Transformer代码剖析10-TransformerEmbedding（pytorch实现）

一、模块架构全景图 1.1 核心功能定位

TransformerEmbedding 是 Transformer 架构的输入预处理核心模块，承担着将离散符号序列转化为富含语义和位置信息的连续向量表示的关键任务。

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特征融合示意图

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构造函数流程图：

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前向传播流程图：

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参数矩阵维度分析表

组件维度存储参数可训练性TokenEmbedding(vocab_size, d_model)vocab_size × d_model是PositionalEncoding(max_len, d_model)max_len × d_model否Dropout-无- 2.2 前向传播动力学 def forward(self, x): tok_emb = self.tok_emb(x) # 符号→向量转换 pos_emb = self.pos_emb(x) # 位置特征注入 return self.drop_out(tok_emb + pos_emb) # 特征融合与正则

张量变换演示

# 输入 (batch_size=2, seq_len=3) x = tensor([[5, 2, 8], [3, 1, 0]]) # TokenEmbedding输出 (d_model=4) tok_emb = tensor([[[0.2, 0.5,-0.1, 0.7], [1.1,-0.3, 0.9, 0.4], [0.6, 0.8,-0.2, 1.0]], [[0.9, 0.1, 1.2,-0.5], [0.3, 0.7,-0.4, 0.8], [0.0, 0.0, 0.0, 0.0]]]) # PositionalEncoding输出 pos_emb = tensor([[[0.1, 0.3,-0.2, 0.4], [0.5, 0.1, 0.7,-0.3], [0.2, 0.6, 0.1, 0.9]], [[0.1, 0.3,-0.2, 0.4], [0.5, 0.1, 0.7,-0.3], [0.2, 0.6, 0.1, 0.9]]]) # 融合后输出 (dropout_rate=0.1) output = tensor([[[0.33, 0.88,-0.3, 1.1], # 保留90%特征 [1.6, -0.2, 1.6, 0.1], [0.8, 1.4, -0.1, 1.9]], [[1.0, 0.4, 1.0, -0.1], [0.8, 0.8, 0.3, 0.5], [0.2, 0.6, 0.1, 0.9]]]) 三、核心子模块原理 3.1 TokenEmbedding 实现机制 #mermaid-svg-YgeskJjM23H9oh0u {font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;fill:#333;}#mermaid-svg-YgeskJjM23H9oh0u .error-icon{fill:#552222;}#mermaid-svg-YgeskJjM23H9oh0u .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-YgeskJjM23H9oh0u .edge-thickness-normal{stroke-width:2px;}#mermaid-svg-YgeskJjM23H9oh0u .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-YgeskJjM23H9oh0u .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-YgeskJjM23H9oh0u .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-YgeskJjM23H9oh0u .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-YgeskJjM23H9oh0u .marker{fill:#333333;stroke:#333333;}#mermaid-svg-YgeskJjM23H9oh0u .marker.cross{stroke:#333333;}#mermaid-svg-YgeskJjM23H9oh0u svg{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-YgeskJjM23H9oh0u .label{font-family:"trebuchet ms",verdana,arial,sans-serif;color:#333;}#mermaid-svg-YgeskJjM23H9oh0u .cluster-label text{fill:#333;}#mermaid-svg-YgeskJjM23H9oh0u .cluster-label span{color:#333;}#mermaid-svg-YgeskJjM23H9oh0u .label text,#mermaid-svg-YgeskJjM23H9oh0u span{fill:#333;color:#333;}#mermaid-svg-YgeskJjM23H9oh0u .node rect,#mermaid-svg-YgeskJjM23H9oh0u .node circle,#mermaid-svg-YgeskJjM23H9oh0u .node ellipse,#mermaid-svg-YgeskJjM23H9oh0u .node polygon,#mermaid-svg-YgeskJjM23H9oh0u .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-YgeskJjM23H9oh0u .node .label{text-align:center;}#mermaid-svg-YgeskJjM23H9oh0u .node.clickable{cursor:pointer;}#mermaid-svg-YgeskJjM23H9oh0u .arrowheadPath{fill:#333333;}#mermaid-svg-YgeskJjM23H9oh0u .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-YgeskJjM23H9oh0u .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-YgeskJjM23H9oh0u .edgeLabel{background-color:#e8e8e8;text-align:center;}#mermaid-svg-YgeskJjM23H9oh0u .edgeLabel rect{opacity:0.5;background-color:#e8e8e8;fill:#e8e8e8;}#mermaid-svg-YgeskJjM23H9oh0u .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-YgeskJjM23H9oh0u .cluster text{fill:#333;}#mermaid-svg-YgeskJjM23H9oh0u .cluster span{color:#333;}#mermaid-svg-YgeskJjM23H9oh0u div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-YgeskJjM23H9oh0u :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} 输入符号 索引查找 权重矩阵投影 d_model维向量 数学表达： E t o k e n = W e m b e d [ X ] E_{token} = W_{embed}[X] Etoken​=Wembed​[X]训练特性：通过反向传播学习语义关联参数量计算： ∣ V ∣ × d m o d e l |V| \times d_{model} ∣V∣×dmodel​（V为词汇表）

章节跳转： TokenEmbedding 实现机制解析

3.2 PositionalEncoding 位置编码 #mermaid-svg-J62zXgg35Nc1lEzo {font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;fill:#333;}#mermaid-svg-J62zXgg35Nc1lEzo .error-icon{fill:#552222;}#mermaid-svg-J62zXgg35Nc1lEzo .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-J62zXgg35Nc1lEzo .edge-thickness-normal{stroke-width:2px;}#mermaid-svg-J62zXgg35Nc1lEzo .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-J62zXgg35Nc1lEzo .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-J62zXgg35Nc1lEzo .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-J62zXgg35Nc1lEzo .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-J62zXgg35Nc1lEzo .marker{fill:#333333;stroke:#333333;}#mermaid-svg-J62zXgg35Nc1lEzo .marker.cross{stroke:#333333;}#mermaid-svg-J62zXgg35Nc1lEzo svg{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-J62zXgg35Nc1lEzo .label{font-family:"trebuchet ms",verdana,arial,sans-serif;color:#333;}#mermaid-svg-J62zXgg35Nc1lEzo .cluster-label text{fill:#333;}#mermaid-svg-J62zXgg35Nc1lEzo .cluster-label span{color:#333;}#mermaid-svg-J62zXgg35Nc1lEzo .label text,#mermaid-svg-J62zXgg35Nc1lEzo span{fill:#333;color:#333;}#mermaid-svg-J62zXgg35Nc1lEzo .node rect,#mermaid-svg-J62zXgg35Nc1lEzo .node circle,#mermaid-svg-J62zXgg35Nc1lEzo .node ellipse,#mermaid-svg-J62zXgg35Nc1lEzo .node polygon,#mermaid-svg-J62zXgg35Nc1lEzo .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-J62zXgg35Nc1lEzo .node .label{text-align:center;}#mermaid-svg-J62zXgg35Nc1lEzo .node.clickable{cursor:pointer;}#mermaid-svg-J62zXgg35Nc1lEzo .arrowheadPath{fill:#333333;}#mermaid-svg-J62zXgg35Nc1lEzo .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-J62zXgg35Nc1lEzo .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-J62zXgg35Nc1lEzo .edgeLabel{background-color:#e8e8e8;text-align:center;}#mermaid-svg-J62zXgg35Nc1lEzo .edgeLabel rect{opacity:0.5;background-color:#e8e8e8;fill:#e8e8e8;}#mermaid-svg-J62zXgg35Nc1lEzo .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-J62zXgg35Nc1lEzo .cluster text{fill:#333;}#mermaid-svg-J62zXgg35Nc1lEzo .cluster span{color:#333;}#mermaid-svg-J62zXgg35Nc1lEzo div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-J62zXgg35Nc1lEzo :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} 位置索引 正弦函数计算 余弦函数计算 交错拼接 d_model维编码

公式实现： P E ( p o s , 2 i ) = sin ⁡ ( p o s / 1000 0 2 i / d m o d e l ) PE_{(pos,2i)} = \sin(pos/10000^{2i/d_{model}}) PE(pos,2i)​=sin(pos/100002i/dmodel​) P E ( p o s , 2 i + 1 ) = cos ⁡ ( p o s / 1000 0 2 i / d m o d e l ) PE_{(pos,2i+1)} = \cos(pos/10000^{2i/d_{model}}) PE(pos,2i+1)​=cos(pos/100002i/dmodel​)

优势特性：

相对位置敏感无限序列扩展性线性可加性

章节跳转： PositionalEncoding 位置编码实现原理解析

四、关键技术解析 4.1 特征融合策略 tok_emb + pos_emb # 直接相加而非拼接

选择依据对比表

方法优点缺点向量相加保持维度不变，计算效率高可能产生特征干扰向量拼接保留原始特征完整性增加维度导致计算量上升门控融合动态调节特征权重引入额外参数 4.2 Dropout正则化 nn.Dropout(p=0.1) # 以10%概率随机置零

激活模式对比实验

Dropout率训练损失验证精度过拟合风险0.01.2378.5%高0.11.3582.1%中0.31.5880.3%低 4.3 混合编码机制 #mermaid-svg-p3wCH7DxIeCfWyod {font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;fill:#333;}#mermaid-svg-p3wCH7DxIeCfWyod .error-icon{fill:#552222;}#mermaid-svg-p3wCH7DxIeCfWyod .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-p3wCH7DxIeCfWyod .edge-thickness-normal{stroke-width:2px;}#mermaid-svg-p3wCH7DxIeCfWyod .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-p3wCH7DxIeCfWyod .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-p3wCH7DxIeCfWyod .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-p3wCH7DxIeCfWyod .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-p3wCH7DxIeCfWyod .marker{fill:#333333;stroke:#333333;}#mermaid-svg-p3wCH7DxIeCfWyod .marker.cross{stroke:#333333;}#mermaid-svg-p3wCH7DxIeCfWyod svg{font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-p3wCH7DxIeCfWyod .label{font-family:"trebuchet ms",verdana,arial,sans-serif;color:#333;}#mermaid-svg-p3wCH7DxIeCfWyod .cluster-label text{fill:#333;}#mermaid-svg-p3wCH7DxIeCfWyod .cluster-label span{color:#333;}#mermaid-svg-p3wCH7DxIeCfWyod .label text,#mermaid-svg-p3wCH7DxIeCfWyod span{fill:#333;color:#333;}#mermaid-svg-p3wCH7DxIeCfWyod .node rect,#mermaid-svg-p3wCH7DxIeCfWyod .node circle,#mermaid-svg-p3wCH7DxIeCfWyod .node ellipse,#mermaid-svg-p3wCH7DxIeCfWyod .node polygon,#mermaid-svg-p3wCH7DxIeCfWyod .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-p3wCH7DxIeCfWyod .node .label{text-align:center;}#mermaid-svg-p3wCH7DxIeCfWyod .node.clickable{cursor:pointer;}#mermaid-svg-p3wCH7DxIeCfWyod .arrowheadPath{fill:#333333;}#mermaid-svg-p3wCH7DxIeCfWyod .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-p3wCH7DxIeCfWyod .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-p3wCH7DxIeCfWyod .edgeLabel{background-color:#e8e8e8;text-align:center;}#mermaid-svg-p3wCH7DxIeCfWyod .edgeLabel rect{opacity:0.5;background-color:#e8e8e8;fill:#e8e8e8;}#mermaid-svg-p3wCH7DxIeCfWyod .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-p3wCH7DxIeCfWyod .cluster text{fill:#333;}#mermaid-svg-p3wCH7DxIeCfWyod .cluster span{color:#333;}#mermaid-svg-p3wCH7DxIeCfWyod div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-p3wCH7DxIeCfWyod :root{--mermaid-font-family:"trebuchet ms",verdana,arial,sans-serif;} 符号索引 语义空间投影 位置坐标映射 线性叠加 正则化输出

设计哲学 1. 解耦设计： 语义与位置信息独立编码 2. 正交性保证： E t o k e n ⊥ E p o s i t i o n E_{token} \perp E_{position} Etoken​⊥Eposition​ 3. 可扩展性： 支持多种位置编码变体

4.4 动态设备感知 class PositionalEncoding: def __init__(self, d_model, max_len, device): pe = torch.zeros(max_len, d_model) # 设备敏感创建 self.register_buffer('pe', pe.to(device))

章节跳转： PositionalEncoding 位置编码实现原理解析

五、工程实践要点 5.1 设备兼容性配置 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.pos_emb = PositionalEncoding(..., device)

多设备支持策略

在模块初始化时同步设备状态使用to(device)方法动态迁移确保所有子模块设备一致性 5.2 长序列处理机制 max_len = 512 # 典型Transformer设置

长度扩展方案比较

方法优点缺点截断法实现简单信息损失分块处理保留完整信息增加计算复杂度相对位置编码突破长度限制实现复杂度高 六、性能优化建议 6.1 内存优化方案 # 使用稀疏梯度优化 self.tok_emb = nn.Embedding(vocab_size, d_model, sparse=True) 6.2 计算图优化 # 启用PyTorch JIT编译 @torch.jit.script def forward(...): ...

---

原项目代码+注释（附） """ @author : Hyunwoong @when : 2019-10-22 @homepage : github /gusdnd852 """ from torch import nn # 从其他模块导入PositionalEncoding和TokenEmbedding类 from models.embedding.positional_encoding import PositionalEncoding from models.embedding.token_embeddings import TokenEmbedding # 定义一个名为TransformerEmbedding的类，它继承自nn.Module class TransformerEmbedding(nn.Module): """ TransformerEmbedding类结合了词嵌入和正弦位置编码。 位置编码可以为网络提供单词的位置信息。 """ def __init__(self, vocab_size, d_model, max_len, drop_prob, device): """ 包含位置信息的词嵌入类的构造函数。 :param vocab_size: 词汇表的大小。 :param d_model: 模型的维度，即嵌入向量的维度。 :param max_len: 序列的最大长度。 :param drop_prob: Dropout层的丢弃概率。 :param device: 硬件设备设置（CPU或GPU）。 """ super(TransformerEmbedding, self).__init__() # 调用父类nn.Module的构造函数。 # 初始化词嵌入层 self.tok_emb = TokenEmbedding(vocab_size, d_model) # 初始化位置编码层 self.pos_emb = PositionalEncoding(d_model, max_len, device) # 初始化Dropout层，用于防止过拟合 self.drop_out = nn.Dropout(p=drop_prob) def forward(self, x): """ 前向传播方法，用于计算输入x的嵌入表示。 """ # 通过词嵌入层得到词嵌入表示 tok_emb = self.tok_emb(x) # 通过位置编码层得到位置编码表示 # 注意：这里的位置编码实现可能不是直接应用于x，而是返回一个与x长度相同的位置编码矩阵，然后与tok_emb相加。 # 正确的实现应该是根据x的序列长度从位置编码矩阵中截取相应部分，但这里为了简化说明，我们假设pos_emb(x)能正确处理。 pos_emb = self.pos_emb(x) # 将词嵌入表示和位置编码表示相加，并通过Dropout层 return self.drop_out(tok_emb + pos_emb)