| import torch | |
| from safetensors.torch import load_file | |
| def load_model(path='model.safetensors'): | |
| return load_file(path) | |
| def popcount8(a7, a6, a5, a4, a3, a2, a1, a0, weights): | |
| """8-bit population count: returns count of 1 bits as 4-bit value""" | |
| inp = torch.tensor([float(a7), float(a6), float(a5), float(a4), | |
| float(a3), float(a2), float(a1), float(a0)]) | |
| # See create_safetensors.py for full implementation | |
| # Returns [y3, y2, y1, y0] where y3y2y1y0 is the count in binary | |
| pass | |
| if __name__ == '__main__': | |
| w = load_model() | |
| print('Popcount8 examples:') | |
| examples = [0b00000000, 0b00000001, 0b01010101, 0b11111111, 0b11110000] | |
| for val in examples: | |
| bits = [(val >> j) & 1 for j in range(7, -1, -1)] | |
| count = sum(bits) | |
| print(f' {val:08b} -> {count}') | |