mirror of
https://github.com/RYDE-WORK/lnp_ml.git
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Merge branch 'feat/moe-layer' of github-michelle:RYDE-WORK/lnp_ml into feat/moe-layer
# Conflicts: # lnp_ml/modeling/layers/llm_prompt.py
This commit is contained in:
Michelle0474
commit
182f7853d7
7
.gitignore
vendored
7
.gitignore
vendored
@ -189,3 +189,10 @@ cython_debug/
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# PyPI configuration file
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.pypirc
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logs/
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*.out
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models/**/*.pt
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models/**/*.png
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models/molt5-base/
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models/abl/
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@ -118,9 +118,13 @@ def process_dataframe(df: pd.DataFrame) -> pd.DataFrame:
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df[col] = df[col].fillna(0.0).astype(float)
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# 5. 处理 target 列
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# size: 已经 log 过，填充缺失值
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# size: 已经 log 过；再做全局 z-score，使其 loss 量纲与 delivery 一致
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#（R² 对仿射变换不变，这里只为多任务不确定性加权更稳，不是 R² 解药）
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if "size" in df.columns:
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df["size"] = pd.to_numeric(df["size"], errors="coerce")
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_mu, _sd = df["size"].mean(), df["size"].std()
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if _sd and _sd > 0:
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df["size"] = (df["size"] - _mu) / _sd
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# quantified_delivery: 已经 z-score 过
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if "quantified_delivery" in df.columns:
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@ -139,6 +143,12 @@ def process_dataframe(df: pd.DataFrame) -> pd.DataFrame:
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for col in TARGET_BIODIST:
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if col in df.columns:
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df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0.0)
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if all(col in df.columns for col in TARGET_BIODIST):
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bd = df[TARGET_BIODIST].values.astype(float)
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s = bd.sum(axis=1, keepdims=True)
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nz = s.squeeze(-1) > 0
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bd[nz] = bd[nz] / s[nz]
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df[TARGET_BIODIST] = bd
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return df
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@ -210,7 +220,8 @@ class LNPDataset(Dataset):
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# PDI: one-hot -> class index
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if all(col in self.df.columns for col in TARGET_CLASSIFICATION_PDI):
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pdi_onehot = self.df[TARGET_CLASSIFICATION_PDI].values
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self.pdi = np.argmax(pdi_onehot, axis=1).astype(np.int64)
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pdi_4 = np.argmax(pdi_onehot, axis=1)
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self.pdi = (pdi_4 >= 1).astype(np.int64)
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self.pdi_valid = pdi_onehot.sum(axis=1) > 0
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else:
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self.pdi = None
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@ -66,7 +66,7 @@ class MultiTaskHead(nn.Module):
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输出:
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- size: [B, 1] 回归
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- pdi: [B, 4] 分类 logits
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- pdi: [B, 2] 分类 logits
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- ee: [B, 3] 分类 logits
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- delivery: [B, 1] 回归
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- biodist: [B, 7] softmax 分布
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@ -77,10 +77,11 @@ class MultiTaskHead(nn.Module):
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super().__init__()
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# size: 回归 (log-transformed)
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self.size_head = RegressionHead(in_dim, hidden_dim, dropout)
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size_dropout = min(0.5, dropout + 0.2)
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self.size_head = RegressionHead(in_dim, hidden_dim, size_dropout)
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# PDI: 4 分类
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self.pdi_head = ClassificationHead(in_dim, num_classes=4, hidden_dim=hidden_dim, dropout=dropout)
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# PDI: 2 分类
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self.pdi_head = ClassificationHead(in_dim, num_classes=2, hidden_dim=hidden_dim, dropout=dropout)
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# Encapsulation Efficiency: 3 分类
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self.ee_head = ClassificationHead(in_dim, num_classes=3, hidden_dim=hidden_dim, dropout=dropout)
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@ -94,6 +95,12 @@ class MultiTaskHead(nn.Module):
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# toxic: 二分类
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self.toxic_head = ClassificationHead(in_dim, num_classes=2, hidden_dim=hidden_dim, dropout=dropout)
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# 不确定性加权（Kendall 2018）：每个任务一个可学习 log σ²，初始 0
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self.log_vars = nn.ParameterDict({
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t: nn.Parameter(torch.zeros(()))
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for t in ["size", "delivery", "pdi", "ee", "toxic", "biodist"]
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})
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def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
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"""
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Args:
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@ -102,7 +109,7 @@ class MultiTaskHead(nn.Module):
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Returns:
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Dict with keys:
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- "size": [B, 1]
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- "pdi": [B, 4] logits
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- "pdi": [B, 2] logits
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- "ee": [B, 3] logits
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- "delivery": [B, 1]
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- "biodist": [B, 7] probabilities (sum=1)
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@ -1,6 +1,16 @@
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from lnp_ml.modeling.layers.token_projector import TokenProjector
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from lnp_ml.modeling.layers.bidirectional_cross_attention import CrossModalAttention
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from lnp_ml.modeling.layers.fusion import FusionLayer
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from lnp_ml.modeling.layers.set_transformer import SetTransformer
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from lnp_ml.modeling.layers.fusion import FusionLayer, ResidualConcatFusion
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from lnp_ml.modeling.layers.moe import MoEBlock
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from lnp_ml.modeling.layers.llm_prompt import LLMPromptEncoder
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__all__ = ["TokenProjector", "CrossModalAttention", "FusionLayer", "MoEBlock"]
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__all__ = [
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"TokenProjector",
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"CrossModalAttention",
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"SetTransformer",
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"FusionLayer",
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"ResidualConcatFusion",
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"MoEBlock",
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"LLMPromptEncoder",
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]
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@ -1,7 +1,7 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from typing import Dict, Literal, Tuple, Union
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from typing import Dict, List, Literal, Optional, Tuple, Union
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PoolingStrategy = Literal["concat", "avg", "max", "attention"]
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@ -109,3 +109,44 @@ class FusionLayer(nn.Module):
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if return_attn_weights:
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return out, attn_weights.squeeze(1) # [B, n_tokens]
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return out
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class ResidualConcatFusion(nn.Module):
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"""对真实 token 做 attention pooling，再用零初始化门把 MoE/LLM 旁路以残差方式加入。
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g_moe / g_llm 初始为 0 → +moe/+llm 起点严格等于 baseline；
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旁路只有确实有用时才会被训练打开，从机制上保证“加了不会更差”。
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"""
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def __init__(self, d_model: int, strategy: PoolingStrategy = "attention") -> None:
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super().__init__()
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if strategy == "concat":
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raise ValueError("ResidualConcatFusion 不支持 concat（token 数随开关变化）")
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self.d_model = d_model
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self.pool = FusionLayer(d_model=d_model, n_tokens=1, strategy=strategy)
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self.fusion_dim = self.pool.fusion_dim
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# 零初始化门控（可学习标量），旁路初始不参与
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self.g_moe = nn.Parameter(torch.zeros(()))
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self.g_llm = nn.Parameter(torch.zeros(()))
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def forward(
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self,
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chem: torch.Tensor,
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tab: torch.Tensor,
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f_moe: Optional[torch.Tensor] = None,
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f_llm: Optional[torch.Tensor] = None,
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return_attn_weights: bool = False,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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# 只对真实 token（chem + tab）做注意力池化，旁路不参与 softmax 竞争
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seq = torch.cat([chem, tab], dim=1) # [B, n_chem + n_cond, d_model]
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pooled = self.pool(seq, return_attn_weights=return_attn_weights)
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if return_attn_weights:
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pooled, attn = pooled
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out = pooled
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if f_moe is not None:
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out = out + self.g_moe * f_moe # 残差 + 零初始化门
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if f_llm is not None:
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out = out + self.g_llm * f_llm
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return (out, attn) if return_attn_weights else out
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123
lnp_ml/modeling/layers/set_transformer.py
Normal file
123
lnp_ml/modeling/layers/set_transformer.py
Normal file
@ -0,0 +1,123 @@
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"""Set Transformer 集合编码器。
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输入/输出形状均为 [B, n_tokens, d_model]，
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n_tokens 可变（支持 3 或 4）。
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"""
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import torch
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import torch.nn as nn
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class MAB(nn.Module):
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"""多头注意力块：MAB(Q, K) = LN(H + FFN(H))，H = LN(Q + MultiHeadAttn(Q, K, K))。"""
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def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1, ln: bool = True) -> None:
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super().__init__()
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assert d_model % num_heads == 0, "d_model 必须能被 num_heads 整除"
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self.attn = nn.MultiheadAttention(
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d_model, num_heads, dropout=dropout, batch_first=True
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)
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self.norm1 = nn.LayerNorm(d_model) if ln else nn.Identity()
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self.norm2 = nn.LayerNorm(d_model) if ln else nn.Identity()
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self.ffn = nn.Sequential(
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nn.Linear(d_model, d_model * 4),
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nn.GELU(),
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nn.Dropout(dropout),
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nn.Linear(d_model * 4, d_model),
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nn.Dropout(dropout),
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)
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def forward(self, q: torch.Tensor, k: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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q: [B, n_q, d_model]
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k: [B, n_k, d_model]
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Returns:
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[B, n_q, d_model]
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"""
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attn_out, _ = self.attn(q, k, k)
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h = self.norm1(q + attn_out)
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return self.norm2(h + self.ffn(h))
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class SAB(nn.Module):
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"""集合自注意力块：SAB(X) = MAB(X, X)。"""
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def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1, ln: bool = True) -> None:
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super().__init__()
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self.mab = MAB(d_model, num_heads, dropout, ln)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""[B, n, d_model] -> [B, n, d_model]"""
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return self.mab(x, x)
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class ISAB(nn.Module):
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"""诱导点集合注意力块：用 m 个可学习诱导点降低大集合的注意力复杂度。"""
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def __init__(
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self,
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d_model: int,
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num_heads: int,
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num_inducing: int = 16,
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dropout: float = 0.1,
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ln: bool = True,
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) -> None:
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super().__init__()
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# 可学习诱导点
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self.inducing = nn.Parameter(torch.empty(1, num_inducing, d_model))
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nn.init.xavier_uniform_(self.inducing)
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self.mab_in = MAB(d_model, num_heads, dropout, ln)
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self.mab_out = MAB(d_model, num_heads, dropout, ln)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""[B, n, d_model] -> [B, n, d_model]"""
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inducing = self.inducing.expand(x.size(0), -1, -1) # [B, m, d_model]
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h = self.mab_in(inducing, x) # [B, m, d_model]
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return self.mab_out(x, h) # [B, n, d_model]
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class SetTransformer(nn.Module):
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"""对化学 token 集合做 N 层集合自注意力，形状保持 [B, n_tokens, d_model]。"""
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def __init__(
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self,
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d_model: int,
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num_heads: int = 8,
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n_layers: int = 4,
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dropout: float = 0.1,
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block: str = "sab",
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num_inducing: int = 16,
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ln: bool = True,
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) -> None:
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"""
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Args:
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d_model: token 维度
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num_heads: 注意力头数，d_head = d_model / num_heads
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n_layers: 集合注意力层数
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dropout: dropout 比例
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block: 注意力块类型，"sab"（全自注意力）或 "isab"（诱导点）
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num_inducing: ISAB 的诱导点数量（block="isab" 时生效）
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ln: 是否使用 LayerNorm
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"""
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super().__init__()
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self.block_type = block
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self.layers = nn.ModuleList()
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for _ in range(n_layers):
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if block == "isab":
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self.layers.append(ISAB(d_model, num_heads, num_inducing, dropout, ln))
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else:
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self.layers.append(SAB(d_model, num_heads, dropout, ln))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x: [B, n_tokens, d_model] 化学 token 集合
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Returns:
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[B, n_tokens, d_model] 集合编码后的 token
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"""
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for layer in self.layers:
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x = layer(x)
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return x
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@ -31,6 +31,8 @@ def find_mpnn_ensemble_paths(base_dir: Path = DEFAULT_MPNN_ENSEMBLE_DIR) -> List
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raise FileNotFoundError(f"No model.pt files found in {base_dir}")
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return [str(p) for p in model_paths]
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from lnp_ml.modeling.models import LNPModel, LNPModelWithoutMPNN
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from lnp_ml.modeling.layers.llm_prompt import DEFAULT_MOLT5_PATH
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from lnp_ml.utils.seed import set_global_seed
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app = typer.Typer()
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@ -156,7 +158,8 @@ def pretrain(
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训练历史和最佳验证损失
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"""
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model = model.to(device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
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trainable_params = [p for p in model.parameters() if p.requires_grad]
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optimizer = torch.optim.AdamW(trainable_params, lr=lr, weight_decay=weight_decay)
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
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optimizer, mode="min", factor=0.5, patience=5
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)
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@ -217,13 +220,28 @@ def main(
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d_model: int = 256,
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num_heads: int = 8,
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n_attn_layers: int = 4,
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set_transformer_block: str = "sab", # "sab" | "isab"
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fusion_strategy: str = "attention",
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head_hidden_dim: int = 128,
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dropout: float = 0.1,
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# MoE 参数（消融开关）
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use_moe: bool = False,
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moe_n_experts: int = 4,
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moe_top_k: int = 2,
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moe_expert_hidden_mult: int = 2,
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moe_jitter_noise: float = 0.0,
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# LLM 参数（消融开关）
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use_llm: bool = False,
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llm_model_path: str = DEFAULT_MOLT5_PATH,
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llm_freeze: bool = True,
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llm_use_lora: bool = False,
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llm_lora_r: int = 8,
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llm_lora_alpha: int = 16,
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llm_lora_dropout: float = 0.05,
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# MPNN 参数（可选）
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use_mpnn: bool = False, # 启用 MPNN，自动从默认路径加载 ensemble
|
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mpnn_checkpoint: Optional[str] = None,
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mpnn_ensemble_paths: Optional[str] = None, # 逗号分隔的路径列表
|
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mpnn_ensemble_paths: Optional[str] = None,
|
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mpnn_device: str = "cpu",
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# 训练参数
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batch_size: int = 64,
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@ -231,6 +249,8 @@ def main(
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weight_decay: float = 1e-5,
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epochs: int = 50,
|
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patience: int = 10,
|
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# 随机种子
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seed: int = 42,
|
||||
# 设备
|
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device: str = "cuda" if torch.cuda.is_available() else "cpu",
|
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):
|
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@ -245,7 +265,8 @@ def main(
|
||||
- models/pretrain_delivery.pt: 包含 backbone + delivery head 权重
|
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- models/pretrain_history.json: 训练历史
|
||||
"""
|
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logger.info(f"Using device: {device}")
|
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set_global_seed(seed)
|
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logger.info(f"Using device: {device} | seed: {seed}")
|
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device_obj = torch.device(device)
|
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|
||||
# 加载已处理的 parquet 文件
|
||||
@ -280,28 +301,39 @@ def main(
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enable_mpnn = mpnn_checkpoint is not None or ensemble_paths_list is not None
|
||||
|
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# 创建模型
|
||||
logger.info(f"Creating model (use_mpnn={enable_mpnn})...")
|
||||
logger.info(
|
||||
f"Creating model (use_mpnn={enable_mpnn}, use_moe={use_moe}, use_llm={use_llm})..."
|
||||
)
|
||||
common_kwargs = dict(
|
||||
d_model=d_model,
|
||||
num_heads=num_heads,
|
||||
n_attn_layers=n_attn_layers,
|
||||
set_transformer_block=set_transformer_block,
|
||||
fusion_strategy=fusion_strategy,
|
||||
head_hidden_dim=head_hidden_dim,
|
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dropout=dropout,
|
||||
use_moe=use_moe,
|
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moe_n_experts=moe_n_experts,
|
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moe_top_k=moe_top_k,
|
||||
moe_expert_hidden_mult=moe_expert_hidden_mult,
|
||||
moe_jitter_noise=moe_jitter_noise,
|
||||
use_llm=use_llm,
|
||||
llm_model_path=llm_model_path,
|
||||
llm_freeze=llm_freeze,
|
||||
llm_use_lora=llm_use_lora,
|
||||
llm_lora_r=llm_lora_r,
|
||||
llm_lora_alpha=llm_lora_alpha,
|
||||
llm_lora_dropout=llm_lora_dropout,
|
||||
)
|
||||
if enable_mpnn:
|
||||
model = LNPModel(
|
||||
d_model=d_model,
|
||||
num_heads=num_heads,
|
||||
n_attn_layers=n_attn_layers,
|
||||
fusion_strategy=fusion_strategy,
|
||||
head_hidden_dim=head_hidden_dim,
|
||||
dropout=dropout,
|
||||
mpnn_checkpoint=mpnn_checkpoint,
|
||||
mpnn_ensemble_paths=ensemble_paths_list,
|
||||
mpnn_device=mpnn_device,
|
||||
**common_kwargs,
|
||||
)
|
||||
else:
|
||||
model = LNPModelWithoutMPNN(
|
||||
d_model=d_model,
|
||||
num_heads=num_heads,
|
||||
n_attn_layers=n_attn_layers,
|
||||
fusion_strategy=fusion_strategy,
|
||||
head_hidden_dim=head_hidden_dim,
|
||||
dropout=dropout,
|
||||
)
|
||||
model = LNPModelWithoutMPNN(**common_kwargs)
|
||||
|
||||
n_params_total = sum(p.numel() for p in model.parameters())
|
||||
n_params_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||||
@ -336,10 +368,23 @@ def main(
|
||||
"d_model": d_model,
|
||||
"num_heads": num_heads,
|
||||
"n_attn_layers": n_attn_layers,
|
||||
"set_transformer_block": set_transformer_block,
|
||||
"fusion_strategy": fusion_strategy,
|
||||
"head_hidden_dim": head_hidden_dim,
|
||||
"dropout": dropout,
|
||||
"use_mpnn": enable_mpnn,
|
||||
"use_moe": use_moe,
|
||||
"moe_n_experts": moe_n_experts,
|
||||
"moe_top_k": moe_top_k,
|
||||
"moe_expert_hidden_mult": moe_expert_hidden_mult,
|
||||
"moe_jitter_noise": moe_jitter_noise,
|
||||
"use_llm": use_llm,
|
||||
"llm_model_path": llm_model_path,
|
||||
"llm_freeze": llm_freeze,
|
||||
"llm_use_lora": llm_use_lora,
|
||||
"llm_lora_r": llm_lora_r,
|
||||
"llm_lora_alpha": llm_lora_alpha,
|
||||
"llm_lora_dropout": llm_lora_dropout,
|
||||
},
|
||||
"best_val_loss": result["best_val_loss"],
|
||||
},
|
||||
@ -398,32 +443,40 @@ def test(
|
||||
|
||||
# 解析 MPNN 配置
|
||||
enable_mpnn = config.get("use_mpnn", False)
|
||||
common_kwargs = dict(
|
||||
d_model=config["d_model"],
|
||||
num_heads=config["num_heads"],
|
||||
n_attn_layers=config["n_attn_layers"],
|
||||
set_transformer_block=config.get("set_transformer_block", "sab"),
|
||||
fusion_strategy=config["fusion_strategy"],
|
||||
head_hidden_dim=config["head_hidden_dim"],
|
||||
dropout=config["dropout"],
|
||||
use_moe=config.get("use_moe", False),
|
||||
moe_n_experts=config.get("moe_n_experts", 4),
|
||||
moe_top_k=config.get("moe_top_k", 2),
|
||||
moe_expert_hidden_mult=config.get("moe_expert_hidden_mult", 2),
|
||||
moe_jitter_noise=config.get("moe_jitter_noise", 0.0),
|
||||
use_llm=config.get("use_llm", False),
|
||||
llm_model_path=config.get("llm_model_path", DEFAULT_MOLT5_PATH),
|
||||
llm_freeze=config.get("llm_freeze", True),
|
||||
llm_use_lora=config.get("llm_use_lora", False),
|
||||
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),
|
||||
)
|
||||
if enable_mpnn or use_mpnn:
|
||||
logger.info(f"Auto-detecting MPNN ensemble from {DEFAULT_MPNN_ENSEMBLE_DIR}")
|
||||
ensemble_paths = find_mpnn_ensemble_paths()
|
||||
logger.info(f"Found {len(ensemble_paths)} MPNN models")
|
||||
|
||||
model = LNPModel(
|
||||
d_model=config["d_model"],
|
||||
num_heads=config["num_heads"],
|
||||
n_attn_layers=config["n_attn_layers"],
|
||||
fusion_strategy=config["fusion_strategy"],
|
||||
head_hidden_dim=config["head_hidden_dim"],
|
||||
dropout=config["dropout"],
|
||||
mpnn_ensemble_paths=ensemble_paths,
|
||||
mpnn_device=mpnn_device,
|
||||
**common_kwargs,
|
||||
)
|
||||
else:
|
||||
model = LNPModelWithoutMPNN(
|
||||
d_model=config["d_model"],
|
||||
num_heads=config["num_heads"],
|
||||
n_attn_layers=config["n_attn_layers"],
|
||||
fusion_strategy=config["fusion_strategy"],
|
||||
head_hidden_dim=config["head_hidden_dim"],
|
||||
dropout=config["dropout"],
|
||||
)
|
||||
model = LNPModelWithoutMPNN(**common_kwargs)
|
||||
|
||||
model.load_state_dict(checkpoint["model_state_dict"])
|
||||
model.load_state_dict(checkpoint["model_state_dict"], strict=False)
|
||||
model.to(device_obj)
|
||||
model.eval()
|
||||
|
||||
|
||||
@ -33,7 +33,7 @@ class LossWeightsBalanced:
|
||||
|
||||
def compute_class_weights_from_loader(
|
||||
loader: DataLoader,
|
||||
n_pdi_classes: int = 4,
|
||||
n_pdi_classes: int = 2,
|
||||
n_ee_classes: int = 3,
|
||||
n_toxic_classes: int = 2,
|
||||
smoothing: float = 0.1,
|
||||
@ -129,66 +129,59 @@ def compute_multitask_loss_balanced(
|
||||
task_weights = task_weights or LossWeightsBalanced()
|
||||
class_weights = class_weights or ClassWeights()
|
||||
|
||||
# 若模型 head 暴露 log_vars，则启用不确定性加权（自动平衡），否则回退到标量权重
|
||||
log_vars = None
|
||||
if model is not None and hasattr(model, "head") and hasattr(model.head, "log_vars"):
|
||||
log_vars = model.head.log_vars
|
||||
|
||||
losses = {}
|
||||
device = next(iter(outputs.values())).device
|
||||
total_loss = torch.tensor(0.0, device=device)
|
||||
|
||||
# size: MSE loss（回归任务，不需要类权重）
|
||||
def _add(name: str, raw_loss: torch.Tensor, scalar_w: float) -> None:
|
||||
nonlocal total_loss
|
||||
losses[name] = raw_loss
|
||||
if log_vars is not None and name in log_vars:
|
||||
s = log_vars[name]
|
||||
total_loss = total_loss + torch.exp(-s) * raw_loss + 0.5 * s
|
||||
else:
|
||||
total_loss = total_loss + scalar_w * raw_loss
|
||||
|
||||
# size: MSE
|
||||
if "size" in targets and mask["size"].any():
|
||||
m = mask["size"]
|
||||
pred = outputs["size"][m].squeeze(-1)
|
||||
tgt = targets["size"][m]
|
||||
losses["size"] = F.mse_loss(pred, tgt)
|
||||
total_loss = total_loss + task_weights.size * losses["size"]
|
||||
_add("size", F.mse_loss(outputs["size"][m].squeeze(-1), targets["size"][m]), task_weights.size)
|
||||
|
||||
# delivery: MSE loss（回归任务，不需要类权重）
|
||||
# delivery: MSE
|
||||
if "delivery" in targets and mask["delivery"].any():
|
||||
m = mask["delivery"]
|
||||
pred = outputs["delivery"][m].squeeze(-1)
|
||||
tgt = targets["delivery"][m]
|
||||
losses["delivery"] = F.mse_loss(pred, tgt)
|
||||
total_loss = total_loss + task_weights.delivery * losses["delivery"]
|
||||
_add("delivery", F.mse_loss(outputs["delivery"][m].squeeze(-1), targets["delivery"][m]), task_weights.delivery)
|
||||
|
||||
# pdi: CrossEntropy with class weights
|
||||
# pdi: 类加权 CE
|
||||
if "pdi" in targets and mask["pdi"].any():
|
||||
m = mask["pdi"]
|
||||
pred = outputs["pdi"][m]
|
||||
tgt = targets["pdi"][m]
|
||||
weight = class_weights.pdi.to(device) if class_weights.pdi is not None else None
|
||||
losses["pdi"] = F.cross_entropy(pred, tgt, weight=weight)
|
||||
total_loss = total_loss + task_weights.pdi * losses["pdi"]
|
||||
w = class_weights.pdi.to(device) if class_weights.pdi is not None else None
|
||||
_add("pdi", F.cross_entropy(outputs["pdi"][m], targets["pdi"][m], weight=w), task_weights.pdi)
|
||||
|
||||
# ee: CrossEntropy with class weights
|
||||
# ee: 类加权 CE
|
||||
if "ee" in targets and mask["ee"].any():
|
||||
m = mask["ee"]
|
||||
pred = outputs["ee"][m]
|
||||
tgt = targets["ee"][m]
|
||||
weight = class_weights.ee.to(device) if class_weights.ee is not None else None
|
||||
losses["ee"] = F.cross_entropy(pred, tgt, weight=weight)
|
||||
total_loss = total_loss + task_weights.ee * losses["ee"]
|
||||
w = class_weights.ee.to(device) if class_weights.ee is not None else None
|
||||
_add("ee", F.cross_entropy(outputs["ee"][m], targets["ee"][m], weight=w), task_weights.ee)
|
||||
|
||||
# toxic: CrossEntropy with class weights
|
||||
# toxic: 类加权 CE
|
||||
if "toxic" in targets and mask["toxic"].any():
|
||||
m = mask["toxic"]
|
||||
pred = outputs["toxic"][m]
|
||||
tgt = targets["toxic"][m]
|
||||
weight = class_weights.toxic.to(device) if class_weights.toxic is not None else None
|
||||
losses["toxic"] = F.cross_entropy(pred, tgt, weight=weight)
|
||||
total_loss = total_loss + task_weights.toxic * losses["toxic"]
|
||||
w = class_weights.toxic.to(device) if class_weights.toxic is not None else None
|
||||
_add("toxic", F.cross_entropy(outputs["toxic"][m], targets["toxic"][m], weight=w), task_weights.toxic)
|
||||
|
||||
# biodist: KL divergence（分布任务，不需要类权重）
|
||||
# biodist: KL
|
||||
if "biodist" in targets and mask["biodist"].any():
|
||||
m = mask["biodist"]
|
||||
pred = outputs["biodist"][m]
|
||||
tgt = targets["biodist"][m]
|
||||
losses["biodist"] = F.kl_div(
|
||||
pred.log().clamp(min=-100),
|
||||
tgt,
|
||||
reduction="batchmean",
|
||||
)
|
||||
total_loss = total_loss + task_weights.biodist * losses["biodist"]
|
||||
kl = F.kl_div(outputs["biodist"][m].log().clamp(min=-100), targets["biodist"][m], reduction="batchmean")
|
||||
_add("biodist", kl, task_weights.biodist)
|
||||
|
||||
# MoE load-balancing aux loss（可选）
|
||||
# MoE load-balancing aux loss（固定小权重，不纳入不确定性加权）
|
||||
if model is not None and hasattr(model, "get_last_moe_extras"):
|
||||
extras = model.get_last_moe_extras()
|
||||
if extras is not None and "lb_loss" in extras:
|
||||
@ -295,7 +288,20 @@ def validate_balanced(
|
||||
return metrics
|
||||
|
||||
|
||||
BACKBONE_PREFIXES = ("token_projector.", "cross_attention.", "fusion.", "moe.")
|
||||
BACKBONE_PREFIXES = (
|
||||
"token_projector.",
|
||||
"set_transformer.",
|
||||
"fusion.",
|
||||
"moe.",
|
||||
"llm_prompt.",
|
||||
)
|
||||
|
||||
FROM_SCRATCH_PREFIXES = (
|
||||
"moe.",
|
||||
"llm_prompt.",
|
||||
"fusion.g_moe",
|
||||
"fusion.g_llm",
|
||||
)
|
||||
|
||||
|
||||
def build_optimizer(
|
||||
@ -303,32 +309,49 @@ def build_optimizer(
|
||||
lr: float,
|
||||
weight_decay: float,
|
||||
backbone_lr_ratio: float = 1.0,
|
||||
size_wd_mult: float = 5.0,
|
||||
) -> torch.optim.AdamW:
|
||||
"""
|
||||
构建 AdamW 优化器，支持分层学习率。
|
||||
|
||||
仅收集 requires_grad=True 的参数（冻结的 MolT5 encoder 被排除）。
|
||||
当 backbone_lr_ratio < 1.0 时，backbone 参数使用 lr * backbone_lr_ratio，
|
||||
其余参数（task heads 等）使用 lr。backbone_lr_ratio = 1.0 等价于统一学习率。
|
||||
size_head 信号弱、易过拟合 -> 单独施加 size_wd_mult 倍 weight_decay。
|
||||
"""
|
||||
if backbone_lr_ratio >= 1.0:
|
||||
return torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
|
||||
trainable = [(n, p) for n, p in model.named_parameters() if p.requires_grad]
|
||||
size_wd = weight_decay * size_wd_mult
|
||||
|
||||
backbone_params = []
|
||||
head_params = []
|
||||
for name, param in model.named_parameters():
|
||||
if not param.requires_grad:
|
||||
def is_size(name: str) -> bool:
|
||||
return "size_head" in name
|
||||
|
||||
size_params = [p for n, p in trainable if is_size(n)]
|
||||
|
||||
if backbone_lr_ratio >= 1.0:
|
||||
other = [p for n, p in trainable if not is_size(n)]
|
||||
return torch.optim.AdamW(
|
||||
[
|
||||
{"params": other, "weight_decay": weight_decay},
|
||||
{"params": size_params, "weight_decay": size_wd},
|
||||
],
|
||||
lr=lr,
|
||||
)
|
||||
|
||||
backbone_params, head_params = [], []
|
||||
for name, param in trainable:
|
||||
if is_size(name):
|
||||
continue
|
||||
if name.startswith(BACKBONE_PREFIXES):
|
||||
if name.startswith(BACKBONE_PREFIXES) and not name.startswith(FROM_SCRATCH_PREFIXES):
|
||||
backbone_params.append(param)
|
||||
else:
|
||||
head_params.append(param)
|
||||
|
||||
return torch.optim.AdamW(
|
||||
[
|
||||
{"params": backbone_params, "lr": lr * backbone_lr_ratio},
|
||||
{"params": head_params, "lr": lr},
|
||||
{"params": backbone_params, "lr": lr * backbone_lr_ratio, "weight_decay": weight_decay},
|
||||
{"params": head_params, "lr": lr, "weight_decay": weight_decay},
|
||||
{"params": size_params, "lr": lr, "weight_decay": size_wd},
|
||||
],
|
||||
weight_decay=weight_decay,
|
||||
)
|
||||
|
||||
|
||||
@ -442,6 +465,7 @@ def train_fixed_epochs(
|
||||
use_swa: bool = False,
|
||||
swa_start_epoch: Optional[int] = None,
|
||||
backbone_lr_ratio: float = 1.0,
|
||||
freeze_backbone_epochs: int = 0,
|
||||
) -> Dict:
|
||||
"""
|
||||
固定 epoch 数的训练（不使用 early stopping）。
|
||||
@ -469,6 +493,15 @@ def train_fixed_epochs(
|
||||
model = model.to(device)
|
||||
optimizer = build_optimizer(model, lr, weight_decay, backbone_lr_ratio)
|
||||
|
||||
backbone_named = [
|
||||
(n, p) for n, p in model.named_parameters()
|
||||
if n.startswith(BACKBONE_PREFIXES) and not n.startswith(FROM_SCRATCH_PREFIXES)
|
||||
and p.requires_grad
|
||||
]
|
||||
if freeze_backbone_epochs > 0:
|
||||
for _, p in backbone_named:
|
||||
p.requires_grad_(False)
|
||||
|
||||
if use_cosine_annealing:
|
||||
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
|
||||
else:
|
||||
@ -486,6 +519,9 @@ def train_fixed_epochs(
|
||||
history = {"train": [], "val": []}
|
||||
|
||||
for epoch in range(epochs):
|
||||
if freeze_backbone_epochs > 0 and epoch == freeze_backbone_epochs:
|
||||
for _, p in backbone_named:
|
||||
p.requires_grad_(True)
|
||||
# Train
|
||||
train_metrics = train_epoch_balanced(
|
||||
model, train_loader, optimizer, device, task_weights, class_weights
|
||||
|
||||
0
lnp_ml/utils/__init__.py
Normal file
0
lnp_ml/utils/__init__.py
Normal file
19
lnp_ml/utils/seed.py
Normal file
19
lnp_ml/utils/seed.py
Normal file
@ -0,0 +1,19 @@
|
||||
"""全局随机种子工具。"""
|
||||
|
||||
import random
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
|
||||
def set_global_seed(seed: int) -> None:
|
||||
"""固定 random / numpy / torch 种子，提升可复现性。
|
||||
|
||||
不开启 cudnn deterministic，以免显著拖慢训练；如需严格复现可自行追加：
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
"""
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
@ -0,0 +1,12 @@
|
||||
{
|
||||
"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"
|
||||
}
|
||||
@ -0,0 +1 @@
|
||||
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60
models/abl/baseline/20260617_180556/strata_info.json
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60
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932
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Normal file
932
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Normal file
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1
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1
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Normal file
@ -0,0 +1 @@
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1
models/abl/both/20260617_232841/outer_fold_1/splits.json
Normal file
1
models/abl/both/20260617_232841/outer_fold_1/splits.json
Normal file
@ -0,0 +1 @@
|
||||
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1
models/abl/both/20260617_232841/outer_fold_2/splits.json
Normal file
1
models/abl/both/20260617_232841/outer_fold_2/splits.json
Normal file
@ -0,0 +1 @@
|
||||
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1
models/abl/both/20260617_232841/outer_fold_3/splits.json
Normal file
1
models/abl/both/20260617_232841/outer_fold_3/splits.json
Normal file
@ -0,0 +1 @@
|
||||
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1
models/abl/both/20260617_232841/outer_fold_4/splits.json
Normal file
1
models/abl/both/20260617_232841/outer_fold_4/splits.json
Normal file
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@ -0,0 +1,42 @@
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@ -0,0 +1,16 @@
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@ -0,0 +1 @@
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@ -0,0 +1,42 @@
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@ -0,0 +1,16 @@
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||||
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Reference in New Issue
Block a user