1355b2ce06
* Add model Qwen-VL-Chat to iGPU-perf * small fix --------- Co-authored-by: ATMxsp01 <shou.xu@intel.com>
85 lines
4.2 KiB
Python
85 lines
4.2 KiB
Python
#
|
|
# 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 code is to support converting of model in load bit
|
|
# for performance tests using load_low_bit
|
|
|
|
import omegaconf
|
|
import time
|
|
import os
|
|
import sys
|
|
import gc
|
|
|
|
from run import LLAMA_IDS, CHATGLM_IDS, LLAVA_IDS, PHI3VISION_IDS, QWENVL_IDS, get_model_path
|
|
|
|
current_dir = os.path.dirname(os.path.realpath(__file__))
|
|
|
|
def save_model_in_low_bit(repo_id,
|
|
local_model_hub,
|
|
low_bit):
|
|
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
|
|
from transformers import AutoTokenizer, LlamaTokenizer
|
|
model_path = get_model_path(repo_id, local_model_hub)
|
|
# Load model in 4 bit,
|
|
# which convert the relevant layers in the model into INT4 format
|
|
st = time.perf_counter()
|
|
if repo_id in CHATGLM_IDS:
|
|
model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, optimize_model=True,
|
|
trust_remote_code=True, use_cache=True).eval()
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
elif repo_id in LLAMA_IDS:
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True,
|
|
use_cache=True).eval()
|
|
tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
elif repo_id in LLAVA_IDS:
|
|
llava_repo_dir = os.environ.get('LLAVA_REPO_DIR')
|
|
sys.path.append(rf"{llava_repo_dir}")
|
|
from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, optimize_model=True,
|
|
trust_remote_code=True, use_cache=True).eval()
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
elif repo_id in PHI3VISION_IDS:
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, optimize_model=True, load_in_low_bit=low_bit,
|
|
_attn_implementation="eager",
|
|
modules_to_not_convert=["vision_embed_tokens"],
|
|
trust_remote_code=True, use_cache=True).eval()
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
elif repo_id in QWENVL_IDS:
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, optimize_model=True, load_in_low_bit=low_bit,
|
|
modules_to_not_convert=['c_fc', 'out_proj'],
|
|
trust_remote_code=True, use_cache=True).eval()
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
else:
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, optimize_model=True, load_in_low_bit=low_bit,
|
|
trust_remote_code=True, use_cache=True).eval()
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
end = time.perf_counter()
|
|
print(">> loading of and converting of model costs {}s".format(end - st))
|
|
|
|
model.save_low_bit(model_path+'-'+low_bit)
|
|
tokenizer.save_pretrained(model_path+'-'+low_bit)
|
|
|
|
del model
|
|
gc.collect()
|
|
|
|
if __name__ == '__main__':
|
|
from omegaconf import OmegaConf
|
|
conf = OmegaConf.load(f'{current_dir}/config.yaml')
|
|
|
|
for model in conf.repo_id:
|
|
save_model_in_low_bit(repo_id=model,
|
|
local_model_hub=conf['local_model_hub'],
|
|
low_bit=conf['low_bit'])
|