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LLM: support models hosted by modelscope (#10106)

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binbin Deng 2024-02-07 16:46:36 +08:00 committed by GitHub
parent 1710ecb990
commit 925f82107e
2 changed files with 31 additions and 2 deletions
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15
python/llm/src/bigdl/llm/transformers/model.py
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@ -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, "

18
python/llm/src/bigdl/llm/transformers/utils.py
View file

@ -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)