| """
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| Threshold Network for MOD-4 Circuit
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|
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| A formally verified threshold network computing Hamming weight mod 4.
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| Uses the algebraic weight pattern [1, 1, 1, -3, 1, 1, 1, -3].
|
| """
|
|
|
| import torch
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| from safetensors.torch import load_file
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|
|
|
|
| class ThresholdMod4:
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| """
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| MOD-4 circuit using threshold logic.
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|
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| Weight pattern: (1, 1, 1, 1-m) repeating for m=4
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| Computes cumulative sum that cycles mod 4.
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| """
|
|
|
| def __init__(self, weights_dict):
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| self.weight = weights_dict['weight']
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| self.bias = weights_dict['bias']
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|
|
| def __call__(self, bits):
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| inputs = torch.tensor([float(b) for b in bits])
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| weighted_sum = (inputs * self.weight).sum() + self.bias
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| return weighted_sum
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|
|
| def get_residue(self, bits):
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| """Returns Hamming weight mod 4."""
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| return sum(bits) % 4
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|
|
| @classmethod
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| def from_safetensors(cls, path="model.safetensors"):
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| return cls(load_file(path))
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|
|
|
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| def forward(x, weights):
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| x = torch.as_tensor(x, dtype=torch.float32)
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| weighted_sum = (x * weights['weight']).sum(dim=-1) + weights['bias']
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| return weighted_sum
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|
|
|
|
| if __name__ == "__main__":
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| weights = load_file("model.safetensors")
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| model = ThresholdMod4(weights)
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|
|
| print("MOD-4 Circuit Tests:")
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| print("-" * 40)
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| for hw in range(9):
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| bits = [1]*hw + [0]*(8-hw)
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| out = model(bits).item()
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| expected_residue = hw % 4
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| print(f"HW={hw}: weighted_sum={out:.0f}, HW mod 4 = {expected_residue}")
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|
|
