9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# this code is to support converting of model in load bit
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# for performance tests using load_low_bit
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import omegaconf
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import time
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import os
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import sys
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import gc
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from run import LLAMA_IDS, CHATGLM_IDS, LLAVA_IDS, get_model_path
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current_dir = os.path.dirname(os.path.realpath(__file__))
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def save_model_in_low_bit(repo_id,
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local_model_hub,
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low_bit):
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from ipex_llm.transformers import AutoModel, AutoModelForCausalLM
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from transformers import AutoTokenizer, LlamaTokenizer
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model_path = get_model_path(repo_id, local_model_hub)
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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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st = time.perf_counter()
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if repo_id in CHATGLM_IDS:
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model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, optimize_model=True,
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trust_remote_code=True, use_cache=True).eval()
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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elif repo_id in LLAMA_IDS:
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model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True,
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use_cache=True).eval()
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tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
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elif repo_id in LLAVA_IDS:
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llava_repo_dir = os.environ.get('LLAVA_REPO_DIR')
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sys.path.append(rf"{llava_repo_dir}")
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from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
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model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, optimize_model=True,
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trust_remote_code=True, use_cache=True).eval()
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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else:
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model = AutoModelForCausalLM.from_pretrained(model_path, optimize_model=True, load_in_low_bit=low_bit,
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trust_remote_code=True, use_cache=True).eval()
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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end = time.perf_counter()
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print(">> loading of and converting of model costs {}s".format(end - st))
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model.save_low_bit(model_path+'-'+low_bit)
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tokenizer.save_pretrained(model_path+'-'+low_bit)
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del model
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gc.collect()
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if __name__ == '__main__':
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from omegaconf import OmegaConf
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conf = OmegaConf.load(f'{current_dir}/config.yaml')
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for model in conf.repo_id:
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save_model_in_low_bit(repo_id=model,
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local_model_hub=conf['local_model_hub'],
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low_bit=conf['low_bit'])
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