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。