mirror of
https://github.com/salesforce/CodeT5.git
synced 2024-10-01 06:35:38 -04:00
84 lines
2.5 KiB
Bash
84 lines
2.5 KiB
Bash
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export HF_DATASETS_CACHE="/export/share/wang.y/workspace/cache_data"
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export TRANSFORMERS_CACHE="/export/share/wang.y/workspace/cache_model"
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export XDG_CACHE_HOME="/export/share/wang.y/workspace/cache_model"
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export WORKDIR="/export/share/wang.y/workspace/CodeT5_release"
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export PYTHONPATH=$WORKDIR
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TASK=${1}
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SUB_TASK=${2}
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MODEL_TAG=${3}
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GPU=${4}
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DATA_NUM=${5}
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BS=${6}
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LR=${7}
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SRC_LEN=${8}
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TRG_LEN=${9}
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PATIENCE=${10}
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EPOCH=${11}
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WARMUP=${12}
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GPU_TYPE=${13}
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RES_FN=${14}
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MODEL_PATH='/export/share/wang.y/workspace/CodeT5_release/pretrained_models'
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if [[ $DATA_NUM == -1 ]]; then
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DATA_TAG='all'
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else
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DATA_TAG=$DATA_NUM
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EPOCH=1
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fi
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FULL_MODEL_TAG=${MODEL_TAG}_${DATA_TAG}_lr${LR}_bs${BS}_src${SRC_LEN}_trg${TRG_LEN}_pat${PATIENCE}_e${EPOCH}
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if [[ ${SUB_TASK} == none ]]; then
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OUTPUT_DIR=saved_models/${TASK}/${FULL_MODEL_TAG}
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else
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OUTPUT_DIR=saved_models/${TASK}/${SUB_TASK}/${FULL_MODEL_TAG}
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fi
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CACHE_DIR=${OUTPUT_DIR}/cache_data
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RES_DIR=${OUTPUT_DIR}/prediction
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LOG=${OUTPUT_DIR}/train.log
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mkdir -p ${OUTPUT_DIR}
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mkdir -p ${CACHE_DIR}
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mkdir -p ${RES_DIR}
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if [[ $MODEL_TAG == roberta ]]; then
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MODEL_TYPE=roberta
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TOKENIZER=roberta-base
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MODEL_NAME=roberta-base
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elif [[ $MODEL_TAG == codebert ]]; then
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MODEL_TYPE=roberta
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TOKENIZER=roberta-base
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MODEL_NAME=microsoft/codebert-base
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elif [[ $MODEL_TAG == bart_base ]]; then
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MODEL_TYPE=bart
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TOKENIZER=facebook/bart-base
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MODEL_NAME=facebook/bart-base
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elif [[ $MODEL_TAG == codet5_small ]]; then
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MODEL_TYPE=codet5
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TOKENIZER=roberta-base
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MODEL_NAME=${MODEL_PATH}/codet5_small
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elif [[ $MODEL_TAG == codet5_base ]]; then
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MODEL_TYPE=codet5
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TOKENIZER=roberta-base
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MODEL_NAME=${MODEL_PATH}/codet5_base
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fi
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if [[ ${TASK} == 'clone' ]]; then
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RUN_FN=${WORKDIR}/run_clone.py
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else
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RUN_FN=${WORKDIR}/run_gen.py
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fi
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CUDA_VISIBLE_DEVICES=${GPU} \
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/export/home/miniconda3/envs/${GPU_TYPE}/bin/python ${RUN_FN} ${MULTI_TASK_AUG} \
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--do_test --do_train --do_eval --do_eval_bleu --save_last_checkpoints --always_save_model \
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--task ${TASK} --sub_task ${SUB_TASK} --model_type ${MODEL_TYPE} --data_num ${DATA_NUM} \
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--num_train_epochs ${EPOCH} --warmup_steps ${WARMUP} --learning_rate ${LR}e-5 --patience ${PATIENCE} \
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--tokenizer_name=${TOKENIZER} --model_name_or_path=${MODEL_NAME} --output_dir ${OUTPUT_DIR} \
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--data_dir ${WORKDIR}/data --cache_path ${CACHE_DIR} --res_dir ${RES_DIR} --res_fn ${RES_FN} \
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--train_batch_size ${BS} --eval_batch_size ${BS} --max_source_length ${SRC_LEN} --max_target_length ${TRG_LEN} \
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2>&1 | tee ${LOG}
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