| import torch | |
| from safetensors.torch import load_file | |
| def load_model(path='model.safetensors'): | |
| return load_file(path) | |
| def prefix_or(x3, x2, x1, x0, w): | |
| inp = torch.tensor([float(x3), float(x2), float(x1), float(x0)]) | |
| y3 = int((inp @ w['y3.weight'].T + w['y3.bias'] >= 0).item()) | |
| y2 = int((inp @ w['y2.weight'].T + w['y2.bias'] >= 0).item()) | |
| y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item()) | |
| y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item()) | |
| return y3, y2, y1, y0 | |
| if __name__ == '__main__': | |
| w = load_model() | |
| print('Prefix-OR selected tests:') | |
| for i in [0b0000, 0b0001, 0b0010, 0b0100, 0b1000, 0b1111]: | |
| x3, x2, x1, x0 = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1 | |
| y3, y2, y1, y0 = prefix_or(x3, x2, x1, x0, w) | |
| print(f'{x3}{x2}{x1}{x0} -> {y3}{y2}{y1}{y0}') | |