File size: 1,871 Bytes
9605203 |
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 |
#!/bin/bash
#SBATCH --job-name=catalogue-jsonl-to-meg-ds # job name
#SBATCH --ntasks=1 # number of MP tasks
#SBATCH --nodes=1
#SBATCH --cpus-per-task=40 # 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/catalogue-jsonl-to-meg-ds/%x-%j.out # output file name
#SBATCH --account=six@cpu
#SBATCH --array=0-11
#SBATCH --partition=cpu_p1
set -x -e
source $six_ALL_CCFRWORK/start-prod
# We need a specific installation of tokenizers so that it works with bytefallback
conda activate thomas_data_tooling
# ======= Generate meg-ds file ======
DATASET_PATHS=($(ls -d /gpfsscratch/rech/six/commun/bigscience-datasets/pii_no_id_no_num/post/*.jsonl))
DATASET_PATH=${DATASET_PATHS[$SLURM_ARRAY_TASK_ID]}
TOKENIZER_NAME_OR_PATH=bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
DATASET_NAME_WITH_JSONL=$(basename $DATASET_PATH)
DATASET_NAME=${DATASET_NAME_WITH_JSONL:0:-6}
LANG=$(basename $(dirname $DATASET_PATH))
SAVE_MEG_DS_DATASET=$six_ALL_CCFRSCRATCH/bigscience-datasets/oscar_pii_v2/$LANG/"$DATASET_NAME"/meg_ds_"${TOKENIZER_NAME_OR_PATH//\//_}"
mkdir -p $(dirname $SAVE_MEG_DS_DATASET)
if [[ -f "$SAVE_MEG_DS_DATASET"_text_document.bin ]];
then
echo "$SAVE_MEG_DS_DATASET exists."
exit 0
fi
export HF_DATASETS_OFFLINE=1
export TRANSFORMERS_OFFLINE=1
cd $six_ALL_CCFRWORK/code/Megatron-DeepSpeed
/usr/bin/time -v python tools/preprocess_data_many_cores.py \
--input $DATASET_PATH \
--output-prefix $SAVE_MEG_DS_DATASET \
--dataset-impl mmap \
--tokenizer-type PretrainedFromHF \
--tokenizer-name-or-path $TOKENIZER_NAME_OR_PATH \
--append-eod \
--workers 40
|