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import torch
from safetensors.torch import load_file
def load_model(path='model.safetensors'):
return load_file(path)
def subtractor4(a3, a2, a1, a0, b3, b2, b1, b0, weights):
"""4-bit subtractor: returns (A - B) mod 16 and borrow out"""
inp = torch.tensor([float(a3), float(a2), float(a1), float(a0),
float(b3), float(b2), float(b1), float(b0)])
# Bit 0
d0_or = int((inp @ weights['d0_or.weight'].T + weights['d0_or.bias'] >= 0).item())
d0_nand = int((inp @ weights['d0_nand.weight'].T + weights['d0_nand.bias'] >= 0).item())
d0 = int((torch.tensor([float(d0_or), float(d0_nand)]) @ weights['d0.weight'].T + weights['d0.bias'] >= 0).item())
bout0 = int((inp @ weights['bout0.weight'].T + weights['bout0.bias'] >= 0).item())
# Bit 1
xor1_or = int((inp @ weights['xor1_or.weight'].T + weights['xor1_or.bias'] >= 0).item())
xor1_nand = int((inp @ weights['xor1_nand.weight'].T + weights['xor1_nand.bias'] >= 0).item())
xor1 = int((torch.tensor([float(xor1_or), float(xor1_nand)]) @ weights['xor1.weight'].T + weights['xor1.bias'] >= 0).item())
d1_in = torch.tensor([float(xor1), float(bout0)])
d1_or = int((d1_in @ weights['d1_or.weight'].T + weights['d1_or.bias'] >= 0).item())
d1_nand = int((d1_in @ weights['d1_nand.weight'].T + weights['d1_nand.bias'] >= 0).item())
d1 = int((torch.tensor([float(d1_or), float(d1_nand)]) @ weights['d1.weight'].T + weights['d1.bias'] >= 0).item())
not_a1 = 1 - a1
bout1 = int((torch.tensor([float(not_a1), float(b1), float(bout0)]) @ weights['bout1.weight'].T + weights['bout1.bias'] >= 0).item())
# Bit 2
xor2_or = int((inp @ weights['xor2_or.weight'].T + weights['xor2_or.bias'] >= 0).item())
xor2_nand = int((inp @ weights['xor2_nand.weight'].T + weights['xor2_nand.bias'] >= 0).item())
xor2 = int((torch.tensor([float(xor2_or), float(xor2_nand)]) @ weights['xor2.weight'].T + weights['xor2.bias'] >= 0).item())
d2_in = torch.tensor([float(xor2), float(bout1)])
d2_or = int((d2_in @ weights['d2_or.weight'].T + weights['d2_or.bias'] >= 0).item())
d2_nand = int((d2_in @ weights['d2_nand.weight'].T + weights['d2_nand.bias'] >= 0).item())
d2 = int((torch.tensor([float(d2_or), float(d2_nand)]) @ weights['d2.weight'].T + weights['d2.bias'] >= 0).item())
not_a2 = 1 - a2
bout2 = int((torch.tensor([float(not_a2), float(b2), float(bout1)]) @ weights['bout2.weight'].T + weights['bout2.bias'] >= 0).item())
# Bit 3
xor3_or = int((inp @ weights['xor3_or.weight'].T + weights['xor3_or.bias'] >= 0).item())
xor3_nand = int((inp @ weights['xor3_nand.weight'].T + weights['xor3_nand.bias'] >= 0).item())
xor3 = int((torch.tensor([float(xor3_or), float(xor3_nand)]) @ weights['xor3.weight'].T + weights['xor3.bias'] >= 0).item())
d3_in = torch.tensor([float(xor3), float(bout2)])
d3_or = int((d3_in @ weights['d3_or.weight'].T + weights['d3_or.bias'] >= 0).item())
d3_nand = int((d3_in @ weights['d3_nand.weight'].T + weights['d3_nand.bias'] >= 0).item())
d3 = int((torch.tensor([float(d3_or), float(d3_nand)]) @ weights['d3.weight'].T + weights['d3.bias'] >= 0).item())
not_a3 = 1 - a3
bout3 = int((torch.tensor([float(not_a3), float(b3), float(bout2)]) @ weights['bout3.weight'].T + weights['bout3.bias'] >= 0).item())
return [d3, d2, d1, d0, bout3]
if __name__ == '__main__':
w = load_model()
print('Subtractor4bit examples:')
examples = [(7, 3), (5, 5), (3, 7), (15, 1), (0, 1)]
for a, b in examples:
a3, a2, a1, a0 = (a >> 3) & 1, (a >> 2) & 1, (a >> 1) & 1, a & 1
b3, b2, b1, b0 = (b >> 3) & 1, (b >> 2) & 1, (b >> 1) & 1, b & 1
result = subtractor4(a3, a2, a1, a0, b3, b2, b1, b0, w)
diff = result[0]*8 + result[1]*4 + result[2]*2 + result[3]
bout = result[4]
print(f' {a:2d} - {b:2d} = {diff:2d} (bout={bout})')
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