[Feat.]: support 310p w8a8 (#6454)

### What this PR does / why we need it?
Introduced 310P W8A8 Quantization Support: New modules and methods have
been added to enable W8A8 static quantization specifically for the
Ascend 310P platform.
Platform-Specific Quantization Configuration Loading: The system now
dynamically loads the appropriate quantization configurations
(AscendCompressedTensorsConfig, AscendModelSlimConfig) based on whether
the current hardware is an Ascend 310P device.
Implemented AscendW8A8LinearMethod310P: A dedicated linear quantization
method for 310P is provided, handling the specifics of weight and
activation quantization, including input parameter broadcasting and
weight data manipulation.
Extended AscendModelSlimConfig for 310P: A specialized configuration
class for 310P integrates the new W8A8 linear method for both standard
linear layers and vocabulary parallel embeddings, ensuring proper
quantization application.

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: Tflowers-0129 <2906339855@qq.com>
Signed-off-by: Shaoxu Cheng <2906339855@qq.com>

tests/e2e/310p/test_offline_inference_w8a8_310p.py

@@ -0,0 +1,22 @@
+import pytest
+
+from tests.e2e.conftest import VllmRunner
+
+
+@pytest.mark.parametrize("dtype", ["float16"])
+@pytest.mark.parametrize("max_tokens", [5])
+def test_qwen3_w8a8_e2e_310p(dtype: str, max_tokens: int) -> None:
+    example_prompts = [
+        "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.",
+    ]
+
+    with VllmRunner(
+        "vllm-ascend/Qwen3-32B-W8A8",
+        tensor_parallel_size=4,
+        dtype=dtype,
+        max_model_len=8192,
+        enforce_eager=True,
+        quantization="ascend",
+        enable_prefix_caching=False,
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)