[Triton]support swiglu_quant triton in w4a8 (#5161)

### What this PR does / why we need it?
support swiglu_quant triton in w4a8
### Does this PR introduce _any_ user-facing change?
No

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: GDzhu01 <809721801@qq.com>

vllm_ascend/ops/fused_moe/moe_mlp.py

@@ -20,6 +20,7 @@ import torch
 import torch_npu
 from torch.nn.functional import pad
 from vllm.forward_context import get_forward_context
+from vllm.triton_utils import HAS_TRITON
 
 from vllm_ascend.ascend_forward_context import MoECommType
 from vllm_ascend.utils import (AscendDeviceType, dispose_tensor,
@@ -243,9 +244,17 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
             if quantized_hidden_states is not None:
                 dispose_tensor(quantized_hidden_states)
             # act_fn: swiglu
-            hidden_states = torch_npu.npu_swiglu(hidden_states)
-            hidden_states, swiglu_out_scale = torch_npu.npu_dynamic_quant(
-                hidden_states)
+            if HAS_TRITON:
+                from vllm_ascend.ops.triton.activation.swiglu_quant import \
+                    swiglu_quant
+                hidden_states, swiglu_out_scale = swiglu_quant(
+                    hidden_states,
+                    group_list=group_list,
+                    group_list_type=group_list_type)
+            else:
+                hidden_states = torch_npu.npu_swiglu(hidden_states)
+                hidden_states, swiglu_out_scale = torch_npu.npu_dynamic_quant(
+                    hidden_states)
         # gmm2: down_proj
         hidden_states = torch_npu.npu_grouped_matmul(
             x=[hidden_states],