Fix load INC load weights compile error due to Transformer 4.45 upgrade.

examples/text-generation/utils.py

@@ -245,8 +245,12 @@ def setup_model(args, model_dtype, model_kwargs, logger):
             args.model_name_or_path, torch_dtype=model_dtype, quantization_config=quantization_config, **model_kwargs
         )
     elif args.load_quantized_model_with_inc:
-        from neural_compressor.torch.quantization import load
+        #TODO: This will be removed in v1.19 Synapse release
+        #Override neural_compressor _load_remaining_pretrained_weight for the Transformer 4.45 release.
+        import neural_compressor.torch.algorithms.weight_only.save_load as nc_sl
+        nc_sl.WOQModelLoader._load_remaining_pretrained_weight = local_load_remaining_pretrained_weight
 
+        from neural_compressor.torch.quantization import load
         model = load(model_name_or_path=args.model_name_or_path, format="huggingface", device="hpu", **model_kwargs)
     elif args.local_quantized_inc_model_path:
         org_model = AutoModelForCausalLM.from_pretrained(
@@ -662,3 +666,46 @@ def initialize_model(args, logger):
     logger.info(f"device: {args.device}, n_hpu: {args.world_size}, bf16: {model_dtype == torch.bfloat16}")
     logger.info(f"Model initialization took {(init_end - init_start):.3f}s")
     return model, assistant_model, tokenizer, generation_config
+
+#TODO:This will be removed from Synapse v1.19 release.
+#This is to override _load_remaining_pretrained_weight for Transformer 4.45 release.
+def local_load_remaining_pretrained_weight(self,model):
+    from transformers.modeling_utils import _load_state_dict_into_meta_model, load_state_dict
+
+    resolved_archive_file = self.kwargs.pop("resolved_archive_file", None)
+    torch_dtype = self.kwargs.pop("torch_dtype", torch.float32)
+    dtype_orig = self.kwargs.pop("dtype_orig", None)
+    offload_folder = self.kwargs.pop("offload_folder", None)
+    offload_state_dict = self.kwargs.pop("offload_state_dict", False)
+
+    # restore default dtype
+    if dtype_orig is not None:
+        torch.set_default_dtype(dtype_orig)
+
+    if not isinstance(resolved_archive_file, list):
+        resolved_archive_file = [resolved_archive_file]
+    for shard_file in resolved_archive_file:
+        state_dict = load_state_dict(shard_file)
+
+        params_dict={
+            "model": model,
+            "state_dict": state_dict,
+            "start_prefix": "",
+            "expected_keys": list(state_dict.keys()),
+            "device_map": {"": self.device},
+            "offload_folder": offload_folder,
+            "state_dict_folder": tempfile.mkdtemp() if offload_state_dict else None,
+            "state_dict_index": {} if offload_state_dict else None,
+            "dtype": torch_dtype,
+            "keep_in_fp32_modules": [],
+        }
+
+        _load_state_dict_into_meta_model(**params_dict)
+
+    # make sure token embedding weights are still tied if needed
+    model.tie_weights()
+
+    # Set model in evaluation mode to deactivate DropOut modules by default
+    model.eval()
+
+    return model