LatentMoE: sparse permute-dispatch + padded bmm

Replace dense all-expert forward (16 experts × all tokens) with
permute-dispatch: sort token-expert pairs by expert id, pad to
[R, C, ℓ] (C = max tokens per expert), run 3 bmm calls for the
batched SiTU-GLU activation, then scatter-add weighted results back.

Routed expert FLOPs drop from R·N to R·C (C ≈ N·k/R under uniform
routing). SiTU parameter structure unchanged; checkpoint compatible.

Tests: sparse-vs-dense fwd/bwd equivalence, unselected expert zero
grad, last_capacity tracking.

kda/models/k3_config.py

@@ -73,7 +73,7 @@ class K3Config:
         if name == "toy":
             return cls()
         if name in {"0.5b", "500m"}:
-            # H * head_dim == hidden. Routed 16: LatentMoE still runs every expert.
+            # H * head_dim == hidden. Routed 16 Top-2; LatentMoE padded bmm.
             # ~482M with tied Qwen3 embeddings. 6×(3 KDA + 1 MLA).
             return cls(
                 hidden_size=768,