作者：黑夜路人
时间：2023年7月
Transformer Block （通用块）实现
看以上整个链路图，其实我们可以很清晰看到这心其实在Encoder环节里面主要是有几个大环节，每一层主要的核心作用如下：
- Multi-headed self Attention（注意力机制层）：通过不同的注意力函数并拼接结果,提高模型的表达能力，主要计算词与词的相关性和长距离次的相关性。
- Normalization layer（归一化层）：对每个隐层神经元进行归一化,使其特征值的均值为0,方差为1，解决梯度爆炸和消失问题。通过归一化,可以将数据压缩在一个合适范围内,避免出现超大或超小值,有利于模型训练，也能改善模型的泛化能力和加速模型收敛以及减少参数量的依赖。
- Feed forward network（前馈神经网络）：对注意力输出结果进行变换。
- Another normalization layer（另一个归一化层）：Weight Normalization用于对模型中层与层之间的权重矩阵进行归一化,其主要目的是解决梯度消失问题
注意力（Attention）实现
参考其他我们了解的信息可以看到里面核心的自注意力层等等，我们把每个层剥离看看核心这一层应该如何实现。
简单看看注意力的计算过程：
这张图所表示的大致运算过程是：
对于每个token，先产生三个向量query，key，value：
query向量类比于询问。某个token问：“其余的token都和我有多大程度的相关呀？”
key向量类比于索引。某个token说：“我把每个询问内容的回答都压缩了下装在我的key里”
value向量类比于回答。某个token说：“我把我自身涵盖的信息又抽取了一层装在我的value里”
注意力计算代码：
def attention(query: Tensor,
key: Tensor,
value: Tensor,
mask: Optional[Tensor] = None,
dropout: float = 0.1):
"""
定义如何计算注意力得分
参数:
query: shape (batch_size, num_heads, seq_len, k_dim)
key: shape(batch_size, num_heads, seq_len, k_dim)
value: shape(batch_size, num_heads, seq_len, v_dim)
mask: shape (batch_size, num_heads, seq_len, seq_len). Since our assumption, here the shape is
(1, 1, seq_len, seq_len)
返回:
out: shape (batch_size, v_dim). 注意力头的输出。注意力分数：形状（seq_len，seq_ln）。
"""
k_dim = query.size(-1)
# shape (seq_len ,seq_len)，row: token，col: token记号的注意力得分
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(k_dim)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e10)
attention_score = F.softmax(scores, dim=-1)
if dropout is not None:
attention_score = dropout(attention_score)
out = torch.matmul(attention_score, value)
return out, attention_score # shape: (seq_len, v_dim), (seq_len, seq_lem)以图中的token a2为例：
它产生一个query，每个query都去和别的token的key做“某种方式”的计算，得到的结果我们称为attention score（即为图中的$$\alpha $$）。则一共得到四个attention score。（attention score又可以被称为attention weight）。
将这四个score分别乘上每个token的value，我们会得到四个抽取信息完毕的向量。
将这四个向量相加，就是最终a2过attention模型后所产生的结果b2。
整个这一层，我们通过代码来进行这个逻辑的简单实现：
class MultiHeadedAttention(nn.Module):
def __init__(self,
num_heads: int,
d_model: int,
dropout: float = 0.1):
super(MultiHeadedAttention, self).__init__()
assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
# 假设v_dim总是等于k_dim
self.k_dim = d_model // num_heads
self.num_heads = num_heads
self.proj_weights = clones(
nn.Linear(d_model, d_model), 4) # W^Q, W^K, W^V, W^O
self.attention_score = None
self.dropout = nn.Dropout(p=dropout)
def forward(self,
query: Tensor,
key: Tensor,
value: Tensor,
mask: Optional[Tensor] = None):
"""
参数:
query: shape (batch_size, seq_len, d_model)
key: shape (batch_size, seq_len, d_model)
value: shape (batch_size, seq_len, d_model)
mask: shape (batch_size, seq_len, seq_len). 由于我们假设所有数据都使用相同的掩码，因此这里的形状也等于（1，seq_len，seq-len）
返回:
out: shape (batch_size, seq_len, d_model). 多头注意力层的输出
"""
if mask is not None:
mask = mask.unsqueeze(1)
batch_size = query.size(0)
# 1) 应用W^Q、W^K、W^V生成新的查询、键、值
query, key, value \
= [proj_weight(x).view(batch_size, -1, self.num_heads, self.k_dim).transpose(1, 2)
for proj_weight, x in zip(self.proj_weights, [query, key, value])] # -1 equals to seq_len
# 2) 计算注意力得分和out
out, self.attention_score = attention(query, key, value, mask=mask,
dropout=self.dropout)
# 3) "Concat" 输出
out = out.transpose(1, 2).contiguous() \
.view(batch_size, -1, self.num_heads * self.k_dim)
# 4) 应用W^O以获得最终输出
out = self.proj_weights[-1](out)
return out
Norm 归一化层实现
# 归一化层,标准化的计算公式
class NormLayer(nn.Module):
def __init__(self, features, eps=1e-6):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.a_2 * (x - mean) / (std + self.eps) + self.b_2前馈神经网络实现
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff=2048, dropout=0.1):
super().__init__()
# 设置 d_ff 缺省值为2048
self.linear_1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.linear_2 = nn.Linear(d_ff, d_model)
def forward(self, x):
x = self.dropout(F.relu(self.linear_1(x)))
x = self.linear_2(x)Encoder（编码器曾）实现
Encoder 就是将前面介绍的整个链路部分，全部组装迭代起来，完成将源编码到中间编码的转换。
class EncoderLayer(nn.Module):
def __init__(self, d_model, heads, dropout=0.1):
super().__init__()
self.norm_1 = Norm(d_model)
self.norm_2 = Norm(d_model)
self.attn = MultiHeadAttention(heads, d_model, dropout=dropout)
self.ff = FeedForward(d_model, dropout=dropout)
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
def forward(self, x, mask):
x2 = self.norm_1(x)
x = x + self.dropout_1(self.attn(x2, x2, x2, mask))
x2 = self.norm_2(x)
x = x + self.dropout_2(self.ff(x2))
return xclass Encoder(nn.Module):
def __init__(self, vocab_size, d_model, N, heads, dropout):
super().__init__()
self.N = N
self.embed = Embedder(d_model, vocab_size)
self.pe = PositionalEncoder(d_model, dropout=dropout)
self.layers = get_clones(EncoderLayer(d_model, heads, dropout), N)
self.norm = Norm(d_model)
def forward(self, src, mask):
x = self.embed(src)
x = self.pe(x)
for i in range(self.N):
x = self.layers[i](x, mask)
return self.norm(x)Decoder（解码器层）实现
Decoder部分和 Encoder 的部分非常的相似，它主要是把 Encoder 生成的中间编码，转换为目标编码。
class DecoderLayer(nn.Module):
def __init__(self, d_model, heads, dropout=0.1):
super().__init__()
self.norm_1 = Norm(d_model)
self.norm_2 = Norm(d_model)
self.norm_3 = Norm(d_model)
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
self.dropout_3 = nn.Dropout(dropout)
self.attn_1 = MultiHeadAttention(heads, d_model, dropout=dropout)
self.attn_2 = MultiHeadAttention(heads, d_model, dropout=dropout)
self.ff = FeedForward(d_model, dropout=dropout)
def forward(self, x, e_outputs, src_mask, trg_mask):
x2 = self.norm_1(x)
x = x + self.dropout_1(self.attn_1(x2, x2, x2, trg_mask))
x2 = self.norm_2(x)
x = x + self.dropout_2(self.attn_2(x2, e_outputs, e_outputs,
src_mask))
x2 = self.norm_3(x)
x = x + self.dropout_3(self.ff(x2))
return xclass Decoder(nn.Module):
def __init__(self, vocab_size, d_model, N, heads, dropout):
super().__init__()
self.N = N
self.embed = Embedder(vocab_size, d_model)
self.pe = PositionalEncoder(d_model, dropout=dropout)
self.layers = get_clones(DecoderLayer(d_model, heads, dropout), N)
self.norm = Norm(d_model)
def forward(self, trg, e_outputs, src_mask, trg_mask):
x = self.embed(trg)
x = self.pe(x)
for i in range(self.N):
x = self.layers[i](x, e_outputs, src_mask, trg_mask)
return self.norm(x)Transformer 实现
把整个链路结合，包括Encoder和Decoder，最终就能够形成一个Transformer框架的基本MVP实现。
class Transformer(nn.Module):
def __init__(self, src_vocab, trg_vocab, d_model, N, heads, dropout):
super().__init__()
self.encoder = Encoder(src_vocab, d_model, N, heads, dropout)
self.decoder = Decoder(trg_vocab, d_model, N, heads, dropout)
self.out = nn.Linear(d_model, trg_vocab)
def forward(self, src, trg, src_mask, trg_mask):
e_outputs = self.encoder(src, src_mask)
d_output = self.decoder(trg, e_outputs, src_mask, trg_mask)
output = self.out(d_output)
return output代码说明
如果想要学习阅读整个代码，访问 black-transformer 项目，github访问地址：
取代你的不是AI，而是比你更了解AI和更会使用AI的人！
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