| # QM9 molecular generation - validity retained as denoising steps fall. | |
| # | |
| # This is the axis the discrete graph-diffusion literature competes on. DiGress | |
| # and its successors use hundreds of denoising steps; the question is how much | |
| # validity survives at few steps, and that depends on the training schedule and | |
| # the decoding rule rather than on model scale - which is why it is winnable at | |
| # this budget when text8 bits-per-character is not. | |
| # | |
| # Both of the project's findings are under test here at once, and QM9 is the | |
| # strongest possible case for the second. Its marginals are extremely | |
| # position-dependent: node slot 8 is NEVER occupied, and edge slots range from | |
| # never-bonded to always-bonded, giving a mean total-variation distance of 0.435 | |
| # between a slot and the average slot. Finding 2 therefore predicts that a | |
| # relative-only encoding badly caps few-step generation here, and that absolute | |
| # position information produces a large gain. Registered before the runs. | |
| #SBATCH -J mol1 | |
| #SBATCH -p gpu-h100 | |
| #SBATCH --gpus=h100:1 | |
| #SBATCH -c 8 | |
| #SBATCH --mem=48g | |
| #SBATCH -t 3:00:00 | |
| #SBATCH -o logs/mol1-%A_%a.out | |
| #SBATCH --array=0-7%8 | |
| source /etc/profile | |
| SCR=/scratch/zt1/project/msml612/user/$USER | |
| module load python/3.10.10 | |
| export PYTHONPATH= | |
| cd $SCR/pcmd/src | |
| MODES=(mdlm matched); PES=(rope ape); SEEDS=(0 1) | |
| I=$SLURM_ARRAY_TASK_ID | |
| M=${MODES[$((I / 4))]}; P=${PES[$(((I % 4) / 2))]}; S=${SEEDS[$((I % 2))]} | |
| NAME="mol1-$M-$P-s$S" | |
| echo "=== $NAME ===" | |
| $SCR/hf_env/bin/python -u train_mol.py --mode $M --pe $P --seed $S --K 8 \ | |
| --root $SCR/pcmd/data/qm9 --d 384 --layers 8 --heads 8 --bs 256 \ | |
| --steps 60000 --lr 1e-4 --warmup 2000 --n_gen 4096 --gen_bs 512 \ | |
| --out $SCR/pcmd/runs/$NAME | |