fix(quantization): accept QuantizeAlgorithmConfig in get_modelike_from_algo_cfg (#201)

modelopt/torch/quantization/mode.py

@@ -372,6 +372,8 @@ def get_modelike_from_algo_cfg(algo_cfg: QuantizeAlgoCfgType) -> ModeConfigList:
             f"Nested lists received as config! config: {algo_cfg}"
         )
         return [get_modelike_from_algo_cfg(c)[0] for c in algo_cfg]
+    if isinstance(algo_cfg, QuantizeAlgorithmConfig):
+        algo_cfg = algo_cfg.model_dump()
     if algo_cfg is None or isinstance(algo_cfg, str):
         algo_name, algo_cfg = algo_cfg, {}
     elif isinstance(algo_cfg, dict):

tests/unit/torch/quantization/test_mode.py

@@ -19,11 +19,12 @@
 
 from modelopt.torch.opt.config import ModeloptField
 from modelopt.torch.opt.mode import _ModeRegistryCls
-from modelopt.torch.quantization.config import QuantizeAlgorithmConfig
+from modelopt.torch.quantization.config import MaxCalibConfig, QuantizeAlgorithmConfig
 from modelopt.torch.quantization.mode import (
     BaseCalibrateModeDescriptor,
     CalibrateModeRegistry,
     QuantizeModeRegistry,
+    get_modelike_from_algo_cfg,
 )
 
 
@@ -60,3 +61,19 @@ def config_class(self) -> QuantizeAlgorithmConfig:
         @CalibrateModeRegistry.register_mode
         class TestIncorrectCalibrateModeDescriptor:
             pass
+
+
+def test_get_modelike_from_algo_cfg_accepts_quantize_algorithm_config():
+    """Regression test for #201: ``QuantizeAlgorithmConfig`` instances must be accepted."""
+    cfg_obj = MaxCalibConfig(method="max", distributed_sync=False)
+    cfg_dict = cfg_obj.model_dump()
+
+    from_obj = get_modelike_from_algo_cfg(cfg_obj)
+    from_dict = get_modelike_from_algo_cfg(cfg_dict)
+
+    assert from_obj == from_dict
+    assert from_obj[0][1] == cfg_dict
+
+    # List handling should also accept ``QuantizeAlgorithmConfig`` instances.
+    from_list = get_modelike_from_algo_cfg([cfg_obj])
+    assert from_list == from_obj