优化这段代码让其在gpu上可以更快运行：import torch.nn as nnclass MyLinear(nn.Module): def __init__(self, in_features, out_features): super(MyLinear, self).__init__() self.linears = nn.ModuleList([nn.Linear(in_features, out_features) for i in range(5)]) def forward(self, x): chunks = torch.chunk(x, 5, dim=1) chunks = torch.stack(chunks, dim=0) weights = torch.stack([linear.weight for linear in self.linears], dim=0) outputs = torch.matmul(chunks, weights.transpose(1, 2)).reshape(x.shape[0], -1) return outputs

可以使用 PyTorch 内置的函数 `to()` 将张量移动到 GPU 上，从而在 GPU 上更快地运行代码。

下面是优化后的代码：

import torch.nn as nn

class MyLinear(nn.Module):
    def __init__(self, in_features, out_features):
        super(MyLinear, self).__init__()
        self.linears = nn.ModuleList([nn.Linear(in_features, out_features).to('cuda') for i in range(5)])
        
    def forward(self, x):
        chunks = torch.chunk(x, 5, dim=1)
        chunks = torch.stack(chunks, dim=0).to('cuda')
        weights = torch.stack([linear.weight.to('cuda') for linear in self.linears], dim=0)
        outputs = torch.matmul(chunks, weights.transpose(1, 2)).reshape(x.shape[0], -1)
        return outputs

此代码在创建每个线性层时使用 `.to('cuda')` 将其权重张量移动到 GPU 上。在 `forward()` 方法中，使用 `.to('cuda')` 将 `chunks` 张量和 `weights` 张量移动到 GPU 上，然后使用 `torch.matmul()` 进行矩阵乘法运算，并使用 `.reshape()` 将输出张量重新变形为二维张量，最后将其返回。