129 lines
5 KiB
Python
129 lines
5 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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import time
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import argparse
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def convert(repo_id_or_model_path, model_family, tmp_path):
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from bigdl.llm import llm_convert
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original_llm_path = repo_id_or_model_path
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bigdl_llm_path = llm_convert(
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model=original_llm_path,
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outfile='./',
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outtype='int4',
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tmp_path=tmp_path,
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model_family=model_family)
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return bigdl_llm_path
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def load(model_path, model_family, n_threads):
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from bigdl.llm.transformers import BigdlForCausalLM
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llm = BigdlForCausalLM.from_pretrained(
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pretrained_model_name_or_path=model_path,
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model_family=model_family,
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n_threads=n_threads)
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return llm
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def inference(llm, repo_id_or_model_path, model_family, prompt):
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if model_family in ['llama', 'gptneox', 'bloom', 'starcoder']:
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# ------ Option 1: Use bigdl-llm based tokenizer
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print('-'*20, ' bigdl-llm based tokenizer ', '-'*20)
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st = time.time()
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# please note that the prompt here can either be a string or a list of string
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tokens_id = llm.tokenize(prompt)
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output_tokens_id = llm.generate(tokens_id, max_new_tokens=32)
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output = llm.batch_decode(output_tokens_id)
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print(f'Inference time: {time.time()-st} s')
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print(f'Output:\n{output}')
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# ------- Option 2: Use HuggingFace transformers tokenizer
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print('-'*20, ' HuggingFace transformers tokenizer ', '-'*20)
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print('Please note that the loading of HuggingFace transformers tokenizer may take some time.\n')
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# here is only a workaround for default example model 'decapoda-research/llama-7b-hf' in LLaMA family,
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# due to its out-of-date 'tokenizer_class' defined in its tokenizer_config.json.
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# for most cases, you could use `AutoTokenizer`.
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if model_family == 'llama':
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from transformers import LlamaTokenizer
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tokenizer = LlamaTokenizer.from_pretrained(repo_id_or_model_path)
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else:
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(repo_id_or_model_path)
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st = time.time()
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# please note that the prompt here can either be a string or a list of string
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tokens_id = tokenizer(prompt).input_ids
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output_tokens_id = llm.generate(tokens_id, max_new_tokens=32)
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output = tokenizer.batch_decode(output_tokens_id)
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print(f'Inference time: {time.time()-st} s')
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print(f'Output:\n{output}')
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# Option 3: fast forward
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print('-'*20, ' fast forward ', '-'*20)
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st = time.time()
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output = llm(prompt, # please note that the prompt here can ONLY be a string
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max_tokens=32)
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print(f'Inference time (fast forward): {time.time()-st} s')
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print(f'Output:\n{output}')
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def main():
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parser = argparse.ArgumentParser(description='INT4 pipeline example')
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parser.add_argument('--thread-num', type=int, default=2, required=True,
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help='Number of threads to use for inference')
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parser.add_argument('--model-family', type=str, default='llama', required=True,
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help="The model family of the large language model (supported option: 'llama', "
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"'gptneox', 'bloom', 'starcoder')")
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parser.add_argument('--repo-id-or-model-path', type=str, required=True,
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help='The path to the huggingface checkpoint folder')
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parser.add_argument('--prompt', type=str, default='Q: What is CPU? A:',
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help='Prompt to infer')
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parser.add_argument('--tmp-path', type=str, default='/tmp',
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help='path to store intermediate model during the conversion process')
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args = parser.parse_args()
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repo_id_or_model_path = args.repo_id_or_model_path
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# Step 1: convert original model to BigDL llm model
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bigdl_llm_path = convert(repo_id_or_model_path=repo_id_or_model_path,
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model_family=args.model_family,
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tmp_path=args.tmp_path)
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# Step 2: load int4 model
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llm = load(model_path=bigdl_llm_path,
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model_family=args.model_family,
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n_threads=args.thread_num)
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# Step 3: inference
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inference(llm=llm,
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repo_id_or_model_path=repo_id_or_model_path,
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model_family=args.model_family,
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prompt=args.prompt)
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if __name__ == '__main__':
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main()
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