实现一个Linear层(torch.nn.Linear)功能，分别基于torch.nn.Module和torch.autograd.Function

好的，下面分别给出基于`torch.nn.Module`和`torch.autograd.Function`的实现示例。

基于`torch.nn.Module`的实现示例：

import torch
import torch.nn as nn

class LinearLayer(nn.Module):
    def __init__(self, input_size, output_size):
        super(LinearLayer, self).__init__()
        self.weight = nn.Parameter(torch.Tensor(output_size, input_size))
        self.bias = nn.Parameter(torch.Tensor(output_size))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            nn.init.uniform_(self.bias, -bound, bound)

    def forward(self, input):
        return torch.matmul(input, self.weight.t()) + self.bias

基于`torch.autograd.Function`的实现示例：

import torch
from torch.autograd import Function

class LinearFunction(Function):
    @staticmethod
    def forward(ctx, input, weight, bias=None):
        ctx.save_for_backward(input, weight, bias)
        output = torch.matmul(input, weight.t())
        if bias is not None:
            output += bias.unsqueeze(0).expand_as(output)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input, weight, bias = ctx.saved_tensors
        grad_input = grad_weight = grad_bias = None

        if ctx.needs_input_grad[0]:
            grad_input = torch.matmul(grad_output, weight)
        if ctx.needs_input_grad[1]:
            grad_weight = torch.matmul(grad_output.t(), input)
        if bias is not None and ctx.needs_input_grad[2]:
            grad_bias = grad_output.sum(0)

        return grad_input, grad_weight, grad_bias

class LinearLayer(Function):
    @staticmethod
    def forward(ctx, input, weight, bias=None):
        ctx.save_for_backward(input, weight, bias)
        output = torch.matmul(input, weight.t())
        if bias is not None:
            output += bias.unsqueeze(0).expand_as(output)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input, weight, bias = ctx.saved_tensors
        grad_input = grad_weight = grad_bias = None

        if ctx.needs_input_grad[0]:
            grad_input = torch.matmul(grad_output, weight)
        if ctx.needs_input_grad[1]:
            grad_weight = torch.matmul(grad_output.t(), input)
        if bias is not None and ctx.needs_input_grad[2]:
            grad_bias = grad_output.sum(0)

        return grad_input, grad_weight, grad_bias

这两个示例分别基于`torch.nn.Module`和`torch.autograd.Function`实现了一个Linear层。你可以根据需要选择其中一种实现方式。希望对你有所帮助！