Fix error during merging adapter (#11145)

python/llm/src/ipex_llm/transformers/training_patch.py

@@ -47,6 +47,22 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+#
+# Some parts of this file is adapted from
+# https://github.com/huggingface/peft/blob/v0.5.0/src/peft/config.py
+# Copyright [yyyy] [name of copyright owner]
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 
 def patch_prepare_ipex(self, *args):
@@ -58,6 +74,7 @@ from transformers.utils import (
     is_sagemaker_mp_enabled,
     is_accelerate_available,
     is_torch_xpu_available,
+    is_peft_available,
     is_sagemaker_dp_enabled,
     is_torch_tpu_available,
     is_torch_npu_available)
@@ -69,6 +86,8 @@ import torch.distributed as dist
 import os
 import warnings
 from datetime import timedelta
+from huggingface_hub import hf_hub_download
+from ipex_llm.utils.common import invalidInputError
 
 if is_accelerate_available():
     from accelerate.state import AcceleratorState, PartialState
@@ -196,3 +215,50 @@ Accelerator._prepare_ipex = patch_prepare_ipex
 # patch transformer for xpu DDP traing
 from transformers import TrainingArguments
 TrainingArguments._setup_devices = _setup_devices
+
+CONFIG_NAME = "adapter_config.json"
+
+
+@classmethod
+def from_pretrained(cls, pretrained_model_name_or_path, subfolder=None, **kwargs):
+    # Avoid circular dependency .. TODO: fix this with a larger refactor
+    from peft.mapping import PEFT_TYPE_TO_CONFIG_MAPPING
+
+    path = (
+        os.path.join(pretrained_model_name_or_path, subfolder)
+        if subfolder is not None
+        else pretrained_model_name_or_path
+    )
+
+    hf_hub_download_kwargs, class_kwargs, _ = cls._split_kwargs(kwargs)
+
+    if os.path.isfile(os.path.join(path, CONFIG_NAME)):
+        config_file = os.path.join(path, CONFIG_NAME)
+    else:
+        try:
+            config_file = hf_hub_download(pretrained_model_name_or_path, CONFIG_NAME,
+                                          subfolder=subfolder, **hf_hub_download_kwargs)
+        except Exception:
+            invalidInputError(False,
+                              f"Can't find '{CONFIG_NAME}' at '{pretrained_model_name_or_path}'")
+
+    loaded_attributes = cls.from_json_file(config_file)
+
+    if "peft_type" in loaded_attributes:
+        peft_type = loaded_attributes["peft_type"]
+        config_cls = PEFT_TYPE_TO_CONFIG_MAPPING[peft_type]
+    else:
+        config_cls = cls
+
+    config = config_cls(**class_kwargs)
+
+    for key, value in loaded_attributes.items():
+        if hasattr(config, key):
+            setattr(config, key, value)
+
+    return config
+
+# patch peft for merging adapter into the original model
+if is_peft_available():
+    from peft.config import PeftConfigMixin
+    PeftConfigMixin.from_pretrained = from_pretrained