#!/bin/bash # cd /mnt/bn/algo-masp-nas-2/xiangchen/repo/LLaVA cd /opt/tiger/masp_models pip install --upgrade pip pip install -e . echo "$PWD" ports=(`echo $METIS_WORKER_0_PORT | tr ',' ' '`) port=${ports[0]} echo "total workers: ${ARNOLD_WORKER_NUM}" echo "cur worker id: ${ARNOLD_ID}" echo "gpus per worker: ${ARNOLD_WORKER_GPU}" echo "master ip: ${METIS_WORKER_0_HOST}" echo "master port: ${port}" #export OMP_NUM_THREADS=8 #export NCCL_IB_DISABLE=0 #export NCCL_IB_GID_INDEX=3 #export NCCL_IB_HCA=${ARNOLD_RDMA_DEVICE} #export NCCL_SOCKET_IFNAME=eth0 # export NCCL_DEBUG=INFO env="$1" cmd="$2" echo $env echo $cmd deepspeed \ --num_nodes=$ARNOLD_WORKER_NUM \ --num_gpus=$ARNOLD_WORKER_GPU \ --master_port=$port \ --master_addr $METIS_WORKER_0_HOST \ llava/train/train_mem.py \ --deepspeed ./scripts/zero2.json \ --model_name_or_path mistralai/Mistral-7B-Instruct-v0.1 \ --version v1 \ --dataset_config /mnt/bn/algo-masp-nas-2/xiangchen/repo/LLaVA/llava/configs/gpt4v_increasing_ablation/finetune_videollava.yaml \ --vision_tower google/siglip-large-patch16-256 \ --pretrain_mm_mlp_adapter /mnt/bn/algo-masp-nas-2/xiangchen/model/masp_models/checkpoints/llava-pretrain-googlesiglip_projector/mm_projector.bin \ --mm_vision_select_layer -2 \ --mm_use_start_end True \ --mm_use_patch_token False \ --image_aspect_ratio pad \ --num_token_per_image 256 \ --num_query_token 256 \ --bf16 True \ --output_dir /mnt/bn/masp-nas/xiangchen/model/masp_models/checkpoints/llava-mistral-googlesiglip_llava_800k \ --group_by_modality_length True \ --num_train_epochs 1 \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size 4 \ --gradient_accumulation_steps 4 \ --evaluation_strategy "no" \ --save_strategy "steps" \ --save_steps 2000 \ --save_total_limit 1 \ --learning_rate 1e-5 \ --weight_decay 0. \ --warmup_ratio 0.03 \ --lr_scheduler_type "cosine" \ --logging_steps 1 \ --tf32 True \ --model_max_length 4096 \ --gradient_checkpointing True \ --dataloader_num_workers 2 \ --lazy_preprocess True \ --report_to none