add model validator to quantization scheme

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>

src/compressed_tensors/quantization/quant_scheme.py

@@ -13,14 +13,14 @@
 # limitations under the License.
 
 from copy import deepcopy
-from typing import List, Optional
+from typing import Any, Dict, List, Optional
 
 from compressed_tensors.quantization.quant_args import (
     QuantizationArgs,
     QuantizationStrategy,
     QuantizationType,
 )
-from pydantic import BaseModel
+from pydantic import BaseModel, model_validator
 
 
 __all__ = [
@@ -47,12 +47,31 @@ class QuantizationScheme(BaseModel):
     input_activations: Optional[QuantizationArgs] = None
     output_activations: Optional[QuantizationArgs] = None
 
+    @model_validator(mode="after")
+    def validate_model_after(model: "QuantizationArgs") -> Dict[str, Any]:
+        inputs = model.input_activations
+        weights = model.weights
+        outputs = model.output_activations
+
+        if inputs is not None:
+            if inputs.actorder is not None:
+                raise ValueError("Cannot apply actorder to input activations")
+
+        if weights is not None:
+            if weights.dynamic:
+                raise ValueError("Cannot apply dynamic quantization to weights")
+
+        if outputs is not None:
+            if outputs.actorder is not None:
+                raise ValueError("Cannot apply actorder to output activations")
+
+        return model
+
     @classmethod
     def default_scheme(
         cls,
         targets: Optional[List[str]] = None,
     ):
-
         if targets is None:
             # default to quantizing all Linear layers
             targets = ["Linear"]