MultiHeadAttention实现详解

Transformer自2017年推出之后，已经横扫NLP领域，成为当之无愧的state-of-the-art。原始paper “Attention is All you Need”中对attention提出了通用的query/key/value抽象，开始时觉得很难理解，后来随着读的文献更多，慢慢体会到了其中的意思。关于Transformer和attention的各种解释类文章有很多，不再赘述，本文仅就其中的核心，MultiHeadAttention的实现进行源码剖析。

Transformer的实现有很多，但我看到实现得最clean的还是 # The Annotated Transformer。它的实现是一个最基本的版本，但五脏俱全，理解原理再合适不过。

MultiHeadAttention

如图所示，所谓Multi-Head Attention其实是把QKV的计算并行化，原始attention计算d_model维的向量，而Multi-Head Attention则是将d_model维向量先经过一个Linear Layer，再分解为h个Head计算attention，最终将这些attention向量连在一起后再经过一层Linear Layer输出。所以在整个过程中需要4个输入和输出维度都是d_model的Linear Layer，而整个Model的输入是(batch_size, seq_length, d_model)，输出也是(batch_size, seq_length, d_model)。

先上原始代码：

class MultiHeadedAttention(nn.Module): def __init__(self, h, d_model, dropout=0.1): 'Take in model size and number of heads.' super(MultiHeadedAttention, self).__init__() assert d_model % h == 0 # We assume d_v always equals d_k self.d_k = d_model // h self.h = h self.linears = clones(nn.Linear(d_model, d_model), 4) self.attn = None self.dropout = nn.Dropout(p=dropout)
def forward(self, query, key, value, mask=None): 'Implements Figure 2' if mask is not None: # Same mask applied to all h heads. mask = mask.unsqueeze(1) nbatches = query.size(0)
# 1) Do all the linear projections in batch from d_model => h x d_k query, key, value = \ [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) for l, x in zip(self.linears, (query, key, value))]
# 2) Apply attention on all the projected vectors in batch. x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
# 3) 'Concat' using a view and apply a final linear. x = x.transpose(1, 2).contiguous() \ .view(nbatches, -1, self.h * self.d_k) return self.linears[-1](x)

这段代码中最费解的地方：

# 1) Do all the linear projections in batch from d_model => h x d_kquery, key, value = \    [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)     for l, x in zip(self.linears, (query, key, value))]

前面提到MultiHeadAttention需要4个Linear Layer，而上面这段代码用到了其中前三个，最后一个用在最后一行self.linears[-1]。重写下这段代码：

query, key, value = [l(x) for l, x in zip(self.linears, (query, key, value))]query, key, value = [x.view(nbatches, -1, self.h, self.d_k).transpose(1, 2) for x in (query, key, value)]

第一行把QKV分别经过一层Linear变换，tensor size不变，第二行将QKV的d_model维向量分解为h * d_k。

跑一个self-attention的实例，作为输入，query/key/value的shape为(batch_size, seq_lengh, d_model):

h = 8d_model = 512batch_size = 1seq_length = 10model = MultiHeadAttention(h, d_model)
query = torch.randn([batch_size, seq_length, d_model])key = queryvalue = query
print ('Input size: ' + str(query.size()))

将代码中的tensor变换维度加上注释，类重命名为MultiHeadAttention，可运行的完整代码如下：

import torchimport torch.nn as nnimport torch.nn.functional as Fimport math, copy
def clones(module, N): 'Produce N identical layers.' return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
def attention(query, key, value, mask=None, dropout=None): 'Compute 'Scaled Dot Product Attention'' d_k = query.size(-1) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: scores = scores.masked_fill(mask == 0, -1e9) p_attn = F.softmax(scores, dim = -1) if dropout is not None: p_attn = dropout(p_attn) return torch.matmul(p_attn, value), p_attn
class MultiHeadAttention(nn.Module): def __init__(self, h, d_model, dropout=0.1): 'Take in model size and number of heads.' super(MultiHeadAttention, self).__init__() assert d_model % h == 0 # We assume d_v always equals d_k self.d_k = d_model // h self.h = h self.linears = clones(nn.Linear(d_model, d_model), 4) # create 4 linear layers self.attn = None self.dropout = nn.Dropout(p=dropout)
def forward(self, query, key, value, mask=None): if mask is not None: # Same mask applied to all h heads. mask = mask.unsqueeze(1) batch_size = query.size(0)
print ('Before transform query: ' + str(query.size())) # (batch_size, seq_length, d_model)
query, key, value = [l(x) for l, x in zip(self.linears, (query, key, value))] # (batch_size, seq_length, d_model), use first 3 self.linears query, key, value = [x.view(batch_size, -1, self.h, self.d_k).transpose(1, 2) for x in (query, key, value)] # (batch_size, h, seq_length, d_k)
print ('After transform query: ' + str(query.size()))
# 2) Apply attention on all the projected vectors in batch. x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
# 3) 'Concat' using a view and apply a final linear. x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.h * self.d_k) return self.linears[-1](x)

h = 8d_model = 512batch_size = 1seq_length = 10model = MultiHeadAttention(h, d_model)
query = torch.randn([batch_size, seq_length, d_model])key = queryvalue = query
print ('Input size: ' + str(query.size()))
m = model(query, key, value)
print ('Output size: ' + str(m.size()))

运行结果：

Input size: torch.Size([1, 10, 512])Before transform query: torch.Size([1, 10, 512])After transform query: torch.Size([1, 8, 10, 64])Output size: torch.Size([1, 10, 512])

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