transformer机器翻译代码

以下是使用PyTorch实现Transformer模型进行机器翻译的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F


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)


class Transformer(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout=0.1):
        super(Transformer, self).__init__()
        self.d_model = d_model
        self.embedding_src = nn.Embedding(src_vocab_size, d_model)
        self.embedding_tgt = nn.Embedding(tgt_vocab_size, d_model)
        self.pos_encoder = PositionalEncoding(d_model, dropout)
        encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout)
        encoder_norm = nn.LayerNorm(d_model)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
        decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout)
        decoder_norm = nn.LayerNorm(d_model)
        self.decoder = nn.TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm)
        self.out = nn.Linear(d_model, tgt_vocab_size)
        self._reset_parameters()

    def _reset_parameters(self):
        nn.init.xavier_uniform_(self.out.weight)
        nn.init.constant_(self.out.bias, 0.)

    def forward(self, src, tgt):
        src_mask = self._generate_square_subsequent_mask(src.size(0)).to(src.device)
        tgt_mask = self._generate_square_subsequent_mask(tgt.size(0)).to(tgt.device)
        src_embeddings = self.embedding_src(src) * math.sqrt(self.d_model)
        tgt_embeddings = self.embedding_tgt(tgt) * math.sqrt(self.d_model)
        src_embeddings = self.pos_encoder(src_embeddings)
        tgt_embeddings = self.pos_encoder(tgt_embeddings)
        memory = self.encoder(src_embeddings, src_key_padding_mask=None, mask=src_mask)
        output = self.decoder(tgt_embeddings, memory, tgt_mask=tgt_mask, memory_mask=src_mask)
        output = self.out(output)
        return output

    def _generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.))
        return mask

在使用该模型进行机器翻译时，需要使用交叉熵损失函数和学习率调度器，可以参考以下代码：

model = Transformer(src_vocab_size, tgt_vocab_size, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout).to(device)
criterion = nn.CrossEntropyLoss(ignore_index=pad_idx)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)

for epoch in range(num_epochs):
    model.train()
    for batch in dataloader:
        src = batch[0].to(device)
        tgt = batch[1].to(device)
        output = model(src, tgt[:-1])
        loss = criterion(output.reshape(-1, output.size(-1)), tgt[1:].reshape(-1))
        optimizer.zero_grad()
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1)
        optimizer.step()
        scheduler.step()

其中，`src_vocab_size`和`tgt_vocab_size`分别表示源语言和目标语言的词汇表大小，`d_model`表示模型的维度，`nhead`表示多头注意力的头数，`num_encoder_layers`表示编码器层数，`num_decoder_layers`表示解码器层数，`dim_feedforward`表示前馈层的维度，`dropout`表示Dropout的概率，`pad_idx`表示填充符的索引。在训练过程中，需要将目标序列向右移动一位，并且在计算损失时忽略填充符。