Remove unused row_idx in token_dispatcher (#3442)

### What this PR does / why we need it?
The `row_idx` parameter is no longer used since
PR[#2689](https://github.com/vllm-project/vllm-ascend/pull/2689), so
remove it across multiple files to remove unnecessary calculations and
parameter passing.

### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
accuracy test passed for Qwen3 235B and DeepSeek V3 671B after this PR.


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: CaranLic <740821011@qq.com>

vllm_ascend/ops/moe/experts_selector.py

@@ -21,17 +21,6 @@ import torch_npu
 from vllm.forward_context import get_forward_context
 
 
-def return_row_idx(hidden_states, top_k):
-    num_tokens = hidden_states.shape[0]
-    row_idx_len = num_tokens * top_k
-    row_idx = (torch.arange(0,
-                            row_idx_len,
-                            dtype=torch.int32,
-                            device=hidden_states.device).view(
-                                top_k, -1).permute(1, 0).contiguous())
-    return row_idx
-
-
 def select_experts(hidden_states: torch.Tensor,
                    router_logits: torch.Tensor,
                    top_k: int,
@@ -71,7 +60,7 @@ def select_experts(hidden_states: torch.Tensor,
     if weight_prefetch_method:
         weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(
             hidden_states, "gate_up")
-    topk_weights, topk_ids, row_idx = _select_experts_with_fusion_ops(
+    topk_weights, topk_ids = _select_experts_with_fusion_ops(
         hidden_states=hidden_states,
         router_logits=router_logits,
         top_k=top_k,
@@ -99,9 +88,7 @@ def select_experts(hidden_states: torch.Tensor,
             e_score_correction_bias=e_score_correction_bias,
             global_num_experts=global_num_experts,
         )
-    if row_idx is None:
-        row_idx = return_row_idx(hidden_states, top_k)
-    return topk_weights, topk_ids, row_idx
+    return topk_weights, topk_ids
 
 
 def _native_grouped_topk(
@@ -187,7 +174,7 @@ def _select_experts_with_fusion_ops(
         routed_scaling_factor=1.0,
         global_num_experts: int = -1):
 
-    topk_weights, topk_ids, row_idx = None, None, None
+    topk_weights, topk_ids = None, None
     # NOTE: now npu_moe_gating_top_k can only support 'group_count=256' pattern
     is_deepseek_v3_r1 = global_num_experts == 256
     if is_deepseek_v3_r1:
@@ -205,14 +192,13 @@ def _select_experts_with_fusion_ops(
             # y2_flag=False, # old api; should the third output be output
             routed_scaling_factor=1,
             eps=float(1e-20))
-        row_idx = return_row_idx(hidden_states, top_k)
     if not use_grouped_topk and custom_routing_function is None and scoring_func == "softmax":
-        topk_weights, topk_ids, row_idx = torch_npu.npu_moe_gating_top_k_softmax(
+        topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k_softmax(
             x=router_logits, finished=None, k=top_k)
         topk_ids = topk_ids.to(torch.int32)
         topk_weights = _renormalize_topk_weights(topk_weights, renormalize)
 
-    return topk_weights, topk_ids, row_idx
+    return topk_weights, topk_ids
 
 
 def _native_select_experts(