Fix manual mapping registration and kimi_k2 layer name mapping (#7347)

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

This PR fixes the layer name mapping logic in `AscendModelSlimConfig`
for quantization config loading.
1. **kimi_k2 model layer name mapping issue**: The `kimi_k2` model has a
unique layer naming convention that differs from the standard
`hf_to_vllm` mapping. One layer was defined in the mapper but was not
being correctly applied, causing quantization config lookup failures.
2. **Manual mapping registration timing issue**: The manual mapping
check in `apply_vllm_mapper` was executed before `vllm_config` was
initialized, causing `model_type` to be unavailable. This prevented some
models with manual mappings from being correctly registered.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
Tested with `kimi_k2` model to verify the special layer name mapping
works correctly. Also tested with other models that have manual mappings
defined in `QUANT_MODEL_PREFIX_MAPPINGS` to ensure the registration
timing fix works properly.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: Matrix_K <zhangke144@huawei.com>
Signed-off-by: Feng-xiaosuo <tengchang1@huawei.com>
Co-authored-by: Matrix_K <zhangke144@huawei.com>
Co-authored-by: Wang Kunpeng <1289706727@qq.com>

vllm_ascend/quantization/modelslim_config.py

@@ -50,7 +50,8 @@ MODELSLIM_CONFIG_FILENAME = "quant_model_description.json"
 logger = init_logger(__name__)
 
 # key: model_type
-# value: orig_to_new_prefix
+# value: vLLM prefix -> HF prefix mapping (used to convert vLLM layer names to HF format
+# for looking up keys in quant_model_description.json)
 QUANT_MODEL_PREFIX_MAPPINGS: dict[str, dict[str, str]] = {
     "qwen3_vl_moe": {
         "visual.": "model.visual.",
@@ -78,6 +79,8 @@ QUANT_MODEL_PREFIX_MAPPINGS: dict[str, dict[str, str]] = {
     },
     "qwen2_5_omni_text": {
         "language_model.": "thinker.",
+        "language_model.lm_head.": "thinker.lm_head.",
+        "language_model.model.": "thinker.model.",
     },
     "glm4v_moe": {
         "visual.": "model.visual.",
@@ -89,15 +92,11 @@ QUANT_MODEL_PREFIX_MAPPINGS: dict[str, dict[str, str]] = {
         "language_model.lm_head.": "lm_head.",
         "language_model.model.": "model.language_model.",
     },
-    "qwen3_5": {
+    "kimi_k2": {
-        "visual.": "model.visual.",
+        "language_model.layers.": "language_model.model.layers.",
-        "language_model.lm_head.": "lm_head.",
+        # mm projector
-        "language_model.model.": "model.language_model.",
+        "mm_projector.proj.0": "mm_projector.linear_1",
-    },
+        "mm_projector.proj.2": "mm_projector.linear_2",
-    "qwen3_5_moe": {
-        "visual.": "model.visual.",
-        "language_model.lm_head.": "lm_head.",
-        "language_model.model.": "model.language_model.",
     },
 }
 
@@ -440,6 +439,10 @@ class AscendModelSlimConfig(QuantizationConfig):
                 new_k = k.replace("weight_packed", "weight")
                 extra_quant_dict[new_k] = self.quant_description[k]
         self.quant_description.update(extra_quant_dict)
+        # Initialize attributes for type checking
+        self.model_type: str | None = None
+        self.hf_to_vllm_mapper: WeightsMapper | None = None
+        self.vllm_to_hf_mapper: WeightsMapper | None = None
         self._add_kvcache_quant_metadata()
 
     def __repr__(self) -> str:
@@ -478,12 +481,73 @@ class AscendModelSlimConfig(QuantizationConfig):
                 return ASCEND_QUANTIZATION_METHOD
         return None
 
+    # TODO: Modify the key values in self.quant_description instead of flipping the hf_to_vllm_mapper
+    def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
+        """Apply the vLLM model-specific mapper to this quantization config.
+
+        This method is called by vLLM to apply the model-specific weight mapper
+        to the quantization configuration. It creates a reverse mapper to convert
+        vLLM prefixes back to HF format for looking up keys in quant_config.json.
+
+        Args:
+            hf_to_vllm_mapper: The WeightsMapper instance provided by vLLM
+                that contains model-specific prefix mappings (HF to vLLM).
+        """
+        # Check if we already have a valid vllm_to_hf_mapper for this hf_to_vllm_mapper
+        if hasattr(self, "hf_to_vllm_mapper") and self.hf_to_vllm_mapper is hf_to_vllm_mapper:
+            # Same mapper instance, no need to recreate
+            return
+
+        # Store the original mapper
+        self.hf_to_vllm_mapper = hf_to_vllm_mapper
+
+        # Try different ways to get the mapping based on WeightsMapper implementation
+        mapping_attrs = ["orig_to_new_prefix"]
+        orig_to_new_prefix = {}
+
+        for attr_name in mapping_attrs:
+            if hasattr(hf_to_vllm_mapper, attr_name):
+                orig_to_new_prefix = getattr(hf_to_vllm_mapper, attr_name)
+                break
+
+        # Create reverse mapping (vLLM -> HF), skipping empty values
+        vllm_to_hf_mapping = {}
+        for orig_prefix, new_prefix in orig_to_new_prefix.items():
+            # Skip empty values to avoid invalid keys in reverse mapping
+            if new_prefix:
+                vllm_to_hf_mapping[new_prefix] = orig_prefix
+
+        # Create and store the reverse WeightsMapper instance
+        if vllm_to_hf_mapping:
+            self.vllm_to_hf_mapper = WeightsMapper(orig_to_new_prefix=vllm_to_hf_mapping)
+            logger.debug(f"Created reverse mapping from hf_to_vllm_mapper: {vllm_to_hf_mapping}")
+        else:
+            logger.info("No valid reverse mapping found for WeightsMapper.")
+
     def quant_prefix_mapper(self, model_type: str, prefix: str) -> str:
-        # TODO (Levi-JQ): will be removed when QuantizationConfig.apply_vllm_mapper is implemented
+        # Store model_type for reference
+        self.model_type = model_type
+
+        # Check if manual mapping exists for this model type
+        # Manual mapping takes priority and is used exclusively to avoid conflicts
+        if model_type in QUANT_MODEL_PREFIX_MAPPINGS:
+            manual_mapping = QUANT_MODEL_PREFIX_MAPPINGS[model_type]
+            # Manual mapping is already in vLLM -> HF direction, use directly
+            mapper = WeightsMapper(orig_to_new_prefix=manual_mapping)
+            return mapper._map_name(prefix)
+
+        # Use the reverse mapper (vLLM to HF) if available
+        if hasattr(self, "vllm_to_hf_mapper") and self.vllm_to_hf_mapper:
+            return self.vllm_to_hf_mapper._map_name(prefix)
+
+        # Fall back to manual mapping for backward compatibility (simplified)
+        # This is only used if apply_vllm_mapper wasn't called or failed
         prefix_mapping = QUANT_MODEL_PREFIX_MAPPINGS.get(model_type)
         if prefix_mapping:
-            hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix=prefix_mapping)
+            # Manual mapping is already in vLLM -> HF direction, use directly
-            return hf_to_vllm_mapper._map_name(prefix)
+            mapper = WeightsMapper(orig_to_new_prefix=prefix_mapping)
+            return mapper._map_name(prefix)
+
         return prefix
 
     def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["QuantizeMethodBase"]:
@@ -512,9 +576,6 @@ class AscendModelSlimConfig(QuantizationConfig):
             self.packed_modules_mapping = packed_modules_model_mapping[model_type]
         prefix = self.quant_prefix_mapper(model_type, prefix)
 
-        if model_type != "kimi_k2":
-            if prefix.startswith("language_model"):
-                prefix = prefix.split(".", 1)[-1]
         if isinstance(layer, LinearBase):
             if self.is_layer_skipped_ascend(prefix, self.packed_modules_mapping):
                 # Delayed import to avoid circular import