File size: 3,593 Bytes
7bf820e | 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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import torch
from safetensors.torch import save_file
# Input order: i7, i6, i5, i4, i3, i2, i1, i0 (i7 = highest priority)
# Layer 1: hk = "ik is the highest active input"
# Layer 2: y2, y1, y0 (3-bit encoding), v (valid)
weights = {}
# Layer 1: 8 neurons detecting "this input is highest active"
# layer1.hk detects input k being highest
for k in range(8):
w = [0.0] * 8
# Input ik is at position (7-k) in the input array
w[7-k] = 1.0
# All higher-priority inputs (indices k+1 to 7) need to be 0
# These are at positions 0 to (6-k) in the input array
for j in range(7-k):
w[j] = -1.0
bias = -1.0
weights[f'layer1.h{k}.weight'] = torch.tensor([w], dtype=torch.float32)
weights[f'layer1.h{k}.bias'] = torch.tensor([bias], dtype=torch.float32)
# Layer 2: combine h outputs
# h array will be [h0, h1, h2, h3, h4, h5, h6, h7]
# y2 = h4 OR h5 OR h6 OR h7 (indices 4,5,6,7 have bit 2 set)
weights['layer2.y2.weight'] = torch.tensor([[0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]], dtype=torch.float32)
weights['layer2.y2.bias'] = torch.tensor([-1.0], dtype=torch.float32)
# y1 = h2 OR h3 OR h6 OR h7 (indices 2,3,6,7 have bit 1 set)
weights['layer2.y1.weight'] = torch.tensor([[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0]], dtype=torch.float32)
weights['layer2.y1.bias'] = torch.tensor([-1.0], dtype=torch.float32)
# y0 = h1 OR h3 OR h5 OR h7 (indices 1,3,5,7 have bit 0 set)
weights['layer2.y0.weight'] = torch.tensor([[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0]], dtype=torch.float32)
weights['layer2.y0.bias'] = torch.tensor([-1.0], dtype=torch.float32)
# v = any h active
weights['layer2.v.weight'] = torch.tensor([[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]], dtype=torch.float32)
weights['layer2.v.bias'] = torch.tensor([-1.0], dtype=torch.float32)
save_file(weights, 'model.safetensors')
def priority_encode(inputs):
inp = torch.tensor([float(x) for x in inputs])
# Layer 1: h[k] = hk output
h = []
for k in range(8):
hk = int((inp @ weights[f'layer1.h{k}.weight'].T + weights[f'layer1.h{k}.bias'] >= 0).item())
h.append(hk)
h_tensor = torch.tensor([float(x) for x in h])
# Layer 2
y2 = int((h_tensor @ weights['layer2.y2.weight'].T + weights['layer2.y2.bias'] >= 0).item())
y1 = int((h_tensor @ weights['layer2.y1.weight'].T + weights['layer2.y1.bias'] >= 0).item())
y0 = int((h_tensor @ weights['layer2.y0.weight'].T + weights['layer2.y0.bias'] >= 0).item())
v = int((h_tensor @ weights['layer2.v.weight'].T + weights['layer2.v.bias'] >= 0).item())
return y2, y1, y0, v
print("Verifying priorityencoder8...")
errors = 0
for val in range(256):
inputs = [(val >> (7-j)) & 1 for j in range(8)]
y2, y1, y0, v = priority_encode(inputs)
# Find highest active input (i7 has highest priority)
highest = -1
for k in range(7, -1, -1):
if inputs[7-k]: # inputs[7-k] is ik
highest = k
break
if highest == -1:
exp_v = 0
exp_y2, exp_y1, exp_y0 = 0, 0, 0
else:
exp_v = 1
exp_y2 = (highest >> 2) & 1
exp_y1 = (highest >> 1) & 1
exp_y0 = highest & 1
if v != exp_v or (v == 1 and (y2 != exp_y2 or y1 != exp_y1 or y0 != exp_y0)):
errors += 1
if errors <= 3:
print(f"ERROR: val={val}, inputs={inputs}, got ({y2},{y1},{y0},{v}), expected ({exp_y2},{exp_y1},{exp_y0},{exp_v})")
if errors == 0:
print("All 256 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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