[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/e2e/multicard/test_offline_inference_distributed.py

@@ -166,7 +166,22 @@ def test_models_distributed_Qwen3_W8A8():
     with VllmRunner(
             snapshot_download("vllm-ascend/Qwen3-8B-W8A8"),
             max_model_len=8192,
-            enforce_eager=True,
+            dtype="auto",
+            tensor_parallel_size=2,
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+def test_models_distributed_Qwen3_W4A8DYNAMIC():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+
+    with VllmRunner(
+            snapshot_download("vllm-ascend/Qwen3-8B-W4A8"),
+            max_model_len=8192,
             dtype="auto",
             tensor_parallel_size=2,
             quantization="ascend",