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。