mirror of
https://github.com/RYDE-WORK/lnp_ml.git
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117 lines
4.2 KiB
Bash
117 lines
4.2 KiB
Bash
#!/usr/bin/env bash
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# 全量数据 final 模型：3-fold Optuna 调参 + 全量固定 epoch 重训
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# MPNN + MoE + Qwen2.5-7B QLoRA + soft-RAG
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# 被 OOM / 抢占 / SSH 断连杀掉后自动等显存并续跑（Optuna 从 sqlite 恢复）
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set -uo pipefail
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GPU=${GPU:-0}
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SEED=${SEED:-42}
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OUT=${OUT:-models/final}
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N_TRIALS=${N_TRIALS:-20}
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EPOCHS=${EPOCHS:-20} # 与 nested CV 的 EPOCHS 保持一致
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PATIENCE=${PATIENCE:-5} # 与 nested CV 的 PATIENCE 保持一致
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N_FOLDS=${N_FOLDS:-3}
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FIXED_PARAMS=${FIXED_PARAMS:-} # 非空则跳过 Optuna，直接用这份超参
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FIXED_EPOCH_MEAN=${FIXED_EPOCH_MEAN:-11}
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BATCH=${BATCH:-8}
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REG_BYPASS=${REG_BYPASS:-off}
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FREEZE=${FREEZE:-3}
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PRETRAIN=${PRETRAIN:-models/pretrain/mpnn/pretrain_delivery.pt}
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MIN_FREE_MB=${MIN_FREE_MB:-14000}
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MAX_RETRY=${MAX_RETRY:-50}
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RETRY_WAIT=${RETRY_WAIT:-300}
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MIN_OK_SEC=${MIN_OK_SEC:-180} # 存活不足这么久就挂 → 判为配置/代码错误，立即停止重试
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LOG="${OUT}/train.log"
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STUDY="${OUT}/optuna_study.sqlite3"
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export TRANSFORMERS_OFFLINE=1
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export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
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export TOKENIZERS_PARALLELISM=false
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export CUDA_VISIBLE_DEVICES=${GPU}
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mkdir -p "${OUT}"
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# nvidia-smi 不受 CUDA_VISIBLE_DEVICES 影响，-i 用物理卡号
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wait_free() {
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while :; do
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local free
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free=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits -i "${GPU}")
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[ "${free}" -ge "${MIN_FREE_MB}" ] && return 0
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echo "[$(date '+%F %T')] GPU${GPU} 仅空 ${free}MiB < ${MIN_FREE_MB}MiB，60s 后重试" >>"${LOG}"
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sleep 60
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done
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}
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# Optuna 的 study.optimize(n_trials=N) 在续跑时语义是"再跑 N 个"，不是"补到 N 个"。
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# 所以每次重启前先数已完成的 trial 只补差额，否则被抢占几次就会多跑几十个 trial。
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remaining_trials() {
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if [ ! -f "${STUDY}" ]; then echo "${N_TRIALS}"; return; fi
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python - "${STUDY}" "${N_TRIALS}" <<'PY' 2>/dev/null || echo "${N_TRIALS}"
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import sys, optuna
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optuna.logging.set_verbosity(optuna.logging.WARNING)
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try:
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st = optuna.load_study(study_name="final_optuna_cv", storage=f"sqlite:///{sys.argv[1]}")
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done = sum(1 for t in st.trials if t.state == optuna.trial.TrialState.COMPLETE)
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except Exception:
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done = 0
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print(max(0, int(sys.argv[2]) - done))
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PY
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}
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EXTRA=()
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if [ -n "${PRETRAIN}" ] && [ -f "${PRETRAIN}" ]; then
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EXTRA+=(--init-from-pretrain "${PRETRAIN}")
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elif [ -n "${PRETRAIN}" ]; then
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echo "[$(date '+%F %T')] 警告：${PRETRAIN} 不存在，跳过预训练初始化" >>"${LOG}"
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fi
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if [ -n "${FIXED_PARAMS}" ]; then
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if [ ! -f "${FIXED_PARAMS}" ]; then
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echo "[$(date '+%F %T')] 错误：${FIXED_PARAMS} 不存在" >>"${LOG}"; exit 1
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fi
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EXTRA+=(--fixed-params-json "${FIXED_PARAMS}" --fixed-epoch-mean "${FIXED_EPOCH_MEAN}")
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fi
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for attempt in $(seq 1 "${MAX_RETRY}"); do
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wait_free
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NT=$(remaining_trials)
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echo "[$(date '+%F %T')] ATTEMPT ${attempt}/${MAX_RETRY} gpu=${GPU} 本次补 ${NT} 个 trial（目标 ${N_TRIALS}）reg_bypass=${REG_BYPASS} batch=${BATCH}" >>"${LOG}"
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t0=${SECONDS}
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python -u -m lnp_ml.modeling.final_train_optuna_cv \
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--input-path data/interim/internal.csv \
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--output-dir "${OUT}" \
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--seed ${SEED} \
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--n-folds ${N_FOLDS} \
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--n-trials ${NT} \
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--epochs-per-trial ${EPOCHS} \
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--patience ${PATIENCE} \
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--batch-size ${BATCH} \
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--use-mpnn --use-moe \
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--reg-bypass ${REG_BYPASS} \
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--use-llm --use-rag --rag-top-k 4 \
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--use-soft-prompt --llm-use-qlora \
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--llm-model-path models/qwen2.5-7b-instruct \
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--no-llm-freeze \
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--llm-max-length 1536 \
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--freeze-backbone-epochs ${FREEZE} \
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--device cuda \
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${EXTRA[@]+"${EXTRA[@]}"} \
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>>"${LOG}" 2>&1
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rc=$?
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dt=$((SECONDS - t0))
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if [ ${rc} -eq 0 ]; then
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echo "[$(date '+%F %T')] DONE（用时 ${dt}s）→ ${OUT}/model.pt" >>"${LOG}"
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exit 0
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fi
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if [ ${dt} -lt ${MIN_OK_SEC} ]; then
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echo "[$(date '+%F %T')] 仅存活 ${dt}s 即以 ${rc} 退出，判为配置/代码错误而非抢占，停止重试" >>"${LOG}"
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exit ${rc}
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fi
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echo "[$(date '+%F %T')] 运行 ${dt}s 后以 ${rc} 退出，${RETRY_WAIT}s 后续跑" >>"${LOG}"
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sleep "${RETRY_WAIT}"
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done
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echo "[$(date '+%F %T')] 超过 MAX_RETRY 次仍失败，放弃" >>"${LOG}"
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exit 1 |