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#!/bin/bash
#SBATCH --job-name=mmpe_pretrain
#SBATCH --output=/mnt/petrelfs/libozhou/mmpe/output/same_config/pretrain/%j.out
#SBATCH --time=60:00:00
#SBATCH --gres=gpu:8
#SBATCH --partition=s2_bigdata
export OMP_NUM_THREADS=8
export NCCL_IB_DISABLE=0
export NCCL_IB_GID_INDEX=3
export NCCL_SOCKET_IFNAME=eth0
NUM_GPUS=8
NNODES=1
RANK=0
LLM_VERSION="/mnt/hwfile/opendatalab/lbz/vicuna-7b-v1.5"
LLM_VERSION_CLEAN="${LLM_VERSION//\//_}"
VISION_MODEL_VERSION="/mnt/hwfile/opendatalab/lbz/clip-vit-large-336"
VISION_MODEL_VERSION_CLEAN="${VISION_MODEL_VERSION//\//_}"
#四卡batch_size=16,accumulate=2,八卡batch_size=16,accumulate=1
############### Pretrain ################
PROMPT_VERSION=plain
BASE_RUN_NAME="llavanext-${VISION_MODEL_VERSION_CLEAN}-${LLM_VERSION_CLEAN}-mlp2x_gelu-pretrain_blip558k_plain"
echo "BASE_RUN_NAME: ${BASE_RUN_NAME}"
ADDR=`scontrol show hostname $SLURM_JOB_NODELIST | head -n1`
PORT=$((RANDOM % 101 + 20000))
echo $ADDR
echo $PORT
ACCELERATE_CPU_AFFINITY=1 torchrun --nproc_per_node="${NUM_GPUS}" --nnodes="${NNODES}" --node_rank="${RANK}" --master_addr="${ADDR}" --master_port="${PORT}" \
llava/train/train_mem.py \
--deepspeed scripts/zero2.json \
--model_name_or_path ${LLM_VERSION} \
--version ${PROMPT_VERSION} \
--data_path /mnt/hwfile/opendatalab/lbz/llava-pretrain/blip_laion_cc_sbu_558k.json \
--image_folder /mnt/hwfile/opendatalab/lbz/llava-pretrain/images \
--vision_tower ${VISION_MODEL_VERSION} \
--mm_tunable_parts="mm_mlp_adapter" \
--mm_vision_select_layer -2 \
--mm_projector_type mlp2x_gelu \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--use_mmpe True \
--group_by_modality_length True \
--image_aspect_ratio anyres \
--image_grid_pinpoints "[(336, 672), (672, 336), (672, 672), (1008, 336), (336, 1008)]" \
--mm_patch_merge_type spatial_unpad \
--bf16 True \
--output_dir /mnt/petrelfs/libozhou/mmpe/output/same_config/pretrain \
--num_train_epochs 1 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 2 \
--evaluation_strategy "no" \
--save_strategy "no" \
--save_steps 50000 \
--learning_rate 1e-3 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 8192 \
--gradient_checkpointing True \
--dataloader_num_workers 16 \
--lazy_preprocess True \
--report_to wandb \
--run_name pretrain_config \
--attn_implementation sdpa
# You can delete the sdpa attn_implementation if you want to use flash attn |