pytorch transformer 机器翻译代码

PyTorch Transformer模型是一种基于自注意力机制（Self-Attention Mechanism）的神经网络结构，常用于序列到序列（Sequence to Sequence, Seq2Seq）的任务，如机器翻译。下面是一个简单的例子概述如何在PyTorch中实现Transformer模型：

import torch
from torch import nn

class MultiHeadAttention(nn.Module):
    # 自注意力模块
    def __init__(self, d_model, num_heads):
        super().__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        self.head_dim = d_model // num_heads
        self.query_linear = nn.Linear(d_model, d_model)
        self.key_linear = nn.Linear(d_model, d_model)
        self.value_linear = nn.Linear(d_model, d_model)
        self.out_proj = nn.Linear(d_model, d_model)

    def forward(self, query, key, value, mask=None):
        query = self.query_linear(query)
        key = self.key_linear(key)
        value = self.value_linear(value)
        
        batch_size, seq_len, _ = query.shape
        query = query.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)  # (batch, heads, seq_len_q, head_dim)
        key = key.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)  # (batch, heads, seq_len_k, head_dim)
        value = value.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)  # (batch, heads, seq_len_v, head_dim)

        attention_scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_dim)
        if mask is not None:
            attention_scores = attention_scores.masked_fill(mask == 0, -1e9)  # 对填充部分设置极大的负值

        attention_weights = nn.Softmax(dim=-1)(attention_scores)
        context = torch.matmul(attention_weights, value)  # (batch, heads, seq_len_q, head_dim)

        context = context.transpose(1, 2).contiguous().view(batch_size, seq_len, self.d_model)
        out = self.out_proj(context)
        return out

# 简化的Transformer编码器层
class EncoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, dropout=0.1):
        super().__init__()
        self.mha = MultiHeadAttention(d_model, num_heads)
        self.ffn = nn.Sequential(
            nn.Linear(d_model, d_model * 4),  # Position-wise Feedforward Network
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(d_model * 4, d_model)
        )
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src, src_mask):
        attn_output = self.mha(src, src, src, src_mask)
        attn_output = self.dropout(attn_output)
        output = self.norm1(src + attn_output)

        ffn_output = self.ffn(output)
        ffn_output = self.dropout(ffn_output)
        output = self.norm2(output + ffn_output)
        return output

# 示例性主函数，假设有个英文句子和对应的法文句子
def translate_sentence(input_sentence, src_vocab, tgt_vocab):
    # 输入预处理...
    encoder_input = encode(input_sentence, src_vocab)  # 编码输入
    for _ in range(num_encoder_layers):  # 进行多次编码
        encoder_output = encoder_layer(encoder_input, src_mask)

    # ...后续解码过程