File size: 2,351 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 | #!/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=32000 # server memory requested (per node)
#SBATCH -t 96:00:00 # Time limit (hh:mm:ss)
#SBATCH --constraint="[a100|a6000|a5000|3090]"
#SBATCH --ntasks-per-node=1
#SBATCH --gres=gpu:1 # 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>
sbatch \
--export=ALL,MODEL=${MODEL} \
--job-name=eval_lm1b_ppl_${MODEL} \
eval_lm1b_ppl.sh
comment
# Setup environment
cd ../ || exit # Go to the root directory of the repo
source setup_env.sh || exit
export HYDRA_FULL_ERROR=1
# Expecting:
# - MODEL (choices: ar, mdlm, udlm)
# - SEED (optional: default = 1)
if [ -z "${MODEL}" ]; then
echo "MODEL is not set"
exit 1
fi
if [ "${MODEL}" = "ar" ]; then
PARAMETERIZATION="ar"
DIFFUSION="absorbing_state"
TRAIN_T=0
ZERO_RECON_LOSS=False
TIME_COND=False
BATCH_SIZE=128
CKPT="${PWD}/outputs/lm1b/ar"
elif [ "${MODEL}" = "mdlm" ]; then
PARAMETERIZATION="subs"
DIFFUSION="absorbing_state"
TRAIN_T=0
ZERO_RECON_LOSS=False
TIME_COND=False
BATCH_SIZE=128
CKPT="${PWD}/outputs/lm1b/mdlm"
elif [ "${MODEL}" = "udlm" ]; then
PARAMETERIZATION="d3pm"
DIFFUSION="uniform"
TRAIN_T=0
ZERO_RECON_LOSS=True
TIME_COND=True
BATCH_SIZE=64
CKPT="${PWD}/outputs/lm1b/udlm"
else
echo "Invalid MODEL: ${MODEL}"
exit 1
fi
# shellcheck disable=SC2086
python -u -m main \
hydra.output_subdir=null \
hydra.run.dir="${PWD}" \
hydra/job_logging=disabled \
hydra/hydra_logging=disabled \
seed=${SEED} \
mode="ppl_eval" \
eval.checkpoint_path="${CKPT}/checkpoints/last.ckpt" \
eval.generate_samples=False \
loader.eval_batch_size=${BATCH_SIZE} \
data=lm1b \
data.wrap=False \
backbone=dit \
model=small \
model.length=128 \
training.guidance=null \
parameterization=${PARAMETERIZATION} \
diffusion=${DIFFUSION} \
time_conditioning=${TIME_COND} \
zero_recon_loss=${ZERO_RECON_LOSS} \
T=${TRAIN_T}
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