写一个多机多卡训练GPT的代码

这是一个使用 PyTorch 实现 GPT 的多机多卡训练的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义 GPT 模型
class GPT(nn.Module):
    def __init__(self, vocab_size, hidden_size, num_layers):
        super(GPT, self).__init__()
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size=vocab_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)
        self.linear = nn.Linear(hidden_size, vocab_size)
    
    def forward(self, x, hidden=None):
        out, hidden = self.lstm(x, hidden)
        out = self.linear(out)
        return out, hidden

# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 将模型放到设备上
model = GPT(vocab_size, hidden_size, num_layers).to(device)

# 分布式训练
model = nn.DataParallel(model)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练循环
for epoch in range(num_epochs):
    # 准备输入和目标
    inputs = torch.randint(0, vocab_size, (batch_size, sequence_length)).to(device)
    targets = torch.randint(0, vocab_size, (batch_size, sequence_length)).to(device)
    
    # 前向计算
    output, _ = model(inputs)
    
    # 计算损失
    loss = criterion(output.view(-1, vocab_size), targets.view(-1))
    
    # 反向传播和优化
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

在这段代码中，我们首先定义了 GPT 模型的类，然后将模型放到设备上，并使用 `nn.DataParallel` 将