Tree like structure

[snapo]

Is there a way instead of using random , that we use a tree like structure that is always a multiple powers of 2?

Sample

    def forward(self, x):
        # Input layer (8 inputs)
        x = x.view(-1, 8)
        
        # First layer (4 gates)
        l1 = torch.stack([
            self.gates[0](x[:,0], x[:,1]),
            self.gates[1](x[:,2], x[:,3]),
            self.gates[2](x[:,4], x[:,5]),
            self.gates[3](x[:,6], x[:,7]),
        ], dim=1)
        
        # Second layer (2 gates)
        l2 = torch.stack([
            self.gates[4](l1[:,0], l1[:,1]),
            self.gates[5](l1[:,2], l1[:,3]),
        ], dim=1)
        
        # Final output gate
        out = self.gates[6](l2[:,0], l2[:,1])
        return torch.sigmoid(out)  # For binary classification

akin to this image:

where blue are the inputs, black are the diff logic gates, orange is the output

[RefractAI]

I can tell from your comment format that your code is AI generated, why would you even post that?

[snapo]

correct the code is ai generated...
I do not directly need it, but i would like to try it is the correct view.
in my view the difflogic gates (as you know) if you have more than 6 layers get exponentially more difficult to solve.
this approach in my view would allow predictions of a single bit and not face the same "dillemma" situation.
The main reason i did want to try something like this is because of the large language models. Instead of predicting tokens, i would like to try and predict the next bit. For me difflogic seems a reasonable approach as it could later be implemented in hw and makes llm's verry small.

with the diagram above for example assume 2**21 inputs and we only predict the next bit. Bit's are perfect information unlike tokens.
If there is no interest in trying this, absolutely no problem, then i will try to rebuild it with the information from the paper and the opensource cuda kernels.

This was a question and not in any way an attack on you / project.