Revert "GMM custom operator optimization in small batch scenarios (vllm-project#7100)" (#7557)

### What this PR does / why we need it?
This reverts commit 42bcad7e9b. The commit
cause accuracy decrease of qwen3Next, 150 items of gsm8k, 98 -> 91.

- vLLM version: v0.18.0
- vLLM main:
6a9cceb219

Signed-off-by: Your Name <you@example.com>
Co-authored-by: Your Name <you@example.com>

vllm_ascend/device/device_op.py

@@ -17,7 +17,6 @@
 #
 import torch
 import torch_npu
-from vllm.forward_context import get_forward_context
 
 from vllm_ascend.device.mxfp_compat import (
     FLOAT4_E2M1FN_X2_DTYPE,
@@ -28,8 +27,6 @@ from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
 
 
 class BaseDeviceAdaptor:
-    small_batch_gmm_batch_num = 16
-
     @classmethod
     def reshape_and_cache(cls, key, value, key_cache, value_cache, slot_mapping):
         torch_npu._npu_reshape_and_cache(
@@ -49,32 +46,17 @@ class BaseDeviceAdaptor:
         active_expert_range=None,
         quant_mode: int = -1,
     ):
-        # In small batch and non-quantization scenarios, npu_moe_init_routing_v2 is more efficient.
-        # It is expected that further improvements will be made after it is incorporated into CANN on June 30th.
-        if quant_mode == -1 and get_forward_context().num_tokens <= DeviceOperator.small_batch_gmm_batch_num:
-            return torch_npu.npu_moe_init_routing_v2(
-                hidden_states,
-                topk_ids,
-                scale=scale,
-                active_num=active_num,
-                expert_num=expert_num,
-                expert_tokens_num_type=2,
-                expert_tokens_num_flag=expert_tokens_num_flag,
-                active_expert_range=active_expert_range,
-                quant_mode=quant_mode,
-            )
-        else:
-            return torch.ops._C_ascend.npu_moe_init_routing_custom(
-                hidden_states,
-                topk_ids,
-                scale=scale,
-                active_num=active_num,
-                expert_num=expert_num,
-                expert_tokens_num_type=expert_tokens_num_type,
-                expert_tokens_num_flag=expert_tokens_num_flag,
-                active_expert_range=active_expert_range,
-                quant_mode=quant_mode,
-            )
+        return torch.ops._C_ascend.npu_moe_init_routing_custom(
+            hidden_states,
+            topk_ids,
+            scale=scale,
+            active_num=active_num,
+            expert_num=expert_num,
+            expert_tokens_num_type=expert_tokens_num_type,
+            expert_tokens_num_flag=expert_tokens_num_flag,
+            active_expert_range=active_expert_range,
+            quant_mode=quant_mode,
+        )
 
     @staticmethod
     def npu_dynamic_quant(