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"""Build SPAgent GRPO datasets for Sokoban and FifteenPuzzle.
Initial states are sampled from the registered Beyond-MMEnv environments.
Images and transitions use ``spagent.grid_puzzle_protocol`` so the initial
frame, rollout frames, direct-answer replay, and reward logic cannot drift.
Example smoke build:
/path/to/mmenv-pool/.venv/bin/python \
train/build_grid_puzzle_training_data.py \
--count-per-task 10 \
--output-dir outputs/grpo_converted/grid_puzzles_v1_smoke
The FifteenPuzzle upstream generator shuffles without a parity check. This
builder rejects unsolvable boards and records only verified-solvable states.
"""
from __future__ import annotations
import argparse
import copy
import json
import random
import sys
from concurrent.futures import ProcessPoolExecutor
from pathlib import Path
from typing import Any, Dict, Iterable, List, Sequence
REPO_ROOT = Path(__file__).resolve().parents[1]
THINK_WITH_IMAGES_ROOT = REPO_ROOT.parents[1]
DEFAULT_BEYOND_ROOT = THINK_WITH_IMAGES_ROOT / "Beyond-MMEnv-Scaling"
DEFAULT_OUTPUT_DIR = REPO_ROOT / "outputs" / "grpo_converted" / "grid_puzzles_v1"
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from spagent.grid_puzzle_protocol import ( # noqa: E402
FIFTEEN_TASK,
SOKOBAN_TASK,
SUPPORTED_TASKS,
build_system_prompt,
build_user_prompt,
canonical_fifteen_state,
canonical_sokoban_state,
encode_initial_state,
render_state,
state_text,
)
_WORKER_ENV: Any = None
def is_fifteen_solvable(board: Sequence[int], *, size: int = 4) -> bool:
"""Return the standard even-width sliding-puzzle parity test."""
values = [int(value) for value in board]
nonzero = [value for value in values if value != 0]
inversions = sum(
1
for i, left in enumerate(nonzero)
for right in nonzero[i + 1:]
if left > right
)
blank_row_from_bottom = size - (values.index(0) // size)
if size % 2 == 1:
return inversions % 2 == 0
return (blank_row_from_bottom % 2 == 0) != (inversions % 2 == 0)
def _import_gym_v(beyond_root: Path) -> Any:
pool_root = beyond_root / "mmenv-pool"
if not pool_root.is_dir():
raise FileNotFoundError(f"Beyond-MMEnv mmenv-pool not found: {pool_root}")
if str(pool_root) not in sys.path:
sys.path.insert(0, str(pool_root))
try:
import gym_v
import gym_v.envs # noqa: F401
except ImportError as exc:
raise RuntimeError(
"Could not import gym_v. Run this builder with the mmenv-pool "
"virtualenv, for example:\n"
f" {pool_root}/.venv/bin/python {Path(__file__).resolve()}"
) from exc
return gym_v
def _extract_sokoban_state(base: Any) -> Dict[str, Any]:
room_state = base._ta_env.room_state
room_fixed = base._ta_env.room_fixed
rows, cols = room_state.shape
walls = []
goals = []
boxes = []
players = []
for r in range(rows):
for c in range(cols):
fixed = int(room_fixed[r, c])
current = int(room_state[r, c])
if fixed == 0:
walls.append((r, c))
if fixed == 2:
goals.append((r, c))
if current in (3, 4):
boxes.append((r, c))
if current in (5, 6):
players.append((r, c))
if len(players) != 1:
raise RuntimeError(f"Expected exactly one Sokoban player, found {players}")
return canonical_sokoban_state(
rows=rows,
cols=cols,
walls=walls,
goals=goals,
boxes=boxes,
player=players[0],
)
def _extract_fifteen_state(base: Any) -> Dict[str, Any]:
board = [
0 if value is None else int(value)
for row in base._ta_env.board
for value in row
]
return canonical_fifteen_state(board)
def sample_state(env: Any, task_id: str, seed: int) -> Dict[str, Any]:
env.reset(seed=seed)
base = env.unwrapped
if task_id == SOKOBAN_TASK:
return _extract_sokoban_state(base)
if task_id == FIFTEEN_TASK:
return _extract_fifteen_state(base)
raise ValueError(f"Unsupported task_id: {task_id}")
def _init_sampling_worker(beyond_root: str, task_id: str) -> None:
"""Create one reusable environment per sampling worker."""
global _WORKER_ENV
gym_v = _import_gym_v(Path(beyond_root))
try:
from loguru import logger
logger.remove()
except (ImportError, ValueError):
pass
_WORKER_ENV = gym_v.make(task_id)
def _sample_seed_batch(
task_id: str,
seed_start: int,
seed_stop: int,
) -> List[tuple[int, Dict[str, Any] | None, str | None]]:
if _WORKER_ENV is None:
raise RuntimeError("Sampling worker environment was not initialized")
results: List[tuple[int, Dict[str, Any] | None, str | None]] = []
for seed in range(seed_start, seed_stop):
try:
state = sample_state(_WORKER_ENV, task_id, seed)
except Exception as exc:
results.append(
(seed, None, f"{type(exc).__name__}: {exc}")
)
else:
results.append((seed, state, None))
return results
def _task_slug(task_id: str) -> str:
return "sokoban" if task_id == SOKOBAN_TASK else "fifteen_puzzle"
def build_record(
*,
task_id: str,
state: Dict[str, Any],
seed: int,
index: int,
output_dir: Path,
) -> Dict[str, Any]:
slug = _task_slug(task_id)
image_path = output_dir / "images" / slug / f"{slug}_{index:06d}.png"
render_state(task_id, state, image_path)
encoded = encode_initial_state(state)
return {
"id": f"{slug}_{index:06d}",
"task_id": task_id,
"source_env": task_id,
"source_seed": seed,
"messages": [
{"role": "system", "content": build_system_prompt(task_id)},
{
"role": "user",
"content": build_user_prompt(task_id, state, image_path),
},
],
"images": [str(image_path.resolve())],
"initial_state": encoded,
"state_text": state_text(task_id, state),
"solvable": True,
"deterministic": True,
"fully_observed": True,
}
def collect_records(
*,
gym_v: Any,
task_id: str,
count: int,
start_seed: int,
output_dir: Path,
max_seed_attempts: int,
beyond_root: Path,
workers: int,
sample_batch_size: int,
) -> List[Dict[str, Any]]:
records: List[Dict[str, Any]] = []
seen = set()
attempts = 0
def consume(
seed: int,
state: Dict[str, Any] | None,
error: str | None,
) -> None:
nonlocal attempts
attempts += 1
if error is not None or state is None:
print(
f"[skip] {task_id} seed={seed}: "
f"{error or 'missing state'}",
flush=True,
)
return
encoded = encode_initial_state(state)
if encoded in seen:
return
if (
task_id == FIFTEEN_TASK
and not is_fifteen_solvable(state["board"])
):
return
seen.add(encoded)
records.append(
build_record(
task_id=task_id,
state=state,
seed=seed,
index=len(records),
output_dir=output_dir,
)
)
if len(records) % 100 == 0 or len(records) == count:
print(
f"[{task_id}] built {len(records)}/{count} "
f"(scanned {attempts} seeds)",
flush=True,
)
if workers == 1:
seed = start_seed
env = gym_v.make(task_id)
try:
while len(records) < count and attempts < max_seed_attempts:
try:
state = sample_state(env, task_id, seed)
except Exception as exc:
consume(
seed,
None,
f"{type(exc).__name__}: {exc}",
)
else:
consume(seed, state, None)
seed += 1
finally:
env.close()
else:
next_seed = start_seed
with ProcessPoolExecutor(
max_workers=workers,
initializer=_init_sampling_worker,
initargs=(str(beyond_root), task_id),
) as executor:
while len(records) < count and attempts < max_seed_attempts:
remaining = max_seed_attempts - attempts
window = min(
remaining,
workers * sample_batch_size * 4,
)
ranges = []
seed_stop = next_seed + window
for batch_start in range(
next_seed, seed_stop, sample_batch_size
):
ranges.append(
(
task_id,
batch_start,
min(batch_start + sample_batch_size, seed_stop),
)
)
next_seed = seed_stop
batches = executor.map(
_sample_seed_batch,
*(zip(*ranges)),
chunksize=1,
)
done = False
for batch in batches:
for seed, state, error in batch:
if (
len(records) >= count
or attempts >= max_seed_attempts
):
done = True
break
consume(seed, state, error)
if done:
break
if len(records) != count:
raise RuntimeError(
f"Built only {len(records)}/{count} records for {task_id} after "
f"{attempts} seed attempts"
)
return records
def _write_jsonl(path: Path, records: Iterable[Dict[str, Any]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8") as handle:
for record in records:
handle.write(json.dumps(record, ensure_ascii=False) + "\n")
def split_records(
records: Sequence[Dict[str, Any]],
*,
test_ratio: float,
split_seed: int,
) -> tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
shuffled = list(records)
random.Random(split_seed).shuffle(shuffled)
if not shuffled:
return [], []
test_count = int(len(shuffled) * test_ratio)
if test_ratio > 0 and len(shuffled) > 1:
test_count = max(1, test_count)
test_count = min(test_count, max(0, len(shuffled) - 1))
return shuffled[test_count:], shuffled[:test_count]
def pad_records(
records: Sequence[Dict[str, Any]],
*,
count: int,
seed: int,
) -> List[Dict[str, Any]]:
"""Deterministically repeat records when the source has fewer unique states."""
padded = list(records)
if len(padded) >= count:
return padded[:count]
if not padded:
raise ValueError("Cannot pad an empty record collection")
pool = list(records)
random.Random(seed).shuffle(pool)
while len(padded) < count:
source = pool[(len(padded) - len(records)) % len(pool)]
repeated = copy.deepcopy(source)
slug = _task_slug(str(source["task_id"]))
repeated["id"] = f"{slug}_{len(padded):06d}"
repeated["duplicate_of"] = source["id"]
padded.append(repeated)
return padded
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--beyond-root",
type=Path,
default=DEFAULT_BEYOND_ROOT,
help=f"Beyond-MMEnv-Scaling root (default: {DEFAULT_BEYOND_ROOT})",
)
parser.add_argument(
"--output-dir",
type=Path,
default=DEFAULT_OUTPUT_DIR,
help=f"Dataset output directory (default: {DEFAULT_OUTPUT_DIR})",
)
parser.add_argument(
"--task",
action="append",
choices=SUPPORTED_TASKS,
help="Task to build; repeat for multiple tasks. Defaults to both.",
)
parser.add_argument("--count-per-task", type=int, default=2000)
parser.add_argument(
"--sokoban-count",
type=int,
default=None,
help="Override --count-per-task for Games/Sokoban-v0.",
)
parser.add_argument(
"--fifteen-count",
type=int,
default=None,
help="Override --count-per-task for Games/FifteenPuzzle-v0.",
)
parser.add_argument(
"--sokoban-unique-count",
type=int,
default=1600,
help=(
"Unique Sokoban states to collect before deterministic padding. "
"The official fixed 6x6 generator has fewer than 2000 practical "
"unique states."
),
)
parser.add_argument("--test-ratio", type=float, default=0.0)
parser.add_argument("--start-seed", type=int, default=0)
parser.add_argument("--split-seed", type=int, default=0)
parser.add_argument("--max-seed-attempts", type=int, default=100000)
parser.add_argument(
"--workers",
type=int,
default=16,
help="Parallel sampling workers; use 1 for serial generation.",
)
parser.add_argument(
"--sample-batch-size",
type=int,
default=16,
help="Consecutive seeds sampled per worker job.",
)
return parser.parse_args()
def main() -> None:
args = parse_args()
if args.count_per_task <= 0:
raise SystemExit("--count-per-task must be positive")
if args.sokoban_count is not None and args.sokoban_count <= 0:
raise SystemExit("--sokoban-count must be positive")
if args.fifteen_count is not None and args.fifteen_count <= 0:
raise SystemExit("--fifteen-count must be positive")
if args.sokoban_unique_count <= 0:
raise SystemExit("--sokoban-unique-count must be positive")
if not 0 <= args.test_ratio < 1:
raise SystemExit("--test-ratio must be in [0, 1)")
if args.workers <= 0:
raise SystemExit("--workers must be positive")
if args.sample_batch_size <= 0:
raise SystemExit("--sample-batch-size must be positive")
tasks = args.task or list(SUPPORTED_TASKS)
output_dir = args.output_dir.resolve()
output_dir.mkdir(parents=True, exist_ok=True)
gym_v = _import_gym_v(args.beyond_root.resolve())
combined_train: List[Dict[str, Any]] = []
combined_test: List[Dict[str, Any]] = []
manifest_tasks: Dict[str, Any] = {}
for task_offset, task_id in enumerate(tasks):
if task_id == SOKOBAN_TASK:
task_count = args.sokoban_count or args.count_per_task
collection_count = min(task_count, args.sokoban_unique_count)
else:
task_count = args.fifteen_count or args.count_per_task
collection_count = task_count
records = collect_records(
gym_v=gym_v,
task_id=task_id,
count=collection_count,
start_seed=args.start_seed,
output_dir=output_dir,
max_seed_attempts=args.max_seed_attempts,
beyond_root=args.beyond_root.resolve(),
workers=args.workers,
sample_batch_size=args.sample_batch_size,
)
unique_initial_states = len(
{str(record["initial_state"]) for record in records}
)
records = pad_records(
records,
count=task_count,
seed=args.split_seed + task_offset,
)
train, test = split_records(
records,
test_ratio=args.test_ratio,
split_seed=args.split_seed + task_offset,
)
slug = _task_slug(task_id)
_write_jsonl(output_dir / f"{slug}_all_v1.jsonl", records)
_write_jsonl(output_dir / f"{slug}_train_v1.jsonl", train)
_write_jsonl(output_dir / f"{slug}_test_v1.jsonl", test)
combined_train.extend(train)
combined_test.extend(test)
manifest_tasks[task_id] = {
"records": len(records),
"train": len(train),
"test": len(test),
"unique_initial_states": unique_initial_states,
"repeated_records": len(records) - unique_initial_states,
"seed_range_scanned_from": args.start_seed,
}
random.Random(args.split_seed).shuffle(combined_train)
random.Random(args.split_seed + 1).shuffle(combined_test)
_write_jsonl(output_dir / "grid_puzzles_train_v1.jsonl", combined_train)
_write_jsonl(output_dir / "grid_puzzles_test_v1.jsonl", combined_test)
manifest = {
"format": "spagent-grid-puzzles-v1",
"tasks": manifest_tasks,
"combined_train": len(combined_train),
"combined_test": len(combined_test),
"test_ratio": args.test_ratio,
"sampling_workers": args.workers,
"fully_observed": True,
"deterministic": True,
"fifteen_filter": "standard sliding-puzzle parity",
}
(output_dir / "manifest.json").write_text(
json.dumps(manifest, indent=2, ensure_ascii=False) + "\n",
encoding="utf-8",
)
print(f"DONE: {output_dir}")
print(json.dumps(manifest, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
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