Fix triton_fused_moe unit test and benchmark (#9276)

Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com>

test/srt/test_triton_fused_moe.py

@@ -8,6 +8,8 @@ from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.moe.fused_moe_triton.triton_kernels_moe import (
     triton_kernel_moe_forward,
 )
+from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
+from sglang.srt.layers.moe.topk import TopK
 from sglang.test.test_utils import CustomTestCase
 
 
@@ -92,8 +94,22 @@ class TestFusedMOE(CustomTestCase):
         w2_tri = w2_tri.transpose(-2, -1).contiguous()
         score = self.create_random_cuda_tensor((m, e), dtype)
 
+        topk_op = TopK(
+            top_k=topk,
+            renormalize=False,
+            use_grouped_topk=False,
+        )
+        topk_op.use_triton_kernels = True
+        triton_topk_output = topk_op.forward_cuda(
+            hidden_states=a,
+            router_logits=score,
+        )
+
+        moe_runner_config = MoeRunnerConfig(
+            inplace=False,
+        )
         triton_output = triton_kernel_moe_forward(
-            a, w1_tri, w2_tri, score, topk, renormalize=False
+            a, w1_tri, w2_tri, triton_topk_output, moe_runner_config
         )
         torch_output = self.torch_naive_moe(a, w1, w2, score, topk)
         torch.testing.assert_close(triton_output, torch_output, rtol=rtol, atol=atol)