update

src/llmcompressor/modifiers/quantization/calibration.py

@@ -120,8 +120,31 @@ def update_weight_zp_scale(module: Module):
 
     if module.quantization_scheme.weights is not None:
         # set weight scale and zero_point up front, calibration data doesn't affect it
+
+        transform_data = getattr(module, "transform_data", None)
+        if transform_data is not None:
+            # order that the transforms were added to match the order they should be applied
+            untransformed_weight = module.weight.data.clone()
+            for transform_name, transform_values in transform_data.data.items():
+                transform = getattr(module, transform_name)
+                apply = transform_values.get("apply")
+                call_args = transform_values.get("call_args")
+                if call_args:
+                    transformed_weight = apply(
+                        input_tensor=module.weight, transform=transform, **call_args
+                    )
+                else:
+                    transformed_weight = apply(
+                        input_tensor=module.weight, transform=transform
+                    )
+                module.weight.data.copy_(transformed_weight)
+
         call_observer(module=module, base_name="weight")
 
+        # TODO: what do we do here?
+        if transform_data is not None:
+            module.weight.data.copy_(untransformed_weight)
+
 
 def calibrate_activations(module: Module, value: torch.Tensor, base_name: str):
     """

src/llmcompressor/modifiers/quantization/quantization/base.py

@@ -10,6 +10,7 @@
     is_preset_scheme,
     preset_name_to_scheme,
 )
+from compressed_tensors.transforms.transform_config import TransformationConfig
 from loguru import logger
 from pydantic import Field, field_validator
 from torch.nn import Module
@@ -74,6 +75,7 @@ class QuantizationModifier(Modifier):
     """
 
     config_groups: Optional[Dict[str, QuantizationScheme]] = None
+    transforms_config: Optional[TransformationConfig] = None
     ignore: List[str] = Field(default_factory=list)
     targets: Union[str, List[str]] = Field(default_factory=lambda: ["Linear"])
     scheme: Optional[Union[str, Dict[str, Any]]] = None
@@ -210,7 +212,9 @@ def _check_calibration_data(self, config: QuantizationConfig):
     def _apply_modifier_to_model(self, model: Module):
         modifier_as_config = self.create_init_config()
         # Add step to attach kv_cache to the model, if present within the config
-        apply_quantization_config(model, modifier_as_config)
+        apply_quantization_config(
+            model, modifier_as_config, transforms_config=self.transforms_config
+        )
         model.apply(set_unset_kv_cache)
         return modifier_as_config