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"""Corrected gate: the deepest reachable minimum, not descent retention.
Today's lesson: descent retention measures the search operator, not the
energy. Sampled-proposal descent keeps the truth for every candidate
energy, while long tempering on the same energy reaches states well below
it. The only decision-relevant question is therefore
E(truth) <= E(deepest state a strong searcher reaches) ?
This module answers it uniformly for the candidate energies (pairwise
moment kernel, triangle-only, and their sum) with one strong searcher:
long parallel tempering with large proposal batches from random starts,
plus a truth-initialized tempering arm that reports whether the truth
itself survives thermal agitation. Hidden pairs score only.
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import torch
from .common import read_json, seed_everything, write_json
from .synth_triangle_gate import TriangleEnergy, build_fields, standardized
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--data-dir", default="artifacts/synth_v0")
parser.add_argument("--split", choices=["val", "test"], default="test")
parser.add_argument("--samples", type=int, default=256)
parser.add_argument("--merge-distance", type=float, default=30.0)
parser.add_argument("--vision-views", type=int, default=4)
parser.add_argument("--triples", type=int, default=200000)
parser.add_argument(
"--energies",
default="pair,triangle,both",
help="Comma list from pair, triangle, both.",
)
parser.add_argument("--replicas", type=int, default=6)
parser.add_argument("--rounds", type=int, default=1500)
parser.add_argument("--proposals", type=int, default=64)
parser.add_argument("--temp-high", type=float, default=3e-2)
parser.add_argument("--temp-low", type=float, default=1e-4)
parser.add_argument("--exchange-every", type=int, default=20)
parser.add_argument("--device", default="cuda:3")
parser.add_argument("--seed", type=int, default=20260731)
parser.add_argument("--output", default="artifacts/synth_v0/deep_gate.json")
return parser.parse_args()
def temper(
energy: TriangleEnergy,
starts: list[torch.Tensor],
truth: torch.Tensor,
args: argparse.Namespace,
generator: torch.Generator,
label: str,
) -> dict:
size = energy.size
temperatures = torch.logspace(
torch.log10(torch.tensor(args.temp_low)),
torch.log10(torch.tensor(args.temp_high)),
len(starts),
)
states = [start.clone() for start in starts]
energies = [energy.total(state) for state in states]
best = {"energy": min(energies), "accuracy": 0.0}
for round_index in range(args.rounds):
for replica in range(len(states)):
temperature = float(temperatures[replica])
for _ in range(args.proposals):
p = int(torch.randint(0, size, (1,), generator=generator))
q = int(torch.randint(0, size, (1,), generator=generator))
if p == q:
continue
delta = energy.swap_delta(states[replica], p, q)
threshold = -temperature * float(
torch.rand(1, generator=generator).clamp_min(1e-12).log()
)
if delta < threshold:
states[replica][[p, q]] = states[replica][[q, p]]
energies[replica] += delta
if round_index % args.exchange_every == 0:
for replica in range(len(states) - 1):
gap = (energies[replica] - energies[replica + 1]) * (
1.0 / float(temperatures[replica])
- 1.0 / float(temperatures[replica + 1])
)
accept = gap > 0 or float(
torch.rand(1, generator=generator)
) < min(1.0, float(torch.tensor(gap).exp()))
if accept:
states[replica], states[replica + 1] = (
states[replica + 1],
states[replica],
)
energies[replica], energies[replica + 1] = (
energies[replica + 1],
energies[replica],
)
cold = min(range(len(states)), key=lambda r: energies[r])
if energies[cold] < best["energy"]:
best = {
"energy": energies[cold],
"accuracy": float(
(states[cold].cpu() == truth.cpu()).float().mean()
),
"round": round_index,
}
exact = [energy.total(state) for state in states]
cold = min(range(len(states)), key=lambda r: exact[r])
return {
"arm": label,
"best_seen": best,
"final_cold_energy": exact[cold],
"final_cold_accuracy": float(
(states[cold].cpu() == truth.cpu()).float().mean()
),
"final_accuracies": [
float((state.cpu() == truth.cpu()).float().mean()) for state in states
],
}
def main() -> None:
args = parse_args()
seed_everything(args.seed)
manifest = read_json(Path(args.data_dir, "manifest.json"))
rows = manifest[args.split][: args.samples]
visual_field, text_field = build_fields(args, rows, manifest)
device = torch.device(args.device)
size = len(rows)
generator = torch.Generator().manual_seed(args.seed)
hidden = torch.randperm(size, generator=generator)
truth = torch.argsort(hidden).to(device)
text = standardized(text_field[hidden][:, hidden].double().to(device)).float()
visual = standardized(visual_field.double().to(device)).float()
triples = torch.randint(0, size, (args.triples, 3), generator=generator)
triples = triples[
(triples[:, 0] != triples[:, 1])
& (triples[:, 1] != triples[:, 2])
& (triples[:, 0] != triples[:, 2])
].to(device)
weights = {
"pair": (1.0, 0.0),
"triangle": (0.0, 1.0),
"both": (1.0, 1.0),
}
report = {
"protocol": (
"The decision statistic is the deepest energy a strong "
"searcher reaches versus the energy of the truth. Descent "
"retention is reported but not used: it measures the search "
"operator. Hidden pairs score only."
),
"samples": size,
"triples": len(triples),
"energies": {},
}
for name in (item.strip() for item in args.energies.split(",")):
pair_weight, triangle_weight = weights[name]
energy = TriangleEnergy(text, visual, triples, pair_weight, triangle_weight)
true_energy = energy.total(truth)
random_starts = [
torch.argsort(torch.rand(size, generator=generator)).to(device)
for _ in range(args.replicas)
]
from_random = temper(energy, random_starts, truth, args, generator, "random")
from_truth = temper(
energy,
[truth.clone() for _ in range(args.replicas)],
truth,
args,
generator,
"truth",
)
deepest = min(from_random["best_seen"]["energy"], from_truth["best_seen"]["energy"])
entry = {
"true_energy": true_energy,
"from_random": from_random,
"from_truth": from_truth,
"deepest_seen": deepest,
"margin_over_true": deepest / true_energy - 1.0,
"passes": bool(deepest >= true_energy - 1e-9),
"recovery_accuracy": max(
from_random["best_seen"]["accuracy"],
from_random["final_cold_accuracy"],
),
}
report["energies"][name] = entry
print(json.dumps({name: {k: entry[k] for k in ("true_energy", "deepest_seen", "margin_over_true", "passes", "recovery_accuracy")}}))
write_json(args.output, report)
print(f"Wrote {args.output}")
if __name__ == "__main__":
main()
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