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lnp_ml/results/pretrained_encoders/grover_pretrain_s42_summary.json
DicongLi 7eff2b3d02 feat: 支持 MolFormer/GraphMVP/GROVER 离线 embedding 作为化学 token 
三个预训练分子编码器作为额外的 chemical token 接入（三选一，互斥）：
- MolFormer-XL 768d（SMILES 序列，11亿分子预训练）
- GraphMVP 300d（2D 图自监督，ICLR'22）
- GROVER 3200d（图 Transformer，NeurIPS'20）

实现：embedding 离线编码存 npy，模型内按 {smiles: vector} 查表注入，
不改动前向逻辑，默认全关、不影响既有实验。

- models.py: 新增三个 CHEM_KEYS_WITH_*、统一的 _load_offline_emb 查表、
  proj_input_dims 按开关裁剪、forward 注入；子类同步转发参数
- nested_cv_optuna.py / pretrain.py: 新增 --{molformer,graphmvp,grover}-emb/-csv
- scripts_run/encode_embeddings/: 三个离线编码脚本 + README（含权重来源与
  新旧环境兼容补丁说明）
- results/pretrained_encoders/: 五组 15 折结果 summary
  (trials15/inner3/repeats3/seed42, 均带 external 预训练)

注：npy embedding 与预训练权重未入库（可由编码脚本复现）
2026-07-23 20:18:53 +08:00

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