NLP实战8：图解 Transformer笔记

目录

1.Transformer宏观结构

2.Transformer结构细节

2.1输入

2.2编码部分

2.3解码部分

2.4多头注意力机制

2.5线性层和softmax

2.6 损失函数

3.参考代码

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作者：[K同学啊]

Transformer整体结构图，与seq2seq模型类似，Transformer模型结构中的左半部分为编码器（encoder），右半部分为解码器（decoder），接下来拆解Transformer。

1.Transformer宏观结构

Transformer模型类似于seq2seq结构，包含编码部分和解码部分。不同之处在于它能够并行计算整个序列输入，无需按时间步进行逐步处理。

其宏观结构如下：

其中，每层encoder由两部分组成：

• Self-Attention Layer
• Feed Forward Neural Network（前馈神经网络，FFNN）

decoder在encoder的Self-Attention和FFNN中间多加了一个Encoder-Decoder Attention层。该层的作用是帮助解码器集中注意力于输入序列中最相关的部分。

2.Transformer结构细节

2.1输入

Transformer的数据输入与seq2seq不同。除了词向量，Transformer还需要输入位置向量，用于确定每个单词的位置特征和句子中不同单词之间的距离特征。

2.2编码部分

编码部分的输入文本序列经过处理后得到向量序列，送入第一层编码器。每层编码器输出一个向量序列，作为下一层编码器的输入。第一层编码器的输入是融合位置向量的词向量，后续每层编码器的输入则是前一层编码器的输出。

2.3解码部分

最后一个编码器输出一组序列向量，作为解码器的K、V输入。

解码阶段的每个时间步输出一个翻译后的单词。当前时间步的解码器输出作为下一个时间步解码器的输入Q，与编码器的输出K、V共同组成下一步的输入。重复此过程直到输出一个结束符。

解码器中的 Self-Attention 层，和编码器中的 Self-Attention 层的区别：

• 在解码器里，Self-Attention 层只允许关注到输出序列中早于当前位置之前的单词。具体做法是：在 Self-Attention 分数经过 Softmax 层之前，屏蔽当前位置之后的那些位置（将Attention Score设置成-inf）。
• 解码器 Attention层是使用前一层的输出来构造Query 矩阵，而Key矩阵和Value矩阵来自于编码器最终的输出。

2.4多头注意力机制

Transformer论文引入了多头注意力机制（多个注意力头组成），以进一步完善Self-Attention。

• 它扩展了模型关注不同位置的能力
• 多头注意力机制赋予Attention层多个“子表示空间”。

残差链接&Normalize： 编码器和解码器的每个子层（Self-Attention 层和 FFNN）都有一个残差连接和层标准化（layer-normalization），细节如下图

2.5线性层和softmax

Decoder最终输出一个浮点数向量。通过线性层和Softmax，将该向量转换为一个包含模型输出词汇表中每个单词分数的logits向量（假设有10000个英语单词）。Softmax将这些分数转换为概率，使其总和为1。然后选择具有最高概率的数字对应的词作为该时间步的输出单词。

2.6 损失函数

在Transformer训练过程中，解码器的输出和标签一起输入损失函数，以计算损失（loss）。最终，模型通过方向传播（backpropagation）来优化损失。

3.参考代码

class SelfAttention(nn.Module):
	def __init__(self, embed_size, heads):
		super(SelfAttention, self).__init__()
		self.embed_size = embed_size
		self.heads = heads
		self.head_dim = embed_size // heads

		assert (self.head_dim * heads == embed_size), "Embed size needs to be div by heads"

		self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
		self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
		self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
		self.fc_out = nn.Linear(heads * self.head_dim, embed_size)

	def forward(self, values, keys, query, mask):
		N =query.shape[0]
		value_len , key_len , query_len = values.shape[1], keys.shape[1], query.shape[1]

		# split embedding into self.heads pieces
		values = values.reshape(N, value_len, self.heads, self.head_dim)
		keys = keys.reshape(N, key_len, self.heads, self.head_dim)
		queries = query.reshape(N, query_len, self.heads, self.head_dim)
		
		values = self.values(values)
		keys = self.keys(keys)
		queries = self.queries(queries)

		energy = torch.einsum("nqhd,nkhd->nhqk", queries, keys)
		# queries shape: (N, query_len, heads, heads_dim)
		# keys shape : (N, key_len, heads, heads_dim)
		# energy shape: (N, heads, query_len, key_len)

		if mask is not None:
			energy = energy.masked_fill(mask == 0, float("-1e20"))

		attention = torch.softmax(energy/ (self.embed_size ** (1/2)), dim=3)

		out = torch.einsum("nhql, nlhd->nqhd", [attention, values]).reshape(N, query_len, self.heads*self.head_dim)
		# attention shape: (N, heads, query_len, key_len)
		# values shape: (N, value_len, heads, heads_dim)
		# (N, query_len, heads, head_dim)

		out = self.fc_out(out)
		return out


class TransformerBlock(nn.Module):
	def __init__(self, embed_size, heads, dropout, forward_expansion):
		super(TransformerBlock, self).__init__()
		self.attention = SelfAttention(embed_size, heads)
		self.norm1 = nn.LayerNorm(embed_size)
		self.norm2 = nn.LayerNorm(embed_size)

		self.feed_forward = nn.Sequential(
			nn.Linear(embed_size, forward_expansion*embed_size),
			nn.ReLU(),
			nn.Linear(forward_expansion*embed_size, embed_size)
		)
		self.dropout = nn.Dropout(dropout)

	def forward(self, value, key, query, mask):
		attention = self.attention(value, key, query, mask)

		x = self.dropout(self.norm1(attention + query))
		forward = self.feed_forward(x)
		out = self.dropout(self.norm2(forward + x))
		return out


class Encoder(nn.Module):
	def __init__(
			self,
			src_vocab_size,
			embed_size,
			num_layers,
			heads,
			device,
			forward_expansion,
			dropout,
			max_length,
		):
		super(Encoder, self).__init__()
		self.embed_size = embed_size
		self.device = device
		self.word_embedding = nn.Embedding(src_vocab_size, embed_size)
		self.position_embedding = nn.Embedding(max_length, embed_size)

		self.layers = nn.ModuleList(
			[
				TransformerBlock(
					embed_size,
					heads,
					dropout=dropout,
					forward_expansion=forward_expansion,
					)
				for _ in range(num_layers)]
		)
		self.dropout = nn.Dropout(dropout)


	def forward(self, x, mask):
		N, seq_length = x.shape
		positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
		out = self.dropout(self.word_embedding(x) + self.position_embedding(positions))
		for layer in self.layers:
			out = layer(out, out, out, mask)

		return out


class DecoderBlock(nn.Module):
	def __init__(self, embed_size, heads, forward_expansion, dropout, device):
		super(DecoderBlock, self).__init__()
		self.attention = SelfAttention(embed_size, heads)
		self.norm = nn.LayerNorm(embed_size)
		self.transformer_block = TransformerBlock(
			embed_size, heads, dropout, forward_expansion
		)

		self.dropout = nn.Dropout(dropout)

	def forward(self, x, value, key, src_mask, trg_mask):
		attention = self.attention(x, x, x, trg_mask)
		query = self.dropout(self.norm(attention + x))
		out = self.transformer_block(value, key, query, src_mask)
		return out


class Decoder(nn.Module):
	def __init__(
			self,
			trg_vocab_size,
			embed_size,
			num_layers,
			heads,
			forward_expansion,
			dropout,
			device,
			max_length,
	):
		super(Decoder, self).__init__()
		self.device = device
		self.word_embedding = nn.Embedding(trg_vocab_size, embed_size)
		self.position_embedding = nn.Embedding(max_length, embed_size)
		self.layers = nn.ModuleList(
			[DecoderBlock(embed_size, heads, forward_expansion, dropout, device)
			for _ in range(num_layers)]
			)
		self.fc_out = nn.Linear(embed_size, trg_vocab_size)
		self.dropout = nn.Dropout(dropout)

	def forward(self, x ,enc_out , src_mask, trg_mask):
		N, seq_length = x.shape
		positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
		x = self.dropout((self.word_embedding(x) + self.position_embedding(positions)))

		for layer in self.layers:
			x = layer(x, enc_out, enc_out, src_mask, trg_mask)

		out =self.fc_out(x)
		return out


class Transformer(nn.Module):
	def __init__(
			self,
			src_vocab_size,
			trg_vocab_size,
			src_pad_idx,
			trg_pad_idx,
			embed_size = 256,
			num_layers = 6,
			forward_expansion = 4,
			heads = 8,
			dropout = 0,
			device="cuda",
			max_length=100
		):
		super(Transformer, self).__init__()
		self.encoder = Encoder(
			src_vocab_size,
			embed_size,
			num_layers,
			heads,
			device,
			forward_expansion,
			dropout,
			max_length
			)
		self.decoder = Decoder(
			trg_vocab_size,
			embed_size,
			num_layers,
			heads,
			forward_expansion,
			dropout,
			device,
			max_length
			)


		self.src_pad_idx = src_pad_idx
		self.trg_pad_idx = trg_pad_idx
		self.device = device


	def make_src_mask(self, src):
		src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)
		# (N, 1, 1, src_len)
		return src_mask.to(self.device)

	def make_trg_mask(self, trg):
		N, trg_len = trg.shape
		trg_mask = torch.tril(torch.ones((trg_len, trg_len))).expand(
			N, 1, trg_len, trg_len
		)
		return trg_mask.to(self.device)

	def forward(self, src, trg):
		src_mask = self.make_src_mask(src)
		trg_mask = self.make_trg_mask(trg)
		enc_src = self.encoder(src, src_mask)
		out = self.decoder(trg, enc_src, src_mask, trg_mask)
		return out