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b9301f1 | 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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 | """Unit tests: atom-payload production under field_obligatory_hard."""
from __future__ import annotations
import unittest
import torch
from src.atom_native import AtomNativeModel
from src.io.atomizer import Atomizer
from src.toroidal.atom import ToroidalAtom
class AtomPayloadProdTests(unittest.TestCase):
def _tiny(self, **kwargs) -> AtomNativeModel:
return AtomNativeModel(
d_model=16,
n_modes=16,
n_atoms_max=64,
max_payload_bytes=8,
atomizer=Atomizer(max_span_bytes=8),
field_max_rms=3.0,
field_obligatory_hard=True,
**kwargs,
)
def test_payload_bytes_preferred_vs_empty_atoms(self) -> None:
"""With distinct atom payloads, logit mass prefers those bytes vs empty."""
torch.manual_seed(11)
model = self._tiny()
surf = model.surface
model.eval()
alpha = torch.randn(16, 16) * 0.5
persist = torch.zeros(16)
atom_r = torch.zeros(16)
legacy = torch.zeros(16)
# Distinct printable payloads (not overlapping the empty baseline).
payloads = [b"ABC", b"xyz"]
target_bytes = sorted({b for p in payloads for b in p})
other_bytes = [b for b in range(32, 127) if b not in target_bytes][:6]
with torch.no_grad():
empty = surf(
legacy,
alpha=alpha,
persistent_state=persist,
atom_r=atom_r,
atom_payloads=[],
)
filled = surf(
legacy,
alpha=alpha,
persistent_state=persist,
atom_r=atom_r,
atom_payloads=payloads,
)
pe = empty["byte_logits"].softmax(dim=-1)
pf = filled["byte_logits"].softmax(dim=-1)
mass_empty = float(pe[:, target_bytes].sum().item())
mass_filled = float(pf[:, target_bytes].sum().item())
mass_other_f = float(pf[:, other_bytes].sum().item())
self.assertGreater(
mass_filled,
mass_empty * 1.5 + 1e-6,
msg=f"payload mass filled={mass_filled:.6f} empty={mass_empty:.6f}",
)
self.assertGreater(
mass_filled,
mass_other_f,
msg=f"target mass {mass_filled:.6f} should beat other {mass_other_f:.6f}",
)
def test_payload_vanishes_when_alpha_zero(self) -> None:
"""Hard contract: α=0 ⇒ ~0 logits even with rich atom payloads."""
torch.manual_seed(3)
model = self._tiny()
model.eval()
with torch.no_grad():
out = model.surface(
torch.randn(16),
alpha=torch.zeros(16, 16),
persistent_state=torch.randn(16),
atom_r=torch.randn(16),
atom_payloads=[b"Hello!!", b"Bonjour"],
)
flat = torch.cat(
[out["byte_logits"].reshape(-1), out["length_logits"].reshape(-1)]
)
self.assertLess(float(flat.norm().item()), 1e-5)
def test_merge_concatenates_payloads(self) -> None:
"""MERGE of two living atoms concatenates their payloads."""
model = self._tiny(enable_merge=True, merge_coherence_threshold=0.0, merge_energy_floor=0.0)
d = 16
a = ToroidalAtom(
r=torch.ones(1, d),
phi=torch.zeros(1, d),
omega=torch.ones(1, d),
E=torch.ones(1, d) * 0.5,
kappa=torch.ones(1, d),
M=torch.ones(1, d),
tau=torch.ones(1, d),
rho=torch.zeros(1, 1),
payload=b"AB",
)
b = ToroidalAtom(
r=torch.ones(1, d),
phi=torch.zeros(1, d),
omega=torch.ones(1, d),
E=torch.ones(1, d) * 0.5,
kappa=torch.ones(1, d),
M=torch.ones(1, d),
tau=torch.ones(1, d),
rho=torch.zeros(1, 1),
payload=b"CD",
)
model.core.atoms.extend([a, b])
n_before = len(model.core.atoms)
merges, _ = model._maybe_merge_atoms()
self.assertGreaterEqual(merges, 1)
self.assertLess(len(model.core.atoms), n_before)
payloads = [getattr(x, "payload", b"") for x in model.core.atoms.atoms]
self.assertTrue(any(p == b"ABCD" or b"AB" in p and b"CD" in p for p in payloads), payloads)
def test_payload_modules_trainable_decoder_still_frozen(self) -> None:
model = self._tiny()
surf = model.surface
for p in surf.byte_decoder.parameters():
self.assertFalse(p.requires_grad)
self.assertTrue(surf.byte_embed.weight.requires_grad)
self.assertTrue(surf.payload_to_byte.weight.requires_grad)
self.assertTrue(surf.payload_copy_scale.requires_grad)
# Gradients hit payload path on a hard forward with payloads.
alpha = torch.randn(16, 16) * 0.5
out = surf(
torch.zeros(16),
alpha=alpha,
persistent_state=torch.zeros(16),
atom_r=torch.zeros(16),
atom_payloads=[b"xy"],
)
out["byte_logits"].pow(2).mean().backward()
self.assertIsNotNone(surf.byte_embed.weight.grad)
self.assertGreater(float(surf.byte_embed.weight.grad.abs().sum()), 0.0)
def test_per_atom_alpha_local_diversifies_logits(self) -> None:
"""Distinct living payload sets → surface logits cos clearly < 0.99.
Replaces mean-pool/global-hist homogenization: α-local per-atom copy-bias
must let different prompt atoms diversify decode without train.
"""
torch.manual_seed(42)
model = self._tiny()
surf = model.surface
model.eval()
alpha = torch.randn(16, 16) * 0.5
persist = torch.zeros(16)
atom_r = torch.zeros(16)
legacy = torch.zeros(16)
payloads_a = [b"Bonjour!", b"salut.."]
payloads_b = [b"zzzzzzzz", b"yyyyyyyy"]
energies = [1.0, 1.0]
with torch.no_grad():
out_a = surf(
legacy,
alpha=alpha,
persistent_state=persist,
atom_r=atom_r,
atom_payloads=payloads_a,
atom_energies=energies,
)
out_b = surf(
legacy,
alpha=alpha,
persistent_state=persist,
atom_r=atom_r,
atom_payloads=payloads_b,
atom_energies=energies,
)
la = torch.cat(
[out_a["byte_logits"].reshape(-1), out_a["length_logits"].reshape(-1)]
)
lb = torch.cat(
[out_b["byte_logits"].reshape(-1), out_b["length_logits"].reshape(-1)]
)
cos = float(
torch.nn.functional.cosine_similarity(
la.unsqueeze(0), lb.unsqueeze(0)
).item()
)
self.assertLess(
cos,
0.99,
msg=f"per-atom α-local should diversify logits; cos={cos:.6f}",
)
def test_alpha_local_weights_prefer_aligned_atom(self) -> None:
"""Higher energy on one distinct atom boosts that atom's bytes vs the other."""
torch.manual_seed(7)
model = self._tiny()
surf = model.surface
model.eval()
alpha = torch.randn(16, 16) * 0.5
payloads = [b"AAAA", b"zzzz"]
with torch.no_grad():
favor_a = surf(
torch.zeros(16),
alpha=alpha,
persistent_state=torch.zeros(16),
atom_r=torch.zeros(16),
atom_payloads=payloads,
atom_energies=[5.0, 0.05],
)
favor_z = surf(
torch.zeros(16),
alpha=alpha,
persistent_state=torch.zeros(16),
atom_r=torch.zeros(16),
atom_payloads=payloads,
atom_energies=[0.05, 5.0],
)
pa = favor_a["byte_logits"].softmax(dim=-1)
pz = favor_z["byte_logits"].softmax(dim=-1)
mass_A_when_A = float(pa[:, ord("A")].sum().item())
mass_A_when_Z = float(pz[:, ord("A")].sum().item())
mass_z_when_Z = float(pz[:, ord("z")].sum().item())
mass_z_when_A = float(pa[:, ord("z")].sum().item())
self.assertGreater(
mass_A_when_A,
mass_A_when_Z,
msg=f"A-mass favor_A={mass_A_when_A:.6f} favor_Z={mass_A_when_Z:.6f}",
)
self.assertGreater(
mass_z_when_Z,
mass_z_when_A,
msg=f"z-mass favor_Z={mass_z_when_Z:.6f} favor_A={mass_z_when_A:.6f}",
)
if __name__ == "__main__":
unittest.main()
|