scripts/selfplay/7b_gpu567.sh

#!/bin/bash
set -x

# Force use only GPU 4,5,6
export CUDA_VISIBLE_DEVICES=4,5,6

# 로그 설정 - /data/azr로 통합
TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
LOG_DIR="/data/azr/logs"
LOG_FILE="$LOG_DIR/7b_gpu567_$TIMESTAMP.log"

# 로그 디렉토리 생성
mkdir -p $LOG_DIR

# 로그 시작
echo "🚀 AZR 7B GPU567 Training Started at $(date)" | tee -a $LOG_FILE
echo "📁 Log file: $LOG_FILE" | tee -a $LOG_FILE
echo "🎯 GPUs: 4,5,6" | tee -a $LOG_FILE
echo "========================================" | tee -a $LOG_FILE

# 모든 출력을 로그파일과 터미널에 동시 출력
exec > >(tee -a $LOG_FILE) 2>&1

# FlashAttention 설정 (해결 방안 적용)
export VLLM_ATTENTION_BACKEND=FLASH_ATTN
export VLLM_USE_FLASH_ATTN_2=1
export VLLM_WORKER_MULTIPROC_METHOD=spawn
export TRANSFORMERS_CACHE_DTYPE=bfloat16
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512

# FlashAttention 호환성을 위한 추가 설정
export TORCH_DTYPE=bfloat16
export VLLM_TORCH_DTYPE=bfloat16
export RAY_memory_monitor_refresh_ms=0
export RAY_LOGGING_LEVEL=DEBUG
export RAY_DEDUP_LOGS=0
export HYDRA_FULL_ERROR=1
export PYTHONPATH="${PYTHONPATH}:$(pwd)/verl"

# FlashAttention 호환성을 위한 설정
export HF_HOME=/data/.cache/huggingface
unset TRANSFORMERS_CACHE

OUTPUT_SEED_PATH=${OUTPUT_SEED_PATH:-/data/azr/data/7b_seed_io.jsonl}
OUTPUT_ERROR_SEED_PATH=${OUTPUT_ERROR_SEED_PATH:-/data/azr/data/7b_error_seed_io.jsonl}
OUTPUT_CODE_F_SEED_PATH=${OUTPUT_CODE_F_SEED_PATH:-/data/azr/data/7b_code_f_seed_io.jsonl}

python -m absolute_zero_reasoner.main_azr_ppo \
    data.shuffle=True \
    actor_rollout_ref.ref.include_ref=False \
    algorithm.adv_estimator=reinforce_plus_plus \
    data.train_files=data/code_reason/test_answer.parquet \
    data.val_files=data/code_reason/test_answer.parquet \
    +trainer.checkpoint_dir=/data/azr/checkpoints/7b_gpu567 \
    +trainer.save_dir=/data/azr/models/7b_gpu567 \
    hydra.run.dir=/data/azr/outputs/7b_gpu567 \
    data.train_batch_size=24 \
    data.val_batch_size=492 \
    data.max_prompt_length=4096 \
    data.max_response_length=6144 \
    azr.data_selection_strategy.content_max_length=4000 \
    actor_rollout_ref.model.path=Qwen/Qwen2.5-7B \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.model.use_remove_padding=True \
    +actor_rollout_ref.model.torch_dtype=bfloat16 \
    +actor_rollout_ref.model.fsdp_config.model_dtype=bf16 \
    +actor_rollout_ref.actor.fsdp_config.model_dtype=bf16 \
    +actor_rollout_ref.ref.fsdp_config.model_dtype=bf16 \
    actor_rollout_ref.actor.ppo_mini_batch_size=48 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \
    actor_rollout_ref.actor.use_kl_loss=False \
    actor_rollout_ref.actor.kl_loss_coef=0.0 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.model.pretrained_tokenizer=True \
    actor_rollout_ref.actor.fsdp_config.param_offload=True \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \
    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=32 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.max_num_batched_tokens=32768 \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
    actor_rollout_ref.rollout.enforce_eager=True \
    actor_rollout_ref.rollout.free_cache_engine=True \
    actor_rollout_ref.rollout.enable_chunked_prefill=False \
    actor_rollout_ref.rollout.n=1 \
    actor_rollout_ref.rollout.temperature=1.0 \
    actor_rollout_ref.rollout.dtype=bfloat16 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    algorithm.kl_ctrl.kl_coef=0.0 \
    trainer.critic_warmup=0 \
    trainer.logger=['console','wandb'] \
    trainer.project_name='azr' \
    trainer.experiment_name='azr_7b_gpu567' \
    trainer.n_gpus_per_node=3 \
    trainer.nnodes=1 \
    trainer.save_freq=10 \
    trainer.remove_previous_ckpt_in_save=True \
    trainer.del_local_ckpt_after_load=True \
    trainer.test_freq=10 \
    +trainer.val_before_train=False \
    reward_fn.extraction_type=answer_conditional \
    reward_fn.math_metric=math_verify \
    trainer.log_val_generations=0 \
    azr.data_selection_strategy.update_iteration=1 \
    azr.seed_dataset=$OUTPUT_SEED_PATH \
    azr.output_seed_path=$OUTPUT_SEED_PATH \
    azr.error_seed_dataset=$OUTPUT_ERROR_SEED_PATH \
    azr.output_error_seed_path=$OUTPUT_ERROR_SEED_PATH \
    azr.code_f_seed_dataset=$OUTPUT_CODE_F_SEED_PATH \
    azr.output_code_f_seed_path=$OUTPUT_CODE_F_SEED_PATH \
    azr.pretrain_pred_steps=-1 \
    azr.executor=qwq \
    azr.ast_check=True \
    azr.reward.n_samples=4 \
    azr.problem_types=['code_i','code_o','code_f'] \
    azr.data_selection_strategy.banned_keywords_for_errors_and_exceptions=['raise'] \
    trainer.debug=False \
    azr.reward.generation_reward_config.complexity_reward.coef=0.0 \
    azr.reward.generation_reward_config.complexity_reward.max=0.0 \
    azr.reward.generation_reward_config.complexity_reward.enabled=False \
    azr.reward.generation_reward_config.mean_edit_distance_reward.coef=0.0 \
    azr.reward.generation_reward_config.mean_edit_distance_reward.max=0.0 \
    azr.reward.generation_reward_config.mean_edit_distance_reward.enabled=False \
    azr.reward.generation_reward_config.halstead_reward.coef=0.0 \
    azr.reward.generation_reward_config.halstead_reward.max=0.0 \
    azr.reward.generation_reward_config.halstead_reward.enabled=False \
    azr.reward.generation_reward_config.answer_diversity_reward.coef=0.0 \
    azr.reward.generation_reward_config.answer_diversity_reward.max=0.0 \
    azr.reward.generation_reward_config.answer_diversity_reward.enabled=False \
    azr.reward.generation_reward_config.answer_diversity_reward.hierarchical=False \
    azr.pred_data_mix_strategy=max_new \
    azr.data_selection_strategy.seed_batch_factor=4 \
    azr.data_selection_strategy.valid_program_filter=all \
    azr.data_selection_strategy.max_programs=16384 \
    azr.data_selection_strategy.batched_estimate=False \
    azr.reward.generation_reward_config.intrinsic_combine_method=sum \
    azr.gen_data_probabilities_strategy=uniform \
    trainer.resume_mode=auto \
    azr.data_selection_strategy.composite_start_step=-1 \
    azr.data_selection_strategy.composite_chance=0.0 \
    azr.reward.generation_reward_config.remove_comments=False \
    azr.reward.generation_reward_config.remove_after_return=False \
    azr.reward.generation_reward_config.use_original_code_as_ref=True \
    azr.reward.generation_reward_config.remove_print=False \
    azr.data_selection_strategy.composite_function_n_min=0 \
    azr.data_selection_strategy.composite_function_n_max=0 \
    azr.reward.code_f_reward_type=binary \
    trainer.wandb_run_id=null \
    trainer.total_epochs=30 \
    azr.save_generated_data=True \
    azr.save_data_path="/data/azr/generated/generated_programs_7b_gpu567" \
    azr.save_valid_data=True \
    azr.save_invalid_data=True \
    azr.save_frequency=10 \
    azr.save_final_datasets=True $@

# 실행 완료 로그
echo "========================================" | tee -a $LOG_FILE
echo "🎉 AZR 7B GPU567 Training Completed at $(date)" | tee -a $LOG_FILE
echo "📁 Final log saved to: $LOG_FILE" | tee -a $LOG_FILE