首页优化这段代码来提高运行速度：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) for i in range(5)]) def forward(self, x): chunks = torch.chunk(x, 5, dim=1) outputs = [linear(chunk) for linear, chunk in zip(self.linears, chunks)] return torch.cat(outputs, dim=1)

优化这段代码来提高运行速度：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) for i in range(5)]) def forward(self, x): chunks = torch.chunk(x, 5, dim=1) outputs = [linear(chunk) for linear, chunk in zip(self.linears, chunks)] return torch.cat(outputs, dim=1)

时间: 2024-03-13 10:46:38 浏览: 185

可以使用 PyTorch 内置的函数 `torch.matmul()` 和 `torch.transpose()` 来优化代码，从而提高运行速度。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) 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 ``` 此代码使用 `torch.chunk()` 将输入张量 `x` 沿着维度 `1` 分成 $5$ 个张量，然后使用 `torch.stack()` 将它们沿着新的维度 `0` 拼接在一起。接着使用 `torch.stack()` 将每个线性层的权重张量沿着新的维度 `0` 拼接在一起，然后使用 `torch.matmul()` 进行矩阵乘法运算，将 `chunks` 张量与 `weights` 张量的转置张量相乘，并将结果沿着维度 `1` 和 `2` 进行转置。最后使用 `torch.reshape()` 将输出张量重新变形为二维张量，得到最终的输出张量。

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