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Add Qwen2.5-7B MolRAG encoder branch + dual-seed results

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- llm_prompt.py: RAG prompt encoding (use_rag), Qwen support, set_retrieval_pool (leak-safe, train-only pool)
- models.py: use_rag/rag_top_k passthrough to LLMPromptEncoder
- nested_cv_optuna.py: --use-rag CLI, per-fold retrieval pool (防泄漏), slim checkpoint (skip frozen LLM weights)
- Qwen+RAG results: seed42 delivery R2=0.2151, seed123=0.1558 (mean 0.185), best LLM encoder so far
This commit is contained in:
Michelle0474 2026-06-28 20:42:27 +08:00
parent c0aa30f2bb
commit 0fc72a9474
157 changed files with 17357 additions and 1029 deletions
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135
lnp_ml/modeling/layers/llm_prompt.py
View File

@ -30,15 +30,36 @@ class LLMPromptEncoder(nn.Module):
lora_alpha: int = 16,
lora_dropout: float = 0.05,
max_length: int = 128,
use_rag: bool = False,
rag_top_k: int = 4,
) -> None:
super().__init__()
from transformers import AutoTokenizer, T5EncoderModel
from transformers import AutoTokenizer, T5EncoderModel, AutoModel
self.use_lora = use_lora
self.max_length = max_length
self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
self.encoder = T5EncoderModel.from_pretrained(model_name_or_path)
self.hidden_size = self.encoder.config.d_model
_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:
self.encoder = AutoModel.from_pretrained(
model_name_or_path, trust_remote_code=True, torch_dtype=torch.float16)
self.hidden_size = self.encoder.config.hidden_size
else:
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)
@ -52,18 +73,115 @@ class LLMPromptEncoder(nn.Module):
)
# 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
for p in self.encoder.parameters():
p.requires_grad = False
# 不同架构注意力层命名不同：
# T5: q/k/v/o；Roberta/ChemBERTa: query/key/value；Qwen/Llama: q_proj/k_proj/v_proj/o_proj
_enc_name = type(self.encoder).__name__.lower()
if getattr(self, "_is_qwen", False) or "qwen" in _enc_name or "llama" in _enc_name:
_targets = ["q_proj", "k_proj", "v_proj", "o_proj"]
elif "t5" in _enc_name or hasattr(self.encoder.config, "d_model"):
_targets = ["q", "k", "v", "o"]
else:
_targets = ["query", "key", "value"]
cfg = LoraConfig(
r=r, lora_alpha=alpha, lora_dropout=dropout,
target_modules=["q", "k", "v", "o"], bias="none",
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。"""
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_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._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）。"""
from rdkit import DataStructs
from lnp_ml.modeling.retrieval import _smiles_to_fp
qfp = _smiles_to_fp(query_smiles)
if qfp is None or self._rag_pool_fps is None:
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
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])))
return out
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）。"""
blocks = []
for rank, (smi, sim, lbl) in enumerate(neighbors, 1):
blocks.append(
f"Retrieved sample {rank}:\nSMILES: {smi}\n"
f"Similarity score: {sim:.3f}\nKnown outcome - delivery_log: {lbl:.3f}"
)
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"
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"
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."
)
@torch.no_grad()
def _encode_rag(self, smiles, device):
"""RAG 编码：每个分子→检索→构造prompt→Qwen编码→取最后有效token。带缓存。"""
feats = []
to_compute = []
keys = []
for s in smiles:
key = f"RAG::{self._rag_pool_id}::{s}"
keys.append(key)
if key not in self._cache:
to_compute.append(s)
# 逐个构造 prompt（检索是 CPU 操作）
if to_compute:
uniq = list(dict.fromkeys(to_compute))
prompts = [self._build_rag_prompt(s, self._retrieve_topk(s)) for s in uniq]
for i in range(0, len(uniq), 4):
bt = prompts[i:i+4]
enc = self.tokenizer(
bt, padding=True, truncation=True,
max_length=self.max_length, return_tensors="pt",
).to(device)
out = self.encoder(**enc).last_hidden_state # [B,L,H]
lengths = enc["attention_mask"].sum(1) - 1 # 最后有效token位置
b = torch.arange(out.size(0), device=device)
last_tok = out[b, lengths.long(), :] # [B,H]
for s, v in zip(uniq[i:i+4], last_tok):
self._cache[f"RAG::{self._rag_pool_id}::{s}"] = v.float().cpu()
return torch.stack([self._cache[k] for k in keys]).to(device)
def _mean_pool(self, last_hidden: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
m = mask.unsqueeze(-1).float() # [B, L, 1]
return (last_hidden * m).sum(1) / m.sum(1).clamp(min=1e-6)
@ -96,8 +214,11 @@ class LLMPromptEncoder(nn.Module):
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]。"""
device = self.proj_down[0].weight.device
feat = self._encode_frozen(smiles, device) if self._frozen \
else self._encode_trainable(smiles, 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.proj_down(feat)
def clear_cache(self) -> None:

30
lnp_ml/modeling/models.py
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@ -98,6 +98,10 @@ class LNPModel(nn.Module):
llm_lora_r: int = 8,
llm_lora_alpha: int = 16,
llm_lora_dropout: float = 0.05,
use_rag: bool = False,
rag_top_k: int = 4,
use_retrieval: bool = False,
retr_feature_dim: int = 0,
) -> None:
super().__init__()
@ -170,10 +174,20 @@ class LNPModel(nn.Module):
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,
)
else:
self.llm_prompt = None
# ============ 检索增强 (可选) ============
self.use_retrieval = use_retrieval
self.retriever = None # 由 nested_cv 每折用训练集构建后赋值
if use_retrieval:
self.retr_proj = nn.Linear(retr_feature_dim, d_model)
else:
self.retr_proj = None
# ============ Residual Concat + Fusion ============
self.fusion = ResidualConcatFusion(d_model=d_model, strategy=fusion_strategy)
@ -234,7 +248,13 @@ class LNPModel(nn.Module):
if self.llm_prompt is not None and smiles is not None:
f_llm = self.llm_prompt(smiles)
return self.fusion(chem, tab, f_moe=f_moe, f_llm=f_llm)
f_retr = None
if self.retr_proj is not None and self.retriever is not None and smiles is not None:
_feats = self.retriever.query_batch(smiles, exclude_self="auto")
_feats_t = torch.as_tensor(_feats, dtype=chem.dtype, device=chem.device)
f_retr = self.retr_proj(_feats_t)
return self.fusion(chem, tab, f_moe=f_moe, f_llm=f_llm, f_retr=f_retr)
def forward_from_projected(
self,
@ -415,6 +435,10 @@ class LNPModelWithoutMPNN(LNPModel):
llm_lora_r: int = 8,
llm_lora_alpha: int = 16,
llm_lora_dropout: float = 0.05,
use_rag: bool = False,
rag_top_k: int = 4,
use_retrieval: bool = False,
retr_feature_dim: int = 0,
) -> None:
dims = input_dims or DEFAULT_INPUT_DIMS.copy()
dims.pop("mpnn", None)
@ -436,6 +460,10 @@ class LNPModelWithoutMPNN(LNPModel):
moe_expert_hidden_mult=moe_expert_hidden_mult,
moe_jitter_noise=moe_jitter_noise,
use_llm=use_llm,
use_rag=use_rag,
rag_top_k=rag_top_k,
use_retrieval=use_retrieval,
retr_feature_dim=retr_feature_dim,
llm_model_path=llm_model_path,
llm_freeze=llm_freeze,
llm_use_lora=llm_use_lora,

2095
lnp_ml/modeling/nested_cv_optuna.py
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13
models/abl_qwen_rag/qwen_rag_seed123/20260628_151005/outer_fold_0/best_params.json Normal file
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@ -0,0 +1,13 @@
{
"dropout": 0.10258134968793176,
"lr": 0.0008654084806997698,
"weight_decay": 0.0005263493803628638,
"backbone_lr_ratio": 0.010620667618802197,
"llm_lora_r": 32,
"d_model": 256,
"num_heads": 8,
"n_attn_layers": 4,
"fusion_strategy": "attention",
"head_hidden_dim": 128,
"set_transformer_block": "sab"
}

1
models/abl_qwen_rag/qwen_rag_seed123/20260628_151005/outer_fold_0/epoch_mean.json Normal file
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@ -0,0 +1 @@
{"epoch_mean": 15}

321
models/abl_qwen_rag/qwen_rag_seed123/20260628_151005/outer_fold_0/history.json Normal file
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@ -0,0 +1,321 @@
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