Revert "Refactor quantization layer name mapping to leverage vLLM built-in mappers" (#7237)

Reverts vllm-project/vllm-ascend#7050, which breaks kimi-k2.5 and
qwen-omin.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e

vllm_ascend/quantization/modelslim_config.py

@@ -48,9 +48,22 @@ MODELSLIM_CONFIG_FILENAME = "quant_model_description.json"
 logger = init_logger(__name__)
 
 # key: model_type
-# value: vLLM prefix -> HF prefix mapping (used to convert vLLM layer names to HF format
-# for looking up keys in quant_model_description.json)
+# value: orig_to_new_prefix
 QUANT_MODEL_PREFIX_MAPPINGS: dict[str, dict[str, str]] = {
+    "qwen3_vl_moe": {
+        "visual.": "model.visual.",
+        "language_model.lm_head.": "lm_head.",
+        "language_model.model.": "model.language_model.",
+    },
+    "qwen3_vl_text": {
+        "visual.": "model.visual.",
+        "language_model.lm_head.": "lm_head.",
+        "language_model.model.": "model.language_model.",
+    },
+    "kimi_k25": {
+        "mm_projector.linear_1": "mm_projector.proj.0",
+        "mm_projector.linear_2": "mm_projector.proj.2",
+    },
     "qwen3_omni_moe": {
         "language_model.lm_head.": "thinker.lm_head.",
         "language_model.model.": "thinker.model.",
@@ -63,8 +76,6 @@ 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.",
@@ -76,6 +87,16 @@ QUANT_MODEL_PREFIX_MAPPINGS: dict[str, dict[str, str]] = {
         "language_model.lm_head.": "lm_head.",
         "language_model.model.": "model.language_model.",
     },
+    "qwen3_5": {
+        "visual.": "model.visual.",
+        "language_model.lm_head.": "lm_head.",
+        "language_model.model.": "model.language_model.",
+    },
+    "qwen3_5_moe": {
+        "visual.": "model.visual.",
+        "language_model.lm_head.": "lm_head.",
+        "language_model.model.": "model.language_model.",
+    },
 }
 
 # key: model_type
@@ -417,10 +438,6 @@ 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
 
     def __repr__(self) -> str:
         return "AscendModelSlimConfig:\n" + super().__repr__()
@@ -458,74 +475,12 @@ class AscendModelSlimConfig(QuantizationConfig):
                 return ASCEND_QUANTIZATION_METHOD
         return None
 
-    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
-
-        # Check if manual mapping exists for this model type
-        # Manual mapping takes priority and is used exclusively to avoid conflicts
-        if hasattr(self, "model_type") and self.model_type in QUANT_MODEL_PREFIX_MAPPINGS:
-            manual_mapping = QUANT_MODEL_PREFIX_MAPPINGS[self.model_type]
-            # Manual mapping is already in vLLM -> HF direction, use directly
-            self.vllm_to_hf_mapper = WeightsMapper(orig_to_new_prefix=manual_mapping)
-            logger.debug(f"Using manual mapping for {self.model_type}: {manual_mapping}")
-            return
-
-        # No manual mapping, use hf_to_vllm_mapper and reverse it
-        # 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:
-        # Store model_type for backward compatibility mappings
-        self.model_type = model_type
-
-        # 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
+        # TODO (Levi-JQ): will be removed when QuantizationConfig.apply_vllm_mapper is implemented
         prefix_mapping = QUANT_MODEL_PREFIX_MAPPINGS.get(model_type)
         if prefix_mapping:
-            # Manual mapping is already in vLLM -> HF direction, use directly
-            mapper = WeightsMapper(orig_to_new_prefix=prefix_mapping)
-            return mapper._map_name(prefix)
-
+            hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix=prefix_mapping)
+            return hf_to_vllm_mapper._map_name(prefix)
         return prefix
 
     def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["QuantizeMethodBase"]:
@@ -556,6 +511,9 @@ class AscendModelSlimConfig(QuantizationConfig):
 
         from vllm.model_executor.layers.attention import Attention
 
+        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