Add no_combine support to cutlass_moe_fp4 (#25688)

Co-authored-by: Hanming Lu <69857889+hanming-lu@users.noreply.github.com>

python/sglang/srt/layers/moe/cutlass_moe.py

@@ -359,6 +359,7 @@ def cutlass_moe_fp4(
     topk_ids: torch.Tensor,
     params: CutlassMoEParams,
     apply_router_weight_on_input: bool = False,
+    no_combine: bool = False,
 ):
     """
     MoE implementation for FP4 Inputs
@@ -492,6 +493,8 @@ def cutlass_moe_fp4(
     del int_fp4, int_blockscale
     c2 = shuffle_rows(c2, c_map, (m_a * num_topk, params.hidden_size))
     c2 = c2.view(m_a, num_topk, params.hidden_size)
+    if no_combine:
+        return c2.to(out_dtype)
     if not apply_router_weight_on_input:
         c2 = c2 * topk_weights.view(m_a, num_topk, 1).to(out_dtype)
     return c2.sum(dim=1).to(out_dtype)

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -2175,6 +2175,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
             topk_ids=topk_ids,
             params=layer.cutlass_moe_params,
             apply_router_weight_on_input=moe_runner_config.apply_router_weight_on_input,
+            no_combine=moe_runner_config.no_combine,
         ).to(x.dtype)
         # Scale by routed_scaling_factor is fused into select_experts.
         return StandardCombineInput(hidden_states=output)