使用管道并行性训练 Transformer 模型 ¶

译者：片刻小哥哥

项目地址：https://pytorch.apachecn.org/2.0/tutorials/intermediate/pipeline_tutorial

原始地址：https://pytorch.org/tutorials/intermediate/pipeline_tutorial.html

作者 : Pritam Damania

本教程演示如何使用管道并行性跨 多个 GPU 训练大型 Transformer 模型。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管道并行性 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

定义模型 ¶

-

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。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。

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

if sys.platform == 'win32':
    print('Windows platform is not supported for pipeline parallelism')
    sys.exit(0)
if torch.cuda.device_count() < 2:
    print('Need at least two 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)

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。

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.register_buffer('pe', pe)

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

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：

[\开始{bmatrix} \文本{A} & \文本{B} & \文本{C} & \ldots & \文本{X} & \文本{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。

import torch
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("cuda")

def batchify(data, bsz):
    # 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()
    return data.to(device)

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

生成输入和目标序列的函数 ¶

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。

bptt = 25
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 12 个转换器层 ( nn.TransformerEncoderLayer )。这将创建一个具有 ~14 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 转换器层来初始化管道。

注意

为了提高效率，我们确保 nn.Sequential 传递给 Pipe 仅包含两个元素(对应于两个 GPU)，这 允许 Pipe 仅处理两个分区并避免任何 跨分区开销。

ntokens = len(vocab) # the size of vocabulary
emsize = 4096 # embedding dimension
nhid = 4096 # the dimension of the feedforward network model in ``nn.TransformerEncoder``
nlayers = 12 # 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"],
    )
)

num_gpus = 2
partition_len = ((nlayers - 1) // num_gpus) + 1

# Add encoder in the beginning.
tmp_list = [Encoder(ntokens, emsize, dropout).cuda(0)]
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(device))

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

from torch.distributed.pipeline.sync import Pipe

# Build the pipeline.
chunks = 8
model = Pipe(torch.nn.Sequential(*module_list), chunks = chunks)


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

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

Total parameters in model: 1,444,261,998

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。

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(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('| 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_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(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。

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('-' * 89)
    print('| 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('-' * 89)

    if val_loss < best_val_loss:
        best_val_loss = val_loss
        best_model = model

    scheduler.step()

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:384: UserWarning:

To get the last learning rate computed by the scheduler, please use `get_last_lr()`.

| epoch   1 |    10/   50 batches | lr 5.00 | ms/batch 2983.68 | loss 51.97 | ppl 37278238304344674926592.00
| epoch   1 |    20/   50 batches | lr 5.00 | ms/batch 2660.24 | loss 39.16 | ppl 101468412802272112.00
| epoch   1 |    30/   50 batches | lr 5.00 | ms/batch 2655.40 | loss 45.74 | ppl 73373605537851539456.00
| epoch   1 |    40/   50 batches | lr 5.00 | ms/batch 2658.85 | loss 39.05 | ppl 90831844662671120.00
-----------------------------------------------------------------------------------------
| end of epoch   1 | time: 151.85s | valid loss  1.59 | valid ppl     4.92
-----------------------------------------------------------------------------------------
| epoch   2 |    10/   50 batches | lr 4.51 | ms/batch 2926.71 | loss 38.92 | ppl 79792098193225456.00
| epoch   2 |    20/   50 batches | lr 4.51 | ms/batch 2664.65 | loss 33.86 | ppl 508484255367480.44
| epoch   2 |    30/   50 batches | lr 4.51 | ms/batch 2663.05 | loss 29.47 | ppl 6267626426289.98
| epoch   2 |    40/   50 batches | lr 4.51 | ms/batch 2665.02 | loss 20.07 | ppl 521065165.54
-----------------------------------------------------------------------------------------
| end of epoch   2 | time: 151.45s | valid loss  0.54 | valid ppl     1.71
-----------------------------------------------------------------------------------------
| epoch   3 |    10/   50 batches | lr 4.29 | ms/batch 2926.95 | loss 13.75 | ppl 935925.21
| epoch   3 |    20/   50 batches | lr 4.29 | ms/batch 2663.29 | loss 10.74 | ppl 46322.74
| epoch   3 |    30/   50 batches | lr 4.29 | ms/batch 2659.67 | loss 10.97 | ppl 58152.80
| epoch   3 |    40/   50 batches | lr 4.29 | ms/batch 2665.57 | loss 11.29 | ppl 80130.60
-----------------------------------------------------------------------------------------
| end of epoch   3 | time: 151.42s | valid loss  0.24 | valid ppl     1.27
-----------------------------------------------------------------------------------------

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。

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

===================================================================================

| End of training | test loss  0.21 | test ppl     1.23
===================================================================================

脚本总运行时间： (8分15.341秒)

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