| import torch | |
| from safetensors.torch import load_file | |
| def load_model(path='model.safetensors'): | |
| return load_file(path) | |
| def xor2(a, b, prefix, w): | |
| or_out = int(a * w[f'{prefix}.or.weight'][0] + b * w[f'{prefix}.or.weight'][1] + w[f'{prefix}.or.bias'] >= 0) | |
| nand_out = int(a * w[f'{prefix}.nand.weight'][0] + b * w[f'{prefix}.nand.weight'][1] + w[f'{prefix}.nand.bias'] >= 0) | |
| return int(or_out * w[f'{prefix}.and.weight'][0] + nand_out * w[f'{prefix}.and.weight'][1] + w[f'{prefix}.and.bias'] >= 0) | |
| def parity8(bits, weights): | |
| x01 = xor2(bits[0], bits[1], 'xor_01', weights) | |
| x23 = xor2(bits[2], bits[3], 'xor_23', weights) | |
| x45 = xor2(bits[4], bits[5], 'xor_45', weights) | |
| x67 = xor2(bits[6], bits[7], 'xor_67', weights) | |
| x0123 = xor2(x01, x23, 'xor_0123', weights) | |
| x4567 = xor2(x45, x67, 'xor_4567', weights) | |
| return xor2(x0123, x4567, 'xor_final', weights) | |
| if __name__ == '__main__': | |
| w = load_model() | |
| print('parity8 selected outputs:') | |
| for n_ones in range(9): | |
| bits = [1 if j < n_ones else 0 for j in range(8)] | |
| print(f' {n_ones} ones: {parity8(bits, w)}') | |