178 lines
6.4 KiB
Bash
178 lines
6.4 KiB
Bash
#!/usr/bin/env bash
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# =============================================================================
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# launch_3b_orpo.sh — 8-GPU ORPO fine-tuning launcher for Korean 3B LLM
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#
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# Usage:
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# bash scripts/launch_3b_orpo.sh # 기본 실행
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# bash scripts/launch_3b_orpo.sh --max_steps 200 # 빠른 테스트
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# RUN_NAME=my_orpo bash scripts/launch_3b_orpo.sh # 이름 지정
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#
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# 기반 모델 : eval/outputs/hf_3b_sft_best (SFT v1 best)
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# 데이터 : data/preference/combined_preference.jsonl
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# 출력 : checkpoints/korean_3b_orpo_v1/
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# 로그 : checkpoints/korean_3b_orpo_v1/train.log
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#
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# 체크포인트 크기 예상:
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# model weights: ~6GB (bf16)
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# optimizer states: ~24GB
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# 총 ~30GB/개 × max 5개 = 150GB
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# =============================================================================
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set -euo pipefail
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# ---- Configurable defaults --------------------------------------------------
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RUN_NAME="${RUN_NAME:-korean_3b_orpo_v1}"
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BASE_MODEL="${BASE_MODEL:-eval/outputs/hf_3b_sft_best}"
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DATA_PATH="${DATA_PATH:-data/preference/combined_preference.jsonl}"
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OUTPUT_DIR="checkpoints/${RUN_NAME}"
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CKPT_DIR="checkpoints/${RUN_NAME}"
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LOG_FILE="${CKPT_DIR}/train.log"
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NPROC=8
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MASTER_PORT="${MASTER_PORT:-29502}"
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# ORPO 하이퍼파라미터
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BATCH_SIZE=4
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GRAD_ACCUM=4
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LR=1.2e-5
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BETA=0.25
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EPOCHS=2
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MAX_LENGTH=1536
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WARMUP_RATIO=0.05
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WEIGHT_DECAY=0.01
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EVAL_SPLIT_RATIO=0.05
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EVAL_STEPS=500
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EARLY_STOPPING_PATIENCE=3
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SAVE_TOTAL_LIMIT=5
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SEED=42
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EXTRA_ARGS="$@"
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# ---- B200 / NVSwitch single-node NCCL tuning --------------------------------
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# (launch_3b_pretrain.sh와 동일한 NCCL 설정 유지)
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export NCCL_IB_DISABLE=1
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export NCCL_PROTO=Simple
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export NCCL_MIN_NCHANNELS=16
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export NCCL_MAX_NCHANNELS=16
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# ORPO forward-backward 패스는 pretrain보다 메모리 변동이 크므로 버퍼 128MB 유지
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export NCCL_BUFFSIZE=134217728
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export OMP_NUM_THREADS=9
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export MKL_NUM_THREADS=9
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# OOM 방지: 메모리 단편화 완화 (ORPO는 chosen/rejected 동시 forward → 메모리 민감)
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export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
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# P2P NVLink 직접 통신 활성화
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export NCCL_P2P_LEVEL=NVL
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# Ring + Tree 병행 (3B gradient 크기 기준)
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export NCCL_ALGO=Ring,Tree
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export PYTHONWARNINGS="ignore::UserWarning:torch.library"
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cd "$(dirname "$0")/.."
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# ---- Pre-flight checks ------------------------------------------------------
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if [[ ! -d "${BASE_MODEL}" ]]; then
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echo "ERROR: 기반 모델 디렉토리 없음: ${BASE_MODEL}"
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echo " SFT 완료 후 HF 포맷으로 변환했는지 확인하세요."
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echo " 예: python scripts/convert_to_hf.py --checkpoint <sft_ckpt> --output ${BASE_MODEL}"
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exit 1
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fi
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if [[ ! -f "${DATA_PATH}" ]]; then
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echo "ERROR: 학습 데이터 없음: ${DATA_PATH}"
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echo " 먼저 데이터 통합 스크립트를 실행하세요:"
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echo " python data/prepare_preference_combined.py"
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exit 1
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fi
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if [[ ! -f "train/orpo.py" ]]; then
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echo "ERROR: train/orpo.py 없음"
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exit 1
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fi
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# GPU 메모리 체크
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GPU_MEM=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits 2>/dev/null | head -1 || echo "0")
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if [[ "$GPU_MEM" -gt 0 && "$GPU_MEM" -lt 40000 ]]; then
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echo "WARNING: GPU 메모리 ${GPU_MEM}MB < 40GB. ORPO 3B 학습에 부족할 수 있음."
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fi
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# 중복 프로세스 방지
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EXISTING_PID=$(pgrep -f "orpo.py.*${RUN_NAME}" 2>/dev/null | head -1 || true)
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if [[ -n "$EXISTING_PID" ]]; then
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echo "ERROR: 이미 ORPO 프로세스 실행 중 (PID: ${EXISTING_PID})"
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echo " kill ${EXISTING_PID} 로 먼저 종료하세요."
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exit 1
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fi
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# 디스크 여유 확인 (최소 200GB)
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AVAIL_KB=$(df /PROJECT 2>/dev/null | awk 'NR==2{print $4}' || echo "0")
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if [[ -n "$AVAIL_KB" && "$AVAIL_KB" -gt 0 && "$AVAIL_KB" -lt 209715200 ]]; then
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AVAIL_GB=$(echo "scale=1; $AVAIL_KB / 1048576" | bc 2>/dev/null || echo "?")
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echo "WARNING: /PROJECT 여유 ${AVAIL_GB}GB < 200GB. 체크포인트 저장 공간 부족 가능."
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fi
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mkdir -p "${CKPT_DIR}" "${OUTPUT_DIR}"
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# ---- 데이터 레코드 수 확인 --------------------------------------------------
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DATA_LINES=$(wc -l < "${DATA_PATH}" 2>/dev/null || echo "?")
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echo " 학습 데이터 레코드 수: ${DATA_LINES}"
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# ---- 유효 배치 크기 계산 ----------------------------------------------------
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EFF_BATCH=$((BATCH_SIZE * NPROC * GRAD_ACCUM))
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echo "=================================================================="
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echo " Korean 3B LLM ORPO Fine-Tuning"
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echo " Run name : ${RUN_NAME}"
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echo " Base model : ${BASE_MODEL}"
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echo " Data : ${DATA_PATH} (${DATA_LINES} records)"
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echo " Output dir : ${OUTPUT_DIR}"
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echo " CKPT dir : ${CKPT_DIR}"
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echo " Log file : ${LOG_FILE}"
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echo " Epochs : ${EPOCHS}"
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echo " LR : ${LR}"
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echo " Beta (ORPO) : ${BETA}"
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echo " Batch : ${BATCH_SIZE} (local) × ${NPROC} GPU × ${GRAD_ACCUM} accum = ${EFF_BATCH}"
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echo " Max length : ${MAX_LENGTH}"
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echo " Weight decay : ${WEIGHT_DECAY}"
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echo " Eval steps : ${EVAL_STEPS}"
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echo " Early stop : patience=${EARLY_STOPPING_PATIENCE}"
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echo " Started : $(date)"
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echo "=================================================================="
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torchrun \
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--nproc_per_node=${NPROC} \
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--master_port=${MASTER_PORT} \
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train/orpo.py \
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--model_path "${BASE_MODEL}" \
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--custom_data_path "${DATA_PATH}" \
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--output_dir "${OUTPUT_DIR}" \
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--epochs ${EPOCHS} \
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--lr ${LR} \
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--beta ${BETA} \
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--batch_size ${BATCH_SIZE} \
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--gradient_accumulation_steps ${GRAD_ACCUM} \
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--max_length ${MAX_LENGTH} \
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--weight_decay ${WEIGHT_DECAY} \
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--eval_split_ratio ${EVAL_SPLIT_RATIO} \
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--eval_steps ${EVAL_STEPS} \
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--early_stopping_patience ${EARLY_STOPPING_PATIENCE} \
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--save_total_limit ${SAVE_TOTAL_LIMIT} \
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${EXTRA_ARGS} \
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2>&1 | tee "${LOG_FILE}" \
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| grep -v "UserWarning" \
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| grep -v "Warning only once" \
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| grep -v "Overriding a previously" \
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| grep -v "dispatch key:" \
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| grep -v "previous kernel:" \
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| grep -v "new kernel:" \
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| grep -v "operator: flash_attn" \
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| grep -v "registered at /usr/local" \
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| grep -v "self.m.impl"
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EXIT_CODE=$?
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echo "=================================================================="
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echo " Done : $(date)"
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echo " Exit code: ${EXIT_CODE}"
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if [[ "${EXIT_CODE}" -eq 0 ]]; then
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echo " 모델 저장 위치: ${OUTPUT_DIR}"
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fi
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echo "=================================================================="
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exit $EXIT_CODE
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