transformerxl

Transformer-XL是一种改进的Transformer模型，旨在解决长序列建模的问题。相比于经典Transformer，Transformer-XL引入了可重用的循环机制，使得模型能够处理比固定长度更长的序列。此外，Transformer-XL还引入了相对位置编码，以更好地捕捉序列中不同位置之间的关系。这些改进使得Transformer-XL在许多自然语言处理任务中取得了最先进的结果。

以下是使用Transformer-XL进行语言建模的示例代码：

import torch
from torch.nn import TransformerXL, TransformerXLDecoder, TransformerXLDecoderLayer

# 定义模型参数
vocab_size = 10000
embed_dim = 512
hidden_dim = 1024
num_layers = 6
num_heads = 8
dropout = 0.1
seq_len = 512
batch_size = 16

# 定义模型
encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, dropout=dropout)
encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
decoder_layer = TransformerXLDecoderLayer(d_model=embed_dim, nhead=num_heads, dropout=dropout)
decoder = TransformerXLDecoder(decoder_layer, num_layers=num_layers)
model = TransformerXL(encoder, decoder)

# 定义输入和输出
inputs = torch.randint(low=0, high=vocab_size, size=(seq_len, batch_size))
targets = torch.randint(low=0, high=vocab_size, size=(seq_len, batch_size))

# 前向传播
outputs = model(inputs, targets[:-1, :])

# 计算损失
loss_fn = nn.CrossEntropyLoss()
loss = loss_fn(outputs.view(-1, vocab_size), targets[1:, :].view(-1))

# 反向传播和参数更新
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
optimizer.zero_grad()
loss.backward()
optimizer.step()