(), train_args.model_save_path)
			# 早停
            if early_stopper.step(metric_score):
                print(f"stop from early stopping.")
                break

    
    # 清理
    cleanup()

其中用到的一些函数定义如下。

准备数据加载器：

def prepare_dataloader(
    dataset, rank, world_size, batch_size, pin_memory=False, num_workers=0
):
    # 定义分布式采样器
    sampler = DistributedSampler(
        dataset, num_replicas=world_size, rank=rank, shuffle=False, drop_last=False
    )

    dataloader = DataLoader(
        dataset,
        batch_size=batch_size,
        pin_memory=pin_memory,
        num_workers=num_workers,
        collate_fn=dataset.collate_fn,
        drop_last=False,
        shuffle=False,
        sampler=sampler,
    )

    return dataloader

训练函数：

def train(
    model: nn.Module,
    data_loader: DataLoader,
    criterion: torch.nn.Module,
    optimizer: torch.optim.Optimizer,
    clip: float,
    gradient_accumulation_steps: int,
    scheduler: torch.optim.lr_scheduler._LRScheduler,
    rank: int,
) -> float:
    model.train()  # train mode

    # let all processes sync up before starting with a new epoch of training
    dist.barrier()

    total_loss = 0.0

    tqdm_iter = tqdm(data_loader)

    for step, batch in enumerate(tqdm_iter, start=1):
        # 发送到指定设备
        source, target, labels = [
            x.to(rank) for x in (batch.source, batch.target, batch.labels)
        ]
        logits = model(source, target)

        # loss calculation
        loss = criterion(logits, labels)

        loss.backward()
		# 支持梯度累积
        if step % gradient_accumulation_steps == 0:
            if clip:
                torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
            optimizer.step()
            optimizer.zero_grad(set_to_none=True)
            scheduler.step()

        total_loss += loss.item()

        description = f"[GPU{rank}] TRAIN  loss={loss.item():.6f}, learning rate={scheduler.get_last_lr()[0]:.7f}"

        del loss

        tqdm_iter.set_description(description)

    # average training loss
    avg_loss = total_loss / len(data_loader)

    return avg_loss

主要修改差不多就完了，更详细的可以访问文末的仓库地址。

下面基于一个调好的配置训练一下，看下效果：

class TrainArugment:
    """
    Create a 'data' directory and store the dataset under it
    """

    dataset_path: str = f"{os.path.dirname(__file__)}/data/wmt"
    save_dir = f"{os.path.dirname(__file__)}/model_storage"

    src_tokenizer_file: str = f"{save_dir}/source.model"
    tgt_tokenizer_path: str = f"{save_dir}/target.model"
    model_save_path: str = f"{save_dir}/best_transformer.pt"

    dataframe_file: str = "dataframe.{}.pkl"
    use_dataframe_cache: bool = True
    cuda: bool = True
    num_epochs: int = 40
    batch_size: int = 32
    gradient_accumulation_steps: int = 1
    grad_clipping: int = 0  # 0 dont use grad clip
    betas: Tuple[float, float] = (0.9, 0.98)
    eps: float = 1e-9
    label_smoothing: float = 0
    warmup_steps: int = 4000
    warmup_factor: float = 0.5
    only_test: bool = False
    max_gen_len: int = 60
    use_wandb: bool = False
    patient: int = 5
    gpus = [1, 2, 3]
    seed = 12345
    calc_bleu_during_train: bool = True

这里使用了3块RTX 3090GPU。

训练过程日志为：

Number of GPUs used: 3
Running  DDP on rank 1.
Running  DDP on rank 0.
source tokenizer size: 32000
target tokenizer size: 32000
Loads cached train dataframe.
Loads cached dev dataframe.
Loads cached test dataframe.
The model has 93255680 trainable parameters
begin train with arguments: {'d_model': 512, 'n_heads': 8, 'num_encoder_layers': 6, 'num_decoder_layers': 6, 'd_ff': 2048, 'dropout': 0.1, 'max_positions': 5000, 'source_vocab_size': 32000, 'target_vocab_size': 32000, 'pad_idx': 0, 'norm_first': True, 'dataset_path': 'nlp-in-action/transformers/transformer/data/wmt', 'src_tokenizer_file': 'nlp-in-action/transformers/transformer/model_storage/source.model', 'tgt_tokenizer_path': 'nlp-in-action/transformers/transformer/model_storage/target.model', 'model_save_path': 'nlp-in-action/transformers/transformer/model_storage/best_transformer.pt', 'dataframe_file': 'dataframe.{}.pkl', 'use_dataframe_cache': True, 'cuda': True, 'num_epochs': 40, 'batch_size': 32, 'gradient_accumulation_steps': 1, 'grad_clipping': 0, 'betas': (0.9, 0.98), 'eps': 1e-09, 'label_smoothing': 0, 'warmup_steps': 4000, 'warmup_factor': 0.5, 'only_test': False, 'max_gen_len': 60, 'use_wandb': True, 'patient': 5, 'calc_bleu_during_train': True}
total train steps: 73760
[GPU0] TRAIN  loss=7.039197, learning rate=0.0001612: 100%|██████████| 1844/1844 [03:51<00:00,  7.98it/s]
[GPU1] TRAIN  loss=7.088427, learning rate=0.0001612: 100%|██████████| 1844/1844 [03:58<00:00,  7.74it/s]

  0%|          | 0/264 [00:00<?, ?it/s]
| ID | GPU | MEM |
------------------
|  0 |  1% | 22% |
|  1 | 82% | 80% |
|  2 | 96% | 74% |
|  3 | 88% | 75% |
begin evaluate
100%|██████████| 264/264 [00:06<00:00, 38.75it/s]
100%|██████████| 264/264 [00:06<00:00, 38.41it/s]
calculate bleu score for dev dataset
100%|██████████| 264/264 [00:07<00:00, 37.36it/s]
100%|██████████| 264/264 [03:28<00:00,  1.27it/s]
 98%|█████████▊| 260/264 [03:30<00:03,  1.24it/s][GPU1] end of epoch   1 [ 457s]| train loss: 8.0777 | valid loss: 7.1328 |  valid bleu_score 0.44
100%|██████████| 264/264 [03:33<00:00,  1.23it/s]
100%|██████████| 264/264 [03:34<00:00,  1.23it/s]
[GPU2] end of epoch   1 [ 463s]| train loss: 8.0691 | valid loss: 7.1192 |  valid bleu_score 0.47
  0%|          | 0/1844 [00:00<?, ?it/s][GPU0] end of epoch   1 [ 456s]| train loss: 8.0675 | valid loss: 7.1118 |  valid bleu_score 0.42
Save model with best bleu score :0.42

[GPU0] end of epoch   2 [ 429s]| train loss: 6.5028 | valid loss: 5.8428 |  valid bleu_score 6.66
Save model with best bleu score :6.66


[GPU0] end of epoch   3 [ 422s]| train loss: 5.2749 | valid loss: 4.6848 |  valid bleu_score 16.72
Save model with best bleu score :16.72

[GPU0] end of epoch   4 [ 430s]| train loss: 4.3027 | valid loss: 4.1180 |  valid bleu_score 21.81
Save model with best bleu score :21.81

...

[GPU0] end of epoch  12 [ 415s]| train loss: 2.1461 | valid loss: 3.6046 |  valid bleu_score 26.98
Save model with best bleu score :26.98

[GPU0] end of epoch  17 [ 413s]| train loss: 1.6261 | valid loss: 3.7982 |  valid bleu_score 26.19
[GPU0] stop from early stopping.

wandb: | 3.412 MB of 3.412 MB uploaded
wandb: Run history:
wandb:       train_loss █▆▅▄▃▃▃▂▂▂▂▂▁▁▁▁▁
wandb: valid_bleu_score ▁▃▅▇▇▇███████████
wandb:       valid_loss █▆▃▂▂▁▁▁▁▁▁▁▁▁▁▁▁
wandb: 
wandb: Run summary:
wandb:       train_loss 1.62611
wandb: valid_bleu_score 26.19141
wandb:       valid_loss 3.79825

日志太多了，因此只摘录一部分，设置了随机种子，有条件的可以尝试复现。

从日志可以看到，在第12个epoch后就取得了验证集最佳得分26.98，并且每个epoch耗时从20分钟减少到了430秒，即7分钟左右， 基本上是减少了3倍，和GPU数量一致。

如果仔细分析每个epoch中耗时占比，会发现计算bleu得分和训练耗时和一样多，虽然我们已经对计算bleu得分进行批处理优化，但实际上我们还可以继续优化这个时间。

详见下篇文章~。

代码地址

完整代码点此