if self.weight_method == 'attn': edge_weights = [tf.cast(var, dtype=dtype) for var in self.vars] normalized_weights = tf.nn.softmax(tf.stack(edge_weights)) nodes = tf.stack(nodes, axis=-1) new_node = tf.reduce_sum(nodes * normalized_weights, -1)

这段代码看起来是一个图神经网络中的计算步骤。根据代码的逻辑，首先将self.vars中的变量转换为指定的数据类型dtype。然后使用tf.nn.softmax函数对这些变量进行归一化处理，得到标准化后的权重值。接下来，使用tf.stack函数将节点列表nodes沿着最后一个维度堆叠起来，并且通过乘以标准化的权重值，得到加权后的节点表示。最后，使用tf.reduce_sum函数对加权后的节点表示进行求和操作，得到新的节点表示new_node。

这段代码的功能是将节点列表nodes按照权重进行加权求和，得到新的节点表示。具体的实现逻辑可能需要参考其他部分的代码来进行全面理解。

相关问题

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。

有参考代码吗？

是的，以下是一个使用PyTorch搭建的GPT-2模型的参考代码，供您参考：

import torch
import torch.nn as nn
import torch.nn.functional as F

class GPT2(nn.Module):
    def __init__(self, n_vocab, n_ctx, n_embd, n_head, n_layer):
        super(GPT2, self).__init__()

        self.n_vocab = n_vocab
        self.n_ctx = n_ctx
        self.n_embd = n_embd
        self.n_head = n_head
        self.n_layer = n_layer

        self.wte = nn.Embedding(n_vocab, n_embd)
        self.wpe = nn.Embedding(n_ctx, n_embd)
        self.drop = nn.Dropout(0.1)
        self.h = nn.ModuleList([Block(n_embd, n_head, n_ctx) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd, eps=1e-5)

        self.init_weights()

    def init_weights(self):
        nn.init.normal_(self.wte.weight, std=0.02)
        nn.init.normal_(self.wpe.weight, std=0.01)
        nn.init.normal_(self.ln_f.weight, std=0.02)
        nn.init.zeros_(self.ln_f.bias)

    def forward(self, input_ids, position_ids=None, token_type_ids=None):
        if position_ids is None:
            position_ids = torch.arange(input_ids.shape[1], dtype=torch.long, device=input_ids.device)
            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
        if token_type_ids is None:
            token_type_ids = torch.zeros_like(input_ids)

        input_embeds = self.wte(input_ids)
        position_embeds = self.wpe(position_ids)
        token_type_embeds = self.wte(token_type_ids)
        hidden_states = input_embeds + position_embeds + token_type_embeds
        hidden_states = self.drop(hidden_states)

        for i in range(self.n_layer):
            block = self.h[i]
            hidden_states = block(hidden_states)

        hidden_states = self.ln_f(hidden_states)

        return hidden_states

class Block(nn.Module):
    def __init__(self, n_embd, n_head, n_ctx):
        super(Block, self).__init__()

        self.n_embd = n_embd
        self.n_head = n_head

        self.ln_1 = nn.LayerNorm(n_embd, eps=1e-5)
        self.attn = Attention(n_embd, n_head, n_ctx)
        self.ln_2 = nn.LayerNorm(n_embd, eps=1e-5)
        self.mlp = MLP(n_embd*4, n_embd)

    def forward(self, x):
        h = x

        x = self.ln_1(x)
        x = self.attn(x)
        x = h + x

        h = x

        x = self.ln_2(x)
        x = self.mlp(x)
        x = h + x

        return x

class Attention(nn.Module):
    def __init__(self, n_embd, n_head, n_ctx):
        super(Attention, self).__init__()

        self.n_embd = n_embd
        self.n_head = n_head
        self.split_size = n_embd // n_head
        self.scale = self.split_size ** -0.5

        self.c_attn = nn.Linear(n_embd, n_embd*3)
        self.c_proj = nn.Linear(n_embd, n_embd)

    def split_heads(self, x):
        x = x.view(x.shape[0], x.shape[1], self.n_head, self.split_size)
        return x.permute(0, 2, 1, 3)

    def forward(self, x):
        qkv = self.c_attn(x)
        q, k, v = torch.split(qkv, qkv.shape[-1] // 3, dim=-1)

        q = self.split_heads(q)
        k = self.split_heads(k)
        v = self.split_heads(v)

        scores = torch.matmul(q, k.transpose(-1, -2)) * self.scale
        attn_weights = F.softmax(scores, dim=-1)
        attn_output = torch.matmul(attn_weights, v)
        attn_output = attn_output.permute(0, 2, 1, 3).contiguous()
        attn_output = attn_output.view(attn_output.shape[0], attn_output.shape[1], -1)
        attn_output = self.c_proj(attn_output)

        return attn_output

class MLP(nn.Module):
    def __init__(self, n_embd, n_hidden):
        super(MLP, self).__init__()

        self.n_embd = n_embd
        self.n_hidden = n_hidden

        self.c_fc = nn.Linear(n_embd, n_hidden)
        self.c_proj = nn.Linear(n_hidden, n_embd)

    def forward(self, x):
        x = F.gelu(self.c_fc(x))
        x = self.c_proj(x)
        return x

此代码实现了一个基于GPT-2的语言模型，包括基本的Attention机制、LayerNorm、MLP等模块。您可以根据需要进行修改和扩展。