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"""Freeze a source-disjoint holdout and a quality-ranked capability selection.
This selector never reads benchmark rows, labels, predictions, task IDs, or
question templates. It joins an already sealed non-benchmark teacher release
back to its generation evidence, removes nominal capability rows whose answer
was available too early, and reserves a policy holdout before selecting SFT
rows. A final training selection additionally requires a complete student
screen over the structural candidate set.
"""
from __future__ import annotations
import argparse
import collections
import hashlib
import json
from pathlib import Path
from typing import Any, Iterable
from viss_core.data_v0.canonical import (
canonical_json_line,
file_sha256,
read_jsonl,
stable_id,
)
from viss_core.data_v0.semantic_release import validate_semantic_release
SELECTION_FORMAT = "iclr_video.quality_capability_selection.v1"
HOLDOUT_FORMAT = "iclr_video.nonbenchmark_policy_holdout.v1"
STUDENT_SCREEN_FORMAT = "iclr_video.nonbenchmark_student_screen.v1"
PROFILE_KIND = "nonbenchmark_capability_profile"
PARENT_KIND = "teacher_generated_nonbenchmark_visual_mcq"
PROFILES = (
"backward_epm",
"backward_asi",
"rec_counting",
"sequence_crr",
"sequence_ssr",
)
DEFAULT_HOLDOUT = {profile: 60 for profile in PROFILES}
DEFAULT_TRAIN = {"backward_epm": 120, "backward_asi": 180}
class QualityCapabilitySelectionError(ValueError):
pass
def _target(row: dict[str, Any]) -> str:
values = [
str(item.get("text") or "").strip()
for message in row.get("messages") or []
if message.get("role") == "assistant" and message.get("loss") is True
for item in message.get("content") or []
if item.get("type") == "text"
]
if len(values) != 1 or values[0] not in set("ABCD"):
raise QualityCapabilitySelectionError("row lacks one A-D target")
return values[0]
def _single_parent(row: dict[str, Any], kind: str) -> str:
values = [
str(parent.get("id") or "")
for parent in row.get("parents") or []
if parent.get("kind") == kind
]
if len(values) != 1 or not values[0]:
raise QualityCapabilitySelectionError(f"row lacks one {kind} parent")
return values[0]
def _rank(seed: str, profile: str, answer: str, uid: str) -> str:
return hashlib.sha256(
f"quality-capability-v1:{seed}:{profile}:{answer}:{uid}".encode()
).hexdigest()
def _balanced_select(
rows: Iterable[dict[str, Any]],
*,
quota: int,
seed: str,
profile: str,
) -> list[dict[str, Any]]:
if quota <= 0 or quota % 4:
raise QualityCapabilitySelectionError("balanced quota must be positive / 4")
by_answer: dict[str, list[dict[str, Any]]] = collections.defaultdict(list)
for row in rows:
by_answer[str(row["answer"])].append(row)
per_answer = quota // 4
selected = []
for answer in "ABCD":
values = sorted(
by_answer.get(answer, []),
key=lambda row: (_rank(seed, profile, answer, str(row["uid"])), str(row["uid"])),
)
if len(values) < per_answer:
raise QualityCapabilitySelectionError(
f"{profile}/{answer} has {len(values)} rows; requires {per_answer}"
)
selected.extend(values[:per_answer])
return sorted(selected, key=lambda row: str(row["uid"]))
def _image_paths(request: dict[str, Any]) -> list[str]:
return [
str(image)
for group in request.get("groups") or []
for image in group.get("images") or []
]
def _structural_candidate(
request: dict[str, Any], generation: dict[str, Any]
) -> tuple[bool, dict[str, Any]]:
profile = str(request.get("target_capability") or "")
groups = [
tuple(str(image) for image in group.get("images") or [])
for group in request.get("groups") or []
]
flattened = [image for group in groups for image in group]
evidence_groups = generation.get("evidence_groups") or []
first_sufficient = generation.get("first_sufficient_group")
minimum_sufficient = {
"backward_epm": 1,
"backward_asi": 2,
"rec_counting": 1,
"sequence_crr": 1,
"sequence_ssr": 1,
}.get(profile)
diagnostics = {
"first_sufficient_group": first_sufficient,
"evidence_groups": len(evidence_groups),
"observation_groups": len(groups),
"distinct_observation_groups": len(set(groups)),
"image_positions": len(flattened),
"unique_images": len(set(flattened)),
"duplicate_image_positions": len(flattened) - len(set(flattened)),
}
eligible = (
minimum_sufficient is not None
and generation.get("decision") == "accept"
and generation.get("confidence") == "high"
and isinstance(first_sufficient, int)
and first_sufficient >= minimum_sufficient
and len(evidence_groups) >= 2
and len(set(groups)) >= 2
and len(set(flattened)) >= 6
)
return bool(eligible), diagnostics
def _student_keep(row: dict[str, Any], audit: dict[str, Any]) -> bool:
if audit.get("error") is not None:
return False
target = str(row["answer"])
if str(audit.get("target") or "") != target:
raise QualityCapabilitySelectionError("student audit target diverges")
prediction = str(audit.get("prediction") or "")
margin = float(audit.get("visible_option_margin"))
if not 0.0 <= margin <= 1.0:
raise QualityCapabilitySelectionError("student audit margin is outside [0,1]")
return prediction != target or margin <= 0.25
def _parse_quota(value: str) -> tuple[str, int]:
try:
profile, raw = value.rsplit("=", 1)
count = int(raw)
except (TypeError, ValueError) as exc:
raise argparse.ArgumentTypeError("quota must be PROFILE=COUNT") from exc
if profile not in PROFILES or count <= 0 or count % 4:
raise argparse.ArgumentTypeError("quota must use a known profile and be / 4")
return profile, count
def _excluded_parent_ids(releases: list[Path]) -> set[str]:
excluded: set[str] = set()
for raw in releases:
root = raw.resolve(strict=True)
validate_semantic_release(root, verify_media_hashes=False)
for row in read_jsonl(
root / "trainer/sft.jsonl",
verify_content_hashes=True,
require_canonical=True,
):
excluded.update(
str(parent["id"])
for parent in row.get("parents") or []
if parent.get("kind") == PARENT_KIND and parent.get("id")
)
return excluded
def build(args: argparse.Namespace) -> tuple[dict[str, Any], dict[str, Any]]:
release = args.release.resolve(strict=True)
pack = args.pack.resolve(strict=True)
generation_path = args.generation.resolve(strict=True)
release_manifest = validate_semantic_release(release, verify_media_hashes=False)
pack_manifest_path = pack / "manifest.json"
pack_manifest = json.loads(pack_manifest_path.read_text(encoding="utf-8"))
if (
(release_manifest.get("inputs") or {}).get("pack_manifest_sha256")
!= file_sha256(pack_manifest_path)
or (release_manifest.get("inputs") or {}).get("generation_sha256")
!= file_sha256(generation_path)
):
raise QualityCapabilitySelectionError("release is not bound to pack/generation")
requests = read_jsonl(pack / "requests.jsonl", require_canonical=True)
generation_rows = read_jsonl(generation_path, require_canonical=True)
trainer_rows = read_jsonl(
release / "trainer/sft.jsonl",
verify_content_hashes=True,
require_canonical=True,
)
request_by_uid = {str(row["uid"]): row for row in requests}
generation_by_uid = {str(row["uid"]): row for row in generation_rows}
trainer_by_uid = {str(row["uid"]): row for row in trainer_rows}
if len(request_by_uid) != len(requests) or len(generation_by_uid) != len(generation_rows):
raise QualityCapabilitySelectionError("request/generation UIDs collide")
excluded = _excluded_parent_ids(list(args.exclude_release or []))
joined = []
for request_uid, request in request_by_uid.items():
trainer_uid = stable_id("benchmarkdistillsft", request_uid, digest_length=24)
trainer = trainer_by_uid.get(trainer_uid)
generation = generation_by_uid.get(request_uid)
if trainer is None:
continue
if generation is None:
raise QualityCapabilitySelectionError("filtered row lacks generation record")
parent_id = _single_parent(trainer, PARENT_KIND)
profile = _single_parent(trainer, PROFILE_KIND)
if profile != str(request.get("target_capability") or ""):
raise QualityCapabilitySelectionError("request/trainer profile diverges")
if parent_id in excluded:
continue
joined.append(
{
"uid": trainer_uid,
"request_uid": request_uid,
"parent_id": parent_id,
"profile": profile,
"answer": _target(trainer),
"source_video_basename": str(request["source_video_basename"]),
"request": request,
"generation": generation,
}
)
holdout_quotas = dict(args.holdout_quota or DEFAULT_HOLDOUT)
train_quotas = dict(args.train_quota or DEFAULT_TRAIN)
holdout_rows = []
for profile, quota in sorted(holdout_quotas.items()):
holdout_rows.extend(
_balanced_select(
(row for row in joined if row["profile"] == profile),
quota=quota,
seed=args.seed + ":holdout",
profile=profile,
)
)
holdout_uids = {str(row["uid"]) for row in holdout_rows}
holdout_videos = {str(row["source_video_basename"]) for row in holdout_rows}
if len(holdout_uids) != len(holdout_rows) or len(holdout_videos) != len(holdout_rows):
raise QualityCapabilitySelectionError("policy holdout is not source-video unique")
structural = []
for row in joined:
if row["uid"] in holdout_uids:
continue
eligible, diagnostics = _structural_candidate(
row["request"], row["generation"]
)
if eligible:
structural.append({**row, "diagnostics": diagnostics})
audit_by_uid: dict[str, dict[str, Any]] | None = None
audit_record = None
if args.student_audit is not None:
audit_path = args.student_audit.resolve(strict=True)
audit_value = json.loads(audit_path.read_text(encoding="utf-8"))
if (
audit_value.get("format") != STUDENT_SCREEN_FORMAT
or audit_value.get("benchmark_rows_or_labels_used") is not False
or audit_value.get("source_release_manifest_sha256")
!= file_sha256(release / "manifest.json")
):
raise QualityCapabilitySelectionError("student screen contract diverges")
audit_rows = list(audit_value.get("rows") or [])
audit_by_uid = {str(row.get("uid") or ""): row for row in audit_rows}
if set(audit_by_uid) != {str(row["uid"]) for row in structural}:
raise QualityCapabilitySelectionError("student screen coverage diverges")
audit_record = {"path": str(audit_path), "sha256": file_sha256(audit_path)}
selected_rows = []
if audit_by_uid is not None:
hard = [row for row in structural if _student_keep(row, audit_by_uid[row["uid"]])]
for profile, quota in sorted(train_quotas.items()):
selected_rows.extend(
_balanced_select(
(row for row in hard if row["profile"] == profile),
quota=quota,
seed=args.seed + ":train",
profile=profile,
)
)
def public_row(row: dict[str, Any]) -> dict[str, Any]:
return {
key: row[key]
for key in (
"uid",
"request_uid",
"parent_id",
"profile",
"answer",
"source_video_basename",
)
} | ({"diagnostics": row["diagnostics"]} if "diagnostics" in row else {})
holdout = {
"format": HOLDOUT_FORMAT,
"status": "sealed_before_training_selection",
"source_release": str(release),
"source_release_manifest_sha256": file_sha256(release / "manifest.json"),
"selection_seed": args.seed + ":holdout",
"quotas": holdout_quotas,
"rows": [public_row(row) for row in sorted(holdout_rows, key=lambda row: row["uid"])],
"parent_ids": sorted(row["parent_id"] for row in holdout_rows),
"contracts": {
"source_video_disjoint_from_sft_selection": True,
"may_enter_sft_or_rl": False,
"benchmark_rows_or_labels_used": False,
},
}
selection = {
"format": SELECTION_FORMAT,
"status": (
"sealed_nonbenchmark_quality_selection"
if audit_by_uid is not None
else "awaiting_complete_student_screen"
),
"training_allowed": audit_by_uid is not None,
"source_release": str(release),
"source_release_manifest_sha256": file_sha256(release / "manifest.json"),
"pack_manifest_sha256": file_sha256(pack_manifest_path),
"generation_sha256": file_sha256(generation_path),
"excluded_parent_ids": len(excluded),
"holdout_parent_ids_sha256": hashlib.sha256(
canonical_json_line(sorted(holdout["parent_ids"]))
).hexdigest(),
"structural_candidates": [
public_row(row) for row in sorted(structural, key=lambda row: row["uid"])
],
"structural_counts_by_profile": dict(
sorted(collections.Counter(row["profile"] for row in structural).items())
),
"student_screen": audit_record,
"train_quotas": train_quotas,
"selected_rows": [
public_row(row) for row in sorted(selected_rows, key=lambda row: row["uid"])
],
"parent_ids": sorted(row["parent_id"] for row in selected_rows),
"transformation": "deduplicate_identical_images_keep_first_chronological_occurrence",
"contracts": {
"benchmark_rows_labels_predictions_or_tasks_used": False,
"counterfactual_rows": 0,
"holdout_overlap": 0,
"already_trained_parent_overlap": 0,
"teacher_confidence_high": True,
"two_order_option_consensus_inherited": True,
},
}
return holdout, selection
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--release", type=Path, required=True)
parser.add_argument("--pack", type=Path, required=True)
parser.add_argument("--generation", type=Path, required=True)
parser.add_argument("--exclude-release", type=Path, action="append", default=[])
parser.add_argument("--student-audit", type=Path)
parser.add_argument("--holdout-quota", type=_parse_quota, action="append")
parser.add_argument("--train-quota", type=_parse_quota, action="append")
parser.add_argument("--seed", default="quality-capability-v1")
parser.add_argument("--holdout-output", type=Path, required=True)
parser.add_argument("--selection-output", type=Path, required=True)
args = parser.parse_args()
if args.holdout_quota:
args.holdout_quota = dict(args.holdout_quota)
if args.train_quota:
args.train_quota = dict(args.train_quota)
for path in (args.holdout_output, args.selection_output):
if path.exists():
raise FileExistsError(path)
holdout, selection = build(args)
args.holdout_output.parent.mkdir(parents=True, exist_ok=True)
args.selection_output.parent.mkdir(parents=True, exist_ok=True)
args.holdout_output.write_bytes(canonical_json_line(holdout))
args.selection_output.write_bytes(canonical_json_line(selection))
print(
json.dumps(
{
"holdout_rows": len(holdout["rows"]),
"structural_counts_by_profile": selection["structural_counts_by_profile"],
"selected_rows": len(selection["selected_rows"]),
"training_allowed": selection["training_allowed"],
},
indent=2,
sort_keys=True,
)
)
if __name__ == "__main__":
main()
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