transformer模型代码

抱歉，我是语言模型AI，无法提供代码，但是我可以给你提供一个基于PyTorch实现的Transformer模型的代码示例，供你参考：

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

class MultiheadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        self.head_size = d_model // num_heads
        
        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.out_linear = nn.Linear(d_model, d_model)
        
    def forward(self, q, k, v, mask):
        bs = q.size(0)
        
        # linear transformation
        q = self.q_linear(q).view(bs, -1, self.num_heads, self.head_size)
        k = self.k_linear(k).view(bs, -1, self.num_heads, self.head_size)
        v = self.v_linear(v).view(bs, -1, self.num_heads, self.head_size)
        
        # transpose
        q = q.transpose(1,2)
        k = k.transpose(1,2)
        v = v.transpose(1,2)
        
        # attention
        scores = torch.matmul(q, k.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.d_model, dtype=torch.float32))
        
        if mask is not None:
            mask = mask.unsqueeze(1).unsqueeze(1)
            scores = scores.masked_fill(mask==0, -1e9)
        
        scores = F.softmax(scores, dim=-1)
        output = torch.matmul(scores, v)
        
        # concatenate
        output = output.transpose(1,2).contiguous().view(bs, -1, self.d_model)
        output = self.out_linear(output)
        
        return output

class FeedForward(nn.Module):
    def __init__(self, d_model, d_ff):
        super().__init__()
        self.linear_1 = nn.Linear(d_model, d_ff)
        self.linear_2 = nn.Linear(d_ff, d_model)
        
    def forward(self, x):
        x = self.linear_1(x)
        x = F.relu(x)
        x = self.linear_2(x)
        return x

class Norm(nn.Module):
    def __init__(self, d_model, eps=1e-6):
        super().__init__()
        self.size = d_model
        self.alpha = nn.Parameter(torch.ones(self.size))
        self.bias = nn.Parameter(torch.zeros(self.size))
        self.eps = eps
        
    def forward(self, x):
        norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
        return norm

class EncoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff):
        super().__init__()
        self.multihead_attention = MultiheadAttention(d_model, num_heads)
        self.feedforward = FeedForward(d_model, d_ff)
        self.norm_1 = Norm(d_model)
        self.norm_2 = Norm(d_model)
        
    def forward(self, x, mask):
        attention = self.multihead_attention(x, x, x, mask)
        x = self.norm_1(x + attention)
        feedforward = self.feedforward(x)
        x = self.norm_2(x + feedforward)
        return x

class DecoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff):
        super().__init__()
        self.masked_multihead_attention = MultiheadAttention(d_model, num_heads)
        self.multihead_attention = MultiheadAttention(d_model, num_heads)
        self.feedforward = FeedForward(d_model, d_ff)
        self.norm_1 = Norm(d_model)
        self.norm_2 = Norm(d_model)
        self.norm_3 = Norm(d_model)
        
    def forward(self, x, encoder_output, src_mask, tgt_mask):
        masked_attention = self.masked_multihead_attention(x, x, x, tgt_mask)
        x = self.norm_1(x + masked_attention)
        attention = self.multihead_attention(x, encoder_output, encoder_output, src_mask)
        x = self.norm_2(x + attention)
        feedforward = self.feedforward(x)
        x = self.norm_3(x + feedforward)
        return x

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout, max_len=5000):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)
        self.d_model = d_model
        self.max_len = max_len
        
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float32).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)
        self.register_buffer('pe', pe)
        
    def forward(self, x):
        x = x * math.sqrt(self.d_model)
        seq_len = x.size(1)
        pe = self.pe[:, :seq_len]
        x = x + pe
        return self.dropout(x)

class Transformer(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, d_model=512, num_layers=6, num_heads=8, d_ff=2048, dropout=0.1):
        super().__init__()
        self.src_embedding = nn.Embedding(src_vocab_size, d_model)
        self.tgt_embedding = nn.Embedding(tgt_vocab_size, d_model)
        self.positional_encoding = PositionalEncoding(d_model, dropout)
        self.encoder_layers = nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff) for _ in range(num_layers)])
        self.decoder_layers = nn.ModuleList([DecoderLayer(d_model, num_heads, d_ff) for _ in range(num_layers)])
        self.generator = nn.Linear(d_model, tgt_vocab_size)
        
    def forward(self, src, tgt, src_mask, tgt_mask):
        src_embedded = self.src_embedding(src)
        tgt_embedded = self.tgt_embedding(tgt)
        src_positional_encoded = self.positional_encoding(src_embedded)
        tgt_positional_encoded = self.positional_encoding(tgt_embedded)
        
        # encoder
        encoder_output = src_positional_encoded
        for encoder_layer in self.encoder_layers:
            encoder_output = encoder_layer(encoder_output, src_mask)
        
        # decoder
        decoder_output = tgt_positional_encoded
        for decoder_layer in self.decoder_layers:
            decoder_output = decoder_layer(decoder_output, encoder_output, src_mask, tgt_mask)
        
        # generator
        output = self.generator(decoder_output)
        return output