Transformer代码

以下是一个简单的Transformer模型的Python代码，用于机器翻译任务：

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

class TransformerModel(nn.Module):

    def __init__(self, vocab_size, embedding_size, hidden_size, num_layers, num_heads, dropout_prob):
        super(TransformerModel, self).__init__()

        self.embedding_size = embedding_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.num_heads = num_heads

        self.embedding = nn.Embedding(vocab_size, embedding_size)
        self.positional_encoding = PositionalEncoding(embedding_size, dropout_prob)

        encoder_layer = nn.TransformerEncoderLayer(embedding_size, num_heads, hidden_size, dropout_prob)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers)

        decoder_layer = nn.TransformerDecoderLayer(embedding_size, num_heads, hidden_size, dropout_prob)
        self.decoder = nn.TransformerDecoder(decoder_layer, num_layers)

        self.output_layer = nn.Linear(embedding_size, vocab_size)

    def forward(self, src, tgt):
        src_embeddings = self.embedding(src)
        tgt_embeddings = self.embedding(tgt)

        src_embeddings = src_embeddings * (self.embedding_size ** 0.5)
        src_embeddings = self.positional_encoding(src_embeddings)

        tgt_embeddings = tgt_embeddings * (self.embedding_size ** 0.5)
        tgt_embeddings = self.positional_encoding(tgt_embeddings)

        memory = self.encoder(src_embeddings)
        output = self.decoder(tgt_embeddings, memory)

        output = self.output_layer(output)

        return F.log_softmax(output, dim=-1)

class PositionalEncoding(nn.Module):

    def __init__(self, embedding_size, dropout_prob, max_length=5000):
        super(PositionalEncoding, self).__init__()

        pe = torch.zeros(max_length, embedding_size)
        position = torch.arange(0, max_length, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, embedding_size, 2).float() * (-math.log(10000.0) / embedding_size))
        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)
        self.dropout = nn.Dropout(dropout_prob)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

注：以上代码仅为示例，可能存在一些不完善的地方。