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#!/bin/bash
#SBATCH --job-name=pegasus-base_last # create a short name for your job
#SBATCH --nodes=1 # node count
#SBATCH --ntasks-per-node=8 # number of tasks to run per node
#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --gres=gpu:8 # number of gpus per node
#SBATCH -o %x-%j.log # output and error log file names (%x for job id)
set -x -e
echo "START TIME: $(date)"
MODEL_NAME=pegasus-base_test
config_json="./$MODEL_NAME.ds_config.json"
export MASTER_PORT=$[RANDOM%10000+40000]
MICRO_BATCH_SIZE=4
# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
cat <<EOT > $config_json
{
"zero_optimization": {
"stage": 1
},
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"params": {
"betas": [
0.9,
0.999
],
"eps": 1e-08,
"lr": 1e-04,
"weight_decay": 0.01
},
"type": "Adam"
},
"scheduler": {
"params": {
"warmup_max_lr": 1e-04,
"warmup_min_lr": 1e-05,
"total_num_steps": 80000000,
"warmup_num_steps" : 50000
},
"type": "WarmupDecayLR"
},
"steps_per_print": 100,
"gradient_clipping": 1,
"train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE,
"zero_allow_untested_optimizer": false
}
EOT
export PL_DEEPSPEED_CONFIG_PATH=$config_json
export TORCH_EXTENSIONS_DIR=/cognitive_comp/dongxiaoqun/torch_extendsions
DATA_ARGS="\
--datasets_name wudao_180g_512 \
--num_workers 20 \
--train_batchsize $MICRO_BATCH_SIZE \
--val_batchsize 8 \
--test_batchsize 8 \
--max_seq_length 512 \
--val_datasets_field valid \
"
MODEL_ARGS="\
--model_path /cognitive_comp/dongxiaoqun/pretrained_model/pegasus-base/ \
--learning_rate 1e-5 \
--weight_decay 0.1 \
--warmup_ratio 0.001 \
"
MODEL_CHECKPOINT_ARGS="\
--monitor train_loss \
--save_top_k 3 \
--mode min \
--every_n_train_steps 200 \
--dirpath /cognitive_comp/dongxiaoqun/train_model/fengshen-$MODEL_NAME_debug/ckpt \
--filename model-{step:02d}-{train_loss:.4f} \
--save_last \
"
TRAINER_ARGS="\
--gradient_clip_val 1.0 \
--max_epochs 1 \
--gpus 2 \
--num_nodes 1 \
--strategy ddp \
--log_every_n_steps 100 \
--val_check_interval 0.1 \
--accumulate_grad_batches 8 \
--default_root_dir /cognitive_comp/dongxiaoqun/train_model/fengshen-$MODEL_NAME_debug \
--stopword_path /cognitive_comp/dongxiaoqun/pretrained_model/pegasus-large/stopwords \
"
export options=" \
$DATA_ARGS \
$MODEL_ARGS \
$MODEL_CHECKPOINT_ARGS \
$TRAINER_ARGS \
"
SINGULARITY_PATH=/cognitive_comp/dongxiaoqun/software/docker/pytorch21_06_py3_docker_image_v2.sif
export SCRIPT_PATH=/cognitive_comp/dongxiaoqun/project/idea-ccnl/bug_fix/Fengshenbang-LM/fengshen/examples/pegasus/pretrain_pegasus.py
# python $SCRIPT_PATH $options
source activate
conda activate torchnew
srun --nodes=1 --ntasks-per-node=1 --gres=gpu:2 --cpus-per-task=30 -o ${MODEL_NAME}-%J.log --jobid=226191 bash -c 'python3 $SCRIPT_PATH $options'
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