pytorch transformer模型代码

PyTorch Transformer模型通常基于Transformer架构，这是由Google在2017年提出的用于自然语言处理的强大模型。它主要用于序列到序列的任务，比如机器翻译和文本生成。以下是构建一个简单的Transformer模块的基本步骤：

import torch
from torch import nn

class TransformerBlock(nn.Module):
    def __init__(self, d_model, n_heads, dim_feedforward=2048, dropout=0.1):
        super(TransformerBlock, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
        self.norm1 = nn.LayerNorm(d_model)
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.norm2 = nn.LayerNorm(d_model)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

    def forward(self, src, src_mask=None):
        residual = src
        src = self.norm1(src)
        src = self.self_attn(src, src, src, key_padding_mask=src_mask)[0]
        src = residual + self.dropout(src)
        
        residual = src
        src = self.norm2(src)
        src = self.linear2(self.dropout(F.relu(self.linear1(src))))
        src = residual + self.dropout(src)
        
        return src

class TransformerEncoder(nn.Module):
    def __init__(self, num_layers, d_model, n_heads, dropout=0.1):
        super(TransformerEncoder, self).__init__()
        self.layers = nn.ModuleList([TransformerBlock(d_model, n_heads, dropout) for _ in range(num_layers)])
        self.norm = nn.LayerNorm(d_model)

    def forward(self, src, src_mask):
        for layer in self.layers:
            src = layer(src, src_mask)
        return self.norm(src)

# 示例用法：
encoder = TransformerEncoder(num_layers=6, d_model=512, n_heads=8)
input_tensor = ... # 假设已经填充好输入序列
output = encoder(input_tensor, src_mask=...)

这只是一个简化的版本，实际应用中可能需要添加更多的细节，如位置编码、残差连接等。如果你需要更详细的代码实例或者有其他问题，可以提问：