【NLP练习】Transformer实战-单词预测

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊

任务：自定义输入一段英文文本进行预测

一、定义模型

from tempfile import TemporaryDirectory
from typing import Tuple
from torch import nn,Tensor
from torch.nn import TransformerEncoder, TransformerEncoderLayer
import math, os, torchclass TransformerModel(nn.Module):def __init__(self, ntoken: int, d_model: int, nhead: int, d_hid: int, nlayers: int, dropout: float = 0.5):super().__init__()self.pos_encoder = PositionalEncoding(d_model, dropout)#定义编码器层encoder_layers = TransformerEncoderLayer(d_model, nhead, d_hid, dropout)#定义编码器，pytorch将Transformer编码器进行了打包，这里直接调用即可self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)self.embedding = nn.Embedding(ntoken,d_model)self.d_model = d_modelself.linear = nn.Linear(d_model, ntoken)self.init_weights()#初始化权重def init_weights(self) -> None:initrange = 0.1self.embedding.weight.data.uniform_(-initrange, initrange)self.linear.bias.data.zeros_()self.linear.weight.data.uniform_(-initrange, initrange)def forward(self, src:Tensor, src_mask: Tensor = None) -> Tensor:"""Arguments:src:      Tensor, 形状为[seq_len, batch_size]src_mask: Tensor, 形状为[seq_len, seq_len]Returns:输出的Tensor，形状为[seq_len, batch_size, ntoken]"""src = self.embedding(src) * math.sqrt(self.d_model)src = self.pos_encoder(src)output = self.transformer_encoder(src, src_mask)output = self.linear(output)return output

class PositionalEncoding(nn.Module):def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):super().__init__()self.dropout = nn.Dropout(p = dropout)#生成位置编码的位置张量position = torch.arange(max_len).unsqueeze(1)#计算位置编码的除数项div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))#创建位置编码张量pe = torch.zeros(max_len, 1, d_model)#使用正弦函数计算位置编码中的基数维度部分pe[:, 0, 1::2] = torch.sin(position * div_term)#使用余弦函数计算位置编码中的偶数维度部分pe[:, 0, 1::2] = torch.cos(position * div_term)self.register_buffer('pe', pe)def forward(self, x: Tensor) -> Tensor:"""Arguments:x:      Tensor, 形状为[seq_len, batch_size, embedding_dim]"""#将位置编码添加到输入张量x = x + self.pe[:x.size(0)]#应用dropoutreturn self.dropout(x)

二、加载数据集

本实验使用torchtext生成Wikitext-2数据集。在此之前，你需要安装下面的包：

pip install portalocker
pip install torchdata

import torchtext
from torchtext.datasets.wikitext2 import WikiText2
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator#从torchtext库中导入WikiTetx2数据集
train_iter = WikiText2(split = 'train')#获取基本的英语分词器
tokenizer = get_tokenizer('basic_english')
#通过迭代器构建词汇表
vocab = build_vocab_from_iterator(map(tokenizer, train_iter), specials=['<unk>'])
#将默认索引设置为'<unk>'
vocab.set_default_index(vocab['<unk>'])def data_process(raw_text_iter: dataset.IterableDataset) -> Tensor:"""将原始文本转换为扁平的张量"""data = [torch.tensor(vocab(tokenizer(item)),dtype = torch.long) for item in raw_text_iter]return torch.cat(tuple(filer(lambda t: t.numel() > 0, data)))#由于构建词汇表时"train_iter"被使用了，所以需要重新创建
train_iter, val_iter, test_iter = WikiText2()#队训练、验证和测试数据进行处理
train_data = data_process(train_iter)
val_data = data_process(val_iter)
test_data = data_process(test_iter)#检查是否有可用的CUDA设备，将设备设置为GPU或者CPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')def batchify(data: Tensor, bsz: int) -> Tensor:"""将数据划分为bsz个单独的序列，去除不能完全容纳的额外元素。参数：data: Tensor，形状为``[N]``bsz：int，批大小返回：形状为[N // bsz, bsz]的张量"""seq_len = data.size(0) // bszdata = data[:seq_len * bsz]data = data.view(bsz, seq_len).t().contiguous()return data.to(device)#设置批大小和评估批大小
batch_size = 20
eval_batch_size = 10
#将训练、验证和测试数据进行批处理
train_data = batchify(train_data, batch_size)   #形状为[seq_len, batch_size]
val_data = batchify(val_data, eval_batch_size)
test_data = batchify(test_data, eval_batch_size)

bptt = 35#获取批次数据
def get_batch(source:Tensor, i: int) -> Tuple[Tensor, Tensor]:"""参数：source: Tensor，形状为``[full_seq_len, batch_size]``i : int, 当前批次索引返回：tuple(data, target),-data形状为[seq_len, batch_size],-target形状为[seq_len * batch_size]"""#计算当前批次的序列长度，最大为bptt，确保不超过source的长度seq_len = min(bptt, len(source) - 1 - i)#获取data，从i开始，长度为seq_lendata = source[i:i+seq_len]#获取target，从i+1开始，长度为seq_len，并将其形状转换为一维张量target = source[i+1:i+1+seq_len].reshape(-1)return data, target

三、初始化实例

ntokend = len(vocab)
emsize = 200
d_hid = 200
nlayers = 2
nhead = 2
dropout = 0.2
#创建transformer模型
model = TransformerModel(ntokend,emsize,nhead,d_hid,nlayers,dropout).to(device)

四、训练模型

结合使用CrossEntropyLoss与SGD（随机梯度下降优化器）。训练期间，使用torch.nn.utils.clip_grad_norm_来防止梯度爆炸

import time
criterion = nn.CrossEntropyLoss() #定义交叉熵损失函数
lr = 5.0
optimizer = torch.optim.SGD(model.parameters(),lr = lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gama = 0.95)def train(model: nn.Module) -> None:model.train() #开启训练模式total_loss = 0.log_interval = 200 #start_time = time.time()num_batches = len(train_data) // bpttfor batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):data, targets = get_batch(train_data, i)output = model(data)output_flat = output.view(-1, ntokens)loss = criterion(output_flat, targets) #计算损失optimizer.zero_grad()loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)optimizer.step()total_loss += loss.item()if batch % log_interval == 0 and batch > 0:lr = scheduler.get_last_lr()[0]ms_per_batch = (time.time() - start_time) * 1000 / log_intervalcur_loss = total_loss / log_intervalppl = math.exp(cur_loss)print(f'| epoch{epoch:3d} | {batch:5d} / {num_batches:5d} batches |'f'lr{lr:02.2f} | ms/batch {ms_per_batch:5.2f} |'f'loss {cur_loss:5.2f}|ppl{ppl:8.2f}')total_loss = 0start_time = time.time()def evaluate(model:nn.Module, eval_data:Tensor) -> float:model.eval()total_loss = 0.with torch.no_grad():for i in range(0,eval_data.size(0) - 1, bptt):data, targets = get_batch(eval_data,i)seq_len = data.size(0)output = model(data)output_flat = output.view(-1,ntokens)total_loss += seq_len * criterion(output_flat, targets).item()return total_loss / (len(eval_data) - 1)

best_val_loss = float('inf')
epochs = 1with TemporaryDirectory() as tempdir:best_model_params_path = os.path.join(tempdir, "best_model_params.pt")for epoch in range(1, epochs + 1):epoch_start_time = time.time()train(model)val_loss = evaluate(model, val_data)val_ppl = math_exp(val_loss)elapsed = time.time() - epoch_start_time#打印当前epoch的信息，包括耗时、验证损失和困惑度print('-' * 89)print(f'|end of epoch {epoch:3d} | time:{elapsed: 5.2f}s |'f'valid loss {val_loss:5.2f} | valid ppl {val_ppl: 8.2f}')print('-' * 89)if val_loss < best_val_loss:best_val_loss = val_losstorch.save(model.state_dict(), best_model_params_path)scheduler.step()    #更新学习率model.load_state_dict(torch.load(best_model_params_path))