decoder的key_pos是如何使用，能否提供代码例子

在ChitGPT中，Decoder的key_pos是通过以下方式使用的：

首先，我们需要在Decoder的初始化函数中定义key_pos：

class Decoder(nn.Module):
    def __init__(self, num_layers, d_model, num_heads, dff, vocab_size, max_seq_len, rate=0.1):
        super(Decoder, self).__init__()
        self.d_model = d_model
        self.num_layers = num_layers
        
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.pos_encoding = positional_encoding(max_seq_len, d_model)
        
        self.dec_layers = [DecoderLayer(d_model, num_heads, dff, rate) for _ in range(num_layers)]
        self.dropout = nn.Dropout(rate)
        self.final_layer_norm = nn.LayerNorm(d_model)
        
        # Define key_pos
        self.key_pos = torch.arange(0, max_seq_len, dtype=torch.long).unsqueeze(0)

    def forward(self, x, enc_output, look_ahead_mask, padding_mask):
        seq_len = x.size(1)
        attention_weights = {}
        
        # Adding embedding and position encoding.
        x = self.embedding(x)  # (batch_size, target_seq_len, d_model)
        x *= torch.sqrt(torch.FloatTensor([self.d_model])).to(x.device)
        x += self.pos_encoding[:, :seq_len, :]
        
        # Applying dropout to the input of the decoder.
        x = self.dropout(x)
        
        for i in range(self.num_layers):
            x, block1, block2 = self.dec_layers[i](x, enc_output, look_ahead_mask, padding_mask)
            
            # Save attention weights
            attention_weights[f'decoder_layer{i+1}_block1'] = block1
            attention_weights[f'decoder_layer{i+1}_block2'] = block2
        
        # Applying a final layer normalization for decoder output.
        x = self.final_layer_norm(x)
        
        return x, attention_weights

然后，在DecoderLayer中，我们可以使用key_pos来计算注意力分数：

class DecoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(DecoderLayer, self).__init__()

        self.mha1 = MultiHeadAttention(d_model, num_heads)
        self.mha2 = MultiHeadAttention(d_model, num_heads)

        self.ffn = point_wise_feed_forward_network(d_model, dff)

        self.layer_norm1 = nn.LayerNorm(d_model)
        self.layer_norm2 = nn.LayerNorm(d_model)
        self.layer_norm3 = nn.LayerNorm(d_model)

        self.dropout1 = nn.Dropout(rate)
        self.dropout2 = nn.Dropout(rate)
        self.dropout3 = nn.Dropout(rate)

    def forward(self, x, enc_output, look_ahead_mask, padding_mask):
        # Multi-head attention layer with masking for decoder self-attention
        attn1, block1 = self.mha1(x, x, x, look_ahead_mask)
        attn1 = self.dropout1(attn1)
        out1 = self.layer_norm1(attn1 + x)

        # Multi-head attention layer with padding masking for encoder-decoder attention
        key_pos = self.mha2.decoder_key_pos.unsqueeze(0)
        attn2, block2 = self.mha2(out1, enc_output, enc_output, padding_mask, key_pos=key_pos)
        attn2 = self.dropout2(attn2)
        out2 = self.layer_norm2(attn2 + out1)

        # Point wise feed forward network
        ffn_output = self.ffn(out2)
        ffn_output = self.dropout3(ffn_output)
        out3 = self.layer_norm3(ffn_output + out2)

        return out3, block1, block2

在这个例子中，我们使用 `self.mha2` 来计算Decoder与Encoder之间的注意力，其中 `key_pos` 是 `self.mha2` 中的一个参数，它被设置为 `self.mha2.decoder_key_pos.unsqueeze(0)`，这将 `key_pos` 转换为一个形状为 `(1, max_seq_len)` 的张量，从而与encoder输出的形状相同。在计算注意力分数时，`key_pos` 用于查找encoder输出中每个位置的位置编码，以便在进行注意力计算时使用。