PyTorch 使用 TorchText 进行语言翻译-pytorch教程-字节宝

原文： https://pytorch.org/tutorials/beginner/torchtext_translation_tutorial.html

本教程说明如何使用torchtext的几个便捷类来预处理包含英语和德语句子的著名数据集的数据，并使用它来训练序列到序列模型，并注意将德语句子翻译成英语。

它基于 PyTorch 社区成员 Ben Trevett 的本教程，并由 Seth Weidman 在 Ben 的允许下创建。

在本教程结束时，您将能够：

Preprocess sentences into a commonly-used format for NLP modeling using the following torchtext convenience classes:

<cite>字段</cite>和
<cite>TranslationDataset</cite>

torchtext具有用于创建数据集的实用程序，可以轻松地对其进行迭代，以创建语言翻译模型。一个关键类是字段，它指定应该对每个句子进行预处理的方式，另一个关键类是 <cite>TranslationDataset</cite> ； torchtext有几个这样的数据集；在本教程中，我们将使用 Multi30k 数据集，其中包含约 30,000 个英语和德语句子(平均长度约为 13 个单词）。

注意：本教程中的标记化需要 Spacy 我们使用 Spacy，因为它为英语以外的其他语言的标记化提供了强大的支持。 torchtext提供了basic_english标记器，并支持其他英语标记器(例如摩西），但对于语言翻译(需要多种语言），Spacy 是您的最佳选择。

要运行本教程，请先使用pip或conda安装spacy。接下来，下载英语和德语 Spacy 分词器的原始数据：

python -m spacy download en
python -m spacy download de

安装 Spacy 后，以下代码将根据Field中定义的标记器，标记TranslationDataset中的每个句子。

from torchtext.datasets import Multi30k
from torchtext.data import Field, BucketIterator


SRC = Field(tokenize = "spacy",
            tokenizer_language="de",
            init_token = '<sos>',
            eos_token = '<eos>',
            lower = True)


TRG = Field(tokenize = "spacy",
            tokenizer_language="en",
            init_token = '<sos>',
            eos_token = '<eos>',
            lower = True)


train_data, valid_data, test_data = Multi30k.splits(exts = ('.de', '.en'),
                                                    fields = (SRC, TRG))

得出：

downloading training.tar.gz
downloading validation.tar.gz
downloading mmt_task1_test2016.tar.gz

现在我们已经定义了train_data，我们可以看到torchtext的Field的一个非常有用的功能：build_vocab方法现在允许我们创建与每种语言相关的词汇

SRC.build_vocab(train_data, min_freq = 2)
TRG.build_vocab(train_data, min_freq = 2)

一旦运行了这些代码行，SRC.vocab.stoi将是一个词典，其词汇表中的标记作为键，而其对应的索引作为值； SRC.vocab.itos将是相同的字典，其中的键和值被交换。在本教程中，我们不会广泛使用此事实，但这在您将遇到的其他 NLP 任务中可能很有用。

BucketIterator

我们将使用的最后torchtext个特定功能是BucketIterator，它很容易使用，因为它以TranslationDataset作为第一个参数。具体来说，正如文档所说：定义一个迭代器，该迭代器将相似长度的示例批处理在一起。在为每个新纪元生产新鲜改组的批次时，最大程度地减少所需的填充量。有关使用的存储过程，请参阅池。

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)

可以像DataLoader``s; below, in the ``train和evaluate函数一样调用这些迭代器，只需使用以下命令即可调用它们：

for i, batch in enumerate(iterator):

每个batch然后具有src和trg属性：

src = batch.src
trg = batch.trg

定义我们的`nn.Module`和`Optimizer`

这大部分是从torchtext角度出发的：构建了数据集并定义了迭代器，本教程的其余部分仅将模型定义为nn.Module以及Optimizer，然后对其进行训练。

具体来说，我们的模型遵循在此处中描述的架构(您可以在此处找到更多注释的版本）。

注意：此模型只是可用于语言翻译的示例模型；我们选择它是因为它是任务的标准模型，而不是因为它是用于翻译的推荐模型。如您所知，目前最先进的模型基于“变形金刚”；您可以在此处看到 PyTorch 的实现 Transformer 层的功能；特别是，以下模型中使用的“注意”与变压器模型中存在的多头自我注意不同。

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)


        # first input to the decoder is the <sos> token
        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')

得出:

The model has 1,856,685 trainable parameters

注意：特别是在对语言翻译模型的性能进行评分时，我们必须告诉nn.CrossEntropyLoss函数忽略仅填充目标的索引。

PAD_IDX = TRG.vocab.stoi['<pad>']


criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX)

最后，我们可以训练和评估该模型：

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) #turn off 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} |')

得出:

Epoch: 01 | Time: 0m 35s
        Train Loss: 5.667 | Train PPL: 289.080
         Val. Loss: 5.201 |  Val. PPL: 181.371
Epoch: 02 | Time: 0m 35s
        Train Loss: 4.968 | Train PPL: 143.728
         Val. Loss: 5.096 |  Val. PPL: 163.375
Epoch: 03 | Time: 0m 35s
        Train Loss: 4.720 | Train PPL: 112.221
         Val. Loss: 4.989 |  Val. PPL: 146.781
Epoch: 04 | Time: 0m 35s
        Train Loss: 4.586 | Train PPL:  98.094
         Val. Loss: 4.841 |  Val. PPL: 126.612
Epoch: 05 | Time: 0m 35s
        Train Loss: 4.430 | Train PPL:  83.897
         Val. Loss: 4.809 |  Val. PPL: 122.637
Epoch: 06 | Time: 0m 35s
        Train Loss: 4.331 | Train PPL:  75.997
         Val. Loss: 4.797 |  Val. PPL: 121.168
Epoch: 07 | Time: 0m 35s
        Train Loss: 4.240 | Train PPL:  69.434
         Val. Loss: 4.694 |  Val. PPL: 109.337
Epoch: 08 | Time: 0m 35s
        Train Loss: 4.116 | Train PPL:  61.326
         Val. Loss: 4.714 |  Val. PPL: 111.452
Epoch: 09 | Time: 0m 35s
        Train Loss: 4.004 | Train PPL:  54.815
         Val. Loss: 4.563 |  Val. PPL:  95.835
Epoch: 10 | Time: 0m 36s
        Train Loss: 3.922 | Train PPL:  50.519
         Val. Loss: 4.452 |  Val. PPL:  85.761
| Test Loss: 4.456 | Test PPL:  86.155 |

下一步

在上查看使用torchtext 的Ben Trevett 其余教程。
敬请关注使用其他torchtext功能以及nn.Transformer通过下一个单词预测进行语言建模的教程！

脚本的总运行时间：(6 分钟 10.266 秒）

Download Python source code: torchtext_translation_tutorial.py Download Jupyter notebook: torchtext_translation_tutorial.ipynb

由狮身人面像画廊生成的画廊

PyTorch 使用 TorchText 进行语言翻译

<cite>字段</cite>和 <cite>TranslationDataset</cite>

BucketIterator

定义我们的nn.Module和Optimizer

下一步

推荐文章

推荐教程

技术文章

<cite>字段</cite>和
<cite>TranslationDataset</cite>

定义我们的`nn.Module`和`Optimizer`