1. 位置编码模块：
import torch
import torch.nn as nn
import mathclass PositonalEncoding(nn.Module):def __init__ (self, d_model, dropout, max_len=5000):super(PositionalEncoding, self).__init__()self.dropout = nn.Dropout(p=dropout)# [[1, 2, 3],# [4, 5, 6],# [7, 8, 9]]pe = torch.zeros(max_len, d_model)# [[0],# [1],# [2]]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)# 位置编码固定，不更新参数# 保存模型时会保存缓冲区，在引入模型时缓冲区也被引入self.register_buffer('pe', pe)def forward(self, x):# 不计算梯度x = x + self.pe[:, :x.size(1)].requires_grad_(False)
2. 多头注意力模块
class MultiHeadAttention(nn.Module):def __init__(self, d_model, num_heads):super(MultiHeadAttention, self).__init__()assert d_model % num_heads == 0self.d_k = d_model // num_headsself.num_heads = num_headsself.W_q = nn.Linear(d_model, d_model)self.W_k = nn.Linear(d_model, d_model)self.W_v = nn.Linear(d_model, d_model)self.dropout = nn.Dropout(dropout)self.W_o = nn.Linear(d_model, d_model)def forward(self, query, key, value, mask=None):batch_size = query.size(0)Q = self.W_q(query).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)K = self.W_k(key).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)V = self.W_v(value).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)if mask is not None:scores = scores.masked_fill(mask == 0, -1e9)attn_weights = torch.softmax(scores, dim=-1)context = torch.matmul(attn_weights, V)context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.d_k * self.num_heads)return self.W_o(context)
3. 编码器层
class EncoderLayer(nn.Module):def __init__(self, d_model, num_heads, d_ff, dropout = 0.1):super().__init__()self.atten = MultiHeadAttention(d_model, num_heads)self.feed_forward = nn.Sequential(nn.Linear(d_model, d_ff),nn.ReLU(),nn.Linear(d_ff, d_model))self.norm1 = nn.LayerNorm(d_model)self.norm2 = nn.LayerNorm(d_model)self.dropout = nn.Dropout(dropout)def forward(self, x, mask=None):attn_output = self.attn(x, x, x, mask)x = self.norm1(x + self.dropout(attn_output))ff_output = self.feed_forward(x)x = self.norm2(x + self.dropout(ff_output))return x
4. 解码器层
class DecoderLayer(nn.Module):def __init__(self, d_model, num_heads, d_ff, dropout=0.1):super(DecoderLayer, self).__init__()self.self_attn = MultiHeadAttention(d_model, num_heads, dropout)self.cross_attn = MultiHeadAttention(d_model, num_heads, dropout)self.norm1 = nn.LayerNorm(d_model)self.norm2 = nn.LayerNorm(d_model)self.norm3 = nn.LayerNorm(d_model)self.feed_forward = nn.Sequential(nn.Linear(d_model, d_ff),nn.ReLU(),nn.Linear(d_ff, d_model))self.dropout = nn.Dropout(dropout)def forward(self, x, enc_output, src_mask, tgt_mask):attn_output = self.self_attn(x, x, x, tgt_mask)x = self.norm1(x + self.dropout(attn_output))attn_output = self.cross_attn(x, enc_output, enc_output, src_mask)x = self.norm2(x + self.dropout(attn_output))ff_output = self.feed_forward(x)x = self.norm3(x + self.dropout(ff_output))return x
5. 模型整合
class Transformer(nn.module):def __init__(self, src_vocab_size, tgt_vocab_size, d_model=512, num_heads=8, num_layers=6, d_ff=2048, dropout=0.1):super(Transformer, self).__init__()self.encoder_embed = nn.Embedding(src_vocab_size, d_model)self.decoder_embed = nn.Embedding(tgt_vocab_size, d_model)self.pos_encoder = PositionalEncoding(d_model, dropout)self.encoder_layers = nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])self.decoder_layers = nn.ModuleList([DecoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])self.fc_out = nn.Linear(d_model, tgt_vocab_size)def encode(self, src, src_mask):src_embeded = self.encoder_embed(src)src = self.pos_encoder(src_embeded)for layer in self.encoder_layers:src = layer(src, src_mask)return srcdef decode(self, tgt, enc_output, src_mask, tgt_mask):tgt_embeded = self.decoder_embed(tgt)tgt = self.pos_encoder(tgt_embeded)for layer in self.decoder_layers:tgt = layer(tgt, enc_output, src_mask, tgt_mask)return tgtdef forward(self, src, tgt, src_mask, tgt_mask):enc_output = self.encode(src, src_mask)dec_output = self.decode(tgt, enc_output, src_mask, tgt_mask)logits = self.fc_out(dec_output)return logits
