move gptq patching to export stage

optimum/exporters/openvino/__main__.py

@@ -18,7 +18,7 @@
 from typing import Any, Callable, Dict, Optional, Union
 
 from requests.exceptions import ConnectionError as RequestsConnectionError
-from transformers import AutoTokenizer
+from transformers import AutoConfig, AutoTokenizer
 
 from optimum.exporters import TasksManager
 from optimum.exporters.onnx import __main__ as optimum_main
@@ -137,6 +137,41 @@ def main_export(
     original_task = task
     task = TasksManager.map_from_synonym(task)
 
+    # Patch the modules to export of GPTQ models w/o GPU
+    do_gptq_patching = False
+    try:
+        config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=trust_remote_code)
+        config_dict = config.to_dict()
+        quantization_config = config_dict.get("quantization_config", None)
+        do_gptq_patching = quantization_config and quantization_config["quant_method"] == "gptq"
+    except Exception:
+        pass
+
+    if do_gptq_patching:
+        import torch
+
+        torch.set_default_dtype(torch.float32)
+        orig_cuda_check = torch.cuda.is_available
+        torch.cuda.is_available = lambda: True
+
+        from optimum.gptq import GPTQQuantizer
+
+        orig_post_init_model = GPTQQuantizer.post_init_model
+
+        def post_init_model(self, model):
+            from auto_gptq import exllama_set_max_input_length
+
+            class StoreAttr(object):
+                pass
+
+            model.quantize_config = StoreAttr()
+            model.quantize_config.desc_act = self.desc_act
+            if self.desc_act and not self.disable_exllama and self.max_input_length is not None:
+                model = exllama_set_max_input_length(model, self.max_input_length)
+            return model
+
+        GPTQQuantizer.post_init_model = post_init_model
+
     framework = TasksManager.determine_framework(model_name_or_path, subfolder=subfolder, framework=framework)
 
     # get the shapes to be used to generate dummy inputs
@@ -324,3 +359,8 @@ def main_export(
         int8=int8,
         model_kwargs=model_kwargs,
     )
+
+    # Unpatch modules after GPTQ export
+    if do_gptq_patching:
+        torch.cuda.is_available = orig_cuda_check
+        GPTQQuantizer.post_init_model = orig_post_init_model

optimum/intel/openvino/modeling_decoder.py

@@ -229,34 +229,6 @@ def _from_transformers(
             if use_cache:
                 task = task + "-with-past"
 
-        # Patch the modules to export of GPTQ models w/o GPU
-        do_gptq_patching = False
-        config_dict = config.to_dict()
-        quantization_config = config_dict.get("quantization_config", None)
-        do_gptq_patching = quantization_config and quantization_config["quant_method"] == "gptq"
-        if do_gptq_patching:
-            torch.set_default_dtype(torch.float32)
-            orig_cuda_check = torch.cuda.is_available
-            torch.cuda.is_available = lambda: True
-
-            from optimum.gptq import GPTQQuantizer
-
-            orig_post_init_model = GPTQQuantizer.post_init_model
-
-            def post_init_model(self, model):
-                from auto_gptq import exllama_set_max_input_length
-
-                class StoreAttr(object):
-                    pass
-
-                model.quantize_config = StoreAttr()
-                model.quantize_config.desc_act = self.desc_act
-                if self.desc_act and not self.disable_exllama and self.max_input_length is not None:
-                    model = exllama_set_max_input_length(model, self.max_input_length)
-                return model
-
-            GPTQQuantizer.post_init_model = post_init_model
-
         main_export(
             model_name_or_path=model_id,
             output=save_dir_path,
@@ -271,11 +243,6 @@ class StoreAttr(object):
             int8=load_in_8bit,
         )
 
-        # Unpatch modules after GPTQ export
-        if do_gptq_patching:
-            torch.cuda.is_available = orig_cuda_check
-            GPTQQuantizer.post_init_model = orig_post_init_model
-
         config.is_decoder = True
         config.is_encoder_decoder = False
         config.save_pretrained(save_dir_path)
@@ -504,7 +471,7 @@ def _from_pretrained(
         elif model_type == "gpt-bigcode":
             init_cls = OVGPTBigCodeForCausalLM
         else:
-            init_cls = OVModelForCausalLM
+            init_cls = cls
 
         return init_cls(model=model, config=config, model_save_dir=model_cache_path.parent, **kwargs)