[hot-fix] fix GroupedTopKRouterFL routing and MLA decode method name

vllm_fl/dispatch/backends/flaggems/impl/mla.py

@@ -119,7 +119,7 @@ def _flash_attn_varlen_diff_headdims(
             return attn_out, lse
         return attn_out
 
-    def _forward_decode(
+    def forward_mqa(
         self,
         q: Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]],
         kv_c_and_k_pe_cache: torch.Tensor,

vllm_fl/ops/fused_moe/router.py

@@ -169,7 +169,17 @@ def _compute_routing(
         *,
         input_ids: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
-        if not self._valid_grouping(router_logits):
+        # Mirror upstream GroupedTopKRouter._compute_routing, which checks
+        # expert/grouping validity via an inner `valid_grouping()` closure
+        # rather than a `_valid_grouping` method. The earlier port called a
+        # non-existent `self._valid_grouping`, raising AttributeError.
+        def valid_grouping() -> bool:
+            num_experts = router_logits.shape[-1]
+            if num_experts <= self.num_expert_group:
+                return False
+            return num_experts % self.num_expert_group == 0
+
+        if not valid_grouping():
             if self.e_score_correction_bias is not None:
                 topk_weights, topk_ids = fused_topk_bias(
                     hidden_states=hidden_states,