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#!/bin/bash
#SBATCH --job-name=predict-cmrc
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --gres=gpu:1 # number of gpus
#SBATCH --cpus-per-task=4 # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH -o $YOUR_SLURM_LOG_PATH/%x-%j.log
#SBATCH -e $YOUR_SLURM_LOG_PATH/%x-%j.err
#
set -x -e
echo "START TIME: $(date)"
MICRO_BATCH_SIZE=8
ROOT_DIR=$YOUR_PROJECT_DIR
DOWNLOAD_MODEL_PATH=$YOUR_PROJECT_DIR/Randeng-T5-784M-QA-Chinese/
#YOUR_MODEL_DIR
if [ ! -d ${ROOT_DIR} ];then
mkdir ${ROOT_DIR}
echo ${ROOT_DIR} created!!!!!!!!!!!!!!
else
echo ${ROOT_DIR} exist!!!!!!!!!!!!!!!
fi
ZERO_STAGE=1
config_json="$ROOT_DIR/ds_config.randeng_t5_dialog_784M.$SLURM_JOBID.json"
export MASTER_PORT=$[RANDOM%10000+30000]
cat <<EOT > $config_json
{
"train_micro_batch_size_per_gpu": ${MICRO_BATCH_SIZE},
"steps_per_print": 100,
"gradient_clipping": 1.0,
"zero_optimization": {
"stage": $ZERO_STAGE,
"contiguous_gradients": false,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 50000000,
"allgather_bucket_size": 500000000
},
}
EOT
export PL_DEEPSPEED_CONFIG_PATH=$config_json
export TORCH_EXTENSIONS_DIR=$YOUR_HOME/tmp/torch_extendsions
# strategy=ddp
strategy=deepspeed_stage_1
TRAINER_ARGS="
--max_epochs 10 \
--gpus 1 \
--num_nodes 1 \
--strategy ${strategy} \
--default_root_dir $ROOT_DIR \
--save_ckpt_path $ROOT_DIR/ckpt \
--save_top_k 5 \
--every_n_train_steps 100\
--monitor val_rougeL_fmeasure \
--mode max \
--save_last \
--check_val_every_n_epoch 1 \
--num_workers 4 \
--dataloader_workers 4 \
--replace_sampler_ddp False \
--accumulate_grad_batches 2 \
--formator t5style \
--filename model-{epoch:02d}-{val_loss:.4f}-{val_rougeL_fmeasure:.3f} \
--do_eval_only \
--prediction_res_path $ROOT_DIR/predictions_sampling.txt \
--decode_strategy sampling \
--precision 16 \
"
TEST_FILE_PATH=$YOUR_DATA_FILE
DATA_ARGS="
--train_batchsize $MICRO_BATCH_SIZE \
--val_batchsize $MICRO_BATCH_SIZE \
--test_file $TEST_FILE_PATH \
--max_seq_length 512 \
--max_knowledge_length 425 \
--max_target_length 128
"
MODEL_ARGS="
--pretrained_model_path $DOWNLOAD_MODEL_PATH\
--tokenizer_type t5_tokenizer \
--learning_rate 1e-4 \
--weight_decay 1e-2 \
--warmup_ratio 0.1 \
--sheduler_type polynomial \
--min_learning_rate 1e-5 \
"
SCRIPTS_PATH=$YOUR_PROJECT_DIR/Fengshenbang-LM/fengshen/examples/qa_t5/finetune_t5_cmrc.py
export CMD=" \
$SCRIPTS_PATH \
$TRAINER_ARGS \
$MODEL_ARGS \
$DATA_ARGS \
"
echo $CMD
# conda activate fs
# export CUDA_VISIBLE_DEVICES=5
srun python $CMD
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