55 lines
2 KiB
Python
55 lines
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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import torch
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import time
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import argparse
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from transformers import BertTokenizer, BertModel
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from bigdl.llm import optimize_model
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Extract the feature of given text using BERT model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="bert-large-uncased",
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help='The huggingface repo id for the BERT (e.g. `bert-large-uncased`) to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--text', type=str, default="This is an example text for feature extraction.",
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help='Text to extract features')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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# Load model
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model = BertModel.from_pretrained(model_path,
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torch_dtype="auto",
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low_cpu_mem_usage=True)
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# With only one line to enable BigDL-LLM optimization on model
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model = optimize_model(model)
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# Load tokenizer
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tokenizer = BertTokenizer.from_pretrained(model_path)
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# Extract the feature of given text
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text = args.text
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encoded_input = tokenizer(text, return_tensors='pt')
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st = time.time()
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output = model(**encoded_input)
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end = time.time()
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print(f'Time cost: {end-st} s')
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print('-'*20, 'Output', '-'*20)
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print(output)
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