Linear_custom.py 文件源码

def backward(self, grad_output):
        input, weight, bias = self.saved_tensors

        grad_input = grad_weight = grad_bias = None
        if self.needs_input_grad[0]:
            grad_output = grad_output.squeeze()
            grad_input = torch.mm(grad_output, weight)
        if self.needs_input_grad[1]:
            grad_weight = torch.mm(grad_output.t(), input)
        if bias is not None and self.needs_input_grad[2]:
            grad_bias = torch.mv(grad_output.t(), self.add_buffer)

        if bias is not None:
            return grad_input, grad_weight, grad_bias
        else:
            return grad_input, grad_weight