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"""Blind recovery under the triangle energy.
The triangle gate holds the true assignment at N=512 with no counterfeit
below it. The search question is separate and now worth asking again:
tempering with third-order swap deltas, from random starts, with the
hidden order behind a shuffle. Recovery accuracy is the end-to-end world
matching measurement.
"""
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=512)
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=400000)
parser.add_argument("--pair-weight", type=float, default=1.0)
parser.add_argument("--triangle-weight", type=float, default=1.0)
parser.add_argument("--replicas", type=int, default=6)
parser.add_argument("--rounds", type=int, default=4000)
parser.add_argument("--proposals", type=int, default=48)
parser.add_argument("--temp-high", type=float, default=2e-2)
parser.add_argument("--temp-low", type=float, default=1e-4)
parser.add_argument("--exchange-every", type=int, default=20)
parser.add_argument("--greedy-rounds", type=int, default=3000)
parser.add_argument("--device", default="cuda:3")
parser.add_argument("--seed", type=int, default=20260731)
parser.add_argument(
"--output", default="artifacts/synth_v0/triangle_recovery.json"
)
return parser.parse_args()
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)
energy = TriangleEnergy(
text, visual, triples, args.pair_weight, args.triangle_weight
)
true_energy = energy.total(truth)
temperatures = torch.logspace(
torch.log10(torch.tensor(args.temp_low)),
torch.log10(torch.tensor(args.temp_high)),
args.replicas,
)
states = [
torch.argsort(torch.rand(size, generator=generator)).to(device)
for _ in range(args.replicas)
]
energies = [energy.total(state) for state in states]
history = []
for round_index in range(args.rounds):
for replica in range(args.replicas):
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(args.replicas - 1):
gap = (energies[replica] - energies[replica + 1]) * (
1.0 / float(temperatures[replica])
- 1.0 / float(temperatures[replica + 1])
)
if gap > 0 or float(torch.rand(1, generator=generator)) < min(
1.0, float(torch.tensor(gap).exp())
):
states[replica], states[replica + 1] = (
states[replica + 1],
states[replica],
)
energies[replica], energies[replica + 1] = (
energies[replica + 1],
energies[replica],
)
if round_index % 200 == 0:
cold = min(range(args.replicas), key=lambda r: energies[r])
record = {
"round": round_index,
"cold_energy": energies[cold],
"cold_accuracy": float(
(states[cold].cpu() == truth.cpu()).float().mean()
),
"energy_over_true": energies[cold] / true_energy - 1.0,
}
history.append(record)
print(json.dumps(record))
# Greedy polish of the coldest replica.
cold = min(range(args.replicas), key=lambda r: energies[r])
current = states[cold].clone()
for _ in range(args.greedy_rounds):
best_delta, best_pair = 0.0, None
for _ in range(96):
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(current, p, q)
if delta < best_delta:
best_delta, best_pair = delta, (p, q)
if best_pair is None:
break
p, q = best_pair
current[[p, q]] = current[[q, p]]
final = {
"accuracy": float((current.cpu() == truth.cpu()).float().mean()),
"energy": energy.total(current),
}
final["energy_over_true"] = final["energy"] / true_energy - 1.0
report = {
"protocol": (
"Tempering on the pairwise-plus-triangle energy from random "
"starts; text order hidden behind a shuffle; hidden truth "
"scores the outcome only."
),
"samples": size,
"true_energy": true_energy,
"chance_accuracy": 1.0 / size,
"history": history,
"final_polished": final,
"replica_accuracies": [
float((state.cpu() == truth.cpu()).float().mean()) for state in states
],
}
print(json.dumps({"final": final}))
write_json(args.output, report)
print(f"Wrote {args.output}")
if __name__ == "__main__":
main()
|
