diff --git a/vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py b/vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
index ac158a7e..4214e894 100644
--- a/vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
@@ -26,6 +26,7 @@ def rocm_aiter_fused_experts(
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         use_fp8_w8a8: bool = False,
+        apply_router_weight_on_input: bool = False,
         w1_scale: Optional[torch.Tensor] = None,
         w2_scale: Optional[torch.Tensor] = None,
         block_shape: Optional[List[int]] = None,
@@ -39,6 +40,18 @@ def rocm_aiter_fused_experts(
     from vllm.model_executor.layers.quantization.utils.fp8_utils import (
         per_token_group_quant_fp8)
 
+    if apply_router_weight_on_input:
+        assert (topk_weights.dim() == 2
+                ), "`topk_weights` should be in shape (num_tokens, topk)"
+        _, topk = topk_weights.shape
+        assert (
+            topk == 1
+        ), "Only support topk=1 when `apply_router_weight_on_input` is True"
+
+        hidden_states = hidden_states * topk_weights.to(hidden_states.dtype)
+        topk_ids = topk_ids.to(torch.int32)
+        topk_weights = torch.ones_like(topk_weights, dtype=torch.float32)
+
     if envs.VLLM_ROCM_USE_AITER_FP8_BLOCK_SCALED_MOE and use_fp8_w8a8:
         assert w1_scale is not None
         assert w2_scale is not None