transformer代码

以下是Transformer模型的Python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, vocab_size, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout=0.1):
        super(Transformer, self).__init__()

        self.embedding = nn.Embedding(vocab_size, d_model)
        self.pos_encoder = PositionalEncoding(d_model, dropout)
        encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_encoder_layers)
        decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout)
        self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_decoder_layers)
        self.fc = nn.Linear(d_model, vocab_size)

        self.d_model = d_model
        self.nhead = nhead

    def forward(self, src, tgt):
        src = self.embedding(src) * math.sqrt(self.d_model)
        src = self.pos_encoder(src)
        tgt = self.embedding(tgt) * math.sqrt(self.d_model)
        tgt = self.pos_encoder(tgt)
        memory = self.transformer_encoder(src)
        output = self.transformer_decoder(tgt, memory)
        output = self.fc(output)
        return F.log_softmax(output, dim=-1)

在这个示例中，我们定义了一个Transformer模型类，它包含嵌入层、位置编码层、Transformer编码器和解码器、全连接层等组件。其中，嵌入层将输入的词汇索引转换为密集向量表示，位置编码层则为每个词汇位置添加了一些额外的信息，用于模型理解句子中的词汇顺序。编码器和解码器都使用了Transformer结构，它们分别用于编码源语言句子和解码目标语言句子。最后，全连接层将模型输出映射为每个词汇的对数概率值，用于计算交叉熵损失。