Datasets:
File size: 1,967 Bytes
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#!/bin/bash
#SBATCH --job-name=xp3mt # 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=10:00:00 # maximum execution time (HH:MM:SS)
#SBATCH --output=%x-%j.out # output file name
#SBATCH --account=ajs@cpu
#SBATCH --partition=cpu_p1
#SBATCH --qos=qos_cpu-t3
set -x -e
source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
export HF_DATASETS_OFFLINE=1
export TRANSFORMERS_OFFLINE=1
MEGATRON_DEEPSPEED_REPO=$six_ALL_CCFRWORK/code/tr13f-6B3-ml-t0/Megatron-DeepSpeed
TOKENIZER_PATH="bigscience/tokenizer"
#ar bn en es fr gu hi id ig mr ne pa prep.py pt rn sw ta te to_meg.slurm ur vi yo zh
LANGS=(
ar
bn
es
fr
gu
hi
id
ig
mr
ne
pa
pt
sw
ta
te
ur
vi
yo
zh
)
DATA_PATH=/gpfswork/rech/six/commun/bigscience-training/jsonls/xP3mt
for val in {0..20}; do
LANG=${LANGS[$val]}
cd $DATA_PATH/$LANG
# Merge
cat *.jsonl > merged_dups_$LANG.jsonl
# Drop duplicates (~1G / 37G for en) + Shuffle
sort -u merged_dups_$LANG.jsonl | shuf > merged_$LANG.jsonl
OUTPUT=/gpfswork/rech/six/commun/bigscience-training/xp3mt/xp3_$LANG
cd $MEGATRON_DEEPSPEED_REPO
#python tools/preprocess_data.py \
# --input $DATA_PATH/$LANG/merged_$LANG.jsonl \
# --output-prefix $OUTPUT \
# --dataset-impl mmap \
# --json-key inputs \
# --tokenizer-type PretrainedFromHF \
# --tokenizer-name-or-path $TOKENIZER_PATH \
# --workers 35
#python tools/preprocess_data.py \
# --input $DATA_PATH/$LANG/merged_$LANG.jsonl \
# --output-prefix $OUTPUT \
# --dataset-impl mmap \
# --json-key targets \
# --tokenizer-type PretrainedFromHF \
# --tokenizer-name-or-path $TOKENIZER_PATH \
# --append-eod \
# --prepend-space \
# --workers 35
done
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