File size: 9,364 Bytes
a7cdc6a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 | #!/usr/bin/env python3
"""
Can the model mate AT ALL, when the mate is within reach?
This separates three explanations of the conversion failure:
H-conv-3 CAPABILITY : the network cannot represent/execute a mating net
H-conv-2 CREDIT : it could, but outcome-only returns over ~200-ply
games give the mating moves almost no credit
H-conv-4 EXPLORATION : it could, but rarely samples mating lines
DESIGN
Build positions with a KNOWN forced mate in N (N = 1, 2, 3), verified by
exhaustive search using the rules engine only (no engine evaluation is used
as a training signal anywhere -- this is measurement, not supervision).
Then measure, for each N and each recurrent depth:
* mate-in-1 accuracy: does the policy's top legal move deliver mate?
* play-out conversion: from the position, does the model actually win?
A model that scores near 0 on mate-in-1 is capability-limited (H-conv-3).
A model that solves mate-in-1 but fails play-out conversion from won middlegame
positions is credit/exploration-limited (H-conv-2 / H-conv-4).
Positions are generated by random self-play-like walks and then filtered with a
depth-limited search over LEGAL MOVES ONLY. The search is used to *label* test
positions, never to train and never to select the model's move.
"""
from __future__ import annotations
import argparse
import json
import os
import random
import sys
import chess
import torch
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from tinychess.arena import wilson_interval
from tinychess.batching import encode_batch
from tinychess.checkpoint import load_model_only
def mate_in_one_moves(board: chess.Board):
"""All legal moves that deliver immediate checkmate."""
out = []
for mv in board.legal_moves:
board.push(mv)
if board.is_checkmate():
out.append(mv)
board.pop()
return out
def forced_mate_in(board: chess.Board, n: int) -> bool:
"""True if side to move has a forced mate in <= n moves (n plies pairs).
Pure rules-engine minimax over legal moves. Used ONLY to label evaluation
positions; never used as a training target or to choose the model's move.
"""
if board.is_checkmate():
return False
if n <= 0:
return False
for mv in board.legal_moves:
board.push(mv)
if board.is_checkmate():
board.pop()
return True
if board.is_stalemate() or board.is_insufficient_material():
board.pop()
continue
# opponent must have no escape
escaped = False
for reply in board.legal_moves:
board.push(reply)
ok = forced_mate_in(board, n - 1)
board.pop()
if not ok:
escaped = True
break
board.pop()
if not escaped:
return True
return False
def gen_mate_positions(n_mate: int, count: int, seed=0, max_tries=400000):
"""Random walks filtered to positions with a forced mate in exactly n_mate."""
rng = random.Random(seed)
found, tries = [], 0
while len(found) < count and tries < max_tries:
tries += 1
b = chess.Board()
for _ in range(rng.randint(10, 70)):
ms = list(b.legal_moves)
if not ms:
break
b.push(rng.choice(ms))
if b.is_game_over() or b.legal_moves.count() > 30:
continue
if n_mate == 1:
if mate_in_one_moves(b):
found.append(b.fen())
else:
if mate_in_one_moves(b):
continue # want *exactly* n
if forced_mate_in(b, n_mate):
found.append(b.fen())
return found, tries
def top_move(model, board, steps):
pb = encode_batch([board])
with torch.no_grad():
out = model(**pb.model_args(), steps=steps)
logits = out["logits"][0]
idx = int(logits.argmax())
moves = pb.real_moves[0]
return moves[idx] if idx < len(moves) else None
def random_control(fens, n, rng):
"""Baseline: pick a uniformly random legal move. Gives the chance rate."""
hit = 0
for f in fens:
b = chess.Board(f)
mv = rng.choice(list(b.legal_moves))
if n == 1:
hit += int(mv in mate_in_one_moves(b))
else:
b.push(mv)
ok = False
if b.is_checkmate():
ok = True
elif not b.is_game_over():
ok = all(not _escape(b, n) for _ in [0])
b.pop()
hit += int(ok)
return hit
def _escape(b, n):
for reply in b.legal_moves:
b.push(reply)
r = forced_mate_in(b, n - 1)
b.pop()
if not r:
return True
return False
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--ckpts", required=True,
help="comma-separated name=path pairs")
ap.add_argument("--depths", default="1,2,4,8")
ap.add_argument("--n-positions", type=int, default=60)
ap.add_argument("--mate-in", default="1,2")
ap.add_argument("--seed", type=int, default=0)
ap.add_argument("--out", default="runs/mate_capability.json")
a = ap.parse_args()
depths = [int(x) for x in a.depths.split(",")]
mates = [int(x) for x in a.mate_in.split(",")]
suites = {}
for n in mates:
fens, tries = gen_mate_positions(n, a.n_positions, seed=a.seed + n)
suites[n] = fens
print(f"[suite] mate-in-{n}: {len(fens)} positions (from {tries} tries)")
rng = random.Random(12345)
controls = {}
for n, fens in suites.items():
if fens:
h = random_control(fens, n, rng)
ci = wilson_interval(h, 0, len(fens))
controls[f"mate_in_{n}"] = {"solved": h, "n": len(fens),
"acc": round(h / len(fens), 4),
"ci95": [round(ci[0], 3), round(ci[1], 3)],
"mean_legal": round(sum(
chess.Board(f).legal_moves.count()
for f in fens) / len(fens), 1)}
print(f"[control] random-legal mate-in-{n}: {h}/{len(fens)} "
f"(mean {controls[f'mate_in_{n}']['mean_legal']} legal moves)")
res = {"args": vars(a), "suite_sizes": {k: len(v) for k, v in suites.items()},
"random_control": controls, "models": {}}
for spec in a.ckpts.split(","):
name, path = spec.split("=", 1)
model, cfg = load_model_only(path)
trained = int(cfg.max_steps)
if max(depths) > trained and hasattr(model, "core"):
import torch.nn as nn
old = model.core.step_emb.data
new = torch.zeros(max(depths) + 1, old.shape[1])
new[:old.shape[0]] = old
new[old.shape[0]:] = old[-1]
model.core.step_emb = nn.Parameter(new)
model.cfg.max_steps = max(depths)
model.core.cfg.max_steps = max(depths)
entry = {"path": path, "params": model.param_report()["total"],
"trained_max_steps": trained, "by_depth": {}}
print(f"\n=== {name} ({entry['params']:,} params, trained {trained} steps) ===")
for d in depths:
row = {}
for n, fens in suites.items():
if not fens:
continue
hit = 0
for f in fens:
b = chess.Board(f)
mv = top_move(model, b, d)
if mv is None:
continue
if n == 1:
if mv in mate_in_one_moves(b):
hit += 1
else:
b.push(mv)
# after our move, is a forced mate in n-1 still on?
still = False
if not b.is_game_over():
ok = True
for reply in b.legal_moves:
b.push(reply)
r = forced_mate_in(b, n - 1)
b.pop()
if not r:
ok = False
break
still = ok
elif b.is_checkmate():
still = True
b.pop()
hit += int(still)
ci = wilson_interval(hit, 0, len(fens))
row[f"mate_in_{n}"] = {"solved": hit, "n": len(fens),
"acc": round(hit / len(fens), 4),
"ci95": [round(ci[0], 3), round(ci[1], 3)]}
print(f" depth={d:2d} mate-in-{n}: {hit}/{len(fens)} "
f"= {hit/len(fens):.3f} CI[{ci[0]:.2f},{ci[1]:.2f}]")
entry["by_depth"][str(d)] = row
res["models"][name] = entry
os.makedirs(os.path.dirname(os.path.abspath(a.out)), exist_ok=True)
json.dump(res, open(a.out, "w"), indent=2)
print(f"\n[saved] {a.out}")
if __name__ == "__main__":
main()
|