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#!/bin/bash
#SBATCH --job-name=finetune_taiyi # 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)
#SBATCH -x dgx050
# pwd=Fengshenbang-LM/fengshen/examples/pretrain_erlangshen
ROOT_DIR=../../workspace
export TORCH_EXTENSIONS_DIR=${ROOT_DIR}/torch_extendsions
MODEL_NAME=taiyi-stablediffusion-1B
MODEL_ROOT_DIR=$ROOT_DIR/${MODEL_NAME}
if [ ! -d ${MODEL_ROOT_DIR} ];then
mkdir ${MODEL_ROOT_DIR}
fi
NNODES=1
GPUS_PER_NODE=1
MICRO_BATCH_SIZE=1
# 如果你不用Deepspeed的话 下面的一段话都可以删掉 Begin
CONFIG_JSON="$MODEL_ROOT_DIR/${MODEL_NAME}.ds_config.json"
ZERO_STAGE=1
# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
cat <<EOT > $CONFIG_JSON
{
"zero_optimization": {
"stage": ${ZERO_STAGE}
},
"bf16": {
"enabled": true
},
"train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE
}
EOT
export PL_DEEPSPEED_CONFIG_PATH=$CONFIG_JSON
### End
DATA_ARGS="\
--dataloader_workers 2 \
--train_batchsize $MICRO_BATCH_SIZE \
--val_batchsize $MICRO_BATCH_SIZE \
--test_batchsize $MICRO_BATCH_SIZE \
--datasets_path ./demo_dataset \
--datasets_type txt \
--resolution 512 \
"
MODEL_ARGS="\
--model_path IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1 \
--learning_rate 1e-4 \
--weight_decay 1e-1 \
--warmup_ratio 0.01 \
"
MODEL_CHECKPOINT_ARGS="\
--save_last \
--save_ckpt_path ${MODEL_ROOT_DIR}/ckpt \
--load_ckpt_path ${MODEL_ROOT_DIR}/ckpt/last.ckpt \
"
TRAINER_ARGS="\
--max_epoch 10 \
--gpus $GPUS_PER_NODE \
--num_nodes $NNODES \
--strategy deepspeed_stage_${ZERO_STAGE} \
--log_every_n_steps 100 \
--precision bf16 \
--default_root_dir ${MODEL_ROOT_DIR} \
--replace_sampler_ddp False \
--num_sanity_val_steps 0 \
--limit_val_batches 0 \
"
# num_sanity_val_steps， limit_val_batches 通过这俩参数把validation关了
export options=" \
$DATA_ARGS \
$MODEL_ARGS \
$MODEL_CHECKPOINT_ARGS \
$TRAINER_ARGS \
"
python3 finetune.py $options
#srun -N $NNODES --gres=gpu:$GPUS_PER_NODE --ntasks-per-node=$GPUS_PER_NODE --cpus-per-task=20 python3 pretrain_deberta.py $options
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