transformer代码
时间: 2023-10-02 18:07:31 浏览: 46
以下是一个简单的Transformer的代码实现:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Transformer(nn.Module):
def __init__(self, vocab_size, max_seq_len, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout):
super(Transformer, self).__init__()
self.d_model = d_model
self.embedding = nn.Embedding(vocab_size, d_model)
self.pos_encoding = self.positional_encoding(max_seq_len, d_model)
self.dropout = nn.Dropout(p=dropout)
self.encoder_layers = nn.ModuleList([nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout) for _ in range(num_encoder_layers)])
self.encoder = nn.TransformerEncoder(self.encoder_layers, num_layers=num_encoder_layers)
self.decoder_layers = nn.ModuleList([nn.TransformerDecoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout) for _ in range(num_decoder_layers)])
self.decoder = nn.TransformerDecoder(self.decoder_layers, num_layers=num_decoder_layers)
self.fc = nn.Linear(d_model, vocab_size)
def forward(self, x, y):
x = self.embedding(x) * torch.sqrt(self.d_model)
x = x + self.pos_encoding[:x.size(1), :]
x = self.dropout(x)
y = self.embedding(y) * torch.sqrt(self.d_model)
y = y + self.pos_encoding[:y.size(1), :]
y = self.dropout(y)
encoder_out = self.encoder(x)
decoder_out = self.decoder(y, encoder_out)
output = self.fc(decoder_out)
return F.log_softmax(output, dim=-1)
def positional_encoding(self, max_seq_len, d_model):
pos = torch.arange(0, max_seq_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pos_encoding = torch.zeros(max_seq_len, d_model)
pos_encoding[:, 0::2] = torch.sin(pos * div_term)
pos_encoding[:, 1::2] = torch.cos(pos * div_term)
return pos_encoding
```
在这个实现中,我们使用了PyTorch的Transformer模块,其中包含了TransformerEncoder和TransformerDecoder。我们使用nn.ModuleList来存储编码器和解码器中的多个层,然后将它们传递给TransformerEncoder和TransformerDecoder。我们还使用了一个嵌入层将输入的整数序列转换为向量,然后应用位置编码。最后,我们使用一个线性层将解码器输出转换为词汇表中各单词的概率分布,使用对数softmax来计算损失。
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