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59 lines
2.1 KiB
Bash
59 lines
2.1 KiB
Bash
PROJECT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
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export REPO_HOME="${PROJECT_ROOT}" # TODO: change this to your own
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echo "REPO_HOME: $REPO_HOME"
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# on remote
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data_paths="${REPO_HOME}/src/open-r1-multimodal/data_jsonl/gui_multi-image.jsonl"
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image_folders="/data9/shz/project/vlm-r1/VLM-R1/images/gui_multi-image"
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model_path="/data9/shz/ckpt/Qwen2.5-VL-3B-Instruct"
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is_reward_customized_from_vlm_module=False
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reward_methods="all_match"
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echo "data_paths: $data_paths"
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echo "image_folders: $image_folders"
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export EXP_NAME="GUI-multi-image" # TODO: change this to your own experiment name
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TASK_TYPE="gui"
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cd ${REPO_HOME}/src/open-r1-multimodal
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export DEBUG_MODE="true" # Enable Debug if you want to see the rollout of model during RL
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# create the run directory and log file
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mkdir -p ${REPO_HOME}/runs/${EXP_NAME}/log
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export LOG_PATH="${REPO_HOME}/runs/${EXP_NAME}/log/debug_log.$(date +%Y-%m-%d-%H-%M-%S).txt"
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MAX_STEPS=1200 # TODO: change this to your own max steps
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# export WANDB_DISABLED=true
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# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6
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torchrun --nproc_per_node="8" \
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--nnodes="1" \
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--node_rank="0" \
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--master_addr="127.0.0.1" \
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--master_port="12349" \
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src/open_r1/grpo_jsonl.py \
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--use_vllm False \
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--output_dir ${REPO_HOME}/checkpoints/rl/${EXP_NAME} \
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--resume_from_checkpoint True \
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--model_name_or_path $model_path \
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--data_file_paths $data_paths \
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--image_folders $image_folders \
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--is_reward_customized_from_vlm_module $is_reward_customized_from_vlm_module \
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--reward_method $reward_methods \
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--task_type $TASK_TYPE \
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--per_device_train_batch_size 2 \
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--gradient_accumulation_steps 2 \
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--gradient_checkpointing true \
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--logging_steps 1 \
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--num_train_epochs 2 \
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--max_steps $MAX_STEPS \
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--bf16 \
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--attn_implementation flash_attention_2 \
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--run_name ${EXP_NAME} \
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--save_steps 400 \
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--num_generations 8 \
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--max_completion_length 2048 \
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--reward_funcs accuracy format \
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--beta 0.04 \
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--report_to wandb \
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--dataset-name not_used \
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--deepspeed ${REPO_HOME}/src/open-r1-multimodal/local_scripts/zero3.json \
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echo "Training completed for ${EXP_NAME}"
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