帮我实现一个transformer的结构
时间: 2023-05-22 14:01:08 浏览: 61
好的,以下是一个简单的Transformer结构的实现代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Transformer(nn.Module):
def __init__(self, input_dim, hidden_dim, num_layers, num_heads, dropout):
super(Transformer, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.num_heads = num_heads
self.dropout = dropout
self.embedding = nn.Embedding(input_dim, hidden_dim)
self.pos_encoding = PositionalEncoding(hidden_dim, dropout)
self.encoder_layers = nn.ModuleList([EncoderLayer(hidden_dim, num_heads, dropout) for _ in range(num_layers)])
self.decoder_layers = nn.ModuleList([DecoderLayer(hidden_dim, num_heads, dropout) for _ in range(num_layers)])
self.fc = nn.Linear(hidden_dim, input_dim)
def forward(self, src, trg):
src_mask = self.generate_square_subsequent_mask(src.size(1)).to(src.device)
trg_mask = self.generate_square_subsequent_mask(trg.size(1)).to(trg.device)
src_emb = self.embedding(src) * math.sqrt(self.hidden_dim)
trg_emb = self.embedding(trg) * math.sqrt(self.hidden_dim)
src_emb = self.pos_encoding(src_emb)
trg_emb = self.pos_encoding(trg_emb)
for i in range(self.num_layers):
src_emb = self.encoder_layers[i](src_emb, src_mask)
trg_emb = self.decoder_layers[i](trg_emb, src_emb, trg_mask, src_mask)
output = self.fc(trg_emb)
return output
def generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
class PositionalEncoding(nn.Module):
def __init__(self, hidden_dim, dropout, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, hidden_dim)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, hidden_dim, 2).float() * (-math.log(10000.0) / hidden_dim))
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 EncoderLayer(nn.Module):
def __init__(self, hidden_dim, num_heads, dropout):
super(EncoderLayer, self).__init__()
self.self_attn = nn.MultiheadAttention(hidden_dim, num_heads, dropout=dropout)
self.dropout1 = nn.Dropout(p=dropout)
self.norm1 = nn.LayerNorm(hidden_dim)
self.fc = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 4),
nn.ReLU(),
nn.Linear(hidden_dim * 4, hidden_dim),
nn.Dropout(p=dropout)
)
self.norm2 = nn.LayerNorm(hidden_dim)
self.dropout2 = nn.Dropout(p=dropout)
def forward(self, x, mask):
attn_output, _ = self.self_attn(x, x, x, attn_mask=mask)
x = x + self.dropout1(attn_output)
x = self.norm1(x)
fc_output = self.fc(x)
x = x + self.dropout2(fc_output)
x = self.norm2(x)
return x
class DecoderLayer(nn.Module):
def __init__(self, hidden_dim, num_heads, dropout):
super(DecoderLayer, self).__init__()
self.self_attn = nn.MultiheadAttention(hidden_dim, num_heads, dropout=dropout)
self.dropout1 = nn.Dropout(p=dropout)
self.norm1 = nn.LayerNorm(hidden_dim)
self.src_attn = nn.MultiheadAttention(hidden_dim, num_heads, dropout=dropout)
self.dropout2 = nn.Dropout(p=dropout)
self.norm2 = nn.LayerNorm(hidden_dim)
self.fc = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 4),
nn.ReLU(),
nn.Linear(hidden_dim * 4, hidden_dim),
nn.Dropout(p=dropout)
)
self.norm3 = nn.LayerNorm(hidden_dim)
self.dropout3 = nn.Dropout(p=dropout)
def forward(self, x, src, trg_mask, src_mask):
attn_output, _ = self.self_attn(x, x, x, attn_mask=trg_mask)
x = x + self.dropout1(attn_output)
x = self.norm1(x)
attn_output, _ = self.src_attn(x, src, src, attn_mask=src_mask)
x = x + self.dropout2(attn_output)
x = self.norm2(x)
fc_output = self.fc(x)
x = x + self.dropout3(fc_output)
x = self.norm3(x)
return x
```
这是一个基于PyTorch实现的Transformer结构,包括了Encoder和Decoder两个部分,可以用于机器翻译等任务。