能否生成transformer的torch代码
时间: 2023-08-13 17:54:11 浏览: 122
当然可以!以下是一个简单的Transformer模型的PyTorch代码示例:
```python
import torch
import torch.nn as nn
class Transformer(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
super(Transformer, self).__init__()
self.embedding = nn.Embedding(input_size, hidden_size)
self.encoder = Encoder(hidden_size, num_layers, num_heads, dropout)
self.decoder = Decoder(hidden_size, num_layers, num_heads, dropout)
self.fc = nn.Linear(hidden_size, input_size)
def forward(self, src, trg):
src_embedded = self.embedding(src)
trg_embedded = self.embedding(trg)
encoder_output = self.encoder(src_embedded)
decoder_output = self.decoder(trg_embedded, encoder_output)
output = self.fc(decoder_output)
return output
class Encoder(nn.Module):
def __init__(self, hidden_size, num_layers, num_heads, dropout):
super(Encoder, self).__init__()
self.layers = nn.ModuleList([EncoderLayer(hidden_size, num_heads, dropout) for _ in range(num_layers)])
self.dropout = nn.Dropout(dropout)
def forward(self, src):
for layer in self.layers:
src = layer(src)
return src
class EncoderLayer(nn.Module):
def __init__(self, hidden_size, num_heads, dropout):
super(EncoderLayer, self).__init__()
self.multihead_attention = MultiheadAttention(hidden_size, num_heads, dropout)
self.feed_forward = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 4),
nn.ReLU(),
nn.Linear(hidden_size * 4, hidden_size)
)
self.layer_norm1 = nn.LayerNorm(hidden_size)
self.layer_norm2 = nn.LayerNorm(hidden_size)
self.dropout = nn.Dropout(dropout)
def forward(self, src):
src = src + self.dropout(self.multihead_attention(src))
src = self.layer_norm1(src)
src = src + self.dropout(self.feed_forward(src))
src = self.layer_norm2(src)
return src
class MultiheadAttention(nn.Module):
def __init__(self, hidden_size, num_heads, dropout):
super(MultiheadAttention, self).__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.query_linear = nn.Linear(hidden_size, hidden_size)
self.key_linear = nn.Linear(hidden_size, hidden_size)
self.value_linear = nn.Linear(hidden_size, hidden_size)
self.fc = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
batch_size = x.size(0)
query = self.query_linear(x)
key = self.key_linear(x)
value = self.value_linear(x)
query = query.view(batch_size * self.num_heads, -1, self.hidden_size // self.num_heads).transpose(0, 1)
key = key.view(batch_size * self.num_heads, -1, self.hidden_size // self.num_heads).transpose(0, 1)
value = value.view(batch_size * self.num_heads, -1, self.hidden_size // self.num_heads).transpose(0, 1)
scores = torch.matmul(query, key.transpose(1, 2))
scores = scores / (self.hidden_size // self.num_heads) ** 0.5
attention_weights = nn.functional.softmax(scores, dim=-1)
attention_weights = self.dropout(attention_weights)
output = torch.matmul(attention_weights, value)
output = output.transpose(0, 1).contiguous().view(batch_size, -1, self.hidden_size)
output = self.fc(output)
return output
class Decoder(nn.Module):
def __init__(self, hidden_size, num_layers, num_heads, dropout):
super(Decoder, self).__init__()
self.layers = nn.ModuleList([DecoderLayer(hidden_size, num_heads, dropout) for _ in range(num_layers)])
self.dropout = nn.Dropout(dropout)
def forward(self, trg, encoder_output):
for layer in self.layers:
trg = layer(trg, encoder_output)
return trg
class DecoderLayer(nn.Module):
def __init__(self, hidden_size, num_heads, dropout):
super(DecoderLayer, self).__init__()
self.masked_multihead_attention = MultiheadAttention(hidden_size, num_heads, dropout)
self.multihead_attention = MultiheadAttention(hidden_size, num_heads, dropout)
self.feed_forward = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 4),
nn.ReLU(),
nn.Linear(hidden_size * 4, hidden_size)
)
self.layer_norm1 = nn.LayerNorm(hidden_size)
self.layer_norm2 = nn.LayerNorm(hidden_size)
self.layer_norm3 = nn.LayerNorm(hidden_size)
self.dropout = nn.Dropout(dropout)
def forward(self, trg, encoder_output):
trg = trg + self.dropout(self.masked_multihead_attention(trg))
trg = self.layer_norm1(trg)
trg = trg + self.dropout(self.multihead_attention(trg, encoder_output))
trg = self.layer_norm2(trg)
trg = trg + self.dropout(self.feed_forward(trg))
trg = self.layer_norm3(trg)
return trg
```
这是一个简单的Transformer模型,包括Encoder、Decoder和多头自注意力机制等组件。你可以根据自己的需求进行修改和扩展。希望对你有所帮助!