[BugFix][310p][Cherry-pick] Handle null quantization config in ShardedStateLoader310&[Feature][310P] Support W8A8 dynamic linear method (#8296)

### What this PR does / why we need it?
This PR implements the `AscendW8A8DynamicLinearMethod310` quantization
scheme specifically for 310P hardware. It includes the logic for weight
retrieval, per-channel parameter generation, and the application of
dynamic quantization using NPU-specific kernels. Additionally, it
updates `ShardedStateLoader310` to handle quantization configurations
more robustly when generating parameter type maps.

Feedback from the review identified two critical issues in the
implementation:
1. The tensor squeezing logic in the `apply` method incorrectly handles
2D inputs, which may lead to shape mismatches in subsequent layers.
2. The weight tensor in `process_weights_after_loading` is transposed
after being converted to the private NZ format; the transpose operation
should be performed on the ND tensor before conversion to ensure correct
physical layout.

cherry-pick from : #7546 #7725
### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
New unit tests were added in
`tests/ut/_310p/quantization/test_w8a8_dynamic_310.py` to verify the
quantization method, and
`tests/ut/_310p/test_sharded_state_loader_310p.py` was updated to test
the state loader changes.

---------

Signed-off-by: csoulnd <daidaicurry@foxmail.com>

vllm_ascend/_310p/sharded_state_loader_310p.py

@@ -20,6 +20,7 @@ from pathlib import Path
 
 import torch
 from vllm.config.load import LoadConfig
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
 from vllm.model_executor.model_loader import ShardedStateLoader
 
 
@@ -48,10 +49,20 @@ class ShardedStateLoader310(ShardedStateLoader):
         )
 
     @staticmethod
-    def generate_quant_description(model: torch.nn.Module, path: str):
+    def generate_quant_description(
+        model: torch.nn.Module,
+        path: str,
+        quant_config: QuantizationConfig | None = None,
+    ) -> None:
         """Generate a mapping of parameter names to their corresponding quantization types."""
         quant_description = {}
-        quantize_type = model.quant_config.quant_description.get("model_quant_type", "FLOAT")
+        if quant_config is None:
+            quantize_type = "FLOAT"
+        else:
+            try:
+                quantize_type = quant_config.quant_description.get("model_quant_type", "FLOAT")
+            except AttributeError:
+                quantize_type = "FLOAT"
         quant_description["model_quant_type"] = quantize_type
         quant_description["version"] = "1.0.0"
         state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())