注意
点击 这里 下载完整的示例代码
使用分布式数据并行和管道并行训练 Transformer 模型¶
作者: Pritam Damania
本教程演示了如何使用 分布式数据并行 和 管道并行 在多个 GPU 上训练大型 Transformer 模型。本教程是对 使用 nn.Transformer 和 TorchText 进行序列到序列建模 教程的扩展，并扩展了相同的模型以展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
定义模型¶
PositionalEncoding 模块注入有关序列中标记的相对或绝对位置的一些信息。位置编码的维度与嵌入相同，以便两者可以相加。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，我们将把一个 Transformer 模型拆分到两个 GPU 上，并使用管道并行来训练模型。除此之外，我们使用 分布式数据并行 来训练这两个管道的副本。我们有一个进程在 GPU 0 和 1 上驱动一个管道，另一个进程在 GPU 2 和 3 上驱动一个管道。这两个进程然后使用分布式数据并行来训练这两个副本。该模型与 使用 nn.Transformer 和 TorchText 的序列到序列建模 教程中使用的模型完全相同，但被分成两个阶段。大部分参数属于 nn.TransformerEncoder 层。 nn.TransformerEncoder 本身包含 nlayers 个 nn.TransformerEncoderLayer。因此，我们的重点是 nn.TransformerEncoder，我们将模型拆分为一半的 nn.TransformerEncoderLayer 在一个 GPU 上，另一半在另一个 GPU 上。为此，我们将 Encoder 和 Decoder 部分提取到单独的模块中，然后构建一个表示原始 Transformer 模块的 nn.Sequential。
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)
加载和批处理数据¶
训练过程使用 torchtext 中的 Wikitext-2 数据集。要访问 torchtext 数据集，请按照 https://github.com/pytorch/data 上的说明安装 torchdata。
词汇表对象是根据训练数据集构建的，用于将标记数值化为张量。从顺序数据开始， batchify() 函数将数据集排列成列，在数据被分成大小为 batch_size 的批次后，将任何剩余的标记剪掉。例如，使用字母表作为序列（总长度为 26）和批次大小为 4，我们将字母表分成 4 个长度为 6 的序列
\[ \begin{bmatrix} \text{A} & \text{B} & \text{C} & \ldots & \text{X} & \text{Y} & \text{Z} \end{bmatrix} \Rightarrow \begin{bmatrix} \begin{bmatrix}\text{A} \\ \text{B} \\ \text{C} \\ \text{D} \\ \text{E} \\ \text{F}\end{bmatrix} & \begin{bmatrix}\text{G} \\ \text{H} \\ \text{I} \\ \text{J} \\ \text{K} \\ \text{L}\end{bmatrix} & \begin{bmatrix}\text{M} \\ \text{N} \\ \text{O} \\ \text{P} \\ \text{Q} \\ \text{R}\end{bmatrix} & \begin{bmatrix}\text{S} \\ \text{T} \\ \text{U} \\ \text{V} \\ \text{W} \\ \text{X}\end{bmatrix} \end{bmatrix}\]
模型将这些列视为独立的，这意味着无法学习 G 和 F 的依赖关系，但可以实现更高效的批处理。
# 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)
生成输入和目标序列的函数¶
get_batch() 函数为 Transformer 模型生成输入和目标序列。它将源数据细分成长度为 bptt 的块。对于语言建模任务，模型需要以下词语作为 Target。例如，对于 bptt 值为 2，我们将为 i = 0 获得以下两个变量
需要注意的是，块是沿着维度 0 的，与 Transformer 模型中的 S 维度一致。批次维度 N 沿着维度 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
模型规模和管道初始化¶
为了演示使用管道并行训练大型 Transformer 模型，我们适当地扩展了 Transformer 层。我们使用 4096 的嵌入维度、4096 的隐藏大小、16 个注意力头和 8 个总 Transformer 层（nn.TransformerEncoderLayer）。这将创建一个具有 ~10 亿 个参数的模型。
我们需要初始化 RPC 框架，因为管道通过 RRef 依赖于 RPC 框架，这允许未来扩展到跨主机管道。我们需要仅用一个工作进程初始化 RPC 框架，因为我们使用一个进程来驱动多个 GPU。
然后用一个 GPU 上的 8 个 Transformer 层和另一个 GPU 上的 8 个 Transformer 层初始化管道。一个管道在 GPU 0 和 1 之间设置，另一个在 GPU 2 和 3 之间设置。然后使用 DistributedDataParallel 复制这两个管道。
# 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¶
交叉熵损失 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 用于在 epochs 中调整学习率。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，以防止梯度爆炸。
# 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)
循环遍历 epochs。如果验证损失是我们迄今为止见过的最佳损失，则保存模型。在每个 epoch 之后调整学习率。
# 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¶
应用最佳模型以使用测试数据集检查结果。
# 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 分钟 0.000 秒）
