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base: RYDE-WORK:987ecf6386e505ee747642ce972f2c01fa25b4dc
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RYDE-WORK:main
RYDE-WORK:feat/moe-layer
RYDE-WORK:feature/llm-encoder
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compare: RYDE-WORK:0c6828076b92320132fb337787807fd828828455
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RYDE-WORK:feat/moe-layer
RYDE-WORK:feature/llm-encoder
RYDE-WORK:main

2 Commits
987ecf6386 ... 0c6828076b

Author SHA1 Message Date
Michelle0574
0c6828076b Merge remote-tracking branch 'origin/feat/moe-layer' into feat/moe-layer 2026-07-07 17:55:13 +00:00
Michelle0574
22e83122e7 feat: Qwen SoftRAG（RAG prompt/soft token/QLoRA）+ Morgan 检索旁路与实验结果 2026-07-07 17:47:34 +00:00
215 changed files with 2472 additions and 7959 deletions
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.gitignore vendored
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@ -195,4 +195,7 @@ logs/
models/**/*.pt
models/**/*.png
models/molt5-base/
models/abl/
models/abl/
models/qwen2.5-7b-instruct/
models/**/*.sqlite3tests/
models/**/*.sqlite3

239
lnp_ml/modeling/layers/llm_prompt.py
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@ -1,4 +1,4 @@
"""LLM 分子特征分支：用 MolT5 直接编码 SMILES 文本。"""
"""LLM 分子特征分支：SMILES 文本 + chem'/tab soft token 的混合 prompt 编码。"""
import os
from typing import Dict, List, Optional
@ -7,15 +7,20 @@ import torch
import torch.nn as nn
# 权重默认路径，可用环境变量 MOLT5_PATH 覆盖
DEFAULT_MOLT5_PATH = os.environ.get("MOLT5_PATH", "models/molt5-base")
# 检索池支持的额外多任务（除 delivery 外）
_EXTRA_TASKS = ["size", "pdi", "ee", "toxic", "biodist"]
class LLMPromptEncoder(nn.Module):
"""用 MolT5 编码 SMILES 文本，输出分子特征 F_llm [B, d_model]。
"""用 LLM 编码分子，输出 F_llm [B, d_model]。
- 冻结 encoder 时：对每个 SMILES 缓存其句向量，避免重复前向，降方差、提速。
- use_lora=True 时：encoder 可训练（LoRA），不缓存。
三种模式：
- use_soft_prompt=True：原始 SMILES 文本(+RAG 邻居) + chem'/tab soft token，
inputs_embeds 注入，全程带梯度（配合 LoRA/QLoRA 真微调），不缓存特征。
- use_rag=True 且非 soft：旧的冻结+缓存 RAG 路径。
- 其他：冻结缓存 / 可训练 mean-pool。
"""
def __init__(
@ -26,33 +31,50 @@ class LLMPromptEncoder(nn.Module):
model_name_or_path: str = DEFAULT_MOLT5_PATH,
freeze: bool = True,
use_lora: bool = False,
use_qlora: bool = False,
lora_r: int = 8,
lora_alpha: int = 16,
lora_dropout: float = 0.05,
max_length: int = 128,
max_length: int = 256,
use_rag: bool = False,
rag_top_k: int = 4,
use_soft_prompt: bool = False,
) -> None:
super().__init__()
from transformers import AutoTokenizer, T5EncoderModel, AutoModel
self.use_lora = use_lora
self.use_lora = use_lora or use_qlora
self.use_qlora = use_qlora
self.max_length = max_length
self.use_rag = use_rag
self.rag_top_k = rag_top_k
self.use_soft_prompt = use_soft_prompt
_name_l = model_name_or_path.lower()
_is_t5 = "t5" in _name_l
_is_qwen = "qwen" in _name_l
self.use_rag = use_rag
self.rag_top_k = rag_top_k
self._is_qwen = _is_qwen
# Qwen 等大模型 tokenizer 需要 trust_remote_code
self.tokenizer = AutoTokenizer.from_pretrained(
model_name_or_path, trust_remote_code=_is_qwen)
if _is_qwen and self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
# 自动判断架构：T5→T5EncoderModel；Qwen(decoder)→AutoModel(fp16)；其他→AutoModel
if _is_t5:
self.encoder = T5EncoderModel.from_pretrained(model_name_or_path)
self.hidden_size = self.encoder.config.d_model
elif _is_qwen and use_qlora:
from transformers import BitsAndBytesConfig
bnb = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
)
self.encoder = AutoModel.from_pretrained(
model_name_or_path, trust_remote_code=True,
quantization_config=bnb, torch_dtype=torch.bfloat16)
self.hidden_size = self.encoder.config.hidden_size
elif _is_qwen:
# 微调(use_lora)时用 4-bit 量化(QLoRA)省显存；纯冻结时用 fp16
if use_lora:
@ -72,26 +94,44 @@ class LLMPromptEncoder(nn.Module):
self.encoder = AutoModel.from_pretrained(model_name_or_path)
self.hidden_size = self.encoder.config.hidden_size
if use_lora:
self._apply_lora(lora_r, lora_alpha, lora_dropout)
if self.use_lora:
self._apply_lora(lora_r, lora_alpha, lora_dropout, prepare_kbit=use_qlora)
elif freeze:
for p in self.encoder.parameters():
p.requires_grad = False
self._frozen = freeze and not use_lora
self._frozen = freeze and not self.use_lora
# soft token 投影：chem'(d_model) / tab(d_model) -> hidden_size
if use_soft_prompt:
self.chem_to_llm = nn.Linear(d_model, self.hidden_size)
self.tab_to_llm = nn.Linear(d_model, self.hidden_size)
else:
self.chem_to_llm = None
self.tab_to_llm = None
self.proj_down = nn.Sequential(
nn.Linear(self.hidden_size, d_model), nn.LayerNorm(d_model)
)
# 冻结特征缓存：smiles -> [H]（CPU）
self._cache: Dict[str, torch.Tensor] = {}
# RAG 检索池（防泄漏：由 nested_cv 在每个 outer fold 用训练集设置）
self._rag_pool_smiles: List[str] = []
self._rag_pool_labels = None # np.ndarray [N]
self._rag_pool_fps = None # 检索池指纹
self._rag_pool_id: str = "none" # 池子标识，用于缓存隔离
def _apply_lora(self, r: int, alpha: int, dropout: float) -> None:
from peft import LoraConfig, get_peft_model
# 缓存：冻结句向量缓存 / 文本 prompt 缓存
self._cache: Dict[str, torch.Tensor] = {}
self._prompt_cache: Dict[str, str] = {}
# RAG 检索池
self._rag_pool_smiles: List[str] = []
self._rag_pool_labels = None # delivery [N]
self._rag_pool_extra = None # dict: task -> (values, valid)
self._rag_pool_fps = None
self._rag_pool_id: str = "none"
def _apply_lora(self, r, alpha, dropout, prepare_kbit=False):
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
if prepare_kbit:
self.encoder = prepare_model_for_kbit_training(self.encoder)
else:
for p in self.encoder.parameters():
p.requires_grad = False
# 4-bit 量化模型(QLoRA)需先 prepare，才能正确接收梯度
if getattr(self.encoder, "is_loaded_in_4bit", False) or getattr(self.encoder, "is_loaded_in_8bit", False):
@ -108,27 +148,27 @@ class LLMPromptEncoder(nn.Module):
_targets = ["q", "k", "v", "o"]
else:
_targets = ["query", "key", "value"]
cfg = LoraConfig(
r=r, lora_alpha=alpha, lora_dropout=dropout,
target_modules=_targets, bias="none",
)
cfg = LoraConfig(r=r, lora_alpha=alpha, lora_dropout=dropout,
target_modules=_targets, bias="none")
self.encoder = get_peft_model(self.encoder, cfg)
def set_retrieval_pool(self, smiles_list, labels, pool_id: str = "train") -> None:
"""设置 RAG 检索池（防泄漏关键：只传训练集的 smiles 和 delivery 标签）。
labels: array-like [N] delivery 标签。pool_id: 池标识，切换时清 RAG 缓存。"""
# ---------- 检索池 ----------
def set_retrieval_pool(self, smiles_list, labels, pool_id="train", extra_labels=None):
"""设置 RAG 检索池（防泄漏：只传训练集分子与标签）。
labels: delivery [N]; extra_labels: dict task -> (values, valid_bool)。"""
import numpy as np
from lnp_ml.modeling.retrieval import _smiles_to_fp
self._rag_pool_smiles = list(smiles_list)
self._rag_pool_labels = np.asarray(labels, dtype=np.float32).reshape(-1)
self._rag_pool_extra = extra_labels
self._rag_pool_fps = [_smiles_to_fp(s) for s in self._rag_pool_smiles]
if pool_id != self._rag_pool_id:
# 池子变了，清掉 RAG 编码缓存（避免跨 fold 串用）
self._cache = {k: v for k, v in self._cache.items() if not k.startswith("RAG::")}
self._prompt_cache.clear()
self._rag_pool_id = pool_id
def _retrieve_topk(self, query_smiles: str):
"""检索 Top-K 相似邻居，返回 [(smiles, sim, label)]。排除查询分子自己（防泄漏）。"""
"""检索 Top-K 邻居，返回 [dict(smiles, sim, delivery, extra)]；排除查询分子自己。"""
from rdkit import DataStructs
from lnp_ml.modeling.retrieval import _smiles_to_fp
qfp = _smiles_to_fp(query_smiles)
@ -136,38 +176,76 @@ class LLMPromptEncoder(nn.Module):
return []
sims = []
for j, (smi, fp) in enumerate(zip(self._rag_pool_smiles, self._rag_pool_fps)):
if fp is None or smi == query_smiles: # 排除自己
if fp is None or smi == query_smiles:
continue
sims.append((j, DataStructs.TanimotoSimilarity(qfp, fp)))
sims.sort(key=lambda t: t[1], reverse=True)
out = []
for j, sim in sims[:self.rag_top_k]:
out.append((self._rag_pool_smiles[j], sim, float(self._rag_pool_labels[j])))
for j, sim in sims[: self.rag_top_k]:
extra = {}
if self._rag_pool_extra is not None:
for task in _EXTRA_TASKS:
if task in self._rag_pool_extra:
vals, valid = self._rag_pool_extra[task]
extra[task] = vals[j] if bool(valid[j]) else None
else:
extra[task] = None
out.append({
"smiles": self._rag_pool_smiles[j],
"sim": sim,
"delivery": float(self._rag_pool_labels[j]),
"extra": extra,
})
return out
@staticmethod
def _fmt(v, kind="float"):
if v is None:
return "unknown"
if kind == "int":
return str(int(v))
if kind == "vec":
return "[" + ", ".join(f"{x:.3f}" for x in v) + "]"
return f"{float(v):.3f}"
def _build_rag_prompt(self, target_smiles: str, neighbors) -> str:
"""构造 RAG prompt（与探针验证版一致，已验证 R²=0.1357）。"""
"""构造 RAG prompt：原始 SMILES + 邻居多任务结果（numeric）。"""
blocks = []
for rank, (smi, sim, lbl) in enumerate(neighbors, 1):
for rank, nb in enumerate(neighbors, 1):
ex = nb["extra"]
blocks.append(
f"Retrieved sample {rank}:\nSMILES: {smi}\n"
f"Similarity score: {sim:.3f}\nKnown outcome - delivery_log: {lbl:.3f}"
f"Retrieved sample {rank}:\n"
f"SMILES: {nb['smiles']}\n"
f"Similarity score: {nb['sim']:.3f}\n"
f"delivery_log: {self._fmt(nb['delivery'])}\n"
f"size_z: {self._fmt(ex.get('size'))}\n"
f"pdi_class: {self._fmt(ex.get('pdi'), 'int')}\n"
f"ee_class: {self._fmt(ex.get('ee'), 'int')}\n"
f"toxic: {self._fmt(ex.get('toxic'), 'int')}\n"
f"biodist: {self._fmt(ex.get('biodist'), 'vec')}"
)
retrieved_block = "\n\n".join(blocks) if blocks else "(no retrieved samples)"
return (
"Task: Encode the target LNP molecule into a retrieval-aware representation "
"for downstream delivery prediction. Do not output predictions.\n\n"
"for downstream multi-task property prediction. Do not output predictions.\n\n"
f"[Target Molecule]\nSMILES: {target_smiles}\n\n"
"[Retrieved Similar LNP Samples]\n"
"The following are retrieved from the training set by fingerprint similarity, "
"with their known delivery outcomes:\n\n"
"Retrieved from the training set by fingerprint similarity, with their known "
"multi-task outcomes (delivery_log, size_z, pdi_class, ee_class, toxic, biodist; "
"'unknown' means the measurement is missing):\n\n"
f"{retrieved_block}\n\n"
"[Encoding Instructions]\n"
"Capture the target molecular structure and the retrieval evidence "
"(structural similarity and consistency of retrieved delivery outcomes) "
"into your internal representation."
"Capture the target structure and the retrieval evidence (structural similarity "
"and consistency of retrieved outcomes) into your internal representation."
)
def _get_prompt(self, s: str) -> str:
if not self.use_rag:
return s
key = f"{self._rag_pool_id}::{s}"
if key not in self._prompt_cache:
self._prompt_cache[key] = self._build_rag_prompt(s, self._retrieve_topk(s))
return self._prompt_cache[key]
def _rag_encode_batch(self, prompts, device):
"""编码一批 RAG prompt，取最后有效 token。grad 由调用方上下文决定。"""
outs = []
@ -202,44 +280,77 @@ class LLMPromptEncoder(nn.Module):
prompts = [self._build_rag_prompt(s, self._retrieve_topk(s)) for s in smiles]
return self._rag_encode_batch(prompts, device)
def _mean_pool(self, last_hidden: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
m = mask.unsqueeze(-1).float() # [B, L, 1]
# ---------- soft-prompt 编码（带梯度，不缓存特征）----------
def _encode_softrag(self, smiles, chem, tab, device) -> torch.Tensor:
prompts = [self._get_prompt(s) for s in smiles]
enc = self.tokenizer(
prompts, padding=True, truncation=True,
max_length=self.max_length, return_tensors="pt",
).to(device)
embed_layer = self.encoder.get_input_embeddings()
text_embeds = embed_layer(enc["input_ids"]) # [B, L, H]
text_mask = enc["attention_mask"] # [B, L]
soft_list = []
if self.chem_to_llm is not None and chem is not None:
soft_list.append(self.chem_to_llm(chem)) # [B, n_chem, H]
if self.tab_to_llm is not None and tab is not None:
soft_list.append(self.tab_to_llm(tab)) # [B, n_tab, H]
if soft_list:
soft = torch.cat(soft_list, dim=1).to(text_embeds.dtype)
inputs_embeds = torch.cat([soft, text_embeds], dim=1)
soft_mask = torch.ones(soft.size(0), soft.size(1),
device=device, dtype=text_mask.dtype)
attn_mask = torch.cat([soft_mask, text_mask], dim=1)
else:
inputs_embeds = text_embeds
attn_mask = text_mask
out = self.encoder(inputs_embeds=inputs_embeds, attention_mask=attn_mask).last_hidden_state
lengths = attn_mask.sum(1) - 1
b = torch.arange(out.size(0), device=device)
feat = out[b, lengths.long(), :] # [B, H] 最后有效 token
return self.proj_down(feat.float())
# ---------- 旧路径（保留，向后兼容）----------
def _mean_pool(self, last_hidden, mask):
m = mask.unsqueeze(-1).float()
return (last_hidden * m).sum(1) / m.sum(1).clamp(min=1e-6)
@torch.no_grad()
def _encode_frozen(self, smiles: List[str], device: torch.device) -> torch.Tensor:
def _encode_frozen(self, smiles, device):
missing = [s for s in smiles if s not in self._cache]
if missing:
uniq = list(dict.fromkeys(missing))
for i in range(0, len(uniq), 256):
chunk = uniq[i:i + 256]
enc = self.tokenizer(
chunk, padding=True, truncation=True,
max_length=self.max_length, return_tensors="pt",
).to(device)
enc = self.tokenizer(chunk, padding=True, truncation=True,
max_length=self.max_length, return_tensors="pt").to(device)
out = self.encoder(**enc).last_hidden_state
pooled = self._mean_pool(out, enc["attention_mask"])
for s, v in zip(chunk, pooled):
self._cache[s] = v.cpu()
return torch.stack([self._cache[s] for s in smiles]).to(device)
def _encode_trainable(self, smiles: List[str], device: torch.device) -> torch.Tensor:
enc = self.tokenizer(
list(smiles), padding=True, truncation=True,
max_length=self.max_length, return_tensors="pt",
).to(device)
def _encode_trainable(self, smiles, device):
enc = self.tokenizer(list(smiles), padding=True, truncation=True,
max_length=self.max_length, return_tensors="pt").to(device)
out = self.encoder(**enc).last_hidden_state
return self._mean_pool(out, enc["attention_mask"])
def forward(self, smiles: List[str], tab: Optional[torch.Tensor] = None) -> torch.Tensor:
"""Args: smiles [B] SMILES 字符串列表。Returns: [B, d_model]。"""
def forward(self, smiles: List[str], chem: Optional[torch.Tensor] = None,
tab: Optional[torch.Tensor] = None) -> torch.Tensor:
device = self.proj_down[0].weight.device
if self.use_soft_prompt:
return self._encode_softrag(smiles, chem, tab, device)
if self.use_rag:
feat = self._encode_rag(smiles, device)
else:
feat = self._encode_frozen(smiles, device) if self._frozen \
else self._encode_trainable(smiles, device)
return self._encode_softrag(smiles, None, None, device)
feat = self._encode_frozen(smiles, device) if self._frozen \
else self._encode_trainable(smiles, device)
return self.proj_down(feat)
def clear_cache(self) -> None:
self._cache.clear()
self._cache.clear()
self._prompt_cache.clear()

38
lnp_ml/modeling/models.py
View File

@ -20,17 +20,26 @@ PoolingStrategy = Literal["attention", "avg", "max"]
# Token 维度配置
def _infer_desc_dim() -> int:
"""RDKit 描述符数量随版本变化，运行时实测，避免写死导致 BatchNorm 维度不匹配。"""
from rdkit import Chem
from rdkit.Chem import Descriptors
return len(Descriptors.CalcMolDescriptors(Chem.MolFromSmiles("CCO")))
_DESC_DIM = _infer_desc_dim() # 本机 = 210
DEFAULT_INPUT_DIMS = {
# Channel A: 化学特征
"mpnn": 600, # D-MPNN embedding
"morgan": 1024, # Morgan fingerprint
"maccs": 167, # MACCS keys
"desc": 217, # RDKit descriptors
"mpnn": 600, # D-MPNN embedding
"morgan": 1024, # Morgan fingerprint
"maccs": 167, # MACCS keys
"desc": _DESC_DIM, # RDKit descriptors（随版本动态）
# Channel B: 配方/实验条件
"comp": 5, # 配方比例
"phys": 12, # 物理参数 one-hot
"help": 4, # Helper lipid one-hot
"exp": 32, # 实验条件 one-hot
"comp": 5, # 配方比例
"phys": 12, # 物理参数 one-hot
"help": 4, # Helper lipid one-hot
"exp": 32, # 实验条件 one-hot
}
# 化学 / 配方 token 的键顺序
@ -95,6 +104,8 @@ class LNPModel(nn.Module):
llm_model_path: str = DEFAULT_MOLT5_PATH,
llm_freeze: bool = True,
llm_use_lora: bool = False,
llm_use_qlora: bool = False,
use_soft_prompt: bool = False,
llm_lora_r: int = 8,
llm_lora_alpha: int = 16,
llm_lora_dropout: float = 0.05,
@ -171,11 +182,13 @@ class LNPModel(nn.Module):
model_name_or_path=llm_model_path,
freeze=llm_freeze,
use_lora=llm_use_lora,
use_qlora=llm_use_qlora,
lora_r=llm_lora_r,
lora_alpha=llm_lora_alpha,
lora_dropout=llm_lora_dropout,
use_rag=use_rag,
rag_top_k=rag_top_k,
use_soft_prompt=use_soft_prompt,
)
else:
self.llm_prompt = None
@ -246,7 +259,10 @@ class LNPModel(nn.Module):
f_llm = None
if self.llm_prompt is not None and smiles is not None:
f_llm = self.llm_prompt(smiles)
if getattr(self.llm_prompt, "use_soft_prompt", False):
f_llm = self.llm_prompt(smiles, chem=chem, tab=tab)
else:
f_llm = self.llm_prompt(smiles)
f_retr = None
if self.retr_proj is not None and self.retriever is not None and smiles is not None:
@ -432,6 +448,8 @@ class LNPModelWithoutMPNN(LNPModel):
llm_model_path: str = DEFAULT_MOLT5_PATH,
llm_freeze: bool = True,
llm_use_lora: bool = False,
llm_use_qlora: bool = False,
use_soft_prompt: bool = False,
llm_lora_r: int = 8,
llm_lora_alpha: int = 16,
llm_lora_dropout: float = 0.05,
@ -467,6 +485,8 @@ class LNPModelWithoutMPNN(LNPModel):
llm_model_path=llm_model_path,
llm_freeze=llm_freeze,
llm_use_lora=llm_use_lora,
llm_use_qlora=llm_use_qlora,
use_soft_prompt=use_soft_prompt,
llm_lora_r=llm_lora_r,
llm_lora_alpha=llm_lora_alpha,
llm_lora_dropout=llm_lora_dropout,

82
lnp_ml/modeling/nested_cv_optuna.py
View File

@ -246,6 +246,26 @@ def create_model(
)
def _build_rag_pool(full_dataset, idx):
"""从 dataset + 索引构造 RAG 池：返回 (smiles, delivery[N], extra_labels)。"""
import numpy as np
idx = np.asarray(idx)
smiles = [full_dataset.smiles[i] for i in idx]
delivery = full_dataset.delivery[idx].reshape(-1)
extra = {}
if full_dataset.size is not None:
extra["size"] = (full_dataset.size[idx], ~np.isnan(full_dataset.size[idx]))
if full_dataset.pdi is not None:
extra["pdi"] = (full_dataset.pdi[idx], full_dataset.pdi_valid[idx])
if full_dataset.ee is not None:
extra["ee"] = (full_dataset.ee[idx], full_dataset.ee_valid[idx])
if full_dataset.toxic is not None:
extra["toxic"] = (full_dataset.toxic[idx], full_dataset.toxic[idx] >= 0)
if full_dataset.biodist is not None:
extra["biodist"] = (full_dataset.biodist[idx], full_dataset.biodist_valid[idx])
return smiles, delivery, extra
# ============ 评估指标 ============
def evaluate_on_test(
@ -496,7 +516,8 @@ def run_inner_optuna(
val_subset = Subset(full_dataset, actual_val_idx.tolist())
train_loader = DataLoader(
train_subset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn
train_subset, batch_size=batch_size, shuffle=True,
collate_fn=collate_fn, drop_last=True,
)
val_loader = DataLoader(
val_subset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn
@ -525,6 +546,12 @@ def run_inner_optuna(
)
if rdkit_cache is not None:
model.rdkit_encoder._cache = rdkit_cache
# 内层 RAG 池：只用内层训练集（防 inner-val 泄漏）
if llm_kwargs_t.get("use_rag", False) and getattr(model, "llm_prompt", None) is not None:
_s, _d, _ex = _build_rag_pool(full_dataset, actual_train_idx)
model.llm_prompt.set_retrieval_pool(
_s, _d, pool_id=f"inner{inner_fold}", extra_labels=_ex)
# 加载预训练权重
if pretrain_state_dict is not None and pretrain_config is not None:
@ -631,6 +658,29 @@ def _run_single_outer_fold(
fold_dir = Path(fold_dir)
fold_dir.mkdir(parents=True, exist_ok=True)
# ===== 断点续跑 =====
_f_metrics = fold_dir / "test_metrics.json"
_f_bp = fold_dir / "best_params.json"
_f_em = fold_dir / "epoch_mean.json"
# (1) 整折已完成 → 读回结果直接跳过
if _f_metrics.exists() and _f_bp.exists() and _f_em.exists():
logger.info(f"[RESUME] Outer fold {outer_fold} 已完成，跳过。")
with open(_f_metrics) as f:
_tm = json.load(f)
with open(_f_bp) as f:
_bp = json.load(f)
with open(_f_em) as f:
_em = json.load(f)["epoch_mean"]
return {"fold": outer_fold, "best_params": _bp,
"epoch_mean": _em, "test_metrics": _tm}
# (2) 内层已完成、外层中断 → 复用超参，跳过内层 Optuna
if precomputed_best_params is None and _f_bp.exists() and _f_em.exists():
with open(_f_bp) as f:
precomputed_best_params = json.load(f)
with open(_f_em) as f:
precomputed_epoch_mean = json.load(f)["epoch_mean"]
logger.info(f"[RESUME] fold {outer_fold} 复用已存 best_params，跳过内层 Optuna。")
full_dataset = LNPDataset(df)
# === 断点续跑:已完成的 fold 直接跳过(读回磁盘结果)===
_tm = fold_dir / "test_metrics.json"
@ -718,7 +768,8 @@ def _run_single_outer_fold(
test_subset = Subset(full_dataset, outer_test_idx.tolist())
train_loader = DataLoader(
train_subset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn
train_subset, batch_size=batch_size, shuffle=True,
collate_fn=collate_fn, drop_last=True,
)
monitor_loader = DataLoader(
monitor_subset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn
@ -763,11 +814,10 @@ def _run_single_outer_fold(
# ===== RAG 检索池设置（防泄漏：只用训练集分子+标签）=====
if llm_kwargs and llm_kwargs.get("use_rag", False) and getattr(model, "llm_prompt", None) is not None:
_rag_smiles = [full_dataset.smiles[i] for i in outer_train_idx]
_rag_labels = full_dataset.delivery[outer_train_idx].reshape(-1)
_s, _d, _ex = _build_rag_pool(full_dataset, outer_train_idx)
model.llm_prompt.set_retrieval_pool(
_rag_smiles, _rag_labels, pool_id=f"fold{outer_fold}")
logger.info(f"[RAG] 检索池={len(_rag_smiles)} 训练分子（不含测试集，防泄漏）")
_s, _d, pool_id=f"fold{outer_fold}", extra_labels=_ex)
logger.info(f"[RAG] 检索池={len(_s)} 训练分子（多任务标签，不含测试集，防泄漏）")
if pretrain_state_dict is not None and pretrain_config is not None:
loaded = load_pretrain_weights_to_model(
@ -791,7 +841,7 @@ def _run_single_outer_fold(
freeze_backbone_epochs=3,
)
model.load_state_dict(train_result["final_state"])
model.load_state_dict(train_result["final_state"], strict=False)
model = model.to(device)
config = {
@ -853,6 +903,7 @@ def _run_single_outer_fold(
def main(
input_path: Path = INTERIM_DATA_DIR / "internal.csv",
output_dir: Path = MODELS_DIR / "nested_cv",
resume_dir: Optional[Path] = None,
# CV 参数
n_outer_folds: int = 5,
n_inner_folds: int = 3,
@ -888,6 +939,8 @@ def main(
llm_model_path: str = DEFAULT_MOLT5_PATH,
llm_freeze: bool = True,
llm_use_lora: bool = False,
llm_use_qlora: bool = False,
use_soft_prompt: bool = False,
llm_lora_r: int = 8,
llm_lora_alpha: int = 16,
llm_lora_dropout: float = 0.05,
@ -920,6 +973,8 @@ def main(
llm_model_path=llm_model_path,
llm_freeze=llm_freeze,
llm_use_lora=llm_use_lora,
llm_use_qlora=llm_use_qlora,
use_soft_prompt=use_soft_prompt,
llm_lora_r=llm_lora_r,
llm_lora_alpha=llm_lora_alpha,
llm_lora_dropout=llm_lora_dropout,
@ -938,10 +993,15 @@ def main(
else:
logger.warning(f"Pretrain checkpoint not found: {init_from_pretrain}, skipping")
# 创建输出目录（带时间戳）
run_name = datetime.now().strftime("%Y%m%d_%H%M%S")
run_dir = output_dir / run_name
run_dir.mkdir(parents=True, exist_ok=True)
# 创建输出目录（带时间戳；--resume-dir 指定则复用，支持断点续跑）
if resume_dir is not None:
run_dir = Path(resume_dir)
run_dir.mkdir(parents=True, exist_ok=True)
logger.info(f"[RESUME] 复用已有运行目录: {run_dir}")
else:
run_name = datetime.now().strftime("%Y%m%d_%H%M%S")
run_dir = output_dir / run_name
run_dir.mkdir(parents=True, exist_ok=True)
logger.info(f"Output directory: {run_dir}")
# 加载数据

222
lnp_ml/modeling/retrieval.py
View File

@ -1,41 +1,24 @@
"""
检索增强特征模块 (第一层优化)。
"""Morgan 指纹检索：RAG prompt 构造与检索旁路共用。
核心思想 (借鉴 MolRAG 的化学硬先验「结构相似 → 性质相似」):
对每个分子，用 Morgan 指纹在「训练集检索池」中找最相似的 k 个邻居，
把它们的已知标签 (已 z-score 的 quantified_delivery 等) 聚合成一个特征向量，
作为额外旁路注入融合层 (配零初始化门控，保证「加了不会更差」)。
防数据泄漏铁律 (本模块通过接口与逻辑强制保证):
1. 检索池 (pool_smiles / pool_labels) 只能由调用方传入「训练集」分子。
本模块自身不接触任何全局数据，只用传进来的池。
2. exclude_self=True 时，查询分子若在池中 (训练分子查询自己)，
排除指纹完全相同的「自己」(相似度=1.0 的那一个)，否则等于直接看答案。
3. 测试分子查询时，池里全是训练集，测试分子不在池中，
天然不会检索到自己或其他测试分子。
设计原则:
- 纯特征工程，不引入可训练参数 (投影层在 models.py 侧)。
- 池构建时一次性预计算所有指纹，查询时只算查询分子指纹 + 相似度，高效。
- 标签已在 dataset.py 标准化 (z-score)，聚合时直接 (加权) 平均即可。
- _smiles_to_fp: SMILES -> RDKit ExplicitBitVect（供 Tanimoto 相似度）。
- MorganRetriever: 训练集指纹检索器，query_batch 返回每个查询的 3 维检索特征。
"""
from __future__ import annotations
from typing import List, Optional, Sequence
from typing import List, Optional
import numpy as np
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
from rdkit import Chem
from rdkit.Chem import AllChem, DataStructs
from rdkit import RDLogger
# 关闭 RDKit 的噪音日志 (无效 SMILES 警告等)
RDLogger.DisableLog("rdApp.*")
# 与 RDKitFeaturizer 的 morgan token 保持一致
MORGAN_RADIUS = 2
MORGAN_NBITS = 1024
def _smiles_to_fp(smiles: str, radius: int = 2, n_bits: int = 2048):
"""单个 SMILES -> Morgan 指纹 (ExplicitBitVect)。无效 SMILES 返回 None。"""
def _smiles_to_fp(smiles: Optional[str], radius: int = MORGAN_RADIUS, n_bits: int = MORGAN_NBITS):
"""SMILES -> Morgan ExplicitBitVect；非法/空返回 None。"""
if not smiles:
return None
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return None
@ -43,151 +26,60 @@ def _smiles_to_fp(smiles: str, radius: int = 2, n_bits: int = 2048):
class MorganRetriever:
"""基于 Morgan 指纹 + Tanimoto 相似度的分子检索器。
"""基于 Morgan + Tanimoto 的训练集检索器（用于 use_retrieval 旁路）。
用法:
# 只用训练集分子建池
retr = MorganRetriever(
pool_smiles=train_smiles, # List[str]
pool_labels=train_labels, # np.ndarray [N_pool, label_dim]
radius=2, n_bits=2048, k=5,
)
# 查询 (训练分子查询时 exclude_self=True 防泄漏)
feat = retr.query(query_smiles, exclude_self=True) # np.ndarray [label_dim*?]
query_batch 为每个查询返回 3 维特征：
[相似度加权邻居标签均值, top1 相似度, top-k 平均相似度]
"""
def __init__(
self,
pool_smiles: Sequence[str],
pool_labels: np.ndarray,
radius: int = 2,
n_bits: int = 2048,
k: int = 5,
sim_weighted: bool = True,
include_sim_stats: bool = True,
) -> None:
"""
Args:
pool_smiles: 检索池分子的 SMILES (必须只含训练集分子)。
pool_labels: 检索池分子的标签, shape [N_pool, label_dim], 已标准化。
radius: Morgan 指纹半径 (MolRAG 用 2)。
n_bits: 指纹位数。
k: 检索近邻数。
sim_weighted: 聚合邻居标签时是否按相似度加权 (否则等权平均)。
include_sim_stats: 是否在输出特征里附带相似度统计 (top1/mean 相似度)，
让模型知道「邻居有多可靠」。
"""
assert len(pool_smiles) == len(pool_labels), "池 SMILES 与标签数量不一致"
self.radius = radius
self.n_bits = n_bits
self.k = k
self.sim_weighted = sim_weighted
self.include_sim_stats = include_sim_stats
def __init__(self, smiles_list: List[str], labels, k: int = 5) -> None:
self.k = int(k)
self.pool_smiles = list(smiles_list)
_smiles_arr = list(pool_smiles)
_labels_arr = np.asarray(pool_labels, dtype=np.float32)
# 过滤掉标签含 NaN 的分子：它们没有有效标签，不能作为性质参考邻居
_valid_mask = ~np.isnan(_labels_arr).any(axis=1)
self.pool_smiles: List[str] = [_smiles_arr[i] for i in range(len(_smiles_arr)) if _valid_mask[i]]
self.pool_labels = _labels_arr[_valid_mask]
self.label_dim = self.pool_labels.shape[1]
_n_dropped = int((~_valid_mask).sum())
if _n_dropped > 0:
import sys
print(f'[MorganRetriever] 过滤 {_n_dropped} 个 NaN 标签分子，有效检索池={len(self.pool_smiles)}', file=sys.stderr)
labels = np.asarray(labels, dtype=np.float32)
if labels.ndim == 1:
labels = labels.reshape(-1, 1)
self.pool_labels = labels # [N, D]，旁路只用第 0 列
# 池内 SMILES 集合，用于 exclude_self="auto" 时判断查询分子是否在池中
self.pool_smiles_set = set(self.pool_smiles)
fps = [_smiles_to_fp(s) for s in self.pool_smiles]
# 只保留可解析的池分子
self._valid_pos = [i for i, fp in enumerate(fps) if fp is not None]
self._valid_fps = [fps[i] for i in self._valid_pos]
self._valid_labels = self.pool_labels[self._valid_pos, 0] if self._valid_pos else np.zeros(0, np.float32)
self._valid_smiles = [self.pool_smiles[i] for i in self._valid_pos]
self._fp_cache = {} # query 指纹缓存
# 预计算池内所有指纹 (无效分子记 None，检索时跳过)
self.pool_fps = [_smiles_to_fp(s, radius, n_bits) for s in self.pool_smiles]
def _query_fp(self, smiles: str):
if smiles not in self._fp_cache:
self._fp_cache[smiles] = _smiles_to_fp(smiles)
return self._fp_cache[smiles]
# 标签均值，用于无有效邻居时的回退 (用训练集均值，不泄漏)
self.label_mean = self.pool_labels.mean(axis=0)
def query_batch(self, smiles_list: List[str], exclude_self="auto") -> np.ndarray:
return np.stack([self._query_one(s, exclude_self) for s in smiles_list]).astype(np.float32)
@property
def feature_dim(self) -> int:
"""输出特征维度: 聚合标签 (label_dim) [+ 相似度统计 2 维]。"""
return self.label_dim + (2 if self.include_sim_stats else 0)
def _query_one(self, smiles: str, exclude_self) -> np.ndarray:
qfp = self._query_fp(smiles)
if qfp is None or not self._valid_fps:
return np.zeros(3, dtype=np.float32)
def query(self, query_smiles: str, exclude_self="auto") -> np.ndarray:
"""检索查询分子的 top-k 邻居并聚合其标签为特征向量。
# 向量化 Tanimoto（C 层批量），替代 Python 逐条
sims = np.asarray(DataStructs.BulkTanimotoSimilarity(qfp, self._valid_fps), dtype=np.float32)
Args:
query_smiles: 查询分子 SMILES。
exclude_self: 排除自身策略，防数据泄漏的关键。
- "auto" (默认, 推荐): 查询分子若在检索池中(训练分子)，自动排除自己；
不在池中(测试分子)则不排除。无需调用方区分训练/测试。
- True: 强制排除相似度=1.0 的分子。
- False: 不排除(仅当确定查询分子不在池中时使用)。
if exclude_self:
self_mask = np.fromiter((s == smiles for s in self._valid_smiles), dtype=bool, count=len(sims))
sims = np.where(self_mask, -1.0, sims)
Returns:
特征向量 np.ndarray [feature_dim]。无有效邻居时回退到训练集均值。
"""
# auto 模式：查询分子在池中 → 训练分子 → 排除自己；否则不排除
if exclude_self == "auto":
do_exclude = query_smiles in self.pool_smiles_set
else:
do_exclude = bool(exclude_self)
k = min(self.k, int((sims >= 0).sum()))
if k <= 0:
return np.zeros(3, dtype=np.float32)
q_fp = _smiles_to_fp(query_smiles, self.radius, self.n_bits)
if q_fp is None:
# 查询分子无效：回退到训练集均值 + 零相似度
agg = self.label_mean.copy()
if self.include_sim_stats:
agg = np.concatenate([agg, np.array([0.0, 0.0], dtype=np.float32)])
return agg.astype(np.float32)
# argpartition 取 top-k（O(N)），再对这 k 个排序
top = np.argpartition(-sims, k - 1)[:k]
top = top[np.argsort(-sims[top])]
top_sims = sims[top]
top_labels = self._valid_labels[top]
# 与池内每个分子算 Tanimoto 相似度
sims = np.full(len(self.pool_fps), -1.0, dtype=np.float32)
for i, fp in enumerate(self.pool_fps):
if fp is None:
continue
sims[i] = DataStructs.TanimotoSimilarity(q_fp, fp)
# 排除自己: 指纹完全相同 (相似度 >= 1.0 - eps) 的那一个
if do_exclude:
eps = 1e-6
self_mask = sims >= (1.0 - eps)
# 保守起见：把所有相似度=1.0 的都视作潜在自身并排除，
# 因为结构完全相同的分子标签也应相同，留着等于泄漏答案。
sims[self_mask] = -1.0
# 取 top-k (相似度降序)，过滤掉无效 (-1) 的
valid = np.where(sims >= 0.0)[0]
if len(valid) == 0:
# 没有有效邻居：回退到训练集均值
agg = self.label_mean.copy()
if self.include_sim_stats:
agg = np.concatenate([agg, np.array([0.0, 0.0], dtype=np.float32)])
return agg.astype(np.float32)
order = valid[np.argsort(-sims[valid])]
topk_idx = order[: self.k]
topk_sims = sims[topk_idx]
topk_labels = self.pool_labels[topk_idx] # [k', label_dim]
# 聚合邻居标签
if self.sim_weighted and topk_sims.sum() > 1e-8:
w = topk_sims / topk_sims.sum()
agg_label = (w[:, None] * topk_labels).sum(axis=0)
else:
agg_label = topk_labels.mean(axis=0)
if self.include_sim_stats:
sim_stats = np.array(
[float(topk_sims[0]), float(topk_sims.mean())], dtype=np.float32
)
agg = np.concatenate([agg_label.astype(np.float32), sim_stats])
else:
agg = agg_label.astype(np.float32)
return agg.astype(np.float32)
def query_batch(
self, query_smiles_list, exclude_self="auto"
) -> np.ndarray:
"""批量检索。返回 [N_query, feature_dim]。"""
return np.stack(
[self.query(s, exclude_self=exclude_self) for s in query_smiles_list]
)
w = np.clip(top_sims, 0.0, None)
wsum = float(w.sum())
weighted_label = float((w * top_labels).sum() / wsum) if wsum > 0 else float(top_labels.mean())
return np.array([weighted_label, float(top_sims[0]), float(top_sims.mean())], dtype=np.float32)

6
lnp_ml/modeling/trainer_balanced.py
View File

@ -406,7 +406,7 @@ def train_with_early_stopping(
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode="min", factor=0.5, patience=5
)
early_stopping = EarlyStoppingBalanced(patience=patience)
early_stopping = EarlyStoppingBalanced(patience=patience, min_delta=1e-3)
history = {"train": [], "val": []}
best_val_loss = float("inf")
@ -438,9 +438,9 @@ def train_with_early_stopping(
if early_stopping(val_metrics["loss"], epoch):
break
# Restore best model
# Restore best model（QLoRA 4-bit 基座的量化元数据键用 strict=False 忽略）
if best_state is not None:
model.load_state_dict(best_state)
model.load_state_dict(best_state, strict=False)
return {
"history": history,

12
models/abl/baseline/20260617_180556/outer_fold_0/best_params.json
View File

@ -1,12 +0,0 @@
{
"dropout": 0.28242799368681437,
"lr": 0.00037183641805732076,
"weight_decay": 6.290644294586152e-05,
"backbone_lr_ratio": 0.10677482709481352,
"d_model": 256,
"num_heads": 8,
"n_attn_layers": 4,
"fusion_strategy": "attention",
"head_hidden_dim": 128,
"set_transformer_block": "sab"
}

42
models/abl/baseline/20260617_180556/outer_fold_0/test_metrics.json
View File

@ -1,42 +0,0 @@
{
"size": {
"n_samples": 83,
"mse": 0.32790454280151365,
"rmse": 0.572629498717551,
"mae": 0.35871042688208893,
"r2": -0.08876073797082196
},
"delivery": {
"n_samples": 58,
"mse": 0.7250728333301815,
"rmse": 0.8515120864263651,
"mae": 0.7098816664696768,
"r2": 0.07838867510225656
},
"pdi": {
"n_samples": 84,
"accuracy": 0.5476190476190477,
"precision": 0.3435971685971686,
"recall": 0.5359094457455114,
"f1": 0.34977324263038545
},
"ee": {
"n_samples": 84,
"accuracy": 0.7142857142857143,
"precision": 0.6871101871101871,
"recall": 0.707142857142857,
"f1": 0.6756066411238826
},
"toxic": {
"n_samples": 58,
"accuracy": 0.9655172413793104,
"precision": 0.75,
"recall": 0.9821428571428572,
"f1": 0.8242424242424242
},
"biodist": {
"n_samples": 58,
"kl_divergence": 0.4073394583325288,
"js_divergence": 0.092887269703335
}
}

12
models/abl/baseline/20260617_180556/outer_fold_1/best_params.json
View File

@ -1,12 +0,0 @@
{
"dropout": 0.23085562232445592,
"lr": 0.0005227270589225132,
"weight_decay": 0.00461701039547356,
"backbone_lr_ratio": 0.12087164274488635,
"d_model": 256,
"num_heads": 8,
"n_attn_layers": 4,
"fusion_strategy": "attention",
"head_hidden_dim": 128,
"set_transformer_block": "sab"
}

42
models/abl/baseline/20260617_180556/outer_fold_1/test_metrics.json
View File

@ -1,42 +0,0 @@
{
"size": {
"n_samples": 84,
"mse": 0.233876145855699,
"rmse": 0.4836074294876982,
"mae": 0.3760785318556286,
"r2": -2.874543407510119
},
"delivery": {
"n_samples": 61,
"mse": 1.0238964615450643,
"rmse": 1.0118776910007772,
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