File size: 3,260 Bytes
c4b1fea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | #!/bin/bash
#SBATCH -o ../watch_folder/%x_%j.out # output file (%j expands to jobID)
#SBATCH -N 1 # Total number of nodes requested
#SBATCH --get-user-env # retrieve the users login environment
#SBATCH --mem=64000 # server memory requested (per node)
#SBATCH -t 960:00:00 # Time limit (hh:mm:ss)
#SBATCH --constraint="[a100|a6000|a5000|3090]"
#SBATCH --ntasks-per-node=4
#SBATCH --gres=gpu:4 # Type/number of GPUs needed
#SBATCH --open-mode=append # Do not overwrite logs
#SBATCH --requeue # Requeue upon preemption
<<comment
# Usage:
cd scripts/
MODEL=<ar|mdlm|udlm>
PROP=<qed|ring_count>
sbatch \
--export=ALL,MODEL=${MODEL},PROP=${PROP} \
--job-name=train_qm9_pplm_classifier_${PROP}_${MODEL} \
train_qm9_pplm_classifier.sh
comment
# Setup environment
cd ../ || exit # Go to the root directory of the repo
source setup_env.sh
export HYDRA_FULL_ERROR=1
export NCCL_P2P_LEVEL=NVL
# Expecting:
# - MODEL (ar, mdlm, or udlm)
# - PROP (qed or ring_count)
if [ -z "${MODEL}" ]; then
echo "MODEL is not set"
exit 1
fi
if [ -z "${PROP}" ]; then
echo "PROP is not set"
exit 1
fi
LABEL_SMOOTHING=FALSE
RUN_NAME="${PROP}_${MODEL}"
if [ "${MODEL}" = "ar" ]; then
# AR
PARAMETERIZATION="ar"
PRETRAINED_PATH="${PWD}/outputs/qm9/${MODEL}_no-guidance/checkpoints/best.ckpt"
POOLING="attention_mean"
# dummy properties
DIFFUSION="absorbing_state"
T=0
TIME_COND=False
elif [ "${MODEL}" = "mdlm" ]; then
# MDLM
DIFFUSION="absorbing_state"
PARAMETERIZATION="subs"
T=0
TIME_COND=False
PRETRAINED_PATH="${PWD}/outputs/qm9/${MODEL}_no-guidance/checkpoints/best.ckpt"
POOLING="mean"
elif [ "${MODEL}" = "udlm" ]; then
# UDLM
DIFFUSION="uniform"
PARAMETERIZATION="d3pm"
T=0
TIME_COND=True
PRETRAINED_PATH="${PWD}/outputs/qm9/${MODEL}_no-guidance/checkpoints/best.ckpt"
POOLING="mean"
else
echo "MODEL must be one of ar, mdlm, udlm"
exit 1
fi
# To enable preemption re-loading, set `hydra.run.dir` or
srun python -u -m main \
mode=train_classifier \
+is_pplm_classifier=True \
+use_label_smoothing=${LABEL_SMOOTHING} \
eval.checkpoint_path="${PRETRAINED_PATH}" \
parameterization=${PARAMETERIZATION} \
time_conditioning=${TIME_COND} \
diffusion=${DIFFUSION} \
T=${T} \
data=qm9 \
data.label_col="${PROP}" \
data.label_col_pctile=90 \
data.num_classes=2 \
loader.global_batch_size=2048 \
loader.eval_global_batch_size=4096 \
classifier_model=small-classifier \
classifier_backbone=dit \
classifier_model.pooling=${POOLING} \
model.length=32 \
+classifier_model.freeze_encoder=True \
+classifier_model.use_encoder_ema=True \
optim.lr=3e-5 \
lr_scheduler=cosine_decay_warmup \
lr_scheduler.warmup_t=1000 \
lr_scheduler.lr_min=3e-7 \
training.guidance=null \
+training.use_label_smoothing=${LABEL_SMOOTHING} \
callbacks.checkpoint_every_n_steps.every_n_train_steps=5_000 \
callbacks.checkpoint_monitor.monitor=val/cross_entropy \
trainer.val_check_interval=1.0 \
trainer.max_steps=25_000 \
wandb.group=train_classifier \
wandb.name="qm9-pplm_classifier_${RUN_NAME}" \
hydra.run.dir="./outputs/qm9/pplm_classifier/${RUN_NAME}"
|