[Quant] Fix use_mla TypeError and support loading pure-sparsity Compressed Tensors configs (#12711)

vllm/config.py

@@ -1000,8 +1000,9 @@ class ModelConfig:
         # have fp8 for both weights and activations.
         if self.quantization == "compressed-tensors":
             quant_config = self._parse_quant_hf_config()
-            for group_name, cfg in quant_config.get("config_groups",
-                                                    ("", {})).items():
+            for group_name, cfg in quant_config.get("config_groups", {
+                    "": {}
+            }).items():
                 act_cfg = cfg.get("input_activations", {})
                 act_type = None if act_cfg is None else act_cfg.get("type", "")
                 w_cfg = cfg.get("weights", {})

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -424,6 +424,11 @@ class CompressedTensorsConfig(QuantizationConfig):
                                          or input_quant is not None,
                                          weight_quant=weight_quant,
                                          input_quant=input_quant)
+        elif weight_quant is None:
+            logger.warning_once("Acceleration for non-quantized schemes is "
+                                "not supported by Compressed Tensors. "
+                                "Falling back to UnquantizedLinearMethod")
+            return None
         else:
             # Find the quant_scheme
             scheme = self._get_scheme_from_parts(  # type: ignore