Transformer 源代码解析

Transformer 是一种基于自注意力机制的深度神经网络模型，它被广泛应用于自然语言处理任务中。以下是对 Transformer 源代码的简要解析。

模型结构

Transformer 模型主要由以下几部分组成：

Encoder：编码器负责将输入序列转换为隐藏状态。
Decoder：解码器负责根据隐藏状态生成输出序列。
Attention Mechanism：注意力机制是 Transformer 的核心，它允许模型关注输入序列中的不同部分。

代码解析

以下是一些关键代码片段的解析：

Encoder

class Encoder(nn.Module):
    def __init__(self, d_model, nhead, num_layers):
        super(Encoder, self).__init__()
        self.layer = nn.ModuleList([EncoderLayer(d_model, nhead) for _ in range(num_layers)])
        self.norm = nn.LayerNorm(d_model)

    def forward(self, src):
        for layer in self.layer:
            src = layer(src)
        src = self.norm(src)
        return src

Decoder

class Decoder(nn.Module):
    def __init__(self, d_model, nhead, num_layers):
        super(Decoder, self).__init__()
        self.layer = nn.ModuleList([DecoderLayer(d_model, nhead) for _ in range(num_layers)])
        self.norm = nn.LayerNorm(d_model)

    def forward(self, tgt, memory):
        for layer in self.layer:
            tgt, _ = layer(tgt, memory)
        tgt = self.norm(tgt)
        return tgt

Attention Mechanism

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, nhead):
        super(MultiHeadAttention, self).__init__()
        assert d_model % nhead == 0
        self.d_k = d_model // nhead
        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.out_linear = nn.Linear(d_model, d_model)
        self.nhead = nhead

    def forward(self, query, key, value, mask=None):
        # Multi-head attention implementation
        pass

扩展阅读

想了解更多关于 Transformer 的信息，可以阅读以下文章：