| import torch |
| from safetensors.torch import load_file |
|
|
| def load_model(path='model.safetensors'): |
| return load_file(path) |
|
|
| def ffs4(a3, a2, a1, a0, w): |
| """Find first set bit. Returns 3-bit binary position (1-indexed), 0 if no bits set.""" |
| inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)]) |
|
|
| |
| has0 = int((inp @ w['has0.weight'].T + w['has0.bias'] >= 0).item()) |
| has1_first = int((inp @ w['has1_first.weight'].T + w['has1_first.bias'] >= 0).item()) |
| has2_first = int((inp @ w['has2_first.weight'].T + w['has2_first.bias'] >= 0).item()) |
| has3_first = int((inp @ w['has3_first.weight'].T + w['has3_first.bias'] >= 0).item()) |
|
|
| |
| l1 = torch.tensor([float(has0), float(has1_first), float(has2_first), float(has3_first)]) |
| y0 = int((l1 @ w['y0.weight'].T + w['y0.bias'] >= 0).item()) |
| y1 = int((l1 @ w['y1.weight'].T + w['y1.bias'] >= 0).item()) |
| y2 = int((l1 @ w['y2.weight'].T + w['y2.bias'] >= 0).item()) |
|
|
| return [y2, y1, y0] |
|
|
| if __name__ == '__main__': |
| w = load_model() |
| print('ffs4 truth table:') |
| print('input | ffs | y2 y1 y0') |
| print('------+-----+---------') |
| for i in range(16): |
| a3, a2, a1, a0 = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1 |
| result = ffs4(a3, a2, a1, a0, w) |
| ffs_val = result[0] * 4 + result[1] * 2 + result[2] |
| print(f'{a3}{a2}{a1}{a0} | {ffs_val} | {result[0]} {result[1]} {result[2]}') |
|
|
