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#!/bin/bash
#SBATCH --job-name=medical_qa_finetune
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=8
#SBATCH --gres=gpu:8 # number of gpus
#SBATCH -o /cognitive_comp/wuziwei/task/fs_medical_qa_finetune/%x-%j.log
#SBATCH -e /cognitive_comp/wuziwei/task/fs_medical_qa_finetune/%x-%j.err
#SBATCH -x dgx[050,049]
#export NCCL_DEBUG=INFO
# export PATH=$PATH:/cognitive_comp/wuziwei/codes/fengshen/fengshen
set -x -e
echo "START TIME: $(date)"
MICRO_BATCH_SIZE=1
ROOT_DIR=/cognitive_comp/wuziwei/task/fs_medical_qa_finetune
ZERO_STAGE=2
config_json="$ROOT_DIR/training_config.json"
export MASTER_PORT=$[RANDOM%10000+30000]
# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
cat <<EOT > $config_json
{
"zero_optimization": {
"stage": $ZERO_STAGE,
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"allgather_bucket_size": 2e8
},
"optimizer": {
"type": "Adam",
"params": {
"lr": 1e-5,
"betas": [0.9,0.95],
"eps": 1e-8,
"weight_decay": 1e-2
}
},
"scheduler": {
"type": "WarmupLR",
"params":{
"warmup_min_lr": 5e-6,
"warmup_max_lr": 1e-5
}
},
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 32,
"hysteresis": 2,
"min_loss_scale": 1
},
"activation_checkpointing": {
"partition_activations": false,
"contiguous_memory_optimization": false
},
"wall_clock_breakdown": false,
"zero_allow_untested_optimizer": false,
"train_micro_batch_size_per_gpu": 1,
"steps_per_print": 100,
"gradient_clipping": 1.0
}
EOT
# export PL_DEEPSPEED_CONFIG_PATH=$config_json
export PL_DEEPSPEED_CONFIG_PATH=$config_json
export TORCH_EXTENSIONS_DIR=/cognitive_comp/wuziwei/torch_extendsions
TRAINER_ARGS="
--max_epochs 10 \
--gpus 16 \
--num_nodes 2 \
--strategy deepspeed_stage_2 \
--default_root_dir $ROOT_DIR \
--dirpath $ROOT_DIR/ckpt \
--save_top_k 3 \
--monitor train_loss \
--mode min \
--save_last \
"
DATA_DIR=/cognitive_comp/wuziwei/task-data/medical_qa
DATA_ARGS="
--data_dir $DATA_DIR \
--train_batchsize $MICRO_BATCH_SIZE \
--valid_batchsize $MICRO_BATCH_SIZE \
--train_data train.txt \
--valid_data valid.txt \
--test_data test.txt
"
# PRETRAINED_MODEL_PATH=/cognitive_comp/wuziwei/pretrained_model_hf/gpt2
PRETRAINED_MODEL_PATH=/cognitive_comp/wuziwei/pretrained_model_hf/medical_v2
MODEL_ARGS="
--pretrained_model_path ${PRETRAINED_MODEL_PATH} \
--output_save_path $ROOT_DIR/predict.json \
--learning_rate 1e-4 \
--weight_decay 0.1 \
--warmup 0.01 \
"
SCRIPTS_PATH=/cognitive_comp/wuziwei/codes/fengshen/fengshen/examples/GPT_pretrain_finetune/finetune_medicalQA.py
export CMD=" \
$SCRIPTS_PATH \
$TRAINER_ARGS \
$MODEL_ARGS \
$DATA_ARGS \
"
echo $CMD
SINGULARITY_PATH=/cognitive_comp/wuziwei/container/oneflow-cuda11.sif
# singularity exec --nv -B /cognitive_comp/wuziwei/:/cognitive_comp/wuziwei/ $SINGULARITY_PATH python $CMD
# to debug - add echo (it exits and prints what it would have launched)
#run_cmd="$PY_LAUNCHER $CMD"
srun singularity exec --nv -B /cognitive_comp/wuziwei/:/cognitive_comp/wuziwei/ $SINGULARITY_PATH bash -c 'python $CMD'
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