[Quantization][Feature] Support compressed tensors moe w4a8 dynamic weight (#5889)

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

While using the LLM Compressor quantization tool from the VLLM community
to generate quantized weights, the VLLM Ascend engine needs to be
adapted to support the compressed tensors quantization format.

1. Support Moe model W4A8 dynamic weight.

- vLLM version: v0.13.0
- vLLM main:
bde38c11df

---------

Signed-off-by: LHXuuu <scut_xlh@163.com>
Signed-off-by: menogrey <1299267905@qq.com>
Co-authored-by: menogrey <1299267905@qq.com>

tests/e2e/multicard/2-cards/test_quantization.py

@@ -64,3 +64,24 @@ def test_qwen3_moe_w8a8_dynamic_llm_compressor():
     for i in range(len(vllm_output)):
         assert golden_results[i] == vllm_output[i][1]
         print(f"Generated text: {vllm_output[i][1]!r}")
+
+def test_qwen3_moe_w4a8_dynamic_llm_compressor():
+    example_prompts = [
+        "The president of the United States is",
+    ]
+    max_tokens = 5
+    with VllmRunner(
+            "vllm-ascend/Qwen3-30B-A3B-Instruct-2507-quantized.w4a8",
+            tensor_parallel_size=2,
+            max_model_len=4096,
+            gpu_memory_utilization=0.8,
+    ) as vllm_model:
+        vllm_output = vllm_model.generate_greedy(example_prompts, max_tokens)
+
+    golden_results = [
+        'The president of the United States is the head of state and',
+    ]
+
+    for i in range(len(vllm_output)):
+        assert golden_results[i] == vllm_output[i][1]
+        print(f"Generated text: {vllm_output[i][1]!r}")