使用分布式数据并行和管道并行训练 Transformer 模型 ¶

译者：片刻小哥哥

项目地址：https://pytorch.apachecn.org/2.0/tutorials/advanced/ddp_pipeline

原始地址：https://pytorch.org/tutorials/advanced/ddp_pipeline.html

作者 : Pritam Damania

本教程演示如何使用 分布式数据并行 和 管道并行性 。本教程是使用 nn.Transformer 和 TorchText 进行序列到序列建模教程的扩展，并扩展了同一模型演示如何使用分布式数据并行和 管道并行来训练 Transformer 模型。

先决条件:

• 管道并行性
• 使用 nn.Transformer 和 TorchText 进行序列到序列建模
• 分布式数据并行入门

定义模型 ¶

-

PositionalEncoding 模块注入一些有关序列中标记的相对或绝对位置的信息。位置编码与嵌入具有相同的维度，因此可以将两者相加。在这里，我们使用不同频率的 sine 和 cosine 函数。

import sys
import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import tempfile
from torch.nn import TransformerEncoder, TransformerEncoderLayer

class PositionalEncoding(nn.Module):

    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.pe = nn.Parameter(pe, requires_grad=False)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

在本教程中，我们将在两个 GPU 上拆分 Transformer 模型，并使用 管道并行性来训练模型。除此之外，我们还使用分布式数据并行来训练该管道的两个副本。我们有一个进程跨 GPU 0 和 1 驱动管道，另一个进程跨 GPU 2 和 3 驱动管道。然后，这两个进程都使用分布式数据并行来训练两个副本。该模型与 使用 nn.Transformer 和 TorchText 进行序列到序列建模 教程中使用的模型完全相同，但是分为两个阶段。参数数量最多的属于 nn.TransformerEncoder 层。

[nn.TransformerEncoder] ](https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html) 本身由 nlayers 组成 nn.TransformerEncoderLayer . 因此，我们的重点是 nn.TransformerEncoder 并且我们分割模型 这样一半的\ n nn.TransformerEncoderLayer 位于一个 GPU 上， 另一半位于另一个 GPU 上。为此，我们将 Encoder 和 Decoder 部分提取到单独的模块中，然后构建 nn.Sequential 表示原始 Transformer 模块。

if sys.platform == 'win32':
    print('Windows platform is not supported for pipeline parallelism')
    sys.exit(0)
if torch.cuda.device_count() < 4:
    print('Need at least four GPU devices for this tutorial')
    sys.exit(0)

class Encoder(nn.Module):
    def __init__(self, ntoken, ninp, dropout=0.5):
        super(Encoder, self).__init__()
        self.pos_encoder = PositionalEncoding(ninp, dropout)
        self.encoder = nn.Embedding(ntoken, ninp)
        self.ninp = ninp
        self.init_weights()

    def init_weights(self):
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, src):
        # Need (S, N) format for encoder.
        src = src.t()
        src = self.encoder(src) * math.sqrt(self.ninp)
        return self.pos_encoder(src)

class Decoder(nn.Module):
    def __init__(self, ntoken, ninp):
        super(Decoder, self).__init__()
        self.decoder = nn.Linear(ninp, ntoken)
        self.init_weights()

    def init_weights(self):
        initrange = 0.1
        self.decoder.bias.data.zero_()
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, inp):
        # Need batch dimension first for output of pipeline.
        return self.decoder(inp).permute(1, 0, 2)

启动多个进程进行训练 ¶

我们启动两个进程，每个进程在两个 GPU 上驱动自己的管道。 每个进程都会执行 run_worker 。

def run_worker(rank, world_size):

加载并批处理数据 ¶

训练过程使用来自 torchtext 的 Wikitext-2 数据集。 要访问 torchtext 数据集，请按照以下位置的说明安装 torchdata https://github.com/pytorch/data 。\ n

vocab 对象是基于训练数据集构建的，用于将 token 数值化为tensor。从顺序数据开始， batchify() 函数将数据集排列成列，在数据被分成大小 batch_size 的批次后，修剪掉剩余的任何标记。\例如，以字母表为序列(总长度为 26) 且批量大小为 4，我们会将字母表分为 4 个 长度为 6 的序列：

[ \begin{bmatrix} \text{A} & \text{B} & \text{C} & \ldots & \text{X} & \text{Y } & \text{Z} \end{bmatrix} \Rightarrow \begin{bmatrix} \begin{bmatrix}\text{A} \ \text {B} \ \text{C} \ \text{D} \ \text{E} \ \text{F}\end{bmatrix} & \begin{bmatrix}\text{G} \ \text{H} \ \text{I} \ \text{J} \ \文本{K} \ \文本{L}\结束{bmatrix} & \开始{bmatrix}\文本{M} \ \文本{N} \ \ ext{O} \ \text{P} \ \text{Q} \ \text{R}\end{bmatrix} & \begin{bmatrix} \文本{S} \ \文本{T} \ \文本{U} \ \文本{V} \ \文本{W} \ \text{X}\end{bmatrix} \end{bmatrix}]

这些列被模型视为独立的，这意味着

G 的依赖性 和 F 无法学习，但允许更 高效的批处理。

# In 'run_worker'
    def print_with_rank(msg):
        print('[RANK {}]: {}'.format(rank, msg))

    from torchtext.datasets import WikiText2
    from torchtext.data.utils import get_tokenizer
    from torchtext.vocab import build_vocab_from_iterator

    train_iter = WikiText2(split='train')
    tokenizer = get_tokenizer('basic_english')
    vocab = build_vocab_from_iterator(map(tokenizer, train_iter), specials=["<unk>"])
    vocab.set_default_index(vocab["<unk>"])

    def data_process(raw_text_iter):
      data = [torch.tensor(vocab(tokenizer(item)), dtype=torch.long) for item in raw_text_iter]
      return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))

    train_iter, val_iter, test_iter = WikiText2()
    train_data = data_process(train_iter)
    val_data = data_process(val_iter)
    test_data = data_process(test_iter)

    device = torch.device(2 * rank)

    def batchify(data, bsz, rank, world_size, is_train=False):
        # Divide the dataset into ``bsz`` parts.
        nbatch = data.size(0) // bsz
        # Trim off any extra elements that wouldn't cleanly fit (remainders).
        data = data.narrow(0, 0, nbatch * bsz)
        # Evenly divide the data across the ``bsz`` batches.
        data = data.view(bsz, -1).t().contiguous()
        # Divide the data across the ranks only for training data.
        if is_train:
            data_per_rank = data.size(0) // world_size
            data = data[rank * data_per_rank : (rank + 1) * data_per_rank]
        return data.to(device)

    batch_size = 20
    eval_batch_size = 10
    train_data = batchify(train_data, batch_size, rank, world_size, True)
    val_data = batchify(val_data, eval_batch_size, rank, world_size)
    test_data = batchify(test_data, eval_batch_size, rank, world_size)

¶

get_batch() 函数生成变压器模型的输入和目标序列。它将源数据细分为 length bptt 的块。对于语言建模任务，模型需要 以下单词作为 Target 。例如， bptt 值为 2， we’d 会为 i = 0 获取以下两个变量：

需要注意的是，块沿着维度 0，与 中的 S 维度一致变压器模型。批次维度 N 沿维度 1。

# In 'run_worker'
    bptt = 35
    def get_batch(source, i):
        seq_len = min(bptt, len(source) - 1 - i)
        data = source[i:i+seq_len]
        target = source[i+1:i+1+seq_len].view(-1)
        # Need batch dimension first for pipeline parallelism.
        return data.t(), target

模型比例和管道初始化 ¶

为了演示使用管道并行性训练大型 Transformer 模型， 我们适当扩展 Transformer 层。我们使用嵌入维度为 4096、隐藏大小为 4096、16 个注意力头和总共 8 个转换器层 ( nn.TransformerEncoderLayer )。这将创建一个具有 ~10 亿 参数的模型。

我们需要初始化 RPC 框架 因为 Pipe 依赖于 RPC 框架 RRef 允许将来扩展到跨主机管道。由于我们’ 使用单个进程来驱动多个 GPU， 我们只需要使用单个工作线程来初始化 RPC 框架。

然后使用一个 GPU 上的 8 个转换器层和另一个 GPU 上的 8 个转换器层来初始化管道。一个管道跨 GPU 0 和 1 设置， 另一个管道跨 GPU 2 和 3 设置。然后使用 DistributedDataParallel 复制两个管道。

# In 'run_worker'
    ntokens = len(vocab) # the size of vocabulary
    emsize = 4096 # embedding dimension
    nhid = 4096 # the dimension of the feedforward network model in ``nn.TransformerEncoder``
    nlayers = 8 # the number of ``nn.TransformerEncoderLayer`` in ``nn.TransformerEncoder``
    nhead = 16 # the number of heads in the Multihead Attention models
    dropout = 0.2 # the dropout value

    from torch.distributed import rpc
    tmpfile = tempfile.NamedTemporaryFile()
    rpc.init_rpc(
        name="worker",
        rank=0,
        world_size=1,
        rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
            init_method="file://{}".format(tmpfile.name),
            # Specifying _transports and _channels is a workaround and we no longer
            # will have to specify _transports and _channels for PyTorch
            # versions >= 1.8.1
            _transports=["ibv", "uv"],
            _channels=["cuda_ipc", "cuda_basic"],
        )
    )

    # Number of GPUs for model parallelism.
    num_gpus = 2
    partition_len = ((nlayers - 1) // num_gpus) + 1

    # Add encoder in the beginning.
    tmp_list = [Encoder(ntokens, emsize, dropout).cuda(2 * rank)]
    module_list = []

    # Add all the necessary transformer blocks.
    for i in range(nlayers):
        transformer_block = TransformerEncoderLayer(emsize, nhead, nhid, dropout)
        if i != 0 and i % (partition_len) == 0:
            module_list.append(nn.Sequential(*tmp_list))
            tmp_list = []
        device = i // (partition_len)
        tmp_list.append(transformer_block.to(2 * rank + device))

    # Add decoder in the end.
    tmp_list.append(Decoder(ntokens, emsize).cuda(2 * rank + num_gpus - 1))
    module_list.append(nn.Sequential(*tmp_list))

    # Need to use 'checkpoint=never' since as of PyTorch 1.8, Pipe checkpointing
    # doesn't work with DDP.
    from torch.distributed.pipeline.sync import Pipe
    chunks = 8
    model = Pipe(torch.nn.Sequential(
        *module_list), chunks = chunks, checkpoint="never")

    # Initialize process group and wrap model in DDP.
    from torch.nn.parallel import DistributedDataParallel
    import torch.distributed as dist
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '29500'
    dist.init_process_group(
                backend="nccl", rank=rank, world_size=world_size)
    model = DistributedDataParallel(model)

    def get_total_params(module: torch.nn.Module):
        total_params = 0
        for param in module.parameters():
            total_params += param.numel()
        return total_params

    print_with_rank('Total parameters in model: {:,}'.format(get_total_params(model)))

运行模型 ¶

CrossEntropyLoss 用于跟踪损失， SGD 实现随机梯度下降法作为优化器。初始 学习率设置为 5.0。 StepLR 应用于 通过纪元调整学习率。在训练过程中，我们使用 nn.utils.clip_grad_norm_ 函数将所有梯度一起缩放以防止爆炸。

# In 'run_worker'
    criterion = nn.CrossEntropyLoss()
    lr = 5.0 # learning rate
    optimizer = torch.optim.SGD(model.parameters(), lr=lr)
    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)

    import time
    def train():
        model.train() # Turn on the train mode
        total_loss = 0.
        start_time = time.time()
        ntokens = len(vocab)

        # Train only for 50 batches to keep script execution time low.
        nbatches = min(50 * bptt, train_data.size(0) - 1)

        for batch, i in enumerate(range(0, nbatches, bptt)):
            data, targets = get_batch(train_data, i)
            optimizer.zero_grad()
            # Since the Pipe is only within a single host and process the ``RRef``
            # returned by forward method is local to this node and can simply
            # retrieved via ``RRef.local_value()``.
            output = model(data).local_value()
            # Need to move targets to the device where the output of the
            # pipeline resides.
            loss = criterion(output.view(-1, ntokens), targets.cuda(2 * rank + 1))
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
            optimizer.step()

            total_loss += loss.item()
            log_interval = 10
            if batch % log_interval == 0 and batch > 0:
                cur_loss = total_loss / log_interval
                elapsed = time.time() - start_time
                print_with_rank('| epoch {:3d} | {:5d}/{:5d} batches | '
                      'lr {:02.2f} | ms/batch {:5.2f} | '
                      'loss {:5.2f} | ppl {:8.2f}'.format(
                        epoch, batch, nbatches // bptt, scheduler.get_last_lr()[0],
                        elapsed * 1000 / log_interval,
                        cur_loss, math.exp(cur_loss)))
                total_loss = 0
                start_time = time.time()

    def evaluate(eval_model, data_source):
        eval_model.eval() # Turn on the evaluation mode
        total_loss = 0.
        ntokens = len(vocab)
        # Evaluate only for 50 batches to keep script execution time low.
        nbatches = min(50 * bptt, data_source.size(0) - 1)
        with torch.no_grad():
            for i in range(0, nbatches, bptt):
                data, targets = get_batch(data_source, i)
                output = eval_model(data).local_value()
                output_flat = output.view(-1, ntokens)
                # Need to move targets to the device where the output of the
                # pipeline resides.
                total_loss += len(data) * criterion(output_flat, targets.cuda(2 * rank + 1)).item()
        return total_loss / (len(data_source) - 1)

循环纪元。如果验证损失是迄今为止我们’见过的最好的，则保存模型。在每个时期后调整学习率。

# In 'run_worker'
    best_val_loss = float("inf")
    epochs = 3 # The number of epochs
    best_model = None

    for epoch in range(1, epochs + 1):
        epoch_start_time = time.time()
        train()
        val_loss = evaluate(model, val_data)
        print_with_rank('-' * 89)
        print_with_rank('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
              'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
                                         val_loss, math.exp(val_loss)))
        print_with_rank('-' * 89)

        if val_loss < best_val_loss:
            best_val_loss = val_loss
            best_model = model

        scheduler.step()

使用测试数据集评估模型 ¶

应用最佳模型来检查测试数据集的结果。

# In 'run_worker'
    test_loss = evaluate(best_model, test_data)
    print_with_rank('=' * 89)
    print_with_rank('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
        test_loss, math.exp(test_loss)))
    print_with_rank('=' * 89)

# Main execution
import torch.multiprocessing as mp

if __name__=="__main__":
    world_size = 2
    mp.spawn(run_worker, args=(world_size, ), nprocs=world_size, join=True)

输出 ¶

[RANK 0]: | epoch   1 |    10/   50 batches | lr 5.00 | ms/batch 778.97 | loss 43.31 | ppl 6432469059895903232.00
[RANK 1]: | epoch   1 |    10/   50 batches | lr 5.00 | ms/batch 778.90 | loss 44.50 | ppl 21245447128217366528.00
[RANK 0]: | epoch   1 |    20/   50 batches | lr 5.00 | ms/batch 699.89 | loss 44.50 | ppl 21176949187407757312.00
[RANK 1]: | epoch   1 |    20/   50 batches | lr 5.00 | ms/batch 699.87 | loss 44.62 | ppl 23975861229620961280.00
[RANK 0]: | epoch   1 |    30/   50 batches | lr 5.00 | ms/batch 698.86 | loss 41.62 | ppl 1193312915629888256.00
[RANK 1]: | epoch   1 |    30/   50 batches | lr 5.00 | ms/batch 698.87 | loss 40.69 | ppl 471605759847546240.00
[RANK 0]: | epoch   1 |    40/   50 batches | lr 5.00 | ms/batch 698.34 | loss 45.20 | ppl 42812308420836458496.00
[RANK 1]: | epoch   1 |    40/   50 batches | lr 5.00 | ms/batch 698.33 | loss 45.68 | ppl 68839569686012223488.00
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 1]: | end of epoch   1 | time: 40.08s | valid loss  0.80 | valid ppl     2.22
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 0]: | end of epoch   1 | time: 40.09s | valid loss  0.80 | valid ppl     2.22
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 0]: | epoch   2 |    10/   50 batches | lr 4.75 | ms/batch 768.51 | loss 36.34 | ppl 6063529544668166.00
[RANK 1]: | epoch   2 |    10/   50 batches | lr 4.75 | ms/batch 769.23 | loss 37.41 | ppl 17651211266236086.00
[RANK 0]: | epoch   2 |    20/   50 batches | lr 4.75 | ms/batch 699.57 | loss 28.97 | ppl 3798441739584.11
[RANK 1]: | epoch   2 |    20/   50 batches | lr 4.75 | ms/batch 699.56 | loss 29.28 | ppl 5203636967575.47
[RANK 0]: | epoch   2 |    30/   50 batches | lr 4.75 | ms/batch 699.04 | loss 28.43 | ppl 2212498693571.25
[RANK 1]: | epoch   2 |    30/   50 batches | lr 4.75 | ms/batch 699.05 | loss 28.33 | ppl 2015144761281.48
[RANK 0]: | epoch   2 |    40/   50 batches | lr 4.75 | ms/batch 699.10 | loss 23.30 | ppl 13121380184.92
[RANK 1]: | epoch   2 |    40/   50 batches | lr 4.75 | ms/batch 699.09 | loss 23.41 | ppl 14653799192.87
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 0]: | end of epoch   2 | time: 39.97s | valid loss  0.24 | valid ppl     1.27
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 1]: | end of epoch   2 | time: 39.98s | valid loss  0.24 | valid ppl     1.27
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 0]: | epoch   3 |    10/   50 batches | lr 4.51 | ms/batch 769.36 | loss 12.80 | ppl 361681.11
[RANK 1]: | epoch   3 |    10/   50 batches | lr 4.51 | ms/batch 768.97 | loss 12.57 | ppl 287876.61
[RANK 0]: | epoch   3 |    20/   50 batches | lr 4.51 | ms/batch 698.27 | loss 12.01 | ppl 164364.60
[RANK 1]: | epoch   3 |    20/   50 batches | lr 4.51 | ms/batch 698.30 | loss 11.98 | ppl 159095.89
[RANK 0]: | epoch   3 |    30/   50 batches | lr 4.51 | ms/batch 697.75 | loss 10.90 | ppl 54261.91
[RANK 1]: | epoch   3 |    30/   50 batches | lr 4.51 | ms/batch 697.72 | loss 10.89 | ppl 53372.39
[RANK 0]: | epoch   3 |    40/   50 batches | lr 4.51 | ms/batch 699.49 | loss 10.78 | ppl 47948.35
[RANK 1]: | epoch   3 |    40/   50 batches | lr 4.51 | ms/batch 699.50 | loss 10.79 | ppl 48664.42
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 0]: | end of epoch   3 | time: 39.96s | valid loss  0.38 | valid ppl     1.46
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 1]: | end of epoch   3 | time: 39.96s | valid loss  0.38 | valid ppl     1.46
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 0]: ===================================================================================

[RANK 0]: | End of training | test loss  0.33 | test ppl     1.39
[RANK 0]: ===================================================================================

[RANK 1]: ===================================================================================

[RANK 1]: | End of training | test loss  0.33 | test ppl     1.39
[RANK 1]: ===================================================================================

脚本的总运行时间: ( 0 分 0.000 秒)

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apachecn/AiLearning

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