[翻译Pytorch教程]NLP部分：使用TorchText进行语言翻译
翻译自官网教程:LANGUAGE TRANSLATION WITH TORCHTEXT
本教程展示了如何使用torchtext中几个方便的类对包含英语和德语句子对的知名数据集进行预处理，并用其训练一个将德语句子翻译成英语的包含注意力机制的序列到序列模型。
本教程基于来自PyTorch社区成员Ben Trevett的这个教程，经过Ben Trevett的允许由Seth Weidman创建。
完成本教程，你可以学到：
- 使用随后的
torchtext中方便的类将句子预处理成NLP模型的通用格式。- TranslationDataset
- Field
- BucketIterator
Field和TranslationDataset
torchtext 可以创建易于迭代的数据集用于创建语言翻译模型。一个重要的类是Field,其可以指定每个句子如何被处理，另一个是TranslationDataset；torchtext有几个这样的数据集；本教程中使用的是Multi30k dataset数据集，其包含大约30,000个英语和德语的句子对（平均长度约为13）。
注意： 本教程中的标记化（tokenization）依赖Spacy使用Spacy是因为其提供了除了英语外的强有力的标记化支持。torchtext提供了basic_english标记化及其他英语标记化支持（如，Moses）,但是对于语言翻译——需要多种语言，Spacy是最佳选择。
运行本教程，需要使用pip或conda安装spacy。然后下载用于英语和德语Spacy标记化的原始数据。
python -m spacy download en
python -m spacy download de
安装了Spacy之后，随后的代码将基于Field中定义的标记器（tokenizer）将TranslationDataset中的每个句子进行标记化。
%matplotlib inline
注意： 由于网络原因，自动下载Multi30k数据集失败，可以在这里下载该数据集并放到.data/目录下。
from torchtext.datasets import Multi30k
from torchtext.data import Field, BucketIterator
SRC = Field(tokenize = "spacy",
tokenizer_language="de_core_news_sm",
init_token = '' ,
eos_token = '' ,
lower = True)
TRG = Field(tokenize = "spacy",
tokenizer_language="en_core_web_sm",
init_token = '' ,
eos_token = '' ,
lower = True)
train_data, valid_data, test_data = Multi30k.splits(exts = ('.de', '.en'),
fields = (SRC, TRG))
一旦定义了train_data,可以看到torchtext的Field十分有用的特征：build_vocab方法允许我们创建与每种语言相关的词典。
SRC.build_vocab(train_data, min_freq = 2)
TRG.build_vocab(train_data, min_freq = 2)
一旦这些代码被运行，SRC.vocab.stoi将成为键是词汇表中符号的字典且值为其对应的索引；SRC.vocab.itos将成为键和值交换的相同的字典。本教程不会大量使用这个特性，但其在其他NLP任务中可能非常有用。
BucketIterator
我们将要用到的torchtext的最后一个具体特性是BucketIterator，由于它接收TranslationDataset作为其第一个参数，它十分易用。特别是，如说明所述：定义一个可以将相似长度的样本分成一个批次的迭代器。当为每个新的epoch生成重新洗牌的批次时可以降低填充数量。见分装过程中使用的池操作（原句：See pool for the bucketing procedure used）。
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
BATCH_SIZE = 128
train_iterator, valid_iterator, test_iterator = BucketIterator.splits(
(train_data, valid_data, test_data),
batch_size = BATCH_SIZE,
device = device)
迭代器可以像DataLoader一样被调用；如下，在train和evaluate函数中，其调用很简单：
for i, batch in enumerate(iterator):
每个batch 就有了src和trg属性:
src = batch.src
trg = batch.trg
定义自己的nn.Module和Optimizer
这些大部分都来自torchtext的全景图：创建了数据集及定义了迭代器（iterator），教程的的剩余部分只需要简单定义模型，如nn.Module，连同一个优化器Optimizer，然后训练它。
本文的模型是根据这里描述的结构定义的（可以在这里找到更推荐的版本）。
注意：这个模型仅仅是个用于语言翻译的样例模型；选择它是因为它是一个翻译任务的标准模型，不是因为它是翻译的推荐模型。可能你已经知道，目前最先进的模型是基于Transformers的，你可以在这里PyTorch实现Transformer层的功能；请注意下面的模型所用到的注意力（attention）与transformer模型中所用的多头自注意力（multi-headed self-attention）不同。
import random
from typing import Tuple
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch import Tensor
class Encoder(nn.Module):
def __init__(self,
input_dim: int,
emb_dim: int,
enc_hid_dim: int,
dec_hid_dim: int,
dropout: float):
super().__init__()
self.input_dim = input_dim
self.emb_dim = emb_dim
self.enc_hid_dim = enc_hid_dim
self.dec_hid_dim = dec_hid_dim
self.dropout = dropout
self.embedding = nn.Embedding(input_dim, emb_dim)
self.rnn = nn.GRU(emb_dim, enc_hid_dim, bidirectional = True)
self.fc = nn.Linear(enc_hid_dim * 2, dec_hid_dim)
self.dropout = nn.Dropout(dropout)
def forward(self,
src: Tensor) -> Tuple[Tensor]:
embedded = self.dropout(self.embedding(src))
outputs, hidden = self.rnn(embedded)
hidden = torch.tanh(self.fc(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)))
return outputs, hidden
class Attention(nn.Module):
def __init__(self,
enc_hid_dim: int,
dec_hid_dim: int,
attn_dim: int):
super().__init__()
self.enc_hid_dim = enc_hid_dim
self.dec_hid_dim = dec_hid_dim
self.attn_in = (enc_hid_dim * 2) + dec_hid_dim
self.attn = nn.Linear(self.attn_in, attn_dim)
def forward(self,
decoder_hidden: Tensor,
encoder_outputs: Tensor) -> Tensor:
src_len = encoder_outputs.shape[0]
repeated_decoder_hidden = decoder_hidden.unsqueeze(1).repeat(1, src_len, 1)
encoder_outputs = encoder_outputs.permute(1, 0, 2)
energy = torch.tanh(self.attn(torch.cat((
repeated_decoder_hidden,
encoder_outputs),
dim = 2)))
attention = torch.sum(energy, dim=2)
return F.softmax(attention, dim=1)
class Decoder(nn.Module):
def __init__(self,
output_dim: int,
emb_dim: int,
enc_hid_dim: int,
dec_hid_dim: int,
dropout: int,
attention: nn.Module):
super().__init__()
self.emb_dim = emb_dim
self.enc_hid_dim = enc_hid_dim
self.dec_hid_dim = dec_hid_dim
self.output_dim = output_dim
self.dropout = dropout
self.attention = attention
self.embedding = nn.Embedding(output_dim, emb_dim)
self.rnn = nn.GRU((enc_hid_dim * 2) + emb_dim, dec_hid_dim)
self.out = nn.Linear(self.attention.attn_in + emb_dim, output_dim)
self.dropout = nn.Dropout(dropout)
def _weighted_encoder_rep(self,
decoder_hidden: Tensor,
encoder_outputs: Tensor) -> Tensor:
a = self.attention(decoder_hidden, encoder_outputs)
a = a.unsqueeze(1)
encoder_outputs = encoder_outputs.permute(1, 0, 2)
weighted_encoder_rep = torch.bmm(a, encoder_outputs)
weighted_encoder_rep = weighted_encoder_rep.permute(1, 0, 2)
return weighted_encoder_rep
def forward(self,
input: Tensor,
decoder_hidden: Tensor,
encoder_outputs: Tensor) -> Tuple[Tensor]:
input = input.unsqueeze(0)
embedded = self.dropout(self.embedding(input))
weighted_encoder_rep = self._weighted_encoder_rep(decoder_hidden,
encoder_outputs)
rnn_input = torch.cat((embedded, weighted_encoder_rep), dim = 2)
output, decoder_hidden = self.rnn(rnn_input, decoder_hidden.unsqueeze(0))
embedded = embedded.squeeze(0)
output = output.squeeze(0)
weighted_encoder_rep = weighted_encoder_rep.squeeze(0)
output = self.out(torch.cat((output,
weighted_encoder_rep,
embedded), dim = 1))
return output, decoder_hidden.squeeze(0)
class Seq2Seq(nn.Module):
def __init__(self,
encoder: nn.Module,
decoder: nn.Module,
device: torch.device):
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.device = device
def forward(self,
src: Tensor,
trg: Tensor,
teacher_forcing_ratio: float = 0.5) -> Tensor:
batch_size = src.shape[1]
max_len = trg.shape[0]
trg_vocab_size = self.decoder.output_dim
outputs = torch.zeros(max_len, batch_size, trg_vocab_size).to(self.device)
encoder_outputs, hidden = self.encoder(src)
# 解码器的第一个输入是 标记
output = trg[0,:]
for t in range(1, max_len):
output, hidden = self.decoder(output, hidden, encoder_outputs)
outputs[t] = output
teacher_force = random.random() < teacher_forcing_ratio
top1 = output.max(1)[1]
output = (trg[t] if teacher_force else top1)
return outputs
INPUT_DIM = len(SRC.vocab)
OUTPUT_DIM = len(TRG.vocab)
# ENC_EMB_DIM = 256
# DEC_EMB_DIM = 256
# ENC_HID_DIM = 512
# DEC_HID_DIM = 512
# ATTN_DIM = 64
# ENC_DROPOUT = 0.5
# DEC_DROPOUT = 0.5
ENC_EMB_DIM = 32
DEC_EMB_DIM = 32
ENC_HID_DIM = 64
DEC_HID_DIM = 64
ATTN_DIM = 8
ENC_DROPOUT = 0.5
DEC_DROPOUT = 0.5
enc = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, ENC_DROPOUT)
attn = Attention(ENC_HID_DIM, DEC_HID_DIM, ATTN_DIM)
dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, DEC_DROPOUT, attn)
model = Seq2Seq(enc, dec, device).to(device)
def init_weights(m: nn.Module):
for name, param in m.named_parameters():
if 'weight' in name:
nn.init.normal_(param.data, mean=0, std=0.01)
else:
nn.init.constant_(param.data, 0)
model.apply(init_weights)
optimizer = optim.Adam(model.parameters())
def count_parameters(model: nn.Module):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'The model has {count_parameters(model):,} trainable parameters')
输出：
The model has 1,856,685 trainable parameters
注意： 当对一个语言翻译模型的表现评分时，需要使nn.CrossEntropyLoss函数忽略掉目标中简单填充部分的索引。
PAD_IDX = TRG.vocab.stoi['' ]
criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX)
最后，可以训练并验证这个模型了：
import math
import time
def train(model: nn.Module,
iterator: BucketIterator,
optimizer: optim.Optimizer,
criterion: nn.Module,
clip: float):
model.train()
epoch_loss = 0
for _, batch in enumerate(iterator):
src = batch.src
trg = batch.trg
optimizer.zero_grad()
output = model(src, trg)
output = output[1:].view(-1, output.shape[-1])
trg = trg[1:].view(-1)
loss = criterion(output, trg)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()
epoch_loss += loss.item()
return epoch_loss / len(iterator)
def evaluate(model: nn.Module,
iterator: BucketIterator,
criterion: nn.Module):
model.eval()
epoch_loss = 0
with torch.no_grad():
for _, batch in enumerate(iterator):
src = batch.src
trg = batch.trg
output = model(src, trg, 0) #关闭 teacher forcing
output = output[1:].view(-1, output.shape[-1])
trg = trg[1:].view(-1)
loss = criterion(output, trg)
epoch_loss += loss.item()
return epoch_loss / len(iterator)
def epoch_time(start_time: int,
end_time: int):
elapsed_time = end_time - start_time
elapsed_mins = int(elapsed_time / 60)
elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
return elapsed_mins, elapsed_secs
N_EPOCHS = 10
CLIP = 1
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
start_time = time.time()
train_loss = train(model, train_iterator, optimizer, criterion, CLIP)
valid_loss = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')
print(f'\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')
print(f'\t Val. Loss: {valid_loss:.3f} | Val. PPL: {math.exp(valid_loss):7.3f}')
test_loss = evaluate(model, test_iterator, criterion)
print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |')
输出：
Epoch: 01 | Time: 0m 24s
Train Loss: 5.678 | Train PPL: 292.465
Val. Loss: 5.252 | Val. PPL: 190.984
Epoch: 02 | Time: 0m 25s
Train Loss: 5.019 | Train PPL: 151.184
Val. Loss: 5.118 | Val. PPL: 166.988
Epoch: 03 | Time: 0m 25s
Train Loss: 4.741 | Train PPL: 114.590
Val. Loss: 4.970 | Val. PPL: 144.084
Epoch: 04 | Time: 0m 25s
Train Loss: 4.615 | Train PPL: 100.974
Val. Loss: 4.999 | Val. PPL: 148.223
Epoch: 05 | Time: 0m 25s
Train Loss: 4.492 | Train PPL: 89.300
Val. Loss: 5.147 | Val. PPL: 171.969
Epoch: 06 | Time: 0m 25s
Train Loss: 4.416 | Train PPL: 82.793
Val. Loss: 5.004 | Val. PPL: 149.005
Epoch: 07 | Time: 0m 25s
Train Loss: 4.339 | Train PPL: 76.631
Val. Loss: 5.027 | Val. PPL: 152.493
Epoch: 08 | Time: 0m 25s
Train Loss: 4.254 | Train PPL: 70.378
Val. Loss: 4.892 | Val. PPL: 133.206
Epoch: 09 | Time: 0m 25s
Train Loss: 4.210 | Train PPL: 67.342
Val. Loss: 4.801 | Val. PPL: 121.662
Epoch: 10 | Time: 0m 25s
Train Loss: 4.140 | Train PPL: 62.808
Val. Loss: 4.709 | Val. PPL: 110.916
| Test Loss: 4.735 | Test PPL: 113.916 |
下一步
- 在这里查看剩余的
Ben Trevett使用torchtext的教程 。 - 继续关注使用
torchtext其他特性的教程，如nn.Transformer，通过预测下一个单词构建语言模型。