allow HFQuantizer to run run_compressed=False mode

src/compressed_tensors/compressors/base.py

@@ -125,6 +125,8 @@ def compress_module(self, module: Module) -> Optional[Dict[str, torch.Tensor]]:
             return None  # module is not quantized
         quantization_scheme = module.quantization_scheme
         if not hasattr(quantization_scheme, "weights"):
+            # models that ran CompressedLinear.from_linear will
+            # run delattr(module, "weight")
             return None  # weights are not quantized
 
         quantization_args = quantization_scheme.weights

src/compressed_tensors/compressors/model_compressors/model_compressor.py

@@ -103,7 +103,9 @@ def from_pretrained(
         :return: compressor for the configs, or None if model is not compressed
         """
         config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        compression_config = getattr(config, COMPRESSION_CONFIG_NAME, None)
+        compression_config = getattr(config, COMPRESSION_CONFIG_NAME, None) or getattr(
+            config, QUANTIZATION_CONFIG_NAME, None
+        )
         return cls.from_compression_config(compression_config)
 
     @classmethod

src/compressed_tensors/linear/compressed_linear.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Dict, Tuple
+
 import torch
 from compressed_tensors.compressors.base import BaseCompressor
 from compressed_tensors.quantization import (
@@ -53,7 +55,7 @@ def from_linear(
         )
 
         # get the shape and dtype of compressed parameters
-        compression_params = module.compressor.compression_param_info(
+        compression_params: Dict[str, Tuple] = module.compressor.compression_param_info(
             module.weight.shape, quantization_scheme.weights
         )
 

src/compressed_tensors/quantization/lifecycle/apply.py

@@ -106,7 +106,8 @@ def apply_quantization_config(
     model: Module, config: Union[QuantizationConfig, None], run_compressed: bool = False
 ) -> OrderedDict:
     """
-    Initializes the model for quantization in-place based on the given config
+    Initializes the model for quantization in-place based on the given config.
+    Optionally coverts quantizable modules to compressed_linear modules
 
     :param model: model to apply quantization config to
     :param config: quantization config

src/compressed_tensors/quantization/quant_config.py

@@ -35,6 +35,7 @@
 __all__ = [
     "QuantizationStatus",
     "QuantizationConfig",
+    "QuantizationConfigHFQuantizer",
     "LIFECYCLE_ORDER",
     "DEFAULT_QUANTIZATION_METHOD",
     "DEFAULT_QUANTIZATION_FORMAT",
@@ -132,9 +133,9 @@ class QuantizationConfig(BaseModel):
         `k_proj` and `v_proj` in their names. If this is not the case
         and kv_cache_scheme != None, the quantization of kv cache will fail
     :global_compression_ratio: optional informational config to report the model
-    compression ratio acheived by the quantization config
+        compression ratio acheived by the quantization config
     :ignore: optional list of layers to ignore from config_groups. Layers in this list
-    are not quantized even if they match up with a target in config_groups
+        are not quantized even if they match up with a target in config_groups
     """
 
     config_groups: Dict[str, Union[QuantizationScheme, List[str]]]
@@ -262,3 +263,14 @@ def requires_calibration_data(self):
                     return True
 
         return False
+
+
+# For HFQuantizer, be able to adjust run_compressed on model load
+class QuantizationConfigHFQuantizer(QuantizationConfig):
+    """
+    :param run_compressed: param used set run_compressed.
+     Used for `apply_quantization_config` in CompressedTensorsHfQuantizer
+     in transformers
+    """
+
+    run_compressed: bool = True