Error when configs are created with unrecognized fields

src/compressed_tensors/quantization/quant_args.py

@@ -19,7 +19,7 @@
 import torch
 from compressed_tensors.utils import Aliasable
 from compressed_tensors.utils.helpers import deprecated
-from pydantic import BaseModel, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
 
 
 __all__ = [
@@ -358,6 +358,8 @@ def pytorch_dtype(self) -> torch.dtype:
     def get_observer(self) -> str:
         return self.observer
 
+    model_config = ConfigDict(extra="forbid")
+
 
 def round_to_quantized_type(
     tensor: torch.Tensor, args: QuantizationArgs

src/compressed_tensors/quantization/quant_config.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from enum import Enum
-from typing import Dict, List, Optional, Union
+from typing import Annotated, Any, Dict, List, Optional, Union
 
 from compressed_tensors.config import CompressionFormat
 from compressed_tensors.quantization.quant_args import DynamicType, QuantizationArgs
@@ -26,7 +26,7 @@
     module_type,
     parse_out_kv_cache_args,
 )
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 from torch.nn import Module
 
 
@@ -142,6 +142,9 @@ class QuantizationConfig(BaseModel):
     quantization_status: QuantizationStatus = QuantizationStatus.INITIALIZED
     global_compression_ratio: Optional[float] = None
     ignore: Optional[List[str]] = Field(default_factory=list)
+    # `run_compressed` is a dummy, unused arg for backwards compatibility
+    # see: https://github.com/huggingface/transformers/pull/39324
+    run_compressed: Annotated[Any, Field(exclude=True)] = None
 
     def model_post_init(self, __context):
         """
@@ -254,3 +257,5 @@ def requires_calibration_data(self):
                     return True
 
         return False
+
+    model_config = ConfigDict(extra="forbid")

src/compressed_tensors/quantization/quant_scheme.py

@@ -14,15 +14,15 @@
 
 import warnings
 from copy import deepcopy
-from typing import Any, Dict, List, Optional
+from typing import List, Optional
 
 from compressed_tensors.quantization.quant_args import (
     DynamicType,
     QuantizationArgs,
     QuantizationStrategy,
     QuantizationType,
 )
-from pydantic import BaseModel, model_validator
+from pydantic import BaseModel, ConfigDict, model_validator
 
 
 __all__ = [
@@ -81,6 +81,8 @@ def validate_model_after(model: "QuantizationScheme") -> "QuantizationScheme":
 
         return model
 
+    model_config = ConfigDict(extra="forbid")
+
 
 """
 Pre-Set Quantization Scheme Args

src/compressed_tensors/transform/factory/base.py

@@ -14,7 +14,7 @@
 
 from abc import ABC, abstractmethod
 from collections import defaultdict
-from typing import List, Optional, Tuple, Set
+from typing import List, Optional, Set, Tuple
 
 import torch
 import torch.nn.utils.parametrize as P

src/compressed_tensors/transform/transform_args.py

@@ -15,7 +15,7 @@
 from enum import Enum
 from typing import List
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 
 __all__ = ["TransformArgs", "TransformLocation"]
@@ -74,3 +74,5 @@ def is_online(self) -> bool:
             TransformLocation.WEIGHT_INPUT,
             TransformLocation.WEIGHT_OUTPUT,
         )
+
+    model_config = ConfigDict(extra="forbid")

src/compressed_tensors/transform/transform_config.py

@@ -15,7 +15,7 @@
 from typing import Dict
 
 from compressed_tensors.transform import TransformArgs, TransformScheme
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 
 __all__ = ["TransformConfig"]
@@ -32,42 +32,4 @@ class TransformConfig(BaseModel):
 
     config_groups: Dict[str, TransformScheme]
 
-
-# quip / quip sharp
-QUIP = TransformConfig(
-    config_groups={
-        "v": TransformScheme(
-            type="hadamard",
-            apply=[
-                TransformArgs(
-                    targets=["Linear"],
-                    location="input",  # non-mergable
-                ),
-                TransformArgs(
-                    targets=["Linear"],
-                    location="weight_input",
-                    inverse=True,
-                ),
-            ],
-            randomize=True,
-        ),
-        "u": TransformScheme(
-            type="hadamard",
-            apply=[
-                TransformArgs(
-                    targets=["Linear"],
-                    location="weight_output",
-                ),
-                TransformArgs(
-                    targets=["Linear"], location="output", inverse=True  # non-mergable
-                ),
-            ],
-            randomize=True,
-        ),
-    }
-)
-
-
-PRESET_CONFIGS = {
-    "QUIP": QUIP,
-}
+    model_config = ConfigDict(extra="forbid")

src/compressed_tensors/transform/transform_scheme.py

@@ -17,7 +17,7 @@
 import torch
 from compressed_tensors.transform import TransformArgs
 from compressed_tensors.utils import TorchDtype
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 __all__ = ["TransformScheme"]
@@ -46,3 +46,5 @@ class TransformScheme(BaseModel):
     requires_grad: bool = Field(default=False)
     head_dim: Optional[int] = Field(default=None)
     precision: TorchDtype = Field(default=torch.float32)
+
+    model_config = ConfigDict(extra="forbid")

tests/test_transform/factory/test_memory.py

@@ -42,7 +42,7 @@ def test_memory_sharing(type, randomize, requires_grad, offload=False):
         config_groups={
             "": TransformScheme(
                 type=type,
-                randomzied=randomize,
+                randomize=randomize,
                 requires_grad=requires_grad,
                 apply=[
                     TransformArgs(targets="Linear", location="input"),