[ModelOpt] Support Qwen3.5/3.6 VLM quantized prefix mapping

vllm/model_executor/layers/quantization/modelopt.py

@@ -2330,18 +2330,22 @@ def _resolve_quant_algo(self, prefix: str) -> str | None:
         is not found.
         """
         # 1. Direct lookup
-        if prefix in self.quantized_layers:
-            return self.quantized_layers[prefix]["quant_algo"].upper()
+        for candidate in self._quantized_layer_prefix_candidates(prefix):
+            if candidate in self.quantized_layers:
+                return self.quantized_layers[candidate]["quant_algo"].upper()
 
         # 2. Packed / fused layer lookup
         proj_name = prefix.rsplit(".", 1)[-1]
         if self.packed_modules_mapping and proj_name in self.packed_modules_mapping:
             algos: set[str] = set()
             base = prefix.rsplit(".", 1)[0]
-            for shard_name in self.packed_modules_mapping[proj_name]:
-                shard_prefix = f"{base}.{shard_name}"
-                if shard_prefix in self.quantized_layers:
-                    algos.add(self.quantized_layers[shard_prefix]["quant_algo"].upper())
+            for base_candidate in self._quantized_layer_prefix_candidates(base):
+                for shard_name in self.packed_modules_mapping[proj_name]:
+                    shard_prefix = f"{base_candidate}.{shard_name}"
+                    if shard_prefix in self.quantized_layers:
+                        algos.add(
+                            self.quantized_layers[shard_prefix]["quant_algo"].upper()
+                        )
             if len(algos) == 1:
                 return algos.pop()
             if len(algos) > 1:
@@ -2351,13 +2355,32 @@ def _resolve_quant_algo(self, prefix: str) -> str | None:
                 )
 
         # 3. Prefix-based lookup (for RoutedExperts / parent modules)
-        prefix_dot = prefix + "."
-        for key, info in self.quantized_layers.items():
-            if key.startswith(prefix_dot):
-                return info["quant_algo"].upper()
+        for candidate in self._quantized_layer_prefix_candidates(prefix):
+            prefix_dot = candidate + "."
+            for key, info in self.quantized_layers.items():
+                if key.startswith(prefix_dot):
+                    return info["quant_algo"].upper()
 
         return None
 
+    @staticmethod
+    def _quantized_layer_prefix_candidates(prefix: str) -> tuple[str, ...]:
+        candidates = [prefix]
+
+        if prefix.endswith(".lm_head"):
+            candidates.append("lm_head")
+
+        if prefix.startswith("language_model.model."):
+            candidates.append(
+                "model.language_model." + prefix[len("language_model.model.") :]
+            )
+        elif prefix.startswith("model.language_model."):
+            candidates.append(
+                "language_model.model." + prefix[len("model.language_model.") :]
+            )
+
+        return tuple(dict.fromkeys(candidates))
+
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
     ) -> "QuantizeMethodBase | None":