| import torch | |
| from safetensors.torch import load_file | |
| def load_model(path='model.safetensors'): | |
| return load_file(path) | |
| def priority_encode(i3, i2, i1, i0, weights): | |
| """4-to-2 priority encoder. Returns (y1, y0, valid).""" | |
| inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)]) | |
| y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item()) | |
| y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item()) | |
| v = int((inp @ weights['v.weight'].T + weights['v.bias'] >= 0).item()) | |
| return y1, y0, v | |
| if __name__ == '__main__': | |
| w = load_model() | |
| print('Priority Encoder 4') | |
| print('i3 i2 i1 i0 | y1 y0 v | highest') | |
| print('-' * 35) | |
| for val in range(16): | |
| i3, i2, i1, i0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1 | |
| y1, y0, v = priority_encode(i3, i2, i1, i0, w) | |
| highest = 'none' if v == 0 else f'i{2*y1 + y0}' | |
| print(f' {i3} {i2} {i1} {i0} | {y1} {y0} {v} | {highest}') | |