| #SBATCH --job-name=catalogue-jsonl-to-meg-ds # job name | |
| #SBATCH --ntasks=1 # number of MP tasks | |
| #SBATCH --nodes=1 | |
| #SBATCH --cpus-per-task=10 # number of cores per tasks | |
| #SBATCH --hint=nomultithread # we get physical cores not logical | |
| #SBATCH --time=20:00:00 # maximum execution time (HH:MM:SS) | |
| #SBATCH --output=logs/merge-meg-ds/%x-%j.out # output file name | |
| #SBATCH --account=six@cpu | |
| #SBATCH --array=0-0 | |
| #SBATCH --partition=cpu_p1 | |
| set -x -e | |
| source $six_ALL_CCFRWORK/start-prod | |
| # We need a specific installation of tokenizers so that it works with bytefallback | |
| conda activate thomas_data_tooling | |
| TOKENIZER_NAME_OR_PATH=bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles | |
| # ======= Generate merged files ====== | |
| MEG_DS_REPO=$six_ALL_CCFRWORK/code/Megatron-DeepSpeed | |
| pushd $MEG_DS_REPO | |
| BASE_PATH=$six_ALL_CCFRSCRATCH/bigscience-datasets/nigercongo_fusion | |
| LANGUAGES=($(ls $BASE_PATH)) | |
| LANG=${LANGUAGES[$SLURM_ARRAY_TASK_ID]} | |
| SAVE_PATH=$six_ALL_CCFRSCRATCH/bigscience-datasets/merged-meg-ds_v3_pii/$LANG/"${TOKENIZER_NAME_OR_PATH//\//_}"_${LANG}_text_document | |
| # fancy way of collecting all datasets within a folder | |
| DATASETS=$(ls $six_ALL_CCFRSCRATCH/bigscience-datasets/nigercongo_fusion/$LANG/**/*.bin | xargs -I {} python -c "print('{}'[:-4])") | |
| mkdir -p $(dirname $SAVE_PATH) | |
| /usr/bin/time -v python -m tools.merge_preprocessed_data \ | |
| --datasets $DATASETS \ | |
| --output-prefix $SAVE_PATH | |