[Qwen-moe] Remove the minor operation arange (#2373)

### What this PR does / why we need it?
Integrate the arange operator to reduce the time spent and improve
performance

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?

- vLLM version: v0.10.1.1
- vLLM main:
56dcf4e7e9

---------

Signed-off-by: s30076806 <songjiayang2@h-partners.com>

tests/e2e/singlecard/ops/test_fused_moe.py

@@ -92,8 +92,15 @@ def test_fused_experts(
     score = torch.softmax(score, dim=-1, dtype=dtype)
     topk_weights, topk_ids = torch.topk(score, topk)
     topk_ids = topk_ids.to(torch.int32)
+    row_idx = (torch.arange(
+        0,
+        m * topk,
+        device=device,
+        dtype=torch.int32,
+    ).view(topk, -1).permute(1, 0).contiguous())
 
-    output = fused_experts(a, w1, w2, topk_weights, topk_ids, topk, e_map)
+    output = fused_experts(a, w1, w2, topk_weights, topk_ids, row_idx, topk,
+                           e_map)
     torch_output = torch_moe(a, w1, w2, topk_weights, topk_ids, topk, e_map)
     # TODO: The native params are: atol=2e-2, rtol=0, maybe related to the nan problem
     torch.testing.assert_close(output, torch_output, atol=4e-2, rtol=1)
@@ -148,7 +155,7 @@ def test_select_experts(
         mock_native_grouped_topk.side_effect = lambda x, num_groups, k: torch.randn_like(
             x)
 
-        topk_weights, topk_ids = select_experts(
+        topk_weights, topk_ids, row_idx = select_experts(
             hidden_states=hidden_states,
             router_logits=router_logits,
             top_k=topk,
@@ -169,6 +176,7 @@ def test_select_experts(
     assert topk_weights.shape == (m, topk)
     assert topk_ids.shape == (m, topk)
     assert topk_ids.dtype == torch.int32
+    assert row_idx.shape == (m, topk)
 
 
 @pytest.mark.parametrize("device", DEVICE)