[Refactor]refactor 310p ops and add ut (#6591)

### What this PR does / why we need it?
This pull request focuses on a significant refactoring effort within the
vllm-ascend project, specifically targeting operations optimized for the
Ascend 310P hardware. The changes aim to streamline the implementation
of core components like quantization and multi-head attention, making
the codebase more maintainable and robust. Concurrently, new unit tests
have been introduced to ensure the correctness and reliability of these
refactored modules.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
E2E test with qwen3-32b w8a8

- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd

---------

Signed-off-by: pu-zhe <zpuaa@outlook.com>

tests/ut/_310p/quantization/test_modelslim_config_310.py

@@ -0,0 +1,67 @@
+from unittest.mock import MagicMock, patch
+
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
+from vllm.model_executor.layers.linear import LinearBase
+
+from tests.ut.base import TestBase
+from vllm_ascend._310p.quantization.modelslim_config import AscendModelSlimConfig310
+from vllm_ascend.ops.linear import AscendUnquantizedLinearMethod
+
+
+class TestAscendModelSlimConfig310(TestBase):
+    def setUp(self):
+        self.sample_config = {
+            "weight": "INT8",
+            "layer1.weight": "INT8",
+            "layer2.weight": "FLOAT",
+            "fused_layer.weight": "FLOAT",
+            "fused_layer.shard1.weight": "FLOAT",
+            "fused_layer.shard2.weight": "FLOAT",
+            "shard1.weight": "FLOAT",
+            "shard2.weight": "FLOAT",
+        }
+        self.ascend_config = AscendModelSlimConfig310(self.sample_config)
+        self.ascend_config.packed_modules_mapping = None
+
+    def test_get_quant_method_for_linear_310(self):
+        mock_config = MagicMock()
+        mock_config.model_config.hf_config.model_type = None
+        linear_layer = MagicMock(spec=LinearBase)
+        # Test skipped layer
+        with (
+            patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
+            patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=True)
+        ):
+            method = self.ascend_config.get_quant_method(linear_layer, ".attn")
+            self.assertIsInstance(method, AscendUnquantizedLinearMethod)
+
+        # Test quantized layer
+        mock_scheme = MagicMock()
+        with (
+            patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=False),
+            patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
+            patch("vllm_ascend._310p.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme),
+            patch(
+                "vllm_ascend._310p.quantization.modelslim_config.AscendLinearMethod", return_value=MagicMock()
+            ) as mock_ascend_linear,
+        ):
+            method = self.ascend_config.get_quant_method(linear_layer, ".attn")
+            self.assertIs(method, mock_ascend_linear.return_value)
+            mock_ascend_linear.assert_called_once_with(mock_scheme)
+
+    def test_get_quant_method_for_fused_moe_310(self):
+        fused_moe_layer = MagicMock(spec=FusedMoE)
+        fused_moe_layer.moe = MagicMock(spec=FusedMoEConfig)
+        fused_moe_layer.moe_config = MagicMock(spec=FusedMoEConfig)
+        mock_config = MagicMock()
+        mock_config.model_config.hf_config.model_type = None
+        mock_scheme = MagicMock()
+        with (
+            patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=False),
+            patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
+            patch("vllm_ascend._310p.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme),
+            patch("vllm_ascend._310p.quantization.modelslim_config.AscendLinearMethod", return_value=MagicMock()),
+            self.assertRaises(NotImplementedError),
+        ):
+            self.ascend_config.get_quant_method(fused_moe_layer, "moe_layer")