Fix loading of INC quantized model (#452)

optimum/intel/neural_compressor/modeling_base.py

@@ -12,7 +12,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import copy
 import logging
 import os
 from pathlib import Path
@@ -138,7 +137,6 @@ def _from_pretrained(
 
         model_save_dir = Path(model_cache_path).parent
         inc_config = None
-        q_config = None
         msg = None
         try:
             inc_config = INCConfig.from_pretrained(model_id)
@@ -153,54 +151,23 @@ def _from_pretrained(
             # load(model_cache_path)
             model = torch.jit.load(model_cache_path)
             model = torch.jit.freeze(model.eval())
-            return cls(model, config=config, model_save_dir=model_save_dir, **kwargs)
+            return cls(model, config=config, model_save_dir=model_save_dir, inc_config=inc_config, **kwargs)
 
         model_class = _get_model_class(config, cls.auto_model_class._model_mapping)
-        keys_to_ignore_on_load_unexpected = copy.deepcopy(
-            getattr(model_class, "_keys_to_ignore_on_load_unexpected", None)
-        )
-        keys_to_ignore_on_load_missing = copy.deepcopy(getattr(model_class, "_keys_to_ignore_on_load_missing", None))
-        # Avoid unnecessary warnings resulting from quantized model initialization
-        quantized_keys_to_ignore_on_load = [
-            r"zero_point",
-            r"scale",
-            r"packed_params",
-            r"constant",
-            r"module",
-            r"best_configure",
-            r"max_val",
-            r"min_val",
-            r"eps",
-            r"fake_quant_enabled",
-            r"observer_enabled",
-        ]
-        if keys_to_ignore_on_load_unexpected is None:
-            model_class._keys_to_ignore_on_load_unexpected = quantized_keys_to_ignore_on_load
-        else:
-            model_class._keys_to_ignore_on_load_unexpected.extend(quantized_keys_to_ignore_on_load)
-        missing_keys_to_ignore_on_load = [r"weight", r"bias"]
-        if keys_to_ignore_on_load_missing is None:
-            model_class._keys_to_ignore_on_load_missing = missing_keys_to_ignore_on_load
-        else:
-            model_class._keys_to_ignore_on_load_missing.extend(missing_keys_to_ignore_on_load)
+        # Load the state dictionary of the model to verify whether the model to get the quantization config
+        state_dict = torch.load(model_cache_path, map_location="cpu")
+        q_config = state_dict.get("best_configure", None)
 
-        try:
+        if q_config is None:
             model = model_class.from_pretrained(model_save_dir)
-        except AttributeError:
+        else:
             init_contexts = [no_init_weights(_enable=True)]
             with ContextManagers(init_contexts):
                 model = model_class(config)
-
-        model_class._keys_to_ignore_on_load_unexpected = keys_to_ignore_on_load_unexpected
-        model_class._keys_to_ignore_on_load_missing = keys_to_ignore_on_load_missing
-
-        # Load the state dictionary of the model to verify whether the model is quantized or not
-        state_dict = torch.load(model_cache_path, map_location="cpu")
-        if "best_configure" in state_dict and state_dict["best_configure"] is not None:
-            q_config = state_dict["best_configure"]
             try:
                 model = load(model_cache_path, model)
             except Exception as e:
+                # For incompatible torch version check
                 if msg is not None:
                     e.args += (msg,)
                 raise