[Perf]set moe w2_weight default to be nz (#2842)

### What this PR does / why we need it?

This PR sets the default format of GMM w2_weight in w8a8_dynamic to be
NZ to improve performance.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?


- vLLM version: main
- vLLM main:
e40827280b

---------

Signed-off-by: Angazenn <supperccell@163.com>

vllm_ascend/quantization/w8a8_dynamic.py

@@ -23,7 +23,6 @@ from vllm.config import CompilationLevel, get_current_vllm_config
 from vllm.distributed import get_ep_group
 from vllm.forward_context import get_forward_context
 
-import vllm_ascend.envs as envs_ascend
 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
@@ -103,8 +102,9 @@ class AscendW8A8DynamicLinearMethod:
     def process_weights_after_loading(self, layer):
         if self.transpose_weight:
             layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
-        # cast quantized weight tensors in NZ format (29) for higher inference speed
+        # cast quantized weight tensors in NZ format for higher inference speed
-        layer.weight.data = torch_npu.npu_format_cast(layer.weight.data, 29)
+        layer.weight.data = torch_npu.npu_format_cast(layer.weight.data,
+                                                      ACL_FORMAT_FRACTAL_NZ)
         layer.weight_scale.data = layer.weight_scale.data.flatten()
         layer.weight_scale_fp32 = layer.weight_scale.data.to(torch.float32)
         layer.weight_offset.data = layer.weight_offset.data.flatten()
@@ -275,8 +275,7 @@ class AscendW8A8DynamicFusedMoEMethod:
             layer.w2_weight.data = layer.w2_weight.data.transpose(
                 1, 2).contiguous()
         torch_npu.npu_format_cast_(layer.w13_weight, ACL_FORMAT_FRACTAL_NZ)
-        if envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP:
+        torch_npu.npu_format_cast_(layer.w2_weight, ACL_FORMAT_FRACTAL_NZ)
-            torch_npu.npu_format_cast_(layer.w2_weight, ACL_FORMAT_FRACTAL_NZ)
         layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
             layer.w13_weight_scale.data.shape[0], -1)
         layer.w13_weight_scale_fp32 = layer.w13_weight_scale.data.to(

vllm_ascend/torchair/quantization/torchair_w8a8_dynamic.py

@@ -23,7 +23,6 @@ import torch_npu
 from vllm.distributed import GroupCoordinator, get_ep_group
 from vllm.forward_context import get_forward_context
 
-import vllm_ascend.envs as envs_ascend
 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
@@ -1021,8 +1020,7 @@ class TorchairAscendW8A8DynamicFusedMoEMethod:
                 1, 2).contiguous()
             layer.w2_weight.data = layer.w2_weight.data.transpose(
                 1, 2).contiguous()
-        if envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP:
+        torch_npu.npu_format_cast_(layer.w2_weight, ACL_FORMAT_FRACTAL_NZ)
-            torch_npu.npu_format_cast_(layer.w2_weight, ACL_FORMAT_FRACTAL_NZ)
         layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
             layer.w13_weight_scale.data.shape[0], -1)
         layer.w13_weight_scale_fp32 = layer.w13_weight_scale.data.to(