Fix Llama 4 with MXFP4 dynamic quant on MI35x (#9993)

python/sglang/srt/layers/quantization/mxfp4.py

@@ -816,7 +816,10 @@ class Mxfp4DynamicQuantMoEMethod(FusedMoEMethodBase):
         moe_runner_config: MoeRunnerConfig,
     ) -> torch.Tensor:
         topk_weights, topk_ids, _ = topk_output
-
+        if _is_hip:
+            topk_weights = topk_weights.to(
+                torch.float32
+            )  # aiter's moe_sorting requires topk_weights to be FP32
         return fused_moe(
             x,
             layer.w13_weight,

python/sglang/srt/server_args.py

@@ -2336,7 +2336,8 @@ class ServerArgs:
             assert self.attention_backend in {
                 "fa3",
                 "aiter",
-            }, "fa3 or aiter is required for Llama4 model"
+                "triton",
+            }, "fa3, aiter, or triton is required for Llama4 model"
         elif model_arch in [
             "Gemma2ForCausalLM",
             "Gemma3ForCausalLM",