minor

Signed-off-by: realAsma <[email protected]>

Author: realAsma

modelopt/torch/quantization/algorithms.py

@@ -86,12 +86,13 @@ class QuantRecipe(CustomHPType):
 
     Args:
         quant_cfg: str or dict or None. dict is used for custom quantization formats.
-        name: name for custom quantization formats. Ignored if quant_cfg is None or str.
+        name: name for custom quantization formats. Only used if quantization format is a custom
+            format not available in :mod:`modelopt.torch.quantization.config`.
     """
 
     def __init__(self, quant_cfg: str | dict[str, Any] | None = None, name: str | None = None):
         """Initialize the QuantRecipe with the quantization configuration."""
-        name = name or self.get_auto_name_for_config(quant_cfg)
+        name = self.get_auto_name_for_config(quant_cfg) or name
 
         if quant_cfg is None:
             quant_cfg = {"quant_cfg": {"*": {"enable": False}}}
@@ -179,7 +180,7 @@ def __init__(
         nn_modules: list[nn.Module] | None = None,
     ) -> None:
         """Initializes Hparam with original value and choices."""
-        choices = sorted(set((choices if choices else []) + [QuantRecipe(quant_cfg=None)]))
+        choices = sorted({*(choices if choices else []), QuantRecipe(quant_cfg=None)})
         super().__init__(choices, original=choices[0])
         self.nn_modules = nn_modules if nn_modules else []
 

modelopt/torch/quantization/model_quant.py

@@ -413,14 +413,11 @@ def forward_backward_step(model, batch) -> None:
 
         name = QuantRecipe.get_auto_name_for_config(quant_cfg)
         if name is None:
-            if isinstance(quant_cfg, str):
-                raise ValueError(f"Invalid quantization format: {quant_cfg}")
-            else:
-                name = f"CUSTOM_{i}"
-                warnings.warn(
-                    f"Received custom quantization formats for search, auto_quantize results may not be optimal. "
-                    f"This config will be displayed as {name}"
-                )
+            name = f"CUSTOM_{i}"
+            warnings.warn(
+                f"Received custom quantization formats for search, auto_quantize results may not be optimal. "
+                f"This config will be displayed as {name}"
+            )
         processed_quantization_formats.append((quant_cfg, name))
 
     assert len(processed_quantization_formats) > 0, "`quantization_formats` should not be empty"