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ae02605 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 | """The full comparison: our diffusion model against classical solvers and the
published neural results, on a benchmark whose difficulty is stated rather than
assumed.
Published Sudoku numbers are reported on different puzzle distributions, and
quoting them side by side without saying so is exactly the error that cost this
project its SATNet claim. So:
* classical baselines are MEASURED on our test set, not cited;
* our model is reported STRATIFIED BY CLUE COUNT, so a reader can compare at
whatever difficulty a published number used;
* published numbers appear only in a clearly separated block, with their
distribution named.
Three classical baselines, each measuring something different:
propagation naked + hidden singles to fixpoint, NO search. The "free" solver.
backtracking full search. Solves everything, so the informative quantity is
its COST - how many search nodes it needs - which is the honest
yardstick for "how hard is this puzzle really".
greedy-MRV one pass of most-constrained-variable assignment without
backtracking: what a single confident sweep achieves.
For the diffusion model the quantity of interest is board accuracy at a small
fixed number of forward passes, which is the comparison that makes the
generative framing worth anything.
"""
import argparse, glob, json, os, sys
from collections import defaultdict
import numpy as np
import torch
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from model import RecurrentDenoiser, Transformer
import diffusion as dfn
from sudoku import CELLS, GRID, SudokuTokenizer
from gen_hard_sudoku import candidates, count_solutions, PEERS
# published results, with the distribution each was measured on
PUBLISHED = [
("SATNet (Wang et al. ICML 2019)", "SATNet easy split, ~36 clues", 0.983),
("SATNet, evaluated on RRN hard", "RRN hard, 17 clues", 0.061),
("RRN (Palm et al. NeurIPS 2018)", "RRN hard, 17 clues", 0.967),
("Recurrent Transformer (Yang et al. 2023)", "RRN hard, 17 clues", 0.995),
("Recurrent Transformer", "SATNet easy split", 1.000),
]
PARAMS = {"Recurrent Transformer (Yang et al. 2023)": "211k",
"RRN (Palm et al. NeurIPS 2018)": "201k",
"SATNet (Wang et al. ICML 2019)": "618k"}
def units():
us = []
for r in range(GRID):
us.append([r * GRID + c for c in range(GRID)])
for c in range(GRID):
us.append([r * GRID + c for r in range(GRID)])
for br in range(0, GRID, 3):
for bc in range(0, GRID, 3):
us.append([(br + dr) * GRID + bc + dc
for dr in range(3) for dc in range(3)])
return us
UNITS = units()
def propagate(grid):
g = [int(v) for v in grid]
while True:
cand = candidates(g)
if cand is None:
return None
changed = False
for i in range(CELLS):
if g[i] == 0 and bin(cand[i]).count("1") == 1:
g[i] = cand[i].bit_length()
changed = True
if not changed:
for unit in UNITS:
for d in range(9):
bit = 1 << d
spots = [i for i in unit if g[i] == 0 and cand[i] & bit]
if len(spots) == 1:
g[spots[0]] = d + 1
changed = True
if not changed:
return g
def greedy_mrv(grid):
"""Most-constrained-variable, one sweep, no backtracking."""
g = [int(v) for v in grid]
for _ in range(CELLS):
cand = candidates(g)
if cand is None:
return None
best, bi = 10, -1
for i in range(CELLS):
if g[i] == 0:
n = bin(cand[i]).count("1")
if n < best:
best, bi = n, i
if bi < 0:
return g
g[bi] = cand[bi].bit_length() if best == 1 else \
[d + 1 for d in range(9) if cand[bi] & (1 << d)][0]
return g
def backtrack_cost(grid):
"""Solve by full search; return (solution, number of search nodes)."""
g = [int(v) for v in grid]
nodes = [0]
def rec():
nodes[0] += 1
cand = candidates(g)
if cand is None:
return False
best, bi = 10, -1
for i in range(CELLS):
if g[i] == 0:
n = bin(cand[i]).count("1")
if n < best:
best, bi = n, i
if n == 1:
break
if bi < 0:
return True
for d in range(9):
if cand[bi] & (1 << d):
g[bi] = d + 1
if rec():
return True
g[bi] = 0
return False
ok = rec()
return (np.array(g) if ok else None), nodes[0]
p = argparse.ArgumentParser()
p.add_argument("--hard", default="data/sudoku_hard")
p.add_argument("--runs", default="runs")
p.add_argument("--pattern", default="sudh-*")
p.add_argument("--eval_bs", type=int, default=200)
p.add_argument("--out", default="figures/sudoku_compare.json")
a = p.parse_args()
P = np.load(os.path.join(a.hard, "test_puz.npy"))
S = np.load(os.path.join(a.hard, "test_sol.npy"))
clues = (P > 0).sum(1)
print(f"benchmark: {len(P)} puzzles, clues {clues.min()}-{clues.max()} "
f"(mean {clues.mean():.1f})\n", flush=True)
res = {"n": int(len(P)), "clues": {"min": int(clues.min()), "max": int(clues.max()),
"mean": float(clues.mean())}}
print("=" * 88)
print("CLASSICAL BASELINES, measured on THIS test set")
print("=" * 88)
prop_ok = np.array([(lambda r: r is not None and all(v > 0 for v in r)
and np.array_equal(r, S[i]))(propagate(P[i]))
for i in range(len(P))])
mrv_ok = np.array([(lambda r: r is not None and all(v > 0 for v in r)
and np.array_equal(r, S[i]))(greedy_mrv(P[i]))
for i in range(len(P))])
bt = [backtrack_cost(P[i]) for i in range(len(P))]
bt_ok = np.array([g is not None and np.array_equal(g, S[i]) for i, (g, _) in enumerate(bt)])
nodes = np.array([n for _, n in bt])
res["classical"] = {"propagation": float(prop_ok.mean()),
"greedy_mrv": float(mrv_ok.mean()),
"backtracking": float(bt_ok.mean()),
"backtrack_nodes_mean": float(nodes.mean()),
"backtrack_nodes_median": float(np.median(nodes))}
print(f" propagation only (no search) : {prop_ok.mean()*100:6.2f}%")
print(f" greedy MRV, one sweep : {mrv_ok.mean()*100:6.2f}%")
print(f" full backtracking search : {bt_ok.mean()*100:6.2f}% "
f"(mean {nodes.mean():.0f} search nodes, median {np.median(nodes):.0f})")
print(f"\n >>> propagation leaves {(1-prop_ok.mean())*100:.1f}% unsolved, so this")
print(f" >>> benchmark discriminates. Search solves all of it, at a cost.")
print()
print("=" * 88)
print("OUR FROM-SCRATCH MASKED DIFFUSION MODEL")
print("=" * 88)
dev = "cuda" if torch.cuda.is_available() else "cpu"
tok = SudokuTokenizer()
runs = defaultdict(list)
for d in sorted(glob.glob(os.path.join(a.runs, a.pattern))):
if os.path.exists(os.path.join(d, "result.json")):
r = json.load(open(os.path.join(d, "result.json")))
tag = ("recurrent-" if r["args"].get("recurrent") else "feedforward-") \
+ r["args"]["mode"]
runs[tag].append((d, r))
if not runs:
print(" (no trained models yet)")
else:
Pt = torch.from_numpy(P)
St = torch.from_numpy(S - 1)
blank = Pt == 0
def load(d):
cfg = json.load(open(os.path.join(d, "result.json")))["args"]
if cfg.get("recurrent"):
m = RecurrentDenoiser(len(tok), cfg["d"], cfg["layers"], cfg["heads"],
cfg["pe"], max_len=CELLS + 8,
recurrences=cfg.get("R_infer") or cfg["R"]).to(dev)
else:
m = Transformer(len(tok), cfg["d"], cfg["layers"], cfg["heads"],
cfg["pe"], causal=False, max_len=CELLS + 8).to(dev)
m.load_state_dict(torch.load(os.path.join(d, "model.pt"), map_location=dev))
m.eval()
return m
@torch.no_grad()
def run(m, steps):
out = []
for i in range(0, len(Pt), a.eval_bs):
gold = St[i:i + a.eval_bs].to(dev)
bl = blank[i:i + a.eval_bs].to(dev)
x = torch.where(bl, torch.full_like(gold, tok.mask), gold)
y, _ = dfn.fixed_k_decode(m, x, tok, dev, steps, fillable=bl)
out.append(y.cpu().numpy() + 1)
return np.concatenate(out)
res["diffusion"] = {}
for mode, lst in sorted(runs.items()):
npar = json.load(open(os.path.join(lst[0][0], "result.json")))["args"]
print(f"\n {mode} ({len(lst)} seeds, d={npar['d']} layers={npar['layers']}"
+ (f" R={npar.get('R')}→{npar.get('R_infer') or npar.get('R')}"
if npar.get('recurrent') else "") + ")")
print(f" {'passes':>8}{'board':>10}{'vs propagation':>17}")
per_pass = {}
for K in (1, 4, 8, 16, 32, 61):
accs, preds = [], None
for d, _ in lst:
m = load(d)
pr = run(m, K)
accs.append(float((pr == S).all(axis=1).mean()))
preds = pr if preds is None else preds
del m
torch.cuda.empty_cache()
mean = float(np.mean(accs))
per_pass[K] = {"board": mean, "per_seed": accs}
if K == 61:
per_pass[K]["by_clue"] = {
int(c): float((preds[clues == c] == S[clues == c]).all(axis=1).mean())
for c in sorted(set(clues.tolist()))
if (clues == c).sum() >= 20}
print(f" {K:>8}{mean*100:>9.2f}%{mean/max(prop_ok.mean(),1e-9):>15.1f}x")
res["diffusion"][mode] = per_pass
best_mode = max(res["diffusion"], key=lambda m: res["diffusion"][m][61]["board"])
bc = res["diffusion"][best_mode][61].get("by_clue", {})
if bc:
print(f"\n stratified by clue count (mode={best_mode}, 61 passes):")
print(f" {'clues':>7}{'n':>6}{'board':>10}")
for c in sorted(bc):
print(f" {c:>7}{int((clues==c).sum()):>6}{bc[c]*100:>9.2f}%")
print()
print("=" * 88)
print("PUBLISHED NEURAL RESULTS — different puzzle distributions, NOT directly comparable")
print("=" * 88)
for name, dist, acc in PUBLISHED:
pm = PARAMS.get(name, "")
print(f" {acc*100:6.1f}% {name:<42} [{dist}]" + (f" {pm} params" if pm else ""))
print(f"\n Our benchmark is {clues.min()}-{clues.max()} clues (mean {clues.mean():.1f}).")
print(" The 17-clue RRN split is strictly harder than ours; the SATNet split,")
print(" at ~36 clues, is strictly easier and is fully solved by propagation alone.")
print(" The stratified table above is what permits a matched-difficulty comparison.")
os.makedirs(os.path.dirname(a.out) or ".", exist_ok=True)
json.dump(res, open(a.out, "w"), indent=2)
print(f"\nwrote {a.out}")
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