How to perform sequence generation tasks in the PaddlePaddle framework?

Seq2Seq models based on the Transformer architecture are commonly used for sequence generation tasks in the PaddlePaddle framework. Here is a simple example code that demonstrates how to implement a basic sequence generation task in PaddlePaddle.

import paddle
from paddle import nn

class Seq2SeqModel(nn.Layer):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(Seq2SeqModel, self).__init__()
        
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(embedding_dim, nhead=2, dim_feedforward=hidden_dim), num_layers=2)
        self.decoder = nn.TransformerDecoder(nn.TransformerDecoderLayer(embedding_dim, nhead=2, dim_feedforward=hidden_dim), num_layers=2)
        self.linear = nn.Linear(embedding_dim, vocab_size)
        
    def forward(self, src_seq, tgt_seq):
        src_emb = self.embedding(src_seq)
        tgt_emb = self.embedding(tgt_seq)
        
        encoder_output = self.encoder(src_emb)
        decoder_output = self.decoder(tgt_emb, encoder_output)
        
        output = self.linear(decoder_output)
        
        return output

# 定义模型参数
vocab_size = 10000
embedding_dim = 256
hidden_dim = 512

# 创建模型
model = Seq2SeqModel(vocab_size, embedding_dim, hidden_dim)

# 定义损失函数和优化器
loss_fn = nn.CrossEntropyLoss()
optimizer = paddle.optimizer.Adam(parameters=model.parameters())

# 训练模型
for epoch in range(num_epochs):
    for batch in data_loader:
        src_seq, tgt_seq = batch
        
        # 前向传播
        output = model(src_seq, tgt_seq)
        loss = loss_fn(output, tgt_seq)
        
        # 反向传播
        optimizer.clear_grad()
        loss.backward()
        optimizer.step()

In the example above, we defined a simple Seq2Seq model and used the Transformer model as both the encoder and decoder. We started by defining the model architecture, then defined the loss function and optimizer, and finally proceeded with model training. During training, we inputted source sequences and target sequences into the model, computed the loss, and optimized the model parameters through backpropagation. By iteratively training the model, we can obtain a model for sequence generation tasks.