参考链接
-
https://www.bilibili.com/video/BV1JE411g7XF?p=54
-
https://arxiv.org/abs/1706.03762
简述自注意力机制（self-attention）
self-attention可以视为一个特征提取层，给定输入特征
，经过self-attention layer，融合每个输入特征，得到新的特征
。具体如下：
设输入特征为
，分别将其乘以三个矩阵
、
和
得到
（query）、
（key）和
（value）三个矩阵；接下来使用矩阵
和
的乘积得到注意力矩阵
，归一化得到
；最后，将归一化后的注意力矩阵
乘上
，得到最后的输出特征
。
多头自注意力机制（multi-head self-attention）
上述的self-attention中，每个输入特征
乘上矩阵
、
和
后，分别得到一个向量
、
和
，称为单头自注意力机制。如果将这些向量
、
和
分裂为
个就得到
头自注意力机制了。公认多头自注意力机制的效果好于单头的，因为前者可以捕获更多维度的信息。示意图如下：
代码实现
设超参数num_attention_heads为自注意力机制的头数，如此，计算出每个头的维度attention_head_size。
self.num_attention_heads = num_attention_heads
self.attention_head_size = int(hidden_size / num_attention_heads)
self.all_head_size = hidden_size
定义
、
和
三个矩阵。
self.query = nn.Linear(input_size, self.all_head_size)
self.key = nn.Linear(input_size, self.all_head_size)
self.value = nn.Linear(input_size, self.all_head_size)
下面开始逐步计算，需要主要的是计算过程中张量维度的变化。
将输入特征乘以三个矩阵
、
和
，输出的张量此时还没有区分出多个头。维度变化为：input_tensor
%20)
到mixed_query_layer
%20)
mixed_query_layer = self.query(input_tensor)
mixed_key_layer = self.key(input_tensor)
mixed_value_layer = self.value(input_tensor)
切分为num_attention_heads个头，并变换维度。维度变化为：mixed_query_layer
到query_layer
%20)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
矩阵
和
相乘，得到注意力矩阵，并除以向量的维度的开方，防止注意力分数随维度增大而增大。维度变化为：query_layer
到attention_scores
%20)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
注意力矩阵归一化。维度变化为：attention_scores
到attention_probs
attention_probs = nn.Softmax(dim=-1)(attention_scores)
将注意力矩阵乘以矩阵
。维度变化为：ttention_probs
乘以value_layer
到context_layer
。
context_layer = torch.matmul(attention_probs, value_layer)
变换context_layer维度，为了后面将各头得到的结果拼接。这里的contiguous()是将tensor的内存变成连续的，为后面的view()做准备。维度变化为：context_layer
到context_layer
%20)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
将各头的结果拼接起来。维度变化为：context_layer
到context_layer
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
完整代码
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
super(LayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.weight * x + self.bias
class SelfAttention(nn.Module):
def __init__(self, num_attention_heads, input_size, hidden_size, hidden_dropout_prob):
super(SelfAttention, self).__init__()
if hidden_size % num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (hidden_size, num_attention_heads))
self.num_attention_heads = num_attention_heads
self.attention_head_size = int(hidden_size / num_attention_heads)
self.all_head_size = hidden_size
self.query = nn.Linear(input_size, self.all_head_size)
self.key = nn.Linear(input_size, self.all_head_size)
self.value = nn.Linear(input_size, self.all_head_size)
self.attn_dropout = nn.Dropout(attention_probs_dropout_prob)
# 做完self-attention 做一个前馈全连接 LayerNorm 输出
self.dense = nn.Linear(hidden_size, hidden_size)
self.LayerNorm = LayerNorm(hidden_size, eps=1e-12)
self.out_dropout = nn.Dropout(hidden_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, input_tensor):
mixed_query_layer = self.query(input_tensor)
mixed_key_layer = self.key(input_tensor)
mixed_value_layer = self.value(input_tensor)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
# [batch_size heads seq_len seq_len] scores
# [batch_size 1 1 seq_len]
# attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
# Fixme
attention_probs = self.attn_dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
hidden_states = self.dense(context_layer)
hidden_states = self.out_dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
