mirror of
https://github.com/RYDE-WORK/lnp_ml.git
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feat: 支持固定超参训练并部署 4 专家稀疏 MoE 模型
This commit is contained in:
Michelle0574
parent
c0203ebe76
commit
aeddf8e1ee
@ -491,6 +491,8 @@ def main(
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n_trials: int = 20,
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n_trials: int = 20,
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epochs_per_trial: int = 30,
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epochs_per_trial: int = 30,
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patience: int = 10,
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patience: int = 10,
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fixed_params_json: Optional[Path] = None,
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fixed_epoch_mean: Optional[int] = None,
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# 训练参数
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# 训练参数
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batch_size: int = 32,
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batch_size: int = 32,
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# 最终训练参数
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# 最终训练参数
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@ -613,7 +615,17 @@ def main(
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# 预热 RDKit 缓存（在整个训练流程中共享）
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# 预热 RDKit 缓存（在整个训练流程中共享）
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rdkit_cache = warmup_rdkit_cache(full_dataset.smiles)
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rdkit_cache = warmup_rdkit_cache(full_dataset.smiles)
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# 运行 Optuna 调参
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if fixed_params_json is not None:
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if fixed_epoch_mean is None:
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raise typer.BadParameter("--fixed-params-json 必须配合 --fixed-epoch-mean 使用")
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logger.info(f"Skipping Optuna, loading fixed params from {fixed_params_json}")
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with open(fixed_params_json) as f:
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best_params = json.load(f)
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epoch_mean = fixed_epoch_mean
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study = None
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logger.info(f"Fixed params: {best_params}")
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logger.info(f"Fixed epoch_mean: {epoch_mean}")
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else:
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logger.info(f"\nRunning {n_folds}-fold Optuna with {n_trials} trials...")
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logger.info(f"\nRunning {n_folds}-fold Optuna with {n_trials} trials...")
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study_path = output_dir / "optuna_study.sqlite3"
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study_path = output_dir / "optuna_study.sqlite3"
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@ -654,6 +666,7 @@ def main(
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json.dump({"epoch_mean": epoch_mean}, f)
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json.dump({"epoch_mean": epoch_mean}, f)
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# 保存 Optuna 试验历史
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# 保存 Optuna 试验历史
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if study is not None:
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trials_history = []
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trials_history = []
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for trial in study.trials:
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for trial in study.trials:
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trials_history.append({
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trials_history.append({
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@ -691,8 +704,6 @@ def main(
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with open(output_dir / "class_weights.json", "w") as f:
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with open(output_dir / "class_weights.json", "w") as f:
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json.dump(class_weights_info, f, indent=2)
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json.dump(class_weights_info, f, indent=2)
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# 架构超参一次性解析：Optuna 采样值优先于 CLI 默认值。
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# 解析结果必须同时喂给 create_model 和 config，否则 load_model 回读时结构对不上。
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arch = {
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arch = {
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"moe_n_experts": best_params.get("moe_n_experts", moe_n_experts),
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"moe_n_experts": best_params.get("moe_n_experts", moe_n_experts),
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"moe_top_k": best_params.get("moe_top_k", moe_top_k),
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"moe_top_k": best_params.get("moe_top_k", moe_top_k),
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@ -3,7 +3,7 @@
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"lr": 0.000980484886752915,
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"lr": 0.000980484886752915,
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"weight_decay": 0.00014367509113739864,
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"weight_decay": 0.00014367509113739864,
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"backbone_lr_ratio": 0.13093310334961422,
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"backbone_lr_ratio": 0.13093310334961422,
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"moe_n_experts": 2,
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"moe_n_experts": 4,
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"moe_top_k": 2,
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"moe_top_k": 2,
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"moe_expert_hidden_mult": 2,
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"moe_expert_hidden_mult": 2,
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"llm_lora_r": 8,
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"llm_lora_r": 8,
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@ -1,126 +1,126 @@
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{
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{
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"train": [
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"train": [
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{
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{
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"loss": 5.662022154286222,
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"loss": 5.694288951046062,
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"loss_size": 0.9464401806581695,
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"loss_size": 1.015234741259296,
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"loss_pdi": 0.6576592483610477,
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"loss_pdi": 0.6592788083373375,
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"loss_ee": 1.0441054870497506,
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"loss_ee": 1.027098409409793,
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"loss_delivery": 1.0691580114499577,
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"loss_biodist": 0.7718977461446006,
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"loss_biodist": 0.7630386647750746,
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"loss_toxic": 0.47160032813279135,
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"loss_toxic": 0.5493527664890829,
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"loss_moe_lb": 1.9999999887538407,
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"loss_moe_lb": 2.045894391131851,
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"loss_aux_moe": 1.096376850709038,
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"loss_aux_moe": 1.0363417644545716,
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"loss_aux_llm": 1.2355621509113401
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"loss_aux_llm": 1.1829107824080396
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},
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},
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{
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{
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"loss": 4.549718969273117,
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"loss": 4.587394336484513,
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"loss_size": 0.9265210998227011,
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"loss_ee": 0.9480358497151788,
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"loss_delivery": 0.8463795804682205,
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"loss_biodist": 0.48631338606465535,
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"loss_toxic": 0.25031149114991696,
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"loss_toxic": 0.30279909285171974,
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"loss_moe_lb": 1.9999999910030726,
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"loss_aux_llm": 1.0696029401612732
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},
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},
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{
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{
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"loss": 4.183341939494295,
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"loss_delivery": 0.8323076782080362,
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"loss_toxic": 0.1671132598740031,
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"loss_toxic": 0.18268741205563102,
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"loss_moe_lb": 1.9999999865046088,
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"loss_moe_lb": 2.1000781711542382,
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"loss_aux_moe": 0.8456886384003567,
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"loss_aux_moe": 0.9373761130461715,
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"loss_aux_llm": 1.0173823231796049
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"loss_aux_llm": 0.9303386138295228
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},
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},
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{
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{
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"loss": 3.9456988685535936,
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"loss_size": 0.925582969104344,
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"loss_size": 0.888136670766574,
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"loss_pdi": 0.586557297211773,
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"loss_ee": 0.867810919599713,
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"loss_ee": 0.8553679860987753,
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"loss_delivery": 0.7656616399521535,
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"loss_biodist": 0.4162127935099152,
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"loss_toxic": 0.2137195658123226,
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"loss_toxic": 0.15547859341890183,
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"loss_moe_lb": 1.9999999887538407,
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"loss_moe_lb": 2.102432388179707,
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"loss_aux_moe": 0.7506198146784643,
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"loss_aux_moe": 1.1255526001442153,
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"loss_aux_llm": 0.9585366686981804
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"loss_aux_llm": 0.9655606655298539
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},
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},
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{
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{
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"loss": 3.4442428957741216,
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"loss_size": 0.8259714532573268,
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"loss_ee": 0.8086997242468708,
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"loss_ee": 0.7858308424364846,
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"loss_delivery": 0.7543281304808158,
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"loss_delivery": 0.7225946192190332,
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"loss_biodist": 0.3298270061330975,
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"loss_toxic": 0.09599270570566351,
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"loss_toxic": 0.10101612598441963,
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"loss_moe_lb": 1.9999999955015362,
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"loss_moe_lb": 2.1294895545491634,
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"loss_aux_moe": 0.8137425728282839,
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"loss_aux_llm": 1.044384686873769
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"loss_aux_llm": 0.8958881971127582
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},
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},
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{
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{
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"loss": 3.108477974837681,
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"loss_size": 0.7671496489981435,
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"loss_biodist": 0.2828179093183212,
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"loss_toxic": 0.08221106674908749,
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"loss_toxic": 0.08327638518992143,
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"loss_moe_lb": 1.9999999910030726,
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"loss_moe_lb": 2.1596880116552675,
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"loss_aux_moe": 0.7168521201413758,
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"loss_aux_moe": 0.763107772424536,
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"loss_aux_llm": 0.9446638939234445
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"loss_aux_llm": 0.8947458216604197
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},
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},
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{
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{
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"loss": 2.983976681277437,
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"loss": 2.7413068245042047,
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"loss_size": 0.7495474740159962,
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"loss_moe_lb": 1.9999999955015362
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{
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{
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{
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"loss_toxic": 0.1272664367724298,
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"loss_moe_lb": 1.9999999932523043
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{
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{
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{
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"loss": 2.5718357652988075,
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"loss_toxic": 0.05047845465344166,
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"loss_moe_lb": 1.9999999842553768
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}
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}
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],
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],
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"val": []
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"val": []
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Binary file not shown.
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:b96d3867be53a3e23abdf291baeec4a9d610da58dc790220a345e7012187aaa0
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oid sha256:5b1c826c11338f313eea643f839fbc27c4c1e0f0f19d58d672d405cc731caa2a
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size 53008646
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size 58261734
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16
scripts/hparams_sparse_moe.json
Normal file
16
scripts/hparams_sparse_moe.json
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{
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"dropout": 0.19323529834582587,
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"lr": 0.000980484886752915,
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"weight_decay": 0.00014367509113739864,
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"backbone_lr_ratio": 0.13093310334961422,
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"moe_n_experts": 4,
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"moe_top_k": 2,
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"moe_expert_hidden_mult": 2,
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"llm_lora_r": 8,
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"d_model": 256,
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"num_heads": 8,
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"n_attn_layers": 4,
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"fusion_strategy": "attention",
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"head_hidden_dim": 128,
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"set_transformer_block": "sab"
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}
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@ -11,6 +11,8 @@ 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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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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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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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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BATCH=${BATCH:-8}
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REG_BYPASS=${REG_BYPASS:-off}
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REG_BYPASS=${REG_BYPASS:-off}
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FREEZE=${FREEZE:-3}
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FREEZE=${FREEZE:-3}
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@ -63,6 +65,12 @@ if [ -n "${PRETRAIN}" ] && [ -f "${PRETRAIN}" ]; then
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elif [ -n "${PRETRAIN}" ]; then
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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}"
|
echo "[$(date '+%F %T')] 警告：${PRETRAIN} 不存在，跳过预训练初始化" >>"${LOG}"
|
||||||
fi
|
fi
|
||||||
|
if [ -n "${FIXED_PARAMS}" ]; then
|
||||||
|
if [ ! -f "${FIXED_PARAMS}" ]; then
|
||||||
|
echo "[$(date '+%F %T')] 错误：${FIXED_PARAMS} 不存在" >>"${LOG}"; exit 1
|
||||||
|
fi
|
||||||
|
EXTRA+=(--fixed-params-json "${FIXED_PARAMS}" --fixed-epoch-mean "${FIXED_EPOCH_MEAN}")
|
||||||
|
fi
|
||||||
|
|
||||||
for attempt in $(seq 1 "${MAX_RETRY}"); do
|
for attempt in $(seq 1 "${MAX_RETRY}"); do
|
||||||
wait_free
|
wait_free
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user