[3/N][Refactor][Quantization]remove packed_modules_mapping from models (#3021)

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

Some custom models in vllm-ascend define packed_modules_mapping, which
prevent keeping same model class with vllm community. So move these
custom packed_modules_mapping to quant utils.py. After this pr, some
custom models can be removed.

### Does this PR introduce _any_ user-facing change?

tested by CI

### How was this patch tested?

tested by CI

- vLLM version: v0.10.2
- vLLM main:
5089fd749c

Signed-off-by: 22dimensions <waitingwind@foxmail.com>

tests/ut/models/test_qwen3_moe.py

@@ -15,41 +15,11 @@
 import math
 import unittest
 
-import pytest
 import torch
-from vllm.model_executor.models.qwen3_moe import Qwen3MoeForCausalLM
 
-from vllm_ascend.models.qwen3_moe import CustomQwen3MoeForCausalLM
 from vllm_ascend.torchair.models.qwen3_moe import CustomQwen3MoeAttention
 
 
-class TestCustomQwen3MoeForCausalLM:
-
-    def test_class_inheritance(self):
-        assert issubclass(CustomQwen3MoeForCausalLM, Qwen3MoeForCausalLM)
-
-    @pytest.mark.parametrize("key, expected", [
-        ("qkv_proj", ["q_proj", "k_proj", "v_proj"]),
-        ("gate_up_proj", ["gate_proj", "up_proj"]),
-        ("experts",
-         ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]),
-    ])
-    def test_packed_modules_mapping(self, key, expected):
-        assert CustomQwen3MoeForCausalLM.packed_modules_mapping[
-            key] == expected
-
-    def test_packed_modules_mapping_structure(self):
-        expected_mapping = {
-            "qkv_proj": ["q_proj", "k_proj", "v_proj"],
-            "gate_up_proj": ["gate_proj", "up_proj"],
-            "experts": [
-                "experts.0.gate_proj", "experts.0.up_proj",
-                "experts.0.down_proj"
-            ]
-        }
-        assert CustomQwen3MoeForCausalLM.packed_modules_mapping == expected_mapping
-
-
 class DummyRMSNorm:
 
     def __init__(self, dim: int, eps: float = 1e-6):

tests/ut/quantization/test_quant_config.py

@@ -73,9 +73,12 @@ class TestAscendQuantConfig(TestBase):
         self.assertIsNone(result)
 
     def test_get_quant_method_for_linear(self):
+        mock_config = MagicMock()
+        mock_config.model_config.hf_config.model_type = None
         linear_layer = MagicMock(spec=LinearBase)
         # Test skipped layer
-        with patch.object(self.ascend_config,
+        with patch("vllm_ascend.quantization.quant_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")
@@ -83,6 +86,7 @@ class TestAscendQuantConfig(TestBase):
 
         # Test quantized layer
         with patch.object(self.ascend_config, 'is_layer_skipped_ascend', return_value=False), \
+            patch("vllm_ascend.quantization.quant_config.get_current_vllm_config", return_value=mock_config), \
             patch('vllm_ascend.quantization.quant_config.AscendLinearMethod', return_value=MagicMock()) as mock_ascend_linear:
 
             method = self.ascend_config.get_quant_method(linear_layer, ".attn")
@@ -93,14 +97,18 @@ class TestAscendQuantConfig(TestBase):
 
     def test_get_quant_method_for_attention(self):
         attention_layer = MagicMock(spec=Attention)
-        with patch('vllm_ascend.quantization.quant_config.AscendKVCacheMethod',
+        mock_config = MagicMock()
+        mock_config.model_config.hf_config.model_type = None
+        with patch("vllm_ascend.quantization.quant_config.get_current_vllm_config", return_value=mock_config), \
+            patch('vllm_ascend.quantization.quant_config.AscendKVCacheMethod', \
                    return_value=MagicMock()) as mock_ascend_kvcache:
             # Test with fa_quant_type
             method = self.ascend_config.get_quant_method(
                 attention_layer, ".attn")
             self.assertIs(method, mock_ascend_kvcache.return_value)
 
-        with patch('vllm_ascend.quantization.quant_config.AscendKVCacheMethod',
+        with patch("vllm_ascend.quantization.quant_config.get_current_vllm_config", return_value=mock_config), \
+            patch('vllm_ascend.quantization.quant_config.AscendKVCacheMethod', \
                    return_value=MagicMock()) as mock_ascend_kvcache:
             # Test with kv_quant_type
             modified_config = {"kv_quant_type": "C8"}
@@ -113,9 +121,12 @@ class TestAscendQuantConfig(TestBase):
         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
 
         # Test skipped layer
         with patch.object(self.ascend_config, 'is_layer_skipped_ascend', return_value=True), \
+            patch("vllm_ascend.quantization.quant_config.get_current_vllm_config", return_value=mock_config), \
             patch('vllm_ascend.quantization.quant_config.AscendUnquantizedFusedMoEMethod', return_value=MagicMock()) as mock_ascend_moe:
             method = self.ascend_config.get_quant_method(
                 fused_moe_layer, "moe_layer")
@@ -123,6 +134,7 @@ class TestAscendQuantConfig(TestBase):
 
         # Test quantized layer
         with patch.object(self.ascend_config, 'is_layer_skipped_ascend', return_value=False), \
+            patch("vllm_ascend.quantization.quant_config.get_current_vllm_config", return_value=mock_config), \
             patch('vllm_ascend.quantization.quant_config.AscendFusedMoEMethod', return_value=MagicMock()) as mock_ascend_moe:
             method = self.ascend_config.get_quant_method(
                 fused_moe_layer, "moe_layer")