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lnp_ml/models/abl_full/s2_chemeleon/seed42/local_uncommitted.patch

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diff --git a/lnp_ml/interpretability/token_importance.py b/lnp_ml/interpretability/token_importance.py
index ee0d1d8..605a19b 100644
--- a/lnp_ml/interpretability/token_importance.py
+++ b/lnp_ml/interpretability/token_importance.py
@@ -40,10 +40,7 @@ BIODIST_ORGANS = ["lymph_nodes", "heart", "liver", "spleen", "lung", "kidney", "
def get_token_names(model: Union[LNPModel, LNPModelWithoutMPNN]) -> List[str]:
- if model.use_mpnn:
- names = ["mpnn", "morgan", "maccs", "desc", "comp", "phys", "help", "exp"]
- else:
- names = ["morgan", "maccs", "desc", "comp", "phys", "help", "exp"]
+ names = list(model.token_order)
if getattr(model, "moe", None) is not None:
names.append("moe")
return names
@@ -300,7 +297,8 @@ def plot_token_importance(
vals_sorted = normed[order]
n_tokens = len(token_names)
- split_idx = 4 if "mpnn" in token_names else 3
+ _mol_tokens = {"mpnn", "chemeleon", "unimol", "morgan", "maccs", "desc"}
+ split_idx = sum(1 for n in token_names if n in _mol_tokens)
channel_a_set = set(token_names[:split_idx])
colors = [color_a if n in channel_a_set else color_b for n in names_sorted]
diff --git a/lnp_ml/modeling/benchmark.py b/lnp_ml/modeling/benchmark.py
index 6ac6b20..af9cba0 100644
--- a/lnp_ml/modeling/benchmark.py
+++ b/lnp_ml/modeling/benchmark.py
@@ -34,7 +34,7 @@ def find_mpnn_ensemble_paths(base_dir: Path = DEFAULT_MPNN_ENSEMBLE_DIR) -> List
from lnp_ml.modeling.models import LNPModel, LNPModelWithoutMPNN
-
+from lnp_ml.utils.seed import set_global_seed
app = typer.Typer()
@@ -172,6 +172,8 @@ def train_fold(
early_stopping = EarlyStopping(patience=patience)
best_val_loss = float("inf")
+ best_val_rmse = 0.0
+ best_val_r2 = 0.0
best_state = None
history = []
@@ -202,6 +204,8 @@ def train_fold(
if val_metrics["loss"] < best_val_loss:
best_val_loss = val_metrics["loss"]
+ best_val_rmse = val_metrics.get("rmse", 0)
+ best_val_r2 = val_metrics.get("r2", 0)
best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}
logger.info(f" -> New best val_loss: {best_val_loss:.4f}")
@@ -239,8 +243,8 @@ def train_fold(
return {
"fold_idx": fold_idx,
"best_val_loss": best_val_loss,
- "best_val_rmse": history[-1]["val_rmse"] if history else 0,
- "best_val_r2": history[-1]["val_r2"] if history else 0,
+ "best_val_rmse": best_val_rmse,
+ "best_val_r2": best_val_r2,
"epochs_trained": len(history),
}
@@ -255,6 +259,8 @@ def create_model(
use_mpnn: bool = False,
mpnn_ensemble_paths: Optional[List[str]] = None,
mpnn_device: str = "cpu",
+ chemeleon_cache: Optional[str] = None,
+ unimol_cache: Optional[str] = None,
) -> nn.Module:
"""创建模型实例"""
if use_mpnn:
@@ -267,6 +273,8 @@ def create_model(
dropout=dropout,
mpnn_ensemble_paths=mpnn_ensemble_paths,
mpnn_device=mpnn_device,
+ chemeleon_cache_path=chemeleon_cache,
+ unimol_cache_path=unimol_cache,
)
else:
return LNPModelWithoutMPNN(
@@ -276,6 +284,8 @@ def create_model(
fusion_strategy=fusion_strategy,
head_hidden_dim=head_hidden_dim,
dropout=dropout,
+ chemeleon_cache_path=chemeleon_cache,
+ unimol_cache_path=unimol_cache,
)
@@ -295,12 +305,18 @@ def main(
mpnn_checkpoint: Optional[str] = None,
mpnn_ensemble_paths: Optional[str] = None,
mpnn_device: str = "cpu",
+ use_chemeleon: bool = False,
+ chemeleon_cache: str = "data/processed/chemeleon_embeddings.npz",
+ use_unimol: bool = False,
+ unimol_cache: str = "data/processed/unimol_embeddings.npz",
# 训练参数
batch_size: int = 64,
lr: float = 1e-4,
weight_decay: float = 1e-5,
epochs: int = 50,
patience: int = 10,
+ # 随机种子
+ seed: int = 42,
# 设备
device: str = "cuda" if torch.cuda.is_available() else "cpu",
):
@@ -311,6 +327,8 @@ def main(
使用 --use-mpnn 启用 MPNN encoder。
"""
logger.info(f"Using device: {device}")
+ set_global_seed(seed)
+ logger.info(f"Global seed set to {seed}")
device = torch.device(device)
# 解析 MPNN 参数
@@ -349,6 +367,11 @@ def main(
"dropout": dropout,
"use_mpnn": use_mpnn,
"mpnn_ensemble_paths": mpnn_paths,
+ "use_chemeleon": use_chemeleon,
+ "chemeleon_cache": chemeleon_cache if use_chemeleon else None,
+ "use_unimol": use_unimol,
+ "unimol_cache": unimol_cache if use_unimol else None,
+ "seed": seed,
"lr": lr,
"weight_decay": weight_decay,
"batch_size": batch_size,
@@ -405,6 +428,8 @@ def main(
use_mpnn=use_mpnn,
mpnn_ensemble_paths=mpnn_paths,
mpnn_device=device.type,
+ chemeleon_cache=(chemeleon_cache if use_chemeleon else None),
+ unimol_cache=(unimol_cache if use_unimol else None),
)
model = model.to(device)
diff --git a/lnp_ml/modeling/encoders/__init__.py b/lnp_ml/modeling/encoders/__init__.py
index 2ab762a..eb78d5c 100644
--- a/lnp_ml/modeling/encoders/__init__.py
+++ b/lnp_ml/modeling/encoders/__init__.py
@@ -1,5 +1,11 @@
from lnp_ml.modeling.encoders.rdkit_encoder import CachedRDKitEncoder
from lnp_ml.modeling.encoders.mpnn_encoder import CachedMPNNEncoder
+from lnp_ml.modeling.encoders.chemeleon_encoder import CheMeleonEmbeddingEncoder
+from lnp_ml.modeling.encoders.unimol_encoder import UniMolEmbeddingEncoder
-__all__ = ["CachedRDKitEncoder", "CachedMPNNEncoder"]
-
+__all__ = [
+ "CachedRDKitEncoder",
+ "CachedMPNNEncoder",
+ "CheMeleonEmbeddingEncoder",
+ "UniMolEmbeddingEncoder",
+]
\ No newline at end of file
diff --git a/lnp_ml/modeling/final_train_optuna_cv.py b/lnp_ml/modeling/final_train_optuna_cv.py
index d3c7a97..4b0a8b8 100644
--- a/lnp_ml/modeling/final_train_optuna_cv.py
+++ b/lnp_ml/modeling/final_train_optuna_cv.py
@@ -176,6 +176,8 @@ def create_model(
dropout: float = 0.1,
use_mpnn: bool = False,
mpnn_device: str = "cpu",
+ chemeleon_cache: Optional[str] = None,
+ unimol_cache: Optional[str] = None,
# ============ MoE 相关（新增） ============
use_moe: bool = False,
moe_n_experts: int = 4,
@@ -203,6 +205,8 @@ def create_model(
dropout=dropout,
mpnn_ensemble_paths=ensemble_paths,
mpnn_device=mpnn_device,
+ chemeleon_cache_path=chemeleon_cache,
+ unimol_cache_path=unimol_cache,
**moe_kwargs,
)
else:
@@ -213,6 +217,8 @@ def create_model(
fusion_strategy=fusion_strategy,
head_hidden_dim=head_hidden_dim,
dropout=dropout,
+ chemeleon_cache_path=chemeleon_cache,
+ unimol_cache_path=unimol_cache,
**moe_kwargs,
)
@@ -258,6 +264,8 @@ def run_optuna_cv(
batch_size: int = 32,
n_folds: int = 3,
use_mpnn: bool = False,
+ chemeleon_cache: Optional[str] = None,
+ unimol_cache: Optional[str] = None,
seed: int = 42,
study_path: Optional[Path] = None,
pretrain_state_dict: Optional[Dict] = None,
@@ -355,6 +363,8 @@ def run_optuna_cv(
dropout=dropout,
use_mpnn=use_mpnn,
mpnn_device=device.type,
+ chemeleon_cache=chemeleon_cache,
+ unimol_cache=unimol_cache,
use_moe=use_moe,
moe_n_experts=moe_n_experts,
moe_top_k=moe_top_k,
@@ -451,7 +461,12 @@ def main(
load_delivery_head: bool = False,
# MPNN
use_mpnn: bool = False,
- # MoE（新增）
+ # CheMeleon
+ use_chemeleon: bool = False,
+ chemeleon_cache: str = "data/processed/chemeleon_embeddings.npz",
+ use_unimol: bool = False,
+ unimol_cache: str = "data/processed/unimol_embeddings.npz",
+ # MoE
use_moe: bool = False,
moe_n_experts: int = 4,
moe_top_k: int = 2,
@@ -531,6 +546,8 @@ def main(
batch_size=batch_size,
n_folds=n_folds,
use_mpnn=use_mpnn,
+ chemeleon_cache=(chemeleon_cache if use_chemeleon else None),
+ unimol_cache=(unimol_cache if use_unimol else None),
seed=seed,
study_path=study_path,
pretrain_state_dict=pretrain_state_dict,
@@ -599,6 +616,8 @@ def main(
dropout=best_params["dropout"],
use_mpnn=use_mpnn,
mpnn_device=device.type,
+ chemeleon_cache=(chemeleon_cache if use_chemeleon else None),
+ unimol_cache=(unimol_cache if use_unimol else None),
use_moe=use_moe,
moe_n_experts=moe_n_experts,
moe_top_k=moe_top_k,
@@ -651,6 +670,10 @@ def main(
"head_hidden_dim": best_params["head_hidden_dim"],
"dropout": best_params["dropout"],
"use_mpnn": use_mpnn,
+ "use_chemeleon": use_chemeleon,
+ "chemeleon_cache": chemeleon_cache if use_chemeleon else None,
+ "use_unimol": use_unimol,
+ "unimol_cache": unimol_cache if use_unimol else None,
"use_moe": use_moe,
"moe_n_experts": moe_n_experts,
"moe_top_k": moe_top_k,
diff --git a/lnp_ml/modeling/layers/llm_prompt.py b/lnp_ml/modeling/layers/llm_prompt.py
index 21b7e65..b5c6987 100644
--- a/lnp_ml/modeling/layers/llm_prompt.py
+++ b/lnp_ml/modeling/layers/llm_prompt.py
@@ -54,9 +54,14 @@ class LLMPromptEncoder(nn.Module):
_is_t5 = "t5" in _name_l
_is_qwen = "qwen" in _name_l
self._is_qwen = _is_qwen
+ _is_biot5 = "biot5" in _name_l
+ self._is_biot5 = _is_biot5
self.tokenizer = AutoTokenizer.from_pretrained(
- model_name_or_path, trust_remote_code=_is_qwen)
+ model_name_or_path,
+ trust_remote_code=_is_qwen,
+ use_fast=not _is_biot5,
+ )
if _is_qwen and self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
@@ -208,6 +213,19 @@ class LLMPromptEncoder(nn.Module):
return "[" + ", ".join(f"{x:.3f}" for x in v) + "]"
return f"{float(v):.3f}"
+ def _fmt_mol(self, smiles: str) -> str:
+ """按 backbone 期望格式化分子。
+ BioT5：SMILES -> SELFIES，用 <bom>...<eom> 紧贴包裹（官方格式，token 间无空格）；
+ 其他 backbone：原样返回 SMILES。"""
+ if not getattr(self, "_is_biot5", False):
+ return smiles
+ try:
+ import selfies as sf
+ sfs = sf.encoder(smiles) # CCO -> [C][C][O]
+ except Exception:
+ return smiles # 转换失败退回 SMILES，避免整批中断
+ return f"<bom>{sfs}<eom>"
+
def _build_rag_prompt(self, target_smiles: str, neighbors) -> str:
"""构造 RAG prompt：原始 SMILES + 邻居多任务结果（numeric）。"""
blocks = []
@@ -215,7 +233,7 @@ class LLMPromptEncoder(nn.Module):
ex = nb["extra"]
blocks.append(
f"Retrieved sample {rank}:\n"
- f"SMILES: {nb['smiles']}\n"
+ f"Molecule: {self._fmt_mol(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"
@@ -228,7 +246,7 @@ class LLMPromptEncoder(nn.Module):
return (
"Task: Encode the target LNP molecule into a retrieval-aware representation "
"for downstream multi-task property prediction. Do not output predictions.\n\n"
- f"[Target Molecule]\nSMILES: {target_smiles}\n\n"
+ f"[Target Molecule]\nMolecule: {self._fmt_mol(target_smiles)}\n\n"
"[Retrieved Similar LNP Samples]\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; "
@@ -241,7 +259,7 @@ class LLMPromptEncoder(nn.Module):
def _get_prompt(self, s: str) -> str:
if not self.use_rag:
- return s
+ return self._fmt_mol(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))
@@ -326,7 +344,8 @@ class LLMPromptEncoder(nn.Module):
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,
+ mols = [self._fmt_mol(s) for s in chunk]
+ enc = self.tokenizer(mols, 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"])
@@ -335,7 +354,8 @@ class LLMPromptEncoder(nn.Module):
return torch.stack([self._cache[s] for s in smiles]).to(device)
def _encode_trainable(self, smiles, device):
- enc = self.tokenizer(list(smiles), padding=True, truncation=True,
+ mols = [self._fmt_mol(s) for s in smiles]
+ enc = self.tokenizer(mols, 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"])
diff --git a/lnp_ml/modeling/models.py b/lnp_ml/modeling/models.py
index 1428075..87e6643 100644
--- a/lnp_ml/modeling/models.py
+++ b/lnp_ml/modeling/models.py
@@ -4,7 +4,12 @@ import torch
import torch.nn as nn
from typing import Dict, List, Optional, Literal
-from lnp_ml.modeling.encoders import CachedRDKitEncoder, CachedMPNNEncoder
+from lnp_ml.modeling.encoders import (
+ CachedRDKitEncoder,
+ CachedMPNNEncoder,
+ CheMeleonEmbeddingEncoder,
+ UniMolEmbeddingEncoder,
+)
from lnp_ml.modeling.layers import (
TokenProjector,
SetTransformer,
@@ -91,6 +96,10 @@ class LNPModel(nn.Module):
mpnn_checkpoint: Optional[str] = None,
mpnn_ensemble_paths: Optional[List[str]] = None,
mpnn_device: str = "cpu",
+ # CheMeleon encoder
+ chemeleon_cache_path: Optional[str] = None,
+ # UniMol encoder
+ unimol_cache_path: Optional[str] = None,
# 输入维度配置
input_dims: Optional[Dict[str, int]] = None,
# ============ MoE 相关 ============
@@ -121,6 +130,8 @@ class LNPModel(nn.Module):
self.input_dims = input_dims or DEFAULT_INPUT_DIMS
self.d_model = d_model
self.use_mpnn = mpnn_checkpoint is not None or mpnn_ensemble_paths is not None
+ self.use_chemeleon = chemeleon_cache_path is not None
+ self.use_unimol = unimol_cache_path is not None
# ============ Encoders ============
self.rdkit_encoder = CachedRDKitEncoder()
@@ -133,6 +144,18 @@ class LNPModel(nn.Module):
else:
self.mpnn_encoder = None
+ if self.use_chemeleon:
+ self.chemeleon_encoder = CheMeleonEmbeddingEncoder(cache_path=chemeleon_cache_path)
+ self.input_dims = {**self.input_dims, "chemeleon": self.chemeleon_encoder.embed_dim}
+ else:
+ self.chemeleon_encoder = None
+
+ if self.use_unimol:
+ self.unimol_encoder = UniMolEmbeddingEncoder(cache_path=unimol_cache_path)
+ self.input_dims = {**self.input_dims, "unimol": self.unimol_encoder.embed_dim}
+ else:
+ self.unimol_encoder = None
+
# ============ Token Projector ============
proj_input_dims = {k: v for k, v in self.input_dims.items()}
if not self.use_mpnn:
@@ -143,8 +166,16 @@ class LNPModel(nn.Module):
dropout=dropout,
)
- # token 顺序与化学侧 token 数
- self.chem_keys = CHEM_KEYS_WITH_MPNN if self.use_mpnn else CHEM_KEYS_NO_MPNN
+ # token 顺序：可选 embedding（mpnn/chemeleon）排在指纹类 token 之前
+ chem_keys: List[str] = []
+ if self.use_mpnn:
+ chem_keys.append("mpnn")
+ if self.use_chemeleon:
+ chem_keys.append("chemeleon")
+ if self.use_unimol:
+ chem_keys.append("unimol")
+ chem_keys += ["morgan", "maccs", "desc"]
+ self.chem_keys = chem_keys
self.tab_keys = TAB_KEYS
self.token_order = self.chem_keys + self.tab_keys
self.split_idx = len(self.chem_keys)
@@ -233,6 +264,10 @@ class LNPModel(nn.Module):
if self.use_mpnn:
mpnn_features = self.mpnn_encoder(smiles)
all_features["mpnn"] = mpnn_features["mpnn"].to(device)
+ if self.use_chemeleon:
+ all_features["chemeleon"] = self.chemeleon_encoder(smiles)["chemeleon"].to(device)
+ if self.use_unimol:
+ all_features["unimol"] = self.unimol_encoder(smiles)["unimol"].to(device)
all_features["morgan"] = rdkit_features["morgan"].to(device)
all_features["maccs"] = rdkit_features["maccs"].to(device)
all_features["desc"] = rdkit_features["desc"].to(device)
@@ -297,6 +332,15 @@ class LNPModel(nn.Module):
if task is None:
task = "delivery"
+ x_reg = getattr(self, "_last_pooled", None) if getattr(self, "reg_bypass", True) else None
+ x_for = {
+ "size": x_reg if x_reg is not None else fused,
+ "pdi": fused,
+ "ee": fused,
+ "delivery": x_reg if x_reg is not None else fused,
+ "biodist": fused,
+ "toxic": fused,
+ }
task_heads = {
"size": self.head.size_head,
"pdi": self.head.pdi_head,
@@ -305,7 +349,7 @@ class LNPModel(nn.Module):
"biodist": self.head.biodist_head,
"toxic": self.head.toxic_head,
}
- return task_heads[task](fused)
+ return task_heads[task](x_for[task])
def forward_replacing_token(
self,
@@ -347,7 +391,8 @@ class LNPModel(nn.Module):
) -> torch.Tensor:
"""仅预测 delivery（用于 pretrain）。返回 [B, 1]。"""
fused = self.forward_backbone(smiles, tabular)
- return self.head.delivery_head(fused)
+ x_reg = getattr(self, "_last_pooled", None) if getattr(self, "reg_bypass", True) else None
+ return self.head.delivery_head(x_reg if x_reg is not None else fused)
def forward(
self,
@@ -369,6 +414,10 @@ class LNPModel(nn.Module):
self.rdkit_encoder.clear_cache()
if self.mpnn_encoder is not None:
self.mpnn_encoder.clear_cache()
+ if self.chemeleon_encoder is not None:
+ self.chemeleon_encoder.clear_cache()
+ if self.unimol_encoder is not None:
+ self.unimol_encoder.clear_cache()
if self.llm_prompt is not None and hasattr(self.llm_prompt, "clear_cache"):
self.llm_prompt.clear_cache()
@@ -442,6 +491,8 @@ class LNPModelWithoutMPNN(LNPModel):
head_hidden_dim: int = 128,
dropout: float = 0.1,
input_dims: Optional[Dict[str, int]] = None,
+ chemeleon_cache_path: Optional[str] = None,
+ unimol_cache_path: Optional[str] = None,
# ============ MoE 相关 ============
use_moe: bool = False,
moe_n_experts: int = 4,
@@ -478,6 +529,8 @@ class LNPModelWithoutMPNN(LNPModel):
dropout=dropout,
mpnn_checkpoint=None,
mpnn_ensemble_paths=None,
+ chemeleon_cache_path=chemeleon_cache_path,
+ unimol_cache_path=unimol_cache_path,
input_dims=dims,
reg_bypass=reg_bypass,
use_moe=use_moe,
diff --git a/lnp_ml/modeling/nested_cv_optuna.py b/lnp_ml/modeling/nested_cv_optuna.py
index 7c34e63..6b0a72c 100644
--- a/lnp_ml/modeling/nested_cv_optuna.py
+++ b/lnp_ml/modeling/nested_cv_optuna.py
@@ -195,6 +195,8 @@ def create_model(
dropout: float = 0.1,
use_mpnn: bool = False,
mpnn_device: str = "cpu",
+ chemeleon_cache: Optional[str] = None,
+ unimol_cache: Optional[str] = None,
set_transformer_block: str = "sab",
# MoE
use_moe: bool = False,
@@ -218,6 +220,8 @@ def create_model(
moe_jitter_noise=moe_jitter_noise,
use_retrieval=use_retrieval,
retr_feature_dim=retr_feature_dim,
+ chemeleon_cache_path=chemeleon_cache,
+ unimol_cache_path=unimol_cache,
**(llm_kwargs or {}),
)
@@ -418,6 +422,8 @@ def run_inner_optuna(
batch_size: int = 32,
n_inner_folds: int = 3,
use_mpnn: bool = False,
+ chemeleon_cache: Optional[str] = None,
+ unimol_cache: Optional[str] = None,
seed: int = 42,
study_path: Optional[Path] = None,
pretrain_state_dict: Optional[Dict] = None,
@@ -536,6 +542,8 @@ def run_inner_optuna(
dropout=dropout,
use_mpnn=use_mpnn,
mpnn_device=device.type,
+ chemeleon_cache=chemeleon_cache,
+ unimol_cache=unimol_cache,
use_moe=use_moe,
moe_n_experts=moe_ne_t,
moe_top_k=moe_tk_t,
@@ -631,6 +639,8 @@ def _run_single_outer_fold(
batch_size: int,
n_inner_folds: int,
use_mpnn: bool,
+ chemeleon_cache: Optional[str],
+ unimol_cache: Optional[str],
seed: int,
pretrain_state_dict: Optional[Dict],
pretrain_config: Optional[Dict],
@@ -732,6 +742,8 @@ def _run_single_outer_fold(
batch_size=batch_size,
n_inner_folds=n_inner_folds,
use_mpnn=use_mpnn,
+ chemeleon_cache=chemeleon_cache,
+ unimol_cache=unimol_cache,
seed=seed + outer_fold,
study_path=study_path,
pretrain_state_dict=pretrain_state_dict,
@@ -789,6 +801,8 @@ def _run_single_outer_fold(
dropout=best_params["dropout"],
use_mpnn=use_mpnn,
mpnn_device=device.type,
+ chemeleon_cache=chemeleon_cache,
+ unimol_cache=unimol_cache,
use_moe=use_moe,
moe_n_experts=best_params.get("moe_n_experts", moe_n_experts),
moe_top_k=best_params.get("moe_top_k", moe_top_k),
@@ -853,6 +867,10 @@ def _run_single_outer_fold(
"set_transformer_block": best_params.get("set_transformer_block", "sab"),
"dropout": best_params["dropout"],
"use_mpnn": use_mpnn,
+ "use_chemeleon": chemeleon_cache is not None,
+ "chemeleon_cache": chemeleon_cache,
+ "use_unimol": unimol_cache is not None,
+ "unimol_cache": unimol_cache,
"use_moe": use_moe,
"moe_n_experts": moe_n_experts,
"moe_top_k": moe_top_k,
@@ -920,6 +938,11 @@ def main(
load_delivery_head: bool = False,
# MPNN
use_mpnn: bool = False,
+ # CheMeleon
+ use_chemeleon: bool = False,
+ chemeleon_cache: str = "data/processed/chemeleon_embeddings.npz",
+ use_unimol: bool = False,
+ unimol_cache: str = "data/processed/unimol_embeddings.npz",
n_repeats: int = 1,
repeat_seed_step: int = 1000,
# MoE（消融开关）
@@ -1050,6 +1073,8 @@ def main(
batch_size=batch_size,
n_inner_folds=n_inner_folds,
use_mpnn=use_mpnn,
+ chemeleon_cache=(chemeleon_cache if use_chemeleon else None),
+ unimol_cache=(unimol_cache if use_unimol else None),
seed=seed,
pretrain_state_dict=pretrain_state_dict,
pretrain_config=pretrain_config,
diff --git a/lnp_ml/modeling/predict.py b/lnp_ml/modeling/predict.py
index a5cf836..4b02f44 100644
--- a/lnp_ml/modeling/predict.py
+++ b/lnp_ml/modeling/predict.py
@@ -64,6 +64,8 @@ def load_model(
llm_lora_dropout=config.get("llm_lora_dropout", 0.05),
mpnn_ensemble_paths=ensemble_paths,
mpnn_device=mpnn_device,
+ chemeleon_cache_path=config.get("chemeleon_cache"),
+ unimol_cache_path=config.get("unimol_cache"),
)
else:
model = LNPModelWithoutMPNN(
@@ -80,6 +82,8 @@ def load_model(
llm_lora_r=config.get("llm_lora_r", 8),
llm_lora_alpha=config.get("llm_lora_alpha", 16),
llm_lora_dropout=config.get("llm_lora_dropout", 0.05),
+ chemeleon_cache_path=config.get("chemeleon_cache"),
+ unimol_cache_path=config.get("unimol_cache"),
)
model.load_state_dict(checkpoint["model_state_dict"], strict=False)
diff --git a/lnp_ml/modeling/pretrain.py b/lnp_ml/modeling/pretrain.py
index 54a39ea..48154ad 100644
--- a/lnp_ml/modeling/pretrain.py
+++ b/lnp_ml/modeling/pretrain.py
@@ -243,6 +243,10 @@ def main(
mpnn_checkpoint: Optional[str] = None,
mpnn_ensemble_paths: Optional[str] = None,
mpnn_device: str = "cpu",
+ use_chemeleon: bool = False,
+ chemeleon_cache: str = "data/processed/chemeleon_embeddings.npz",
+ use_unimol: bool = False,
+ unimol_cache: str = "data/processed/unimol_embeddings.npz",
# 训练参数
batch_size: int = 64,
lr: float = 1e-4,
@@ -324,6 +328,8 @@ def main(
llm_lora_r=llm_lora_r,
llm_lora_alpha=llm_lora_alpha,
llm_lora_dropout=llm_lora_dropout,
+ chemeleon_cache_path=(chemeleon_cache if use_chemeleon else None),
+ unimol_cache_path=(unimol_cache if use_unimol else None),
)
if enable_mpnn:
model = LNPModel(
@@ -373,6 +379,8 @@ def main(
"head_hidden_dim": head_hidden_dim,
"dropout": dropout,
"use_mpnn": enable_mpnn,
+ "use_chemeleon": use_chemeleon,
+ "use_unimol": use_unimol,
"use_moe": use_moe,
"moe_n_experts": moe_n_experts,
"moe_top_k": moe_top_k,
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