File size: 3,495 Bytes
5e877ac 681e79d 5e877ac 681e79d 5e877ac 681e79d 5e877ac 681e79d 5e877ac 681e79d 5e877ac 681e79d 5e877ac 681e79d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
#!/bin/bash
# Pretrain a multimodal model.
export OMP_NUM_THREADS=8
export NCCL_IB_DISABLE=0
export NCCL_IB_GID_INDEX=3
export NCCL_SOCKET_IFNAME=eth0
export NCCL_DEBUG=INFO
export NCCL_IB_SL=1
export CUDA_DEVICE_MAX_CONNECTIONS=1
export NCCL_BLOCKING_WAIT=1
export NCCL_ASYNC_ERROR_HANDLING=1
export NCCL_TIMEOUT=500
export TORCH_DISTRIBUTED_DEBUG=DETAIL
DATETIME=`date +'%y-%m-%d-%H-%M-%S'`
# Setting for multi nodes training.
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}"
source /mnt/bn/tns-algo-video-public-my2/wangpeng.an/environment/anaconda3/bin/activate multimodal
cd /mnt/bn/tns-algo-video-public-my2/wangpeng.an/model/OmniFusion-main
# Install necessary packages
pip3 install requests
pip3 install attrs
pip3 install aiofiles
pip3 install pynvml
# Print Python executable path, torchrun, deepspeed and PYTHONPATH
echo "Python executable: $(which python)"
echo "torchrun executable: $(which torchrun)"
echo "deepspeed executable: $(which deepspeed)"
echo "PYTHONPATH before torchrun: $PYTHONPATH"
sudo chmod 777 /var/lib/fastrak -R
ACCELERATE_CPU_AFFINITY=1 torchrun --nproc_per_node=$ARNOLD_WORKER_GPU --nnodes=$ARNOLD_WORKER_NUM --node_rank=$ARNOLD_ID --master_addr=$METIS_WORKER_0_HOST --master_port=$port \
llava/train/train_mem.py \
--deepspeed ./scripts/zero2.json \
--model_name_or_path /mnt/bn/tns-algo-video-public-my2/wangpeng.an/model/Meta-Llama-3.1-8B-Instruct \
--version plain \
--data_path /mnt/bn/tns-algo-video-public-my2/wangpeng.an/data/multidataset/video_image_asr_caption_pre_1102.json \
--audio_asr_folder /mnt/bn/tns-algo-video-public-my2/wangpeng.an/data \
--audio_caption_folder /mnt/bn/tns-algo-video-public-my2/wangpeng.an/data/multidataset/caption_data/0818 \
--video_folder /mnt/bn/tns-algo-video-public-my2/wangpeng.an/data/Video-LLaVA \
--image_folder /mnt/bn/tns-algo-video-public-my2/wangpeng.an/data/Video-LLaVA \
--X "Audio_asr" "Audio_caption" "Video" "Image" \
--audio_tower /mnt/bn/tns-algo-video-public-my2/wangpeng.an/model/LanguageBind_Audio_Asr \
--audio_caption_tower /mnt/bn/tns-algo-video-public-my2/wangpeng.an/model/pretrained_model/LanguageBind_Audio \
--video_tower /mnt/bn/tns-algo-video-public-my2/wangpeng.an/model/pretrained_model/LanguageBind_Video_merge \
--image_tower /mnt/bn/tns-algo-video-public-my2/wangpeng.an/model/pretrained_model/LanguageBind_Image \
--mm_projector_type mlp2x_gelu \
--tune_mm_mlp_adapter True \
--mm_vision_select_layer -2 \
--mm_use_x_start_end False \
--mm_use_x_patch_token False \
--bf16 True \
--output_dir ./checkpoints/Video-LLaVA-Pretrain-7B-1102 \
--num_train_epochs 1 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 16 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 2000 \
--save_total_limit 20 \
--learning_rate 1e-3 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--tokenizer_model_max_length 3072 \
--gradient_checkpointing True \
--dataloader_num_workers 8 \
--lazy_preprocess True \
--report_to tensorboard \
--cache_dir "./cache_dir"
|