File size: 2,336 Bytes
d8ea23b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import torch
from safetensors.torch import save_file
weights = {}
# Input order: d0..d15, s3, s2, s1, s0 (20 inputs)
# Layer 1: 16 neurons, each selects di when s = i
# Layer 2: OR gate
layer1_weights = []
layer1_biases = []
for i in range(16):
w = [0.0] * 20
# Data input weight
w[i] = 1.0
# Select weights: +1 if bit should be 1, -1 if bit should be 0
s3_bit = (i >> 3) & 1
s2_bit = (i >> 2) & 1
s1_bit = (i >> 1) & 1
s0_bit = i & 1
w[16] = 1.0 if s3_bit else -1.0 # s3
w[17] = 1.0 if s2_bit else -1.0 # s2
w[18] = 1.0 if s1_bit else -1.0 # s1
w[19] = 1.0 if s0_bit else -1.0 # s0
# Bias: -(1 + popcount(i))
bias = -(1 + bin(i).count('1'))
layer1_weights.append(w)
layer1_biases.append(bias)
weights['layer1.weight'] = torch.tensor(layer1_weights, dtype=torch.float32)
weights['layer1.bias'] = torch.tensor(layer1_biases, dtype=torch.float32)
# Layer 2: OR gate
weights['layer2.weight'] = torch.tensor([[1.0] * 16], dtype=torch.float32)
weights['layer2.bias'] = torch.tensor([-1.0], dtype=torch.float32)
save_file(weights, 'model.safetensors')
# Verify
def mux16(data, s3, s2, s1, s0):
inp = torch.tensor([float(d) for d in data] + [float(s3), float(s2), float(s1), float(s0)])
l1 = (inp @ weights['layer1.weight'].T + weights['layer1.bias'] >= 0).float()
out = (l1 @ weights['layer2.weight'].T + weights['layer2.bias'] >= 0).float()
return int(out.item())
print("Verifying MUX16...")
errors = 0
test_count = 0
for s in range(16):
s3, s2, s1, s0 = (s >> 3) & 1, (s >> 2) & 1, (s >> 1) & 1, s & 1
# Test with selected data = 1, others = 0
data = [0] * 16
data[s] = 1
result = mux16(data, s3, s2, s1, s0)
if result != 1:
errors += 1
print(f"ERROR: s={s}, d[{s}]=1 -> {result}, expected 1")
test_count += 1
# Test with selected data = 0
data[s] = 0
result = mux16(data, s3, s2, s1, s0)
if result != 0:
errors += 1
print(f"ERROR: s={s}, d[{s}]=0 -> {result}, expected 0")
test_count += 1
if errors == 0:
print(f"All {test_count} test cases passed!")
else:
print(f"FAILED: {errors} errors")
mag = sum(t.abs().sum().item() for t in weights.values())
print(f"Magnitude: {mag:.0f}")
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