[4/N][refactor]delete torchair from quantization (#2535)

### What this PR does / why we need it?
After moved torchair related quantization section into
torchair_quantization, split the torchair from the origin quantization

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

### How was this patch tested?
vLLM version: main
vLLM main:
ab9f2cfd19


- vLLM version: v0.10.1.1
- vLLM main:
69244e67e6

Signed-off-by: hust17yixuan <303660421@qq.com>

vllm_ascend/quantization/w4a8_dynamic.py

@@ -24,13 +24,11 @@ from vllm.config import get_current_vllm_config
 from vllm.distributed import get_ep_group
 from vllm.forward_context import get_forward_context
 
-from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.ascend_forward_context import FusedMoEState
 from vllm_ascend.distributed.parallel_state import get_mc2_group
 from vllm_ascend.ops.layers.experts_selector import select_experts
 from vllm_ascend.quantization.w8a8_dynamic import (fused_experts_with_all2all,
                                                    fused_experts_with_mc2)
-from vllm_ascend.torchair.utils import npu_stream_switch, npu_wait_tensor
 
 
 class AscendW4A8DynamicLinearMethod:
@@ -133,9 +131,6 @@ class AscendW4A8DynamicFusedMoEMethod:
 
         self.ep_group = get_ep_group()
 
-        ascend_config = get_ascend_config()
-        self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
-
         vllm_config = get_current_vllm_config()
         self.group_size = vllm_config.quant_config.quant_description.get(
             "group_size", 256)
@@ -284,12 +279,10 @@ class AscendW4A8DynamicFusedMoEMethod:
         fused_moe_state = get_forward_context().fused_moe_state
         shared_gate_up, shared_dequant_scale = None, None
         if shared_experts is not None and fused_moe_state == FusedMoEState.MC2:
-            with npu_stream_switch("moe_secondary", 0):
-                npu_wait_tensor(quantized_x_for_share, router_logits)
-                share_up_out, _ = shared_experts.gate_up_proj(
-                    (quantized_x_for_share, dynamic_scale_for_share))
-                shared_gate_up, shared_dequant_scale = share_up_out[
-                    0], share_up_out[1]
+            share_up_out, _ = shared_experts.gate_up_proj(
+                (quantized_x_for_share, dynamic_scale_for_share))
+            shared_gate_up, shared_dequant_scale = share_up_out[
+                0], share_up_out[1]
 
         # this is a naive implementation for experts load balance so as
         # to avoid accumulating too much tokens on a single rank.
@@ -315,7 +308,6 @@ class AscendW4A8DynamicFusedMoEMethod:
                 log2phy=log2phy,
                 global_redundant_expert_num=global_redundant_expert_num,
                 shared_experts=shared_experts,
-                is_torchair=self.torchair_graph_enabled,
                 quantized_x_for_share=shared_gate_up,
                 dynamic_scale_for_share=shared_dequant_scale,
                 mc2_mask=kwargs.get("mc2_mask", None))