dela
584f7e9e73
Standalone tree split from LLMRL/projects/kda. Includes Triton dt_bias backward fix, train_k3 --preset 0.5b, SFT, Docker runtime, and tests.
163 lines
5.5 KiB
TeX
163 lines
5.5 KiB
TeX
% teach:
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% gap: 读者已知各组件但不知道怎么组装成完整模型
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% takeaway: K3 = Hybrid(3 KDA + 1 MLA) × DecoderBlock(attn + MoE), 末层强制 MLA
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% jump: 为什么每 4 层才放一次 MLA？位置感知只需要周期性提供
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% omit: 0.5b preset 的训练超参
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\section{K3 混合架构}
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\subsection{整体结构}
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\begin{center}
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\texttt{Embedding} $\to$ \texttt{DecoderBlock} $\times L$ $\to$ \texttt{RMSNorm} $\to$ \texttt{LM Head}
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\end{center}
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每个 \texttt{DecoderBlock} 是 Pre-Norm 残差：
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\begin{lstlisting}
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def forward(self, x):
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x = x + self.attn(self.attn_norm(x)) # mixing
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return x + self.ffn(self.ffn_norm(x)) # channel
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\end{lstlisting}
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\subsection{Hybrid Attention Pattern}
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K3 用两种 attention 层交替：
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\begin{center}
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\begin{tabular}{ccccccccc}
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\toprule
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层 & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\
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\midrule
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Attn & KDA & KDA & KDA & \textbf{MLA} & KDA & KDA & KDA & \textbf{MLA} \\
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FFN & MoE & MoE & MoE & MoE & MoE & MoE & MoE & MoE \\
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\bottomrule
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\end{tabular}
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\end{center}
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\noindent 规则：每 4 层放 1 次 MLA（0-based 层 3, 7, 11, ...），\textbf{末层强制 MLA}。
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\begin{codemathtop}{models/k3\_config.py — layer\_types}
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\begin{lstlisting}
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def layer_types(self) -> list[str]:
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"""Hybrid: 3 KDA + 1 MLA per group, last always MLA."""
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types = ["kda"] * self.num_hidden_layers
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for i in range(self.num_hidden_layers):
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if i % 4 == 3:
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types[i] = "mla"
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types[-1] = "mla" # last layer forced
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return types
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def layer_specs(self):
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return [(kind, "moe") for kind in self.layer_types()]
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\end{lstlisting}
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\end{codemathtop}
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\begin{knowledgebox}{为什么 KDA 不需要 RoPE？}
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KDA 的 gate/decay 机制天然提供位置感知：
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远的 token 衰减更多，近的保留更多。
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但 softmax attention（MLA）没有这个机制，所以真实 K3 用 NoPE
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（本复现的 MLA 也是 NoPE）。
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位置感知从 KDA 层``渗透''到 MLA 层——3:1 的比例足够了。
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\end{knowledgebox}
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\subsection{CausalLM 完整数据流}
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\begin{codemathtop}{models/causal\_lm.py — CausalLM}
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\begin{lstlisting}
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class CausalLM(nn.Module):
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def __init__(self, config):
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self.embedding = nn.Embedding(vocab_size, D) # [V, D]
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self.blocks = ModuleList([
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DecoderBlock.from_spec(config, attn, ffn)
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for attn, ffn in config.layer_specs()
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])
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self.norm = RMSNorm(D)
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self.lm_head = nn.Linear(D, vocab_size) # [V, D]
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if config.tie_word_embeddings:
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self.lm_head.weight = self.embedding.weight
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def forward(self, input_ids, labels=None):
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x = self.embedding(input_ids) # [B,T] -> [B,T,D]
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if self.mixer is None: # attnres="off"
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for block in self.blocks:
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x = block(x) # [B,T,D] -> [B,T,D]
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else:
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x = self.mixer(x) # AttnRes 深度残差, 见 §9
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logits = self.lm_head(self.norm(x)) # [B,T,D] -> [B,T,V]
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if labels is None:
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return logits
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# Shifted CE: predict next token
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return cross_entropy(logits[:,:-1], labels[:,1:])
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\end{lstlisting}
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\end{codemathtop}
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\begin{knowledgebox}{残差流是可替换的}
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上面的 \texttt{DecoderBlock} 逐层堆叠（\texttt{x = x + sublayer(norm(x))}）
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是 \texttt{config.attnres="off"} 时的默认路径。
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置为 \texttt{"full"} / \texttt{"block"} 时，\texttt{CausalLM} 会把每个 block
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拆成 attn / ffn 两个原子子层交给 \texttt{mixer}，用\textbf{深度维注意力}
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代替等权残差加法——见 \S9。真实 K3 用的是 \texttt{block} 模式。
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\end{knowledgebox}
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\subsection{两种配置}
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\begin{center}
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\begin{tabular}{lll}
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\toprule
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& \textbf{KDAConfig}（纯 KDA） & \textbf{K3Config}（混合） \\
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\midrule
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Attn & KDA only & 3 KDA + 1 MLA \\
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FFN & SwiGLU & LatentMoE \\
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典型规模 & \textasciitilde8M (toy) & \textasciitilde8M (toy) / \textasciitilde500M (0.5b) \\
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\texttt{layer\_specs()} & \texttt{[("kda","swiglu")] * L} & \texttt{[(kind,"moe") for kind in ...]} \\
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\bottomrule
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\end{tabular}
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\end{center}
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\subsection{K3 toy 尺寸}
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\begin{center}
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\begin{tabular}{llll}
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\toprule
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参数 & 真实 K3 & toy 复现 & 缩比 \\
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\midrule
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$D$ & 7168 & 256 & 28$\times$ \\
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$L$ & 93 & 4 & 23$\times$ \\
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$H = H_V$ & 96 & 8 & 12$\times$ \\
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$K = V$ & 128 & 16 & 8$\times$ \\
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kv\_lora\_rank & 512 & 32 & 16$\times$ \\
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q\_lora\_rank & 1536 & 64 & 24$\times$ \\
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$\ell$ (MoE latent) & 3584 & 128 & 28$\times$ \\
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$n_{\mathrm{routed}}$ / Top-$k$ & 896/16 & 16/2 & 56$\times$ / 8$\times$ \\
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\bottomrule
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\end{tabular}
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\end{center}
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\subsection{DecoderBlock 构建}
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\begin{codemathtop}{layers/block.py — build\_attn / build\_ffn}
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\begin{lstlisting}
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def build_attn(config, kind: str) -> nn.Module:
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if kind == "kda": return KDAAttention.from_config(config)
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if kind == "mla": return GatedMLA.from_config(config)
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def build_ffn(config, kind: str) -> nn.Module:
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if kind == "swiglu": return SwiGLUMLP.from_config(config)
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if kind == "moe": return LatentMoE.from_config(config)
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class DecoderBlock(nn.Module):
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def forward(self, x):
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x = x + self.attn(self.attn_norm(x))
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return x + self.ffn(self.ffn_norm(x))
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\end{lstlisting}
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\end{codemathtop}
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\subsection{本章小结}
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K3 架构 = Hybrid Attention（3 KDA + 1 MLA，末层强制 MLA）+ LatentMoE。
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KDA 层提供线性复杂度的序列混合和位置感知（通过 decay），
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MLA 层提供全局 softmax attention（NoPE，利用 KDA 渗透的位置信息）。
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每层默认是 Pre-Norm 残差 DecoderBlock；\texttt{config.attnres} 可以把这条
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等权残差流换成 AttnRes 深度注意力（\S9）。
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