| # Grid 1 — learnability gate and the first quality-vs-parallelism curves. | |
| # | |
| # Doubles as a gate: if a model cannot solve a probe even at FULLY SEQUENTIAL | |
| # decoding (128 passes for a 128-token canvas), the task is not learnable at | |
| # this scale and no parallel-decoding claim built on it would mean anything. | |
| # Launching a large grid before establishing this is the mistake that cost a | |
| # day on the previous project. | |
| #SBATCH -J g1 | |
| #SBATCH -p gpu-h100 | |
| #SBATCH --gpus=h100:1 | |
| #SBATCH -c 8 | |
| #SBATCH --mem=64g | |
| #SBATCH -t 04:00:00 | |
| #SBATCH -o logs/g1-%A_%a.out | |
| #SBATCH --array=0-3%4 | |
| source /etc/profile | |
| SCR=/scratch/zt1/project/msml612/user/$USER | |
| module load python/3.10.10 | |
| export PYTHONPATH= | |
| PY=$SCR/hf_env/bin/python | |
| cd $SCR/pcmd/src | |
| I=$SLURM_ARRAY_TASK_ID | |
| TASKS=(copy); OBJ=(mdlm selfcorrupt); SEEDS=(0 1) | |
| T=${TASKS[$((I / 4))]} | |
| O=${OBJ[$(((I % 4) / 2))]} | |
| S=${SEEDS[$((I % 2))]} | |
| NAME="g1-$T-$O-s$S" | |
| echo "=== $NAME ===" | |
| $PY train.py --task $T --objective $O --coupling 4 --seed $S \ | |
| --seq_len 128 --n_train 200000 --n_eval 500 --eval_bs 100 \ | |
| --d 384 --layers 6 --heads 6 --bs 64 --steps 20000 --lr 3e-4 \ | |
| --out $SCR/pcmd/runs/$NAME | |