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9a46c96 | 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 | #!/bin/bash
#SBATCH -J sample_mdlm # Job name
#SBATCH -o watch_folder/%x_%j.out # log file (out & err)
#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 --partition=kuleshov # Request partition
#SBATCH --constraint="[a5000|a6000|a100|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
export HYDRA_FULL_ERROR=1
number_sample_batches=16
CUDA_VISIBLE_DEVICES=$1 python main_for_grad_analysis.py \
mode=sample_eval \
loader.batch_size=16 \
loader.eval_batch_size=8 \
data=openwebtext-split \
model=small \
algo=mdlm \
checkpointing.save_dir=/data/remote_cache/patrick/discrete_diffusion/openwebtext-train/2025.10.24/173434/ \
hydra.run.dir=/data/remote_cache/patrick/discrete_diffusion/openwebtext-train/2025.10.24/173434/ \
+eval.save_dir=/home/patrick/.cache/discrete_diffusion/openwebtext-train/mdlm_50k_all/checkpoints \
sampling.num_sample_batches=$number_sample_batches \
sampling.predictor=ancestral_cache \
model.length=1024 \
+wandb.offline=true |