update

Signed-off-by: Suguna Velury <[email protected]>

Author: sugunav14

examples/llm_ptq/multinode_ptq.py

@@ -30,19 +30,12 @@
 from accelerate import Accelerator
 from example_utils import build_quant_cfg, get_tokenizer
 from tqdm import tqdm
-from transformers import (
-    AutoConfig,
-    AutoModelForCausalLM,
-    AutoProcessor,
-    PreTrainedTokenizer,
-    PreTrainedTokenizerFast,
-)
+from transformers import AutoModelForCausalLM, PreTrainedTokenizer, PreTrainedTokenizerFast
 
 import modelopt.torch.opt as mto
 import modelopt.torch.quantization as mtq
 from modelopt.torch.export import get_model_type
 from modelopt.torch.export.convert_hf_config import convert_hf_quant_config_format
-from modelopt.torch.export.model_utils import is_multimodal_model
 from modelopt.torch.export.unified_export_hf import _export_hf_checkpoint
 from modelopt.torch.quantization.config import need_calibration
 from modelopt.torch.quantization.utils import patch_fsdp_mp_dtypes
@@ -250,28 +243,6 @@ def export_model(
     export_dir = Path(export_path)
     export_dir.mkdir(parents=True, exist_ok=True)
 
-    # Check if the model is a multimodal/VLM model
-    is_vlm = is_multimodal_model(model)
-
-    if is_vlm:
-        # Save original model config and the processor config to the export path for VLMs.
-        print(f"Saving original model config to {export_path}")
-
-        config_kwargs = {"trust_remote_code": args.trust_remote_code}
-        if args.attn_implementation is not None:
-            config_kwargs["attn_implementation"] = args.attn_implementation
-        AutoConfig.from_pretrained(args.pyt_ckpt_path, **config_kwargs).save_pretrained(export_path)
-
-        # Try to save processor config if available
-        try:
-            print(f"Saving processor config to {export_path}")
-            AutoProcessor.from_pretrained(
-                args.pyt_ckpt_path, trust_remote_code=args.trust_remote_code
-            ).save_pretrained(export_path)
-        except Exception as e:
-            print(f"Warning: Could not save processor config: {e}")
-            print("This is normal for some VLM architectures that don't use AutoProcessor")
-
     post_state_dict, hf_quant_config = _export_hf_checkpoint(
         model, torch.bfloat16, accelerator=accelerator
     )