MMSRL

Our Multimodal Semantic Role Labeling model for CONSTRAINT 2022
git clone https://esimon.eu/repos/MMSRL.git
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generic_subsample.sh (2222B)

      1 #!/bin/bash
      2 #SBATCH -A lco@gpu
      3 #SBATCH --job-name=mmsrl             # job name
      4 #SBATCH --ntasks=1                   # number of MP tasks
      5 #SBATCH --ntasks-per-node=1          # number of MPI tasks per node
      6 #SBATCH --gres=gpu:1                 # number of GPUs per node
      7 #SBATCH --cpus-per-task=10           # number of cores per tasks
      8 #SBATCH --hint=nomultithread         # we get physical cores not logical
      9 #SBATCH --distribution=block:block   # we pin the tasks on contiguous cores
     10 #SBATCH --time=10:00:00               # maximum execution time (HH:MM:SS)
     11 #SBATCH --output=/gpfswork/rech/lco/url46ht/outputs/generic_subsample/generic_subsample_%j_%x_%A_%a.out  # output file name
     12 #SBATCH --error=/gpfswork/rech/lco/url46ht/outputs/generic_subsample/generic_subsample_%j_%x_%A_%a.err   # error file name
     13 #SBATCH --array=0-104
     14 
     15 
     16 cd $WORK/mmsrl
     17 
     18 module purge
     19 source ~/.bashrc
     20 conda activate pytorch
     21 export DATA_PATH=$WORK/data
     22 export TRANSFORMERS_CACHE=$HOME/.cache/huggingface/transformers
     23 export TRANSFORMERS_OFFLINE=1
     24 
     25 base_config=("--cyclic_subsample=None --subsample_labels=micro"
     26              "--cyclic_subsample=None --subsample_labels=macro"
     27              "--cyclic_subsample=None --subsample_labels=target --frequency_target=[2,1.5,1.75,1]"
     28              "--cyclic_subsample=None --subsample_labels='interpolate_micro_to_macro'"
     29              "--cyclic_subsample=None --subsample_labels=affine --frequency_factor=0.762 --frequency_bias=0.06"
     30              "configs/cyclic.py"
     31              "configs/cyclic.py --cyclic_subsample=cyclic_subsample_shorter")
     32 
     33 base_name=("micro"
     34            "macro"
     35            "supramacro"
     36            "interpolate"
     37            "affine"
     38            "long_cycle"
     39            "short_cycle")
     40 base_bpe=(500 1000 2000)
     41 
     42 mkdir $WORK/outputs/generic_subsample
     43 
     44 
     45 iconfig=$(($SLURM_ARRAY_TASK_ID%7))
     46 ibpe=$(($SLURM_ARRAY_TASK_ID/7%3))
     47 config=${base_config[$iconfig]}
     48 bpe=${base_bpe[$ibpe]}
     49 name=${base_name[$iconfig]}_bpe${bpe}
     50 python -m mmsrl.train configs/generic.py --pooling=mlp $config --patience=26 --batch_per_epoch=$bpe --output_val=\"$WORK/outputs/generic_subsample/val_${name}_${SLURM_ARRAY_TASK_ID}.pkl\" --output_test=\"$WORK/outputs/generic_subsample/test_${name}_${SLURM_ARRAY_TASK_ID}.pkl\"