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https://github.com/RYDE-WORK/lnp_ml.git
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feat: 同步 RAG 融合与训练核心代码(fusion 支持 f_retr、models/trainer/moe/dataset)
This commit is contained in:
DicongLi
parent
3898fb8973
commit
987ecf6386
@ -1,152 +1,156 @@
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import torch
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import torch
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import torch.nn as nn
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.functional as F
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from typing import Dict, List, Literal, Optional, 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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PoolingStrategy = Literal["concat", "avg", "max", "attention"]
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class FusionLayer(nn.Module):
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class FusionLayer(nn.Module):
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"""
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"""
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将多个 token 融合成单个向量。
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将多个 token 融合成单个向量。
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输入: Dict[str, Tensor] 或 [B, n_tokens, d_model]
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输入: Dict[str, Tensor] 或 [B, n_tokens, d_model]
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输出: [B, fusion_dim]
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输出: [B, fusion_dim]
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策略:
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策略:
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- concat: [B, n_tokens, d_model] -> [B, n_tokens * d_model]
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- concat: [B, n_tokens, d_model] -> [B, n_tokens * d_model]
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- avg: [B, n_tokens, d_model] -> [B, d_model]
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- avg: [B, n_tokens, d_model] -> [B, d_model]
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- max: [B, n_tokens, d_model] -> [B, d_model]
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- max: [B, n_tokens, d_model] -> [B, d_model]
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- attention: [B, n_tokens, d_model] -> [B, d_model] (learnable attention pooling)
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- attention: [B, n_tokens, d_model] -> [B, d_model] (learnable attention pooling)
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"""
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"""
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def __init__(
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def __init__(
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self,
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self,
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d_model: int,
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d_model: int,
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n_tokens: int,
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n_tokens: int,
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strategy: PoolingStrategy = "attention",
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strategy: PoolingStrategy = "attention",
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) -> None:
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) -> None:
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"""
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"""
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Args:
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Args:
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d_model: 每个 token 的维度
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d_model: 每个 token 的维度
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n_tokens: token 数量（如 8）
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n_tokens: token 数量（如 8）
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strategy: 融合策略
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strategy: 融合策略
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"""
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"""
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super().__init__()
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super().__init__()
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self.d_model = d_model
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self.d_model = d_model
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self.n_tokens = n_tokens
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self.n_tokens = n_tokens
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self.strategy = strategy
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self.strategy = strategy
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if strategy == "concat":
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if strategy == "concat":
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self.fusion_dim = n_tokens * d_model
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self.fusion_dim = n_tokens * d_model
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else:
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else:
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self.fusion_dim = d_model
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self.fusion_dim = d_model
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# Attention pooling: learnable query
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# Attention pooling: learnable query
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if strategy == "attention":
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if strategy == "attention":
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self.attn_query = nn.Parameter(torch.randn(1, 1, d_model))
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self.attn_query = nn.Parameter(torch.randn(1, 1, d_model))
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self.attn_proj = nn.Linear(d_model, d_model)
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self.attn_proj = nn.Linear(d_model, d_model)
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def forward(
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def forward(
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self,
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self,
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x: Union[Dict[str, torch.Tensor], torch.Tensor],
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x: Union[Dict[str, torch.Tensor], torch.Tensor],
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return_attn_weights: bool = False,
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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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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""
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"""
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Args:
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Args:
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x: Dict[str, Tensor] 每个 [B, d_model]，或已 stack 的 [B, n_tokens, d_model]
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x: Dict[str, Tensor] 每个 [B, d_model]，或已 stack 的 [B, n_tokens, d_model]
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return_attn_weights: 若为 True 且策略为 attention，额外返回 attn_weights [B, n_tokens]
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return_attn_weights: 若为 True 且策略为 attention，额外返回 attn_weights [B, n_tokens]
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Returns:
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Returns:
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return_attn_weights=False: [B, fusion_dim]
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return_attn_weights=False: [B, fusion_dim]
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return_attn_weights=True: ([B, fusion_dim], [B, n_tokens])
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return_attn_weights=True: ([B, fusion_dim], [B, n_tokens])
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"""
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"""
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if isinstance(x, dict):
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if isinstance(x, dict):
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x = torch.stack(list(x.values()), dim=1)
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x = torch.stack(list(x.values()), dim=1)
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if self.strategy == "concat":
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if self.strategy == "concat":
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out = x.flatten(start_dim=1)
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out = x.flatten(start_dim=1)
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return (out, None) if return_attn_weights else out
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return (out, None) if return_attn_weights else out
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elif self.strategy == "avg":
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elif self.strategy == "avg":
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out = x.mean(dim=1)
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out = x.mean(dim=1)
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return (out, None) if return_attn_weights else out
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return (out, None) if return_attn_weights else out
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elif self.strategy == "max":
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elif self.strategy == "max":
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out = x.max(dim=1).values
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out = x.max(dim=1).values
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return (out, None) if return_attn_weights else out
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return (out, None) if return_attn_weights else out
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elif self.strategy == "attention":
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elif self.strategy == "attention":
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return self._attention_pooling(x, return_attn_weights)
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return self._attention_pooling(x, return_attn_weights)
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else:
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else:
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raise ValueError(f"Unknown strategy: {self.strategy}")
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raise ValueError(f"Unknown strategy: {self.strategy}")
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def _attention_pooling(
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def _attention_pooling(
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self, x: torch.Tensor, return_attn_weights: bool = False,
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self, x: torch.Tensor, return_attn_weights: bool = False,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""
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"""
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Attention pooling: 用可学习 query 对 tokens 做加权求和
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Attention pooling: 用可学习 query 对 tokens 做加权求和
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Args:
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Args:
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x: [B, n_tokens, d_model]
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x: [B, n_tokens, d_model]
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return_attn_weights: 是否返回权重
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return_attn_weights: 是否返回权重
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Returns:
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Returns:
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return_attn_weights=False: [B, d_model]
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return_attn_weights=False: [B, d_model]
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return_attn_weights=True: ([B, d_model], [B, n_tokens])
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return_attn_weights=True: ([B, d_model], [B, n_tokens])
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"""
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"""
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B = x.size(0)
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B = x.size(0)
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query = self.attn_query.expand(B, -1, -1)
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query = self.attn_query.expand(B, -1, -1)
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keys = self.attn_proj(x)
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keys = self.attn_proj(x)
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scores = torch.bmm(query, keys.transpose(1, 2)) / (self.d_model ** 0.5)
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scores = torch.bmm(query, keys.transpose(1, 2)) / (self.d_model ** 0.5)
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attn_weights = F.softmax(scores, dim=-1) # [B, 1, n_tokens]
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attn_weights = F.softmax(scores, dim=-1) # [B, 1, n_tokens]
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out = torch.bmm(attn_weights, x).squeeze(1) # [B, d_model]
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out = torch.bmm(attn_weights, x).squeeze(1) # [B, d_model]
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if return_attn_weights:
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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, attn_weights.squeeze(1) # [B, n_tokens]
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return out
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return out
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class ResidualConcatFusion(nn.Module):
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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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"""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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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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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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"""
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def __init__(self, d_model: int, strategy: PoolingStrategy = "attention") -> None:
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def __init__(self, d_model: int, strategy: PoolingStrategy = "attention") -> None:
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super().__init__()
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super().__init__()
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if strategy == "concat":
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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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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.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.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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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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# 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_moe = nn.Parameter(torch.zeros(()))
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self.g_llm = nn.Parameter(torch.zeros(()))
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self.g_llm = nn.Parameter(torch.zeros(()))
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self.g_retr = nn.Parameter(torch.zeros(())) # 检索旁路零初始化门控
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def forward(
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self,
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def forward(
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chem: torch.Tensor,
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self,
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tab: torch.Tensor,
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chem: torch.Tensor,
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f_moe: Optional[torch.Tensor] = None,
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tab: torch.Tensor,
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f_llm: Optional[torch.Tensor] = None,
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f_moe: Optional[torch.Tensor] = None,
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return_attn_weights: bool = False,
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f_llm: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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f_retr: Optional[torch.Tensor] = None,
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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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return_attn_weights: bool = False,
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seq = torch.cat([chem, tab], dim=1) # [B, n_chem + n_cond, d_model]
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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pooled = self.pool(seq, return_attn_weights=return_attn_weights)
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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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if return_attn_weights:
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seq = torch.cat([chem, tab], dim=1) # [B, n_chem + n_cond, d_model]
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pooled, attn = pooled
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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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out = pooled
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pooled, attn = 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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out = pooled
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if f_llm is not None:
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if f_moe is not None:
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out = out + self.g_llm * f_llm
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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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if f_retr is not None:
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out = out + self.g_retr * f_retr # 检索旁路，零初始化门保证起点=不开
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return (out, attn) if return_attn_weights else out
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return (out, attn) if return_attn_weights else out
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@ -1,229 +1,229 @@
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"""
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"""
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MoE (Mixture-of-Experts) 模块：sample-level + 跨模态路由。
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MoE (Mixture-of-Experts) 模块：sample-level + 跨模态路由。
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设计要点（与现有 8-token 架构对齐）：
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设计要点（与现有 8-token 架构对齐）：
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- Router 输入：tab token 池化后的 [B, d]（即配方/实验条件向量）。
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- Router 输入：tab token 池化后的 [B, d]（即配方/实验条件向量）。
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- Expert 输入：chem token flatten 后的 [B, T_chem * d]（化学侧 token）。
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- Expert 输入：chem token flatten 后的 [B, T_chem * d]（化学侧 token）。
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- 路由粒度：每个样本只算一次 router → gates [B, K]。
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- 路由粒度：每个样本只算一次 router → gates [B, K]。
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- Top-k 稀疏激活 + 可选训练态 jitter 噪声。
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- Top-k 稀疏激活 + 可选训练态 jitter 噪声。
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- 返回 load-balancing aux loss，由 trainer 端按权重加进总 loss。
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- 返回 load-balancing aux loss，由 trainer 端按权重加进总 loss。
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输出:
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输出:
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F_moe: [B, d_model] 与单个 token 同维度，便于追加到 fusion 序列中。
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F_moe: [B, d_model] 与单个 token 同维度，便于追加到 fusion 序列中。
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extras: dict 诊断与监控信息（aux loss、gates 等）。
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extras: dict 诊断与监控信息（aux loss、gates 等）。
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"""
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"""
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from typing import Dict, Tuple
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from typing import Dict, Tuple
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import torch
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import torch
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import torch.nn as nn
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.functional as F
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class MoEAttentionPool(nn.Module):
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class MoEAttentionPool(nn.Module):
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"""与 FusionLayer 同款的 attention pooling，参数独立。
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"""与 FusionLayer 同款的 attention pooling，参数独立。
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将一组 token [B, T, d] 池化成单个查询向量 [B, d]，用作 router 的输入。
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将一组 token [B, T, d] 池化成单个查询向量 [B, d]，用作 router 的输入。
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"""
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"""
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def __init__(self, d_model: int) -> None:
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def __init__(self, d_model: int) -> None:
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super().__init__()
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super().__init__()
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self.d_model = d_model
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self.d_model = d_model
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self.query = nn.Parameter(torch.randn(1, 1, d_model))
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self.query = nn.Parameter(torch.randn(1, 1, d_model))
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self.proj = nn.Linear(d_model, d_model)
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self.proj = nn.Linear(d_model, d_model)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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"""
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Args:
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Args:
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x: [B, T, d_model]
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x: [B, T, d_model]
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Returns:
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Returns:
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[B, d_model]
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[B, d_model]
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"""
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"""
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B = x.size(0)
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B = x.size(0)
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q = self.query.expand(B, -1, -1) # [B, 1, d]
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q = self.query.expand(B, -1, -1) # [B, 1, d]
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k = self.proj(x) # [B, T, d]
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k = self.proj(x) # [B, T, d]
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scores = torch.bmm(q, k.transpose(1, 2)) / (self.d_model ** 0.5)
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scores = torch.bmm(q, k.transpose(1, 2)) / (self.d_model ** 0.5)
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weights = F.softmax(scores, dim=-1) # [B, 1, T]
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weights = F.softmax(scores, dim=-1) # [B, 1, T]
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return torch.bmm(weights, x).squeeze(1) # [B, d]
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return torch.bmm(weights, x).squeeze(1) # [B, d]
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class MoERouter(nn.Module):
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class MoERouter(nn.Module):
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"""单层 softmax router + Top-k 稀疏激活。
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"""单层 softmax router + Top-k 稀疏激活。
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Args:
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Args:
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d_model: router 输入维度。
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d_model: router 输入维度。
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n_experts: 专家数量 K。
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n_experts: 专家数量 K。
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top_k: 每个样本激活的专家数。
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top_k: 每个样本激活的专家数。
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jitter_noise: 训练态加在 logits 上的均匀噪声幅度，用于探索；评估态自动关闭。
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jitter_noise: 训练态加在 logits 上的均匀噪声幅度，用于探索；评估态自动关闭。
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"""
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"""
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def __init__(
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def __init__(
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self,
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self,
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d_model: int,
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d_model: int,
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n_experts: int,
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n_experts: int,
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top_k: int = 2,
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top_k: int = 2,
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jitter_noise: float = 0.0,
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jitter_noise: float = 0.0,
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||||||
) -> None:
|
) -> None:
|
||||||
super().__init__()
|
super().__init__()
|
||||||
if not 1 <= top_k <= n_experts:
|
if not 1 <= top_k <= n_experts:
|
||||||
raise ValueError(f"top_k 必须在 [1, n_experts={n_experts}]，收到 {top_k}")
|
raise ValueError(f"top_k 必须在 [1, n_experts={n_experts}]，收到 {top_k}")
|
||||||
self.n_experts = n_experts
|
self.n_experts = n_experts
|
||||||
self.top_k = top_k
|
self.top_k = top_k
|
||||||
self.jitter_noise = jitter_noise
|
self.jitter_noise = jitter_noise
|
||||||
self.linear = nn.Linear(d_model, n_experts)
|
self.linear = nn.Linear(d_model, n_experts)
|
||||||
|
|
||||||
def forward(
|
def forward(
|
||||||
self, q: torch.Tensor,
|
self, q: torch.Tensor,
|
||||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||||
"""
|
"""
|
||||||
Args:
|
Args:
|
||||||
q: [B, d_model] 路由查询向量。
|
q: [B, d_model] 路由查询向量。
|
||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
gates: [B, n_experts] top-k 后再归一化的稀疏概率。
|
gates: [B, n_experts] top-k 后再归一化的稀疏概率。
|
||||||
probs_full: [B, n_experts] 未掩码的 softmax 概率（用于 aux loss）。
|
probs_full: [B, n_experts] 未掩码的 softmax 概率（用于 aux loss）。
|
||||||
expert_mask: [B, n_experts] 0/1 掩码，标记被激活的专家。
|
expert_mask: [B, n_experts] 0/1 掩码，标记被激活的专家。
|
||||||
"""
|
"""
|
||||||
logits = self.linear(q) # [B, K]
|
logits = self.linear(q) # [B, K]
|
||||||
if self.training and self.jitter_noise > 0.0:
|
if self.training and self.jitter_noise > 0.0:
|
||||||
noise = (torch.rand_like(logits) - 0.5) * self.jitter_noise
|
noise = (torch.rand_like(logits) - 0.5) * self.jitter_noise
|
||||||
logits = logits + noise
|
logits = logits + noise
|
||||||
|
|
||||||
probs_full = F.softmax(logits, dim=-1) # [B, K]
|
probs_full = F.softmax(logits, dim=-1) # [B, K]
|
||||||
|
|
||||||
topk_vals, topk_idx = probs_full.topk(self.top_k, dim=-1) # [B, k]
|
topk_vals, topk_idx = probs_full.topk(self.top_k, dim=-1) # [B, k]
|
||||||
expert_mask = torch.zeros_like(probs_full)
|
expert_mask = torch.zeros_like(probs_full)
|
||||||
expert_mask.scatter_(1, topk_idx, 1.0)
|
expert_mask.scatter_(1, topk_idx, 1.0)
|
||||||
|
|
||||||
gates = probs_full * expert_mask
|
gates = probs_full * expert_mask
|
||||||
gates = gates / gates.sum(dim=-1, keepdim=True).clamp(min=1e-9)
|
gates = gates / gates.sum(dim=-1, keepdim=True).clamp(min=1e-9)
|
||||||
return gates, probs_full, expert_mask
|
return gates, probs_full, expert_mask
|
||||||
|
|
||||||
|
|
||||||
class MoEExpert(nn.Module):
|
class MoEExpert(nn.Module):
|
||||||
"""单个专家 MLP：in_dim → hidden_dim → out_dim。"""
|
"""单个专家 MLP：in_dim → hidden_dim → out_dim。"""
|
||||||
|
|
||||||
def __init__(
|
def __init__(
|
||||||
self, in_dim: int, hidden_dim: int, out_dim: int, dropout: float = 0.1,
|
self, in_dim: int, hidden_dim: int, out_dim: int, dropout: float = 0.1,
|
||||||
) -> None:
|
) -> None:
|
||||||
super().__init__()
|
super().__init__()
|
||||||
self.net = nn.Sequential(
|
self.net = nn.Sequential(
|
||||||
nn.Linear(in_dim, hidden_dim),
|
nn.Linear(in_dim, hidden_dim),
|
||||||
nn.GELU(),
|
nn.GELU(),
|
||||||
nn.Dropout(dropout),
|
nn.Dropout(dropout),
|
||||||
nn.Linear(hidden_dim, out_dim),
|
nn.Linear(hidden_dim, out_dim),
|
||||||
)
|
)
|
||||||
|
|
||||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||||
return self.net(x)
|
return self.net(x)
|
||||||
|
|
||||||
|
|
||||||
class MoEBlock(nn.Module):
|
class MoEBlock(nn.Module):
|
||||||
"""
|
"""
|
||||||
Sample-level 跨模态 MoE。
|
Sample-level 跨模态 MoE。
|
||||||
|
|
||||||
流程:
|
流程:
|
||||||
1. tab tokens [B, T_tab, d] -> attention pool -> q_tab [B, d]
|
1. tab tokens [B, T_tab, d] -> attention pool -> q_tab [B, d]
|
||||||
2. q_tab -> Router -> gates [B, K] (+ probs_full / expert_mask)
|
2. q_tab -> Router -> gates [B, K] (+ probs_full / expert_mask)
|
||||||
3. chem tokens [B, T_chem, d] -> flatten -> [B, T_chem * d]
|
3. chem tokens [B, T_chem, d] -> flatten -> [B, T_chem * d]
|
||||||
4. K 个 Expert MLP 并行处理 -> stack [B, K, d]
|
4. K 个 Expert MLP 并行处理 -> stack [B, K, d]
|
||||||
5. 加权求和 -> F_moe [B, d]
|
5. 加权求和 -> F_moe [B, d]
|
||||||
6. 计算 load-balancing aux loss
|
6. 计算 load-balancing aux loss
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
d_model: token 维度。
|
d_model: token 维度。
|
||||||
n_chem_tokens: chem 侧 token 数（决定 expert 输入维度）。
|
n_chem_tokens: chem 侧 token 数（决定 expert 输入维度）。
|
||||||
n_experts: 专家数量 K。
|
n_experts: 专家数量 K。
|
||||||
top_k: 每个样本激活的专家数。
|
top_k: 每个样本激活的专家数。
|
||||||
expert_hidden_mult: expert 中间层维度 = expert_hidden_mult * d_model。
|
expert_hidden_mult: expert 中间层维度 = expert_hidden_mult * d_model。
|
||||||
dropout: expert 内部 dropout。
|
dropout: expert 内部 dropout。
|
||||||
jitter_noise: router 训练态噪声幅度，0.0 表示关闭。
|
jitter_noise: router 训练态噪声幅度，0.0 表示关闭。
|
||||||
"""
|
"""
|
||||||
|
|
||||||
def __init__(
|
def __init__(
|
||||||
self,
|
self,
|
||||||
d_model: int,
|
d_model: int,
|
||||||
n_chem_tokens: int = 4,
|
n_chem_tokens: int = 4,
|
||||||
n_experts: int = 4,
|
n_experts: int = 4,
|
||||||
top_k: int = 2,
|
top_k: int = 2,
|
||||||
expert_hidden_mult: int = 2,
|
expert_hidden_mult: int = 2,
|
||||||
dropout: float = 0.1,
|
dropout: float = 0.1,
|
||||||
jitter_noise: float = 0.0,
|
jitter_noise: float = 0.0,
|
||||||
) -> None:
|
) -> None:
|
||||||
super().__init__()
|
super().__init__()
|
||||||
self.d_model = d_model
|
self.d_model = d_model
|
||||||
self.n_chem_tokens = n_chem_tokens
|
self.n_chem_tokens = n_chem_tokens
|
||||||
self.n_experts = n_experts
|
self.n_experts = n_experts
|
||||||
self.top_k = top_k
|
self.top_k = top_k
|
||||||
|
|
||||||
self.tab_pool = MoEAttentionPool(d_model)
|
self.tab_pool = MoEAttentionPool(d_model)
|
||||||
self.router = MoERouter(
|
self.router = MoERouter(
|
||||||
d_model, n_experts, top_k=top_k, jitter_noise=jitter_noise,
|
d_model, n_experts, top_k=top_k, jitter_noise=jitter_noise,
|
||||||
)
|
)
|
||||||
|
|
||||||
expert_in = d_model * n_chem_tokens
|
expert_in = d_model * n_chem_tokens
|
||||||
expert_hidden = d_model * expert_hidden_mult
|
expert_hidden = d_model * expert_hidden_mult
|
||||||
self.experts = nn.ModuleList([
|
self.experts = nn.ModuleList([
|
||||||
MoEExpert(expert_in, expert_hidden, d_model, dropout=dropout)
|
MoEExpert(expert_in, expert_hidden, d_model, dropout=dropout)
|
||||||
for _ in range(n_experts)
|
for _ in range(n_experts)
|
||||||
])
|
])
|
||||||
|
|
||||||
def forward(
|
def forward(
|
||||||
self,
|
self,
|
||||||
chem: torch.Tensor,
|
chem: torch.Tensor,
|
||||||
tab: torch.Tensor,
|
tab: torch.Tensor,
|
||||||
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
|
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
|
||||||
"""
|
"""
|
||||||
Args:
|
Args:
|
||||||
chem: [B, T_chem, d_model] 化学侧 token（router 不看）。
|
chem: [B, T_chem, d_model] 化学侧 token（router 不看）。
|
||||||
tab: [B, T_tab, d_model] 配方/实验侧 token（router 看）。
|
tab: [B, T_tab, d_model] 配方/实验侧 token（router 看）。
|
||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
F_moe: [B, d_model]
|
F_moe: [B, d_model]
|
||||||
extras: {
|
extras: {
|
||||||
"lb_loss": 标量 load-balancing aux loss（带梯度），
|
"lb_loss": 标量 load-balancing aux loss（带梯度），
|
||||||
"gates": [B, K] 稀疏归一后的门控（detached），
|
"gates": [B, K] 稀疏归一后的门控（detached），
|
||||||
"probs": [B, K] 原始 softmax 概率（detached），
|
"probs": [B, K] 原始 softmax 概率（detached），
|
||||||
}
|
}
|
||||||
"""
|
"""
|
||||||
if chem.size(1) != self.n_chem_tokens:
|
if chem.size(1) != self.n_chem_tokens:
|
||||||
raise ValueError(
|
raise ValueError(
|
||||||
f"chem token 数不匹配：期望 {self.n_chem_tokens}, "
|
f"chem token 数不匹配：期望 {self.n_chem_tokens}, "
|
||||||
f"实际 {chem.size(1)}"
|
f"实际 {chem.size(1)}"
|
||||||
)
|
)
|
||||||
|
|
||||||
q_tab = self.tab_pool(tab) # [B, d]
|
q_tab = self.tab_pool(tab) # [B, d]
|
||||||
gates, probs_full, expert_mask = self.router(q_tab) # 各 [B, K]
|
gates, probs_full, expert_mask = self.router(q_tab) # 各 [B, K]
|
||||||
|
|
||||||
flat = chem.flatten(start_dim=1) # [B, T_chem * d]
|
flat = chem.flatten(start_dim=1) # [B, T_chem * d]
|
||||||
expert_outs = torch.stack(
|
expert_outs = torch.stack(
|
||||||
[expert(flat) for expert in self.experts], dim=1,
|
[expert(flat) for expert in self.experts], dim=1,
|
||||||
) # [B, K, d]
|
) # [B, K, d]
|
||||||
|
|
||||||
F_moe = (gates.unsqueeze(-1) * expert_outs).sum(dim=1) # [B, d]
|
F_moe = (gates.unsqueeze(-1) * expert_outs).sum(dim=1) # [B, d]
|
||||||
|
|
||||||
lb_loss = self._load_balancing_loss(probs_full, expert_mask)
|
lb_loss = self._load_balancing_loss(probs_full, expert_mask)
|
||||||
|
|
||||||
return F_moe, {
|
return F_moe, {
|
||||||
"lb_loss": lb_loss,
|
"lb_loss": lb_loss,
|
||||||
"gates": gates.detach(),
|
"gates": gates.detach(),
|
||||||
"probs": probs_full.detach(),
|
"probs": probs_full.detach(),
|
||||||
}
|
}
|
||||||
|
|
||||||
def _load_balancing_loss(
|
def _load_balancing_loss(
|
||||||
self,
|
self,
|
||||||
probs_full: torch.Tensor,
|
probs_full: torch.Tensor,
|
||||||
expert_mask: torch.Tensor,
|
expert_mask: torch.Tensor,
|
||||||
) -> torch.Tensor:
|
) -> torch.Tensor:
|
||||||
"""Switch Transformer 风格的 load-balancing loss。
|
"""Switch Transformer 风格的 load-balancing loss。
|
||||||
|
|
||||||
f_i = 该 batch 中 expert_i 被激活的样本占比（top-k mask 的均值）
|
f_i = 该 batch 中 expert_i 被激活的样本占比（top-k mask 的均值）
|
||||||
p_i = 该 batch 中 expert_i 的 softmax 概率均值
|
p_i = 该 batch 中 expert_i 的 softmax 概率均值
|
||||||
loss = K * Σ_i f_i * p_i
|
loss = K * Σ_i f_i * p_i
|
||||||
|
|
||||||
理想情况下 f_i 与 p_i 都接近 1/K，loss ≈ 1。
|
理想情况下 f_i 与 p_i 都接近 1/K，loss ≈ 1。
|
||||||
"""
|
"""
|
||||||
f = expert_mask.mean(dim=0) # [K]
|
f = expert_mask.mean(dim=0) # [K]
|
||||||
p = probs_full.mean(dim=0) # [K]
|
p = probs_full.mean(dim=0) # [K]
|
||||||
return self.n_experts * (f * p).sum()
|
return self.n_experts * (f * p).sum()
|
||||||
Loading…
x
Reference in New Issue
Block a user