#!/bin/bash

DATA_PATH=/mnt/petrelfs/share_data/wangweiyun/playground/blipcc_5m_as_10m_grit_14m.json
IMAGE_FOLDER=/mnt/petrelfs/share_data/wangweiyun/playground/data

# SLURM
GPUS=32
GPUS_PER_NODE=${GPUS_PER_NODE:-8}
CPUS_PER_TASK=${CPUS_PER_TASK:-12}
QUOTA_TYPE="reserved"

PROJECT_NAME="finetune/asmv2_13b_stage2_pretrain_${GPUS}gpu"
mkdir -p "logs/$(dirname "${PROJECT_NAME}")"

srun --gres=gpu:"${GPUS_PER_NODE}" \
    --ntasks="${GPUS}" \
    --ntasks-per-node="${GPUS_PER_NODE}" \
    --cpus-per-task="${CPUS_PER_TASK}" \
    --quotatype="${QUOTA_TYPE}" \
python -u llava/train/train_mem.py \
    --deepspeed ./scripts/zero2.json \
    --model_name_or_path checkpoints/asmv2-13b-stage1-ft \
    --version v1 \
    --data_path ${DATA_PATH} \
    --image_folder ${IMAGE_FOLDER} \
    --vision_tower openai/clip-vit-large-patch14-336 \
    --mm_projector_type mlp2x_gelu \
    --mm_vision_select_layer -2 \
    --mm_use_im_start_end False \
    --mm_use_im_patch_token False \
    --image_aspect_ratio pad \
    --group_by_modality_length True \
    --bf16 True \
    --output_dir ./checkpoints/asmv2-13b-stage2-pretrain \
    --num_train_epochs 1 \
    --per_device_train_batch_size 16 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 1 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 1250 \
    --save_total_limit 10000 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --tf32 True \
    --model_max_length 2048 \
    --gradient_checkpointing True \
    --dataloader_num_workers 4 \
    --lazy_preprocess True \
    --report_to tensorboard \
1>"logs/${PROJECT_NAME}.out" \
2>"logs/${PROJECT_NAME}.err" \
