| #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 |