From 925f82107e893ec75d6623ef8a4f3cdce726f35b Mon Sep 17 00:00:00 2001
From: binbin Deng <108676127+plusbang@users.noreply.github.com>
Date: Wed, 7 Feb 2024 16:46:36 +0800
Subject: [PATCH] LLM: support models hosted by modelscope (#10106)

---
 python/llm/src/bigdl/llm/transformers/model.py | 15 ++++++++++++++-
 python/llm/src/bigdl/llm/transformers/utils.py | 18 +++++++++++++++++-
 2 files changed, 31 insertions(+), 2 deletions(-)

diff --git a/python/llm/src/bigdl/llm/transformers/model.py b/python/llm/src/bigdl/llm/transformers/model.py
index 9c71b045..651c63f1 100644
--- a/python/llm/src/bigdl/llm/transformers/model.py
+++ b/python/llm/src/bigdl/llm/transformers/model.py
@@ -124,11 +124,24 @@ class _BaseAutoModelClass:
         :param imatrix: str value, represent filename of importance matrix pretrained on
             specific datasets for use with the improved quantization methods recently
             added to llama.cpp.
+        :param model_hub: str value, options are ``'huggingface'`` and ``'modelscope'``,
+            specify the model hub. Default to be ``'huggingface'``.
         :return: a model instance
         """
         pretrained_model_name_or_path = kwargs.get("pretrained_model_name_or_path", None) \
             if len(args) == 0 else args[0]
-        config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path)
+        model_hub = kwargs.pop("model_hub", "huggingface")
+        invalidInputError(model_hub in ["huggingface", "modelscope"],
+                          "The parameter `model_hub` is supposed to be `huggingface` or "
+                          f"`modelscope`, but got {model_hub}.")
+        if model_hub == "huggingface":
+            config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path)
+        elif model_hub == "modelscope":
+            import modelscope
+            from modelscope.utils.hf_util import get_wrapped_class
+            cls.HF_Model = get_wrapped_class(cls.HF_Model)
+            from .utils import get_modelscope_hf_config
+            config_dict, _ = get_modelscope_hf_config(pretrained_model_name_or_path)
         bigdl_transformers_low_bit = config_dict.pop("bigdl_transformers_low_bit", False)
         invalidInputError(not bigdl_transformers_low_bit,
                           f"Detected model is a low-bit({bigdl_transformers_low_bit}) model, "
diff --git a/python/llm/src/bigdl/llm/transformers/utils.py b/python/llm/src/bigdl/llm/transformers/utils.py
index 06e3a0fd..e7ab41aa 100644
--- a/python/llm/src/bigdl/llm/transformers/utils.py
+++ b/python/llm/src/bigdl/llm/transformers/utils.py
@@ -43,7 +43,7 @@ import os
 from transformers.modeling_utils import _add_variant
 from bigdl.llm.ggml.quantize import ggml_tensor_qtype
 from ..utils.common import invalidInputError
-from typing import Union
+from typing import Union, Optional
 import torch
 from torch import nn
 import logging
@@ -258,3 +258,19 @@ def get_cur_qtype_and_imatrix(qtype, full_module_name, imatrix_data):
         cur_qtype = qtype
 
     return cur_qtype, cur_imatrix
+
+
+def get_modelscope_hf_config(model_id_or_path: str,
+                             revision: Optional[str] = None):
+    # Read hf config dictionary from modelscope hub or local path
+    from modelscope.utils.constant import ModelFile
+    from modelscope.hub.file_download import model_file_download
+    from modelscope.utils.config import Config
+    if not os.path.exists(model_id_or_path):
+        local_path = model_file_download(
+            model_id_or_path, ModelFile.CONFIG, revision=revision)
+    elif os.path.isdir(model_id_or_path):
+        local_path = os.path.join(model_id_or_path, ModelFile.CONFIG)
+    elif os.path.isfile(model_id_or_path):
+        local_path = model_id_or_path
+    return Config._file2dict(local_path)