pytorch中的transformer

在PyTorch中，Transformer是一种常用的深度学习模型，用于处理序列数据，如自然语言处理和机器翻译。Transformer模型由Vaswani等人于2017年提出，并在处理长距离依赖性问题方面取得了很大的成功。

在PyTorch中，可以使用torch.nn.Transformer类来构建Transformer模型。Transformer模型由编码器和解码器组成，每个部分都由多个层堆叠而成。

编码器将输入序列映射到一组连续的表示向量，其中每个位置的向量表示输入序列的不同部分。解码器根据编码器的输出和先前的预测生成输出序列。

以下是使用PyTorch中的Transformer模型的基本步骤：
1. 定义编码器和解码器的嵌入层。
2. 创建编码器和解码器模型实例。
3. 定义损失函数（如交叉熵损失）和优化器（如Adam）。
4. 循环遍历输入序列和目标序列，并将它们传递给编码器和解码器。
5. 计算损失并执行反向传播。
6. 更新模型参数。

相关问题

pytorch 中transformer模型完整代码

以下是一个使用PyTorch实现Transformer模型的完整代码示例：

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

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=0.1)
        
        pe = torch.zeros(max_len, d_model)
        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).transpose(0, 1)
        self.register_buffer('pe', pe)
        
    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)
        

class TransformerModel(nn.Module):
    def __init__(self, ntoken, d_model, nhead, dim_feedforward, num_layers, dropout=0.5):
        super(TransformerModel, self).__init__()
        self.model_type = 'Transformer'
        
        self.embedding = nn.Embedding(ntoken, d_model)
        self.pos_encoder = PositionalEncoding(d_model)
        
        encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)
        
        self.fc = nn.Linear(d_model, ntoken)
        
        self.init_weights()
        
    def _generate_square_subsequent_mask(self, sz):
        mask = torch.triu(torch.ones(sz, sz)) == 1
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask
    
    def init_weights(self):
        initrange = 0.1
        self.embedding.weight.data.uniform_(-initrange, initrange)
        self.fc.bias.data.zero_()
        self.fc.weight.data.uniform_(-initrange, initrange)
        
    def forward(self, src):
        src = self.embedding(src)
        src = self.pos_encoder(src)
        mask = self._generate_square_subsequent_mask(src.size(0)).to(src.device)
        output = self.transformer_encoder(src, mask)
        output = self.fc(output)
        return F.log_softmax(output, dim=-1)
        

# 使用示例：
ntoken = 1000  # 词汇表大小
d_model = 512  # 模型维度
nhead = 8  # 多头注意力头数
dim_feedforward = 2048  # 前馈神经网络隐藏层维度
num_layers = 6  # Transformer编码器层数
dropout = 0.5

model = TransformerModel(ntoken, d_model, nhead, dim_feedforward, num_layers, dropout)
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练过程
def train():
    model.train()
    total_loss = 0.
    for batch in training_data:
        optimizer.zero_grad()
        src, tgt = batch
        output = model(src)
        loss = criterion(output.view(-1, ntoken), tgt.view(-1))
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss / len(training_data)

# 测试过程
def evaluate():
    model.eval()
    total_loss = 0.
    with torch.no_grad():
        for batch in test_data:
            src, tgt = batch
            output = model(src)
            total_loss += criterion(output.view(-1, ntoken), tgt.view(-1)).item()
    return total_loss / len(test_data)

请注意，上述代码中的`training_data`和`test_data`是训练和测试数据的例子，需要根据实际情况进行替换。此外，还需要根据任务的具体要求调整模型的超参数和其他设置。

pytorch 中transformer的各个模块代码

在 PyTorch 中，Transformer 模型的核心组件包括 Encoder（编码器）、Decoder（解码器）以及 Self-Attention（自注意力机制）。下面是它们的代码实现：

1. Encoder（编码器）：

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

class Encoder(nn.Module):
    def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout):
        super().__init__()
        
        self.input_dim = input_dim
        self.hid_dim = hid_dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.pf_dim = pf_dim
        self.dropout = dropout
        
        self.tok_embedding = nn.Embedding(input_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)  # 位置编码
        
        self.layers = nn.ModuleList([EncoderLayer(hid_dim, n_heads, pf_dim, dropout) for _ in range(n_layers)])
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, src, src_mask):
        batch_size = src.shape[0]
        src_len = src.shape[1]
        
        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(src.device)
        
        src = self.dropout((self.tok_embedding(src) * torch.sqrt(torch.FloatTensor([self.hid_dim])).to(src.device)) + self.pos_embedding(pos))
        
        for layer in self.layers:
            src = layer(src, src_mask)
            
        return src

2. Decoder（解码器）：

class Decoder(nn.Module):
    def __init__(self, output_dim, hid_dim, n_layers, n_heads, pf_dim, dropout):
        super().__init__()
        
        self.output_dim = output_dim
        self.hid_dim = hid_dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.pf_dim = pf_dim
        self.dropout = dropout
        
        self.tok_embedding = nn.Embedding(output_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)  # 位置编码
        
        self.layers = nn.ModuleList([DecoderLayer(hid_dim, n_heads, pf_dim, dropout) for _ in range(n_layers)])
        
        self.fc_out = nn.Linear(hid_dim, output_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, trg, enc_src, trg_mask, src_mask):
        batch_size = trg.shape[0]
        trg_len = trg.shape[1]
        
        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(trg.device)
        
        trg = self.dropout((self.tok_embedding(trg) * torch.sqrt(torch.FloatTensor([self.hid_dim])).to(trg.device)) + self.pos_embedding(pos))
        
        for layer in self.layers:
            trg, attention = layer(trg, enc_src, trg_mask, src_mask)
        
        output = self.fc_out(trg)
        
        return output, attention

3. Self-Attention（自注意力机制）：

class SelfAttention(nn.Module):
    def __init__(self, hid_dim, n_heads, dropout):
        super().__init__()
        
        assert hid_dim % n_heads == 0
        
        self.hid_dim = hid_dim
        self.n_heads = n_heads
        self.head_dim = hid_dim // n_heads
        
        self.fc_q = nn.Linear(hid_dim, hid_dim)
        self.fc_k = nn.Linear(hid_dim, hid_dim)
        self.fc_v = nn.Linear(hid_dim, hid_dim)
        
        self.fc_o = nn.Linear(hid_dim, hid_dim)
        
        self.dropout = nn.Dropout(dropout)
        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)
        
    def forward(self, query, key, value, mask=None):
        batch_size = query.shape[0]
        
        Q = self.fc_q(query)
        K = self.fc_k(key)
        V = self.fc_v(value)
        
        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        
        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale
        
        if mask is not None:
            energy = energy.masked_fill(mask == 0, -1e10)
            
        attention = F.softmax(energy, dim=-1)
        
        x = torch.matmul(self.dropout(attention), V)
        x = x.permute(0, 2, 1, 3).contiguous()
        
        x = x.view(batch_size, -1, self.hid_dim)
        
        x = self.fc_o(x)
        
        return x, attention

这些代码展示了 Transformer 模型的核心组件的实现。需要注意的是，这仅仅是这些模块的基本代码实现，实际使用时还需要进行相应的参数初始化、模型组装等步骤。