| # Sudoku on the SATNet benchmark (Wang et al., ICML 2019), 9000/1000 split, | |
| # scored by BOARD-level accuracy - all 81 cells correct - which is the published | |
| # metric. SATNet reports 98.3%. | |
| # | |
| # The registered prediction: Sudoku has a unique answer, so TC = 0 and there is | |
| # no information-theoretic obstacle at all; the whole difficulty is mode (i) plus | |
| # the ORDER of commitment. An entropy-budgeted decoder commits the forced cells | |
| # first, recomputes and repeats, which IS constraint propagation. A fixed-K | |
| # decoder commits a fixed share per pass regardless of certainty. The gap between | |
| # them should be the largest in this project. | |
| # | |
| # Augmentation is by digit relabelling, a genuine Sudoku symmetry that leaves the | |
| # constraint structure untouched. Both arms are run because SATNet did not | |
| # augment, so the un-augmented number is the like-for-like one. | |
| #SBATCH -J sud1 | |
| #SBATCH -p gpu-h100 | |
| #SBATCH --gpus=h100:1 | |
| #SBATCH -c 8 | |
| #SBATCH --mem=48g | |
| #SBATCH -t 4:00:00 | |
| #SBATCH -o logs/sud1-%A_%a.out | |
| #SBATCH --array=0-7%4 | |
| source /etc/profile | |
| SCR=/scratch/zt1/project/msml612/user/$USER | |
| module load python/3.10.10 | |
| export PYTHONPATH= | |
| cd $SCR/pcmd/src | |
| MODES=(mdlm full); AUG=(plain aug); SEEDS=(0 1) | |
| I=$SLURM_ARRAY_TASK_ID | |
| M=${MODES[$((I / 4))]}; A=${AUG[$(((I % 4) / 2))]}; S=${SEEDS[$((I % 2))]} | |
| FLAG=""; [ "$A" = "aug" ] && FLAG="--augment" | |
| NAME="sud1-$M-$A-s$S" | |
| echo "=== $NAME ===" | |
| $SCR/hf_env/bin/python -u train_sudoku.py --mode $M $FLAG --seed $S --pe ape \ | |
| --root $SCR/pcmd/data/sudoku --d 512 --layers 10 --heads 8 --bs 128 \ | |
| --steps 60000 --lr 1e-4 --warmup 2000 --K 8 --lam 1.0 --eval_bs 200 \ | |
| --out $SCR/pcmd/runs/$NAME | |