transformmer代码
时间: 2023-07-02 16:08:39 浏览: 58
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
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.d_k = d_model // num_heads
self.W_Q = nn.Linear(d_model, d_model)
self.W_K = nn.Linear(d_model, d_model)
self.W_V = nn.Linear(d_model, d_model)
self.W_O = nn.Linear(d_model, d_model)
def forward(self, queries, keys, values):
Q = self.W_Q(queries)
K = self.W_K(keys)
V = self.W_V(values)
Q = Q.view(-1, self.num_heads, self.d_k)
K = K.view(-1, self.num_heads, self.d_k)
V = V.view(-1, self.num_heads, self.d_k)
scores = torch.matmul(Q, K.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.d_k).float())
scores = F.softmax(scores, dim=-1)
attention = torch.matmul(scores, V)
attention = attention.view(-1, self.num_heads * self.d_k)
output = self.W_O(attention)
return output
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff):
super(FeedForward, self).__init__()
self.linear1 = nn.Linear(d_model, d_ff)
self.linear2 = nn.Linear(d_ff, d_model)
def forward(self, x):
x = F.relu(self.linear1(x))
x = self.linear2(x)
return x
class EncoderLayer(nn.Module):
def __init__(self, d_model, num_heads, d_ff):
super(EncoderLayer, self).__init__()
self.multi_head_attention = MultiHeadAttention(d_model, num_heads)
self.feed_forward = FeedForward(d_model, d_ff)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
def forward(self, x):
residual = x
x = self.norm1(x + self.multi_head_attention(x, x, x))
x = self.norm2(x + self.feed_forward(x))
return x
class Transformer(nn.Module):
def __init__(self, num_layers, d_model, num_heads, d_ff):
super(Transformer, self).__init__()
self.layers = nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff) for _ in range(num_layers)])
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
```
这个Transformer模型包含了一个多头注意力机制(`MultiHeadAttention`)和一个前馈神经网络(`FeedForward`),以及一个由多个编码层(`EncoderLayer`)组成的Transformer。在这个简单的实现中,我们只考虑了编码器(即只有一个Transformer)。
我们可以使用以下代码来实例化一个Transformer模型,并对一些序列输入进行编码:
```python
transformer = Transformer(num_layers=6, d_model=512, num_heads=8, d_ff=2048)
x = torch.randn(32, 100, 512)
output = transformer(x)
```
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