一个简化的 Transformer 编码器（Encoder）和解码器（Decoder）的 PyTorch 代码示例
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2024-05-16
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`Transformer` 是由 Google 提出的用于自然语言处理（NLP）任务的一个深度学习模型架构，
它基于自注意力（self-attention）机制。以下是一个简化的 `Transformer` 编码器（Encoder）
和解码器（Decoder）的 PyTorch 代码示例。请注意，这个示例仅用于教学目的，并未包含
完整的 `Transformer` 架构（如位置编码、层归一化、残差连接等）。
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
# 这是一个简化的多头注意力机制实现，用于演示
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.d_model = d_model
assert d_model % self.num_heads == 0
self.depth = d_model // self.num_heads
self.wq = nn.Linear(d_model, d_model)
self.wk = nn.Linear(d_model, d_model)
self.wv = nn.Linear(d_model, d_model)
self.dense = nn.Linear(d_model, d_model)
def split_heads(self, x, batch_size):
x = x.reshape(batch_size, -1, self.num_heads, self.depth)
return x.permute(0, 2, 1, 3)
def forward(self, v, k, q, mask):
batch_size = q.shape[0]
q = self.wq(q) # (batch_size, seq_len, d_model)
k = self.wk(k) # (batch_size, seq_len, d_model)
v = self.wv(v) # (batch_size, seq_len, d_model)
q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)
k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)
v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)
# Scaled Dot-Product Attention
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