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"""
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Inference code for mod7-verified threshold network.
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This network computes MOD-7 (Hamming weight mod 7) on 8-bit binary inputs.
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"""
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import torch
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import torch.nn as nn
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from safetensors.torch import load_file
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def heaviside(x):
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return (x >= 0).float()
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class Mod7Network(nn.Module):
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"""
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Verified threshold network for MOD-7 computation.
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Architecture: 8 -> 9 -> 6 -> 7
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"""
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def __init__(self):
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super().__init__()
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self.layer1 = nn.Linear(8, 9)
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self.layer2 = nn.Linear(9, 6)
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self.output = nn.Linear(6, 7)
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def forward(self, x):
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x = x.float()
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x = heaviside(self.layer1(x))
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x = heaviside(self.layer2(x))
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return self.output(x)
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def predict(self, x):
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return self.forward(x).argmax(dim=-1)
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@classmethod
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def from_safetensors(cls, path):
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model = cls()
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weights = load_file(path)
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model.layer1.weight.data = weights['layer1.weight']
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model.layer1.bias.data = weights['layer1.bias']
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model.layer2.weight.data = weights['layer2.weight']
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model.layer2.bias.data = weights['layer2.bias']
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model.output.weight.data = weights['output.weight']
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model.output.bias.data = weights['output.bias']
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return model
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def mod7_reference(x):
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return (x.sum(dim=-1) % 7).long()
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def verify(model):
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inputs = torch.zeros(256, 8)
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for i in range(256):
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for j in range(8):
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inputs[i, j] = (i >> j) & 1
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targets = mod7_reference(inputs)
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predictions = model.predict(inputs)
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correct = (predictions == targets).sum().item()
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print(f"Verification: {correct}/256 ({100*correct/256:.1f}%)")
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return correct == 256
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if __name__ == '__main__':
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model = Mod7Network.from_safetensors('model.safetensors')
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verify(model)
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