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[LLM] Add transformers-like API from_pretrained (#8271)

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* Init commit for bigdl.llm.transformers.AutoModelForCausalLM

* Temp change to avoid name conflicts with external transformers lib

* Support downloading model from huggingface

* Small python style fix

* Change location of transformers to avoid library conflicts

* Add return value for converted ggml binary ckpt path for convert_model

* Avoid repeated loading of shared library and adding some comments

* Small fix

* Path type fix anddocstring fix

* Small fix

* Small fix

* Change cache dir to pwd
This commit is contained in:
Yuwen Hu 2023-06-06 17:04:16 +08:00 committed by GitHub
parent 2ed5842448
commit 64bc123dd3
4 changed files with 137 additions and 4 deletions
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10
python/llm/src/bigdl/llm/ggml/convert_model.py
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@ -39,6 +39,8 @@ def convert_model(input_path: str,
:param dtype: Which quantized precision will be converted.
Now only int4 supported.
:param tmp_path: Which path to store the intermediate model during the conversion process.
:return: the path str to the converted lower precision checkpoint
"""
dtype = dtype.lower()
@ -54,7 +56,7 @@ def convert_model(input_path: str,
tmp_ggml_file_path = next(Path(tmp_ggml_file_path).iterdir())
quantize(input_path=tmp_ggml_file_path,
output_path=output_path,
model_family=model_family,
dtype=dtype)
return quantize(input_path=tmp_ggml_file_path,
output_path=output_path,
model_family=model_family,
dtype=dtype)

3
python/llm/src/bigdl/llm/ggml/quantize.py
View file

@ -60,6 +60,8 @@ def quantize(input_path: str, output_path: str=None,
llama : "q4_0", "q4_1", "q4_2"
bloom : "q4_0", "q4_1"
gptneox : "q4_0", "q4_1", "q4_2", "q5_0", "q5_1", "q8_0"
:return: the path str to the converted ggml binary checkpoint
"""
invalidInputError(model_family in ['llama', 'bloom', 'gptneox'],
"Now we only support quantization of model \
@ -92,3 +94,4 @@ def quantize(input_path: str, output_path: str=None,
p.communicate()
invalidInputError(not p.returncode,
"Fail to quantize {}.".format(str(input_path)))
return str(output_path)

22
python/llm/src/bigdl/llm/ggml/transformers/__init__.py Normal file
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@ -0,0 +1,22 @@
#
# Copyright 2016 The BigDL Authors.
#
# 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.
#
# This would makes sure Python is aware there is more than one sub-package within bigdl,
# physically located elsewhere.
# Otherwise there would be module not found error in non-pip's setting as Python would
# only search the first bigdl package and end up finding only one sub-package.
from .model import AutoModelForCausalLM

106
python/llm/src/bigdl/llm/ggml/transformers/model.py Normal file
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@ -0,0 +1,106 @@
#
# Copyright 2016 The BigDL Authors.
#
# 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.
#
# This would makes sure Python is aware there is more than one sub-package within bigdl,
# physically located elsewhere.
# Otherwise there would be module not found error in non-pip's setting as Python would
# only search the first bigdl package and end up finding only one sub-package.
import os
import traceback
from huggingface_hub import snapshot_download
from bigdl.llm.utils.common import invalidInputError
from bigdl.llm.ggml import convert_model
class AutoModelForCausalLM:
"""
A generic model class that mimics the behavior of
``transformers.AutoModelForCausalLM.from_pretrained`` API
"""
@classmethod
def from_pretrained(cls,
pretrained_model_name_or_path: str,
model_family: str = 'llama',
dtype: str = 'int4',
cache_dir: str = './',
**kwargs):
"""
:param pretrained_model_name_or_path: We support 3 kinds of pretrained model checkpoint
1. path for huggingface checkpoint that are directly pulled from hugginface hub.
This should be a dir path that contains: weight bin, tokenizer config,
tokenizer.model (required for llama) and added_tokens.json (if applied).
For lora fine tuned model, the path should be pointed to a merged weight.
2. path for converted ggml binary checkpoint. The checkpoint should be converted by
``bigdl.llm.ggml.convert_model``.
3. a str for huggingface hub repo id.
:param model_family: the model family of the pretrained checkpoint.
Currently we support ``"llama"``, ``"bloom"``, ``"gptneox"``.
:param dtype: (optional) the data type for weight. Currently we only support ``"int4"``
:param cache_dir: (optional) this parameter will only be used when
``pretrained_model_name_or_path`` is a hugginface checkpoint or hub repo id.
It indicates the saving path for the converted low precision model.
:param **kwargs: keyword arguments which will be passed to the model instance
:return: a model instance
"""
invalidInputError(model_family in ['llama', 'gptneox', 'bloom'],
"Now we only support model family: 'llama', 'gptneox', 'bloom', "
"'{}' is not in the list.".format(model_family))
invalidInputError(dtype.lower() == 'int4',
"Now we only support int4 as date type for weight")
# check whether pretrained_model_name_or_path exists.
# if not, it is likely that the user wants to pass in the repo id.
if not os.path.exists(pretrained_model_name_or_path):
try:
# download from huggingface based on repo id
pretrained_model_name_or_path = snapshot_download(
repo_id=pretrained_model_name_or_path)
except Exception as e:
traceback.print_exc()
# if downloading fails, it could be the case that repo id is invalid,
# or the user pass in the wrong path for checkpoint
invalidInputError(False,
"Downloadng from huggingface repo id {} failed. "
"Please input valid huggingface hub repo id, "
"or provide the valid path to huggingface / "
"ggml binary checkpoint, for pretrained_model_name_or_path"
.format(pretrained_model_name_or_path))
ggml_model_path = pretrained_model_name_or_path
# check whether pretrained_model_name_or_path is a file.
# if not, it is likely that pretrained_model_name_or_path
# points to a huggingface checkpoint
if not os.path.isfile(pretrained_model_name_or_path):
# huggingface checkpoint
ggml_model_path = convert_model(input_path=pretrained_model_name_or_path,
output_path=cache_dir,
model_family=model_family,
dtype=dtype)
if model_family == 'llama':
from bigdl.llm.ggml.model.llama import Llama
return Llama(model_path=ggml_model_path, **kwargs)
elif model_family == 'gptneox':
from bigdl.llm.ggml.model.gptneox import Gptneox
return Gptneox(model_path=ggml_model_path, **kwargs)
elif model_family == 'bloom':
from bigdl.llm.ggml.model.bloom import Bloom
return Bloom(model_path=ggml_model_path, **kwargs)