Though .cuda() “should” do as you said, but I don’t think changing devices for all the torch.tensor  attributes of a class inherited from nn.Module by default is good idea. In your use case it might be helpful, but in some case user may don’t that, so I think that’s the reason why it ain’t do that by default.
One more thing if you want to create just a constant tensor (not a parameter) then you can do that as
self.a_constant_tensor = nn.Parameter(torch.ones(2, 1), requires_grad = False)

and then use it in forward method.
OR you can use buffers, "which is recommended"
import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.weight = torch.nn.Parameter(torch.zeros(2, 1))
        self.bias = torch.nn.Parameter(torch.zeros(1))
        self.register_buffer('a_constant_tensor', torch.tensor([0.5]))

    def forward(self, x):
        # linear regression completely from scratch,
        # using parameters created in __init__
        x = torch.mm(x, self.weight) + self.bias + self.a_constant_tensor
        return x


model = Model().cuda()

Doing this wouldn’t consider self.a_constant_tensor as a parameter, so printing parameters wouldn’t return self.a_constant_tensor -
for param in model.parameters():
    print(param)
# Only prints about self.weight and self.bias
'''
Parameter containing:
tensor([[0.],
        [0.]], device='cuda:0', requires_grad=True)
Parameter containing:
tensor([0.], device='cuda:0', requires_grad=True)
'''