[main][Feature] Support Qwen3 W4A8 quantization (#2060)

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

Adding `W4A8_DYNAMIC` quantization support for linear.
Dense models like Qwen3 can infer with `W4A8_DYNAMIC` quantization.

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

None

### How was this patch tested?

Adding ut case in `tests/ut/quantization/test_w4a8_dynamic.py`
Adding e2e case in
`tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC`
to test qwen3 w4a8_dynamic quantized model

Note the w4a8_dynamic quantized model is quantized by `msit/msmodelslim`
of commit `d0abb0a47e1f1a473b866ad41b737fbc28fb1409`

1. Generate `W4A8_DYNAMIC` quantization weights using `msmodelslim`
```shell
git clone https://gitee.com/ascend/msit.git
cd msit/msmodelslim
git checkout d0abb0a47e1f1a473b866ad41b737fbc28fb1409
bash install.sh
```

2. Serve model using `vllm`
```shell
VLLM_USE_V1=1 python -m vllm.entrypoints.openai.api_server \
  --model vllm-ascend/Qwen3-8B-W4A8 \
  --port 8000 \
  --quantization ascend \
  --tensor_parallel_size 2 \
  --enforce-eager
```

- vLLM version: v0.10.0
- vLLM main:
4cd7fe6cea

---------

Signed-off-by: ZhouXiang <zhouxiang100@huawei.com>

tests/ut/quantization/test_w4a8_dynamic.py

@@ -0,0 +1,27 @@
+import torch
+
+from tests.ut.base import TestBase
+from vllm_ascend.quantization.w4a8_dynamic import AscendW4A8DynamicLinearMethod
+
+
+class TestAscendW4A8DynamicLinearMethod(TestBase):
+
+    def setUp(self):
+        self.method = AscendW4A8DynamicLinearMethod()
+        self.method.group_size = 8
+
+    def test_get_weight(self):
+        weight = self.method.get_weight(8, 32, torch.bfloat16)
+        self.assertEqual(weight["weight"].dtype, torch.int8)
+        self.assertEqual(weight["weight"].shape, (32, 8))
+
+    def test_get_pergroup_param(self):
+        params = self.method.get_pergroup_param(8, 32, torch.bfloat16)
+        self.assertEqual(params["weight_scale"].dtype, torch.bfloat16)
+        self.assertEqual(params["weight_scale"].shape, (32, 1))
+        self.assertEqual(params["weight_offset"].dtype, torch.bfloat16)
+        self.assertEqual(params["weight_offset"].shape, (32, 1))
+        self.assertEqual(params["weight_scale_second"].dtype, torch.bfloat16)
+        self.assertEqual(params["weight_scale_second"].shape, (32, 1))
+        self.assertEqual(params["weight_offset_second"].dtype, torch.bfloat16)
+        self.assertEqual(params["weight_offset_second"].shape, (32, 1))