| # Grid 16 - THE DEFINITIVE FRONTIER, one consistent configuration throughout. | |
| # | |
| # Grid 11 (10 layers, 60k steps) understated the method: it gave 75% at sixteen | |
| # digits, while grid 13 (12 layers, 150k steps) reached 99.9% at TWENTY digits | |
| # with the baseline at exactly zero. Mixing configurations across digit counts | |
| # makes the curve meaningless, so this reruns every length at one setting. | |
| #SBATCH -J g16 | |
| #SBATCH -p gpu-h100 | |
| #SBATCH --gpus=h100:1 | |
| #SBATCH -c 8 | |
| #SBATCH --mem=64g | |
| #SBATCH -t 16:00:00 | |
| #SBATCH -o logs/g16-%A_%a.out | |
| #SBATCH --array=0-29%12 | |
| source /etc/profile | |
| SCR=/scratch/zt1/project/msml612/user/$USER | |
| module load python/3.10.10 | |
| export PYTHONPATH= | |
| cd $SCR/pcmd/src | |
| DIG=(12 16 20 24 28); MODES=(mdlm full); SEEDS=(0 1 2) | |
| I=$SLURM_ARRAY_TASK_ID | |
| D=${DIG[$((I / 6))]}; M=${MODES[$(((I % 6) / 3))]}; S=${SEEDS[$((I % 3))]} | |
| SL=$(( 3 * D + 4 )) | |
| NAME="g16-d$D-$M-s$S" | |
| echo "=== $NAME ===" | |
| $SCR/hf_env/bin/python train_add.py --mode $M --digits $D --seq_len $SL --seed $S \ | |
| --n_train 1500000 --n_eval 400 --eval_bs 100 \ | |
| --d 512 --layers 12 --heads 8 --bs 128 --steps 150000 --lr 1e-4 --warmup 6000 \ | |
| --K 4 --lam 1.0 --out $SCR/pcmd/runs/$NAME | |