transformer的代码实现以及注释
时间: 2023-03-19 10:25:20 浏览: 106
以下是一个简单的 Transformer 模型的代码实现,其中包括注释以帮助理解:
```python
import torch
import torch.nn as nn
class Transformer(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_heads, num_layers, dropout_prob):
super().__init__()
# 学习输入嵌入
self.input_embedding = nn.Embedding(input_dim, hidden_dim)
# 多头自注意力层
self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=dropout_prob)
# 位置编码器
self.positional_encoding = PositionalEncoding(hidden_dim, dropout_prob)
# 编码器层
self.encoder_layers = nn.ModuleList([
EncoderLayer(hidden_dim, num_heads, dropout_prob) for _ in range(num_layers)
])
# 输出层
self.output_layer = nn.Linear(hidden_dim, output_dim)
def forward(self, inputs, mask):
# 嵌入输入并增加位置编码
embedded_inputs = self.input_embedding(inputs)
encoded_inputs = self.positional_encoding(embedded_inputs)
# 使用自注意力层进行编码
self_attended_inputs, _ = self.self_attention(encoded_inputs, encoded_inputs, encoded_inputs, attn_mask=mask)
encoded_inputs = encoded_inputs + self_attended_inputs
# 经过多个编码器层进行编码
for encoder_layer in self.encoder_layers:
encoded_inputs = encoder_layer(encoded_inputs, mask)
# 计算输出
outputs = self.output_layer(encoded_inputs)
return outputs
class PositionalEncoding(nn.Module):
def __init__(self, hidden_dim, dropout_prob, max_length=5000):
super().__init__()
self.dropout = nn.Dropout(p=dropout_prob)
# 生成位置嵌入
position = torch.arange(0, max_length).unsqueeze(1)
div_term = torch.exp(torch.arange(0, hidden_dim, 2) * -(math.log(10000.0) / hidden_dim))
pe = torch.zeros(1, max_length, hidden_dim)
pe[0, :, 0::2] = torch.sin(position * div_term)
pe[0, :, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:, :x.size(1)]
return self.dropout(x)
class EncoderLayer(nn.Module):
def __init__(self, hidden_dim, num_heads, dropout_prob):
super().__init__()
# 多头注意力层和全连接层
self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=dropout_prob)
self.feedforward = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim * 4),
nn.ReLU(),
nn.Linear(hidden_dim * 4, hidden_dim)
)
# Layer Normalization
self.norm1 = nn.LayerNorm(hidden_dim)
self.norm2 = nn.LayerNorm(hidden_dim)
# Dropout
self.dropout = nn.Dropout(p=dropout_prob)
def forward(self, x, mask):
# 多头自注意力和Layer Normalization
attended, _ = self.self_attention(x, x, x, attn_mask=mask)
x = self.norm1(x + self.dropout(attended))
# 前馈神经网络和Layer Normalization
feedforward_output =