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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 :param imatrix: str value, represent filename of importance matrix pretrained on
specific datasets for use with the improved quantization methods recently specific datasets for use with the improved quantization methods recently
added to llama.cpp. 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 :return: a model instance
""" """
pretrained_model_name_or_path = kwargs.get("pretrained_model_name_or_path", None) \ pretrained_model_name_or_path = kwargs.get("pretrained_model_name_or_path", None) \
if len(args) == 0 else args[0] 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) bigdl_transformers_low_bit = config_dict.pop("bigdl_transformers_low_bit", False)
invalidInputError(not bigdl_transformers_low_bit, invalidInputError(not bigdl_transformers_low_bit,
f"Detected model is a low-bit({bigdl_transformers_low_bit}) model, " 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 transformers.modeling_utils import _add_variant
from bigdl.llm.ggml.quantize import ggml_tensor_qtype from bigdl.llm.ggml.quantize import ggml_tensor_qtype
from ..utils.common import invalidInputError from ..utils.common import invalidInputError
from typing import Union from typing import Union, Optional
import torch import torch
from torch import nn from torch import nn
import logging import logging
@ -258,3 +258,19 @@ def get_cur_qtype_and_imatrix(qtype, full_module_name, imatrix_data):
cur_qtype = qtype cur_qtype = qtype
return cur_qtype, cur_imatrix 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)