[main][quantization] Support deepseek w4a8 per-channel quantization (#3011)

### What this PR does / why we need it?
1.Support deepseek w4a8 per-channel quantization
2.The eager mode supports converting weights to the NZ format
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
#### How to get weights using Modelslim

##### Installation steps

git clone https://gitcode.com/Ascend/msit.git
cd msit/msmodelslim
bash install.sh

##### Generate w4a8 per-channel weights

cd /example/DeepSeek
Command reference: msmodelslim/example/DeepSeek/README.md

- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9

---------

Signed-off-by: Wang Kunpeng <1289706727@qq.com>

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -35,6 +35,11 @@ QWEN_DENSE_MODELS = [
     "vllm-ascend/Qwen3-8B-W8A8", "vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8"
 ]
 
+DEEPSEEK_W4A8_MODELS = [
+    "vllm-ascend/DeepSeek-V3-W4A8-Pruing",
+    "vllm-ascend/DeepSeek-V3.1-W4A8-puring"
+]
+
 
 def test_models_distributed_QwQ():
     example_prompts = [
@@ -109,14 +114,15 @@ def test_models_distributed_Qwen3_W4A8DYNAMIC():
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
+@pytest.mark.parametrize("model", DEEPSEEK_W4A8_MODELS)
 @patch.dict(os.environ, {"VLLM_ASCEND_MLA_PA": "1"})
-def test_models_distributed_DeepSeek_W4A8DYNAMIC():
+def test_models_distributed_DeepSeek_W4A8DYNAMIC(model):
     prompts = [
         "Hello, my name is",
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download("vllm-ascend/DeepSeek-V3-W4A8-Pruing"),
+            snapshot_download(model),
             dtype="auto",
             tensor_parallel_size=2,
             quantization="ascend",