#!/bin/bash
# Properly specified baselines, replacing two broken ones.
#
#  0  DIRECT + all-cell supervision: the published recurrent classifier trains
#     on all 81 cells, not just the blanks. Our blanks-only version plateaued at
#     loss 0.34 against diffusion's 0.15, which is the likeliest cause.
#  1  same, second seed - the first attempt had huge seed variance (40.9 / 6.6)
#  2  AR PREFIX-LM: [puzzle][solution], causal, loss on the solution half. The
#     earlier in-place causal version was mis-specified: Sudoku clues are
#     scattered, so a causal model could not attend to clues on its right.
#SBATCH -J fix
#SBATCH -p gpu-h100
#SBATCH --gpus=h100:1
#SBATCH -c 8
#SBATCH --mem=48g
#SBATCH -t 2:00:00
#SBATCH -o logs/fix-%A_%a.out
#SBATCH --array=0-2%3
source /etc/profile
SCR=/scratch/zt1/project/msml612/user/$USER
module load python/3.10.10
export PYTHONPATH=
cd $SCR/pcmd/src
I=$SLURM_ARRAY_TASK_ID
COMMON="--pe ape --hard $SCR/pcmd/data/sudoku_hard --augment --heads 4 --bs 256 \
  --steps 110000 --lr 3e-4 --warmup 3000 --eval_bs 250 --ckpt_every 10000"
case $I in
  0) NAME="fix-directall-s0"; ARGS="--objective direct --all_cells --recurrent --R 32 --R_infer 64 --d 128 --layers 1 --seed 0" ;;
  1) NAME="fix-directall-s1"; ARGS="--objective direct --all_cells --recurrent --R 32 --R_infer 64 --d 128 --layers 1 --seed 1" ;;
  2) NAME="fix-arprefix-s0";  ARGS="--objective ar --d 128 --layers 12 --seed 0" ;;
esac
echo "=== $NAME ==="
$SCR/hf_env/bin/python -u train_sudoku.py $ARGS $COMMON --out $SCR/pcmd/runs/$NAME