src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)这段代码什么意思

这段代码的作用是生成一个源序列掩码，用于在Transformer模型中进行自注意力机制的计算。具体来说，它会首先检查源序列 `src` 中哪些位置是被填充符号（一般为0）所填充的，将这些位置的值设为0，其他位置的值设为1，得到一个与 `src` 相同形状的二元张量。然后，它会通过 `unsqueeze` 方法在第1个和第2个维度上各增加一个维度，使得掩码张量的形状变成 `(batch_size, 1, 1, src_len)`，其中 `batch_size` 是输入数据的批大小，`src_len` 是源序列的长度。这样生成的掩码张量可以直接与注意力矩阵相乘，实现对填充位置的屏蔽，只让真实的输入参与注意力计算。

相关问题

flatten transformer代码

flatten transformer是一种用于自然语言处理任务的模型架构，它基于transformer模型，并在其基础上进行了改进。下面是flatten transformer的代码介绍：

首先，我们需要导入所需的库和模块：

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

接下来，定义flatten transformer的主要模块，包括Encoder、Decoder和Transformer模块。

1. Encoder模块：

class Encoder(nn.Module):
    def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout):
        super().__init__()
        
        self.tok_embedding = nn.Embedding(input_dim, hid_dim)
        self.pos_embedding = nn.Embedding(max_len, hid_dim)
        
        self.layers = nn.ModuleList([EncoderLayer(hid_dim, n_heads, pf_dim, dropout) for _ in range(n_layers)])
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, src, src_mask):
        batch_size = src.shape
        src_len = src.shape
        
        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(device)
        
        src = self.dropout((self.tok_embedding(src) * math.sqrt(self.hid_dim)) + self.pos_embedding(pos))
        
        for layer in self.layers:
            src = layer(src, src_mask)
        
        return src

2. Decoder模块：

class Decoder(nn.Module):
    def __init__(self, output_dim, hid_dim, n_layers, n_heads, pf_dim, dropout):
        super().__init__()
        
        self.tok_embedding = nn.Embedding(output_dim, hid_dim)
        self.pos_embedding = nn.Embedding(max_len, hid_dim)
        
        self.layers = nn.ModuleList([DecoderLayer(hid_dim, n_heads, pf_dim, dropout) for _ in range(n_layers)])
        
        self.fc_out = nn.Linear(hid_dim, output_dim)
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, trg, enc_src, trg_mask, src_mask):
        batch_size = trg.shape
        trg_len = trg.shape
        
        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(device)
        
        trg = self.dropout((self.tok_embedding(trg) * math.sqrt(self.hid_dim)) + self.pos_embedding(pos))
        
        for layer in self.layers:
            trg, attention = layer(trg, enc_src, trg_mask, src_mask)
        
        output = self.fc_out(trg)
        
        return output, attention

3. Transformer模块：

class Transformer(nn.Module):
    def __init__(self, encoder, decoder, src_pad_idx, trg_pad_idx):
        super().__init__()
        
        self.encoder = encoder
        self.decoder = decoder
        self.src_pad_idx = src_pad_idx
        self.trg_pad_idx = trg_pad_idx
        
    def make_src_mask(self, src):
        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)
        return src_mask
    
    def make_trg_mask(self, trg):
        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(3)
        trg_len = trg.shape
        
        trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device=device)).bool()
        
        trg_mask = trg_pad_mask & trg_sub_mask
        
        return trg_mask
    
    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)
        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)
        
        return output, attention

以上是flatten transformer的主要代码介绍，其中包括Encoder、Decoder和Transformer模块的定义和前向传播过程。这些模块通过嵌套调用实现了flatten transformer的功能。

transformer原始代码生成

Transformer是一种用于自然语言处理任务的深度学习模型，它在机器翻译、文本生成等任务中取得了很好的效果。下面是Transformer模型的原始代码生成的步骤：

1. 导入所需的库和模块：

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

2. 定义Transformer模型的主要组件，包括Encoder、Decoder和整个Transformer模型：

class Encoder(nn.Module):
    def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout):
        super().__init__()
        self.input_dim = input_dim
        self.hid_dim = hid_dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.pf_dim = pf_dim
        self.dropout = dropout
        
        self.tok_embedding = nn.Embedding(input_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)
        
        self.layers = nn.ModuleList([EncoderLayer(hid_dim, n_heads, pf_dim, dropout) for _ in range(n_layers)])
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, src, src_mask):
        batch_size = src.shape[0]
        src_len = src.shape[1]
        
        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(src.device)
        
        src = self.dropout((self.tok_embedding(src) * math.sqrt(self.hid_dim)) + self.pos_embedding(pos))
        
        for layer in self.layers:
            src = layer(src, src_mask)
        
        return src

class Decoder(nn.Module):
    def __init__(self, output_dim, hid_dim, n_layers, n_heads, pf_dim, dropout):
        super().__init__()
        self.output_dim = output_dim
        self.hid_dim = hid_dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.pf_dim = pf_dim
        self.dropout = dropout
        
        self.tok_embedding = nn.Embedding(output_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)
        
        self.layers = nn.ModuleList([DecoderLayer(hid_dim, n_heads, pf_dim, dropout) for _ in range(n_layers)])
        
        self.fc_out = nn.Linear(hid_dim, output_dim)
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, trg, enc_src, trg_mask, src_mask):
        batch_size = trg.shape[0]
        trg_len = trg.shape[1]
        
        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(trg.device)
        
        trg = self.dropout((self.tok_embedding(trg) * math.sqrt(self.hid_dim)) + self.pos_embedding(pos))
        
        for layer in self.layers:
            trg, attention = layer(trg, enc_src, trg_mask, src_mask)
        
        output = self.fc_out(trg)
        
        return output, attention

class Transformer(nn.Module):
    def __init__(self, encoder, decoder, src_pad_idx, trg_pad_idx):
        super().__init__()
        self.encoder = encoder
        self.decoder = decoder
        self.src_pad_idx = src_pad_idx
        self.trg_pad_idx = trg_pad_idx
        
    def make_src_mask(self, src):
        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)
        return src_mask
    
    def make_trg_mask(self, trg):
        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(3)
        trg_len = trg.shape[1]
        trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len))).bool()
        trg_mask = trg_pad_mask & trg_sub_mask
        return trg_mask

    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)
        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)
        
        return output, attention

3. 定义Transformer模型的子组件，包括EncoderLayer和DecoderLayer：

class EncoderLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout):
        super().__init__()
        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, src, src_mask):
        _src, _ = self.self_attention(src, src, src, src_mask)
        
        src = self.self_attn_layer_norm(src + self.dropout(_src))
        
        _src = self.positionwise_feedforward(src)
        
        src = self.ff_layer_norm(src + self.dropout(_src))
        
        return src

class DecoderLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout):
        super().__init__()
        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.enc_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout)
        self.encoder_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, trg, enc_src, trg_mask, src_mask):
        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)
        
        trg = self.self_attn_layer_norm(trg + self.dropout(_trg))
        
        _trg, attention = self.encoder_attention(trg, enc_src, enc_src, src_mask)
        
        trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))
        
        _trg = self.positionwise_feedforward(trg)
        
        trg = self.ff_layer_norm(trg + self.dropout(_trg))
        
        return trg, attention

4. 定义Transformer模型的其他辅助组件，包括MultiHeadAttentionLayer和PositionwiseFeedforwardLayer：

class MultiHeadAttentionLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, dropout):
        super().__init__()
        self.hid_dim = hid_dim
        self.n_heads = n_heads
        self.head_dim = hid_dim // n_heads
        
        self.fc_q = nn.Linear(hid_dim, hid_dim)
        self.fc_k = nn.Linear(hid_dim, hid_dim)
        self.fc_v = nn.Linear(hid_dim, hid_dim)
        
        self.fc_o = nn.Linear(hid_dim, hid_dim)
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, query, key, value, mask=None):
        batch_size = query.shape[0]
        
        Q = self.fc_q(query)
        K = self.fc_k(key)
        V = self.fc_v(value)
        
        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        
        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / math.sqrt(self.head_dim)
        
        if mask is not None:
            energy = energy.masked_fill(mask == 0, -1e10)
        
        attention = torch.softmax(energy, dim=-1)
        
        x = torch.matmul(self.dropout(attention), V)
        
        x = x.permute(0, 2, 1, 3).contiguous()
        
        x = x.view(batch_size, -1, self.hid_dim)
        
        x = self.fc_o(x)
        
        return x, attention

class PositionwiseFeedforwardLayer(nn.Module):
    def __init__(self, hid_dim, pf_dim, dropout):
        super().__init__()
        self.fc_1 = nn.Linear(hid_dim, pf_dim)
        self.fc_2 = nn.Linear(pf_dim, hid_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        x = self.dropout(torch.relu(self.fc_1(x)))
        x = self.fc_2(x)
        return x

这些代码片段展示了Transformer模型的主要组件和辅助组件的实现。你可以根据需要进行修改和扩展。注意，这只是一个简化的示例，实际的Transformer模型可能还包括其他组件和功能。