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from __future__ import annotations
import argparse
import json
from pathlib import Path
import torch
import torch.nn.functional as F
from .common import read_json, seed_everything, write_json
from .energy import (
log_sinkhorn,
relation_field_energy,
retrieval_metrics,
standardized_relation,
)
from .io import load_feature_pair, select_rows
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--manifest", default="artifacts/manifest.json")
parser.add_argument("--vision", default="artifacts/vision.pt")
parser.add_argument("--text", default="artifacts/text.pt")
parser.add_argument("--text-orbits")
parser.add_argument("--split", choices=["val", "test"], default="test")
parser.add_argument("--samples", type=int, default=256)
parser.add_argument(
"--reservoir",
choices=["same_set_shuffled", "unpaired"],
default="same_set_shuffled",
)
parser.add_argument("--steps", type=int, default=1_200)
parser.add_argument("--lr", type=float, default=0.12)
parser.add_argument("--temperature-start", type=float, default=0.8)
parser.add_argument("--temperature-end", type=float, default=0.12)
parser.add_argument("--conditional-weight", type=float, default=0.08)
parser.add_argument("--entropy-weight-start", type=float, default=0.005)
parser.add_argument("--entropy-weight-end", type=float, default=0.055)
parser.add_argument("--device", default="cuda:1")
parser.add_argument("--seed", type=int, default=20260729)
parser.add_argument(
"--output", default="artifacts/energy_coupling_test.pt"
)
parser.add_argument(
"--metrics-output", default="artifacts/energy_coupling_test.json"
)
return parser.parse_args()
def main() -> None:
args = parse_args()
seed_everything(args.seed)
manifest = read_json(args.manifest)
vision, text, vision_lookup, text_lookup = load_feature_pair(
args.vision, args.text
)
rows = manifest[args.split][: args.samples]
visual = select_rows(
vision["features"], vision_lookup, rows
).to(args.device)
paired_text = select_rows(
text["features"], text_lookup, rows
).to(args.device)
text_population = select_rows(
text["features"], text_lookup, manifest["text_only_train"]
).to(args.device)
if args.text_orbits:
state = torch.load(
args.text_orbits, map_location="cpu", weights_only=False
)
lookup = {
int(row): index for index, row in enumerate(state["rows"])
}
orbit_mean = F.normalize(
state["features"].float().mean(1), dim=-1
)
paired_text = select_rows(
orbit_mean, lookup, rows
).to(args.device)
text_population = select_rows(
orbit_mean, lookup, manifest["text_only_train"]
).to(args.device)
generator = torch.Generator(device=args.device).manual_seed(args.seed)
target: torch.Tensor | None = None
if args.reservoir == "same_set_shuffled":
permutation = torch.randperm(
len(paired_text), generator=generator, device=args.device
)
anchors = paired_text[permutation]
target = torch.argsort(permutation)
else:
anchor_indices = torch.randperm(
len(text_population),
generator=generator,
device=args.device,
)[: len(visual)]
anchors = text_population[anchor_indices]
initial_permutation = torch.randperm(
len(visual), generator=generator, device=args.device
)
logits = torch.nn.Parameter(
0.02
* torch.randn(
len(visual),
len(visual),
generator=generator,
device=args.device,
)
)
with torch.no_grad():
logits[torch.arange(len(visual), device=args.device), initial_permutation] += 3
visual_relation, visual_standardized = standardized_relation(visual)
optimizer = torch.optim.Adam([logits], lr=args.lr)
history: list[dict] = []
for step in range(args.steps + 1):
progress = min(step / max(args.steps, 1), 1.0)
temperature = max(
args.temperature_end,
args.temperature_start
+ progress
* (args.temperature_end - args.temperature_start),
)
coupling = log_sinkhorn(logits, temperature, iterations=15)
particles = F.normalize(coupling @ anchors, dim=-1)
relation, conditional = relation_field_energy(
visual_relation, visual_standardized, particles
)
entropy = -(
coupling * coupling.clamp_min(1e-12).log()
).sum(-1).mean()
entropy_weight = (
args.entropy_weight_start
+ progress
* (args.entropy_weight_end - args.entropy_weight_start)
)
loss = (
relation
+ args.conditional_weight * conditional
+ entropy_weight * entropy
)
if step % 100 == 0 or step == args.steps:
record = {
"step": step,
"total": float(loss.detach()),
"relation": float(relation.detach()),
"conditional": float(conditional.detach()),
"entropy": float(entropy.detach()),
"temperature": temperature,
"paired_evaluation_only": retrieval_metrics(
particles.detach(), paired_text
),
}
if target is not None:
prediction = coupling.argmax(-1)
record["exact_coupling_evaluation_only"] = {
"accuracy": float((prediction == target).float().mean()),
"true_mass": float(
coupling[
torch.arange(
len(coupling), device=args.device
),
target,
].mean()
),
}
history.append(record)
print(json.dumps(record))
if step == args.steps:
break
optimizer.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_([logits], 5.0)
optimizer.step()
result = {
"protocol": (
"The optimizer sees only frozen within-modality states and "
"energy. Pair identity and paired text are used only for "
"evaluation diagnostics."
),
"mode": "mass_conserving_language_state_coupling",
"reservoir": args.reservoir,
"split": args.split,
"rows": rows,
"args": vars(args),
"history": history,
}
state = {
**result,
"anchors": anchors.detach().cpu(),
"final_coupling": coupling.detach().cpu(),
"final_particles": particles.detach().cpu(),
}
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
torch.save(state, args.output)
write_json(args.metrics_output, result)
print(f"Wrote {args.output} and {args.metrics_output}")
if __name__ == "__main__":
main()
|
