72 lines
2.5 KiB
Python
72 lines
2.5 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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#
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# This file is adapted from
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#
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# https://github.com/SafeAILab/EAGLE/blob/main/eagle/evaluation/speed.py
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#
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# Copyright 2024 SafeAI Lab (SAIL).
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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 argparse
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import json
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from transformers import AutoTokenizer
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import numpy as np
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Application to calculate and compare speeds between base and optimized llm models.')
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parser.add_argument("--base-model-path", type=str, default="meta-llama/Llama-2-7b-chat-hf",
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help="1")
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parser.add_argument("--jsonl-file", type=str, required=True, help='Enter path of model result file')
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args = parser.parse_args()
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tokenizer=AutoTokenizer.from_pretrained(args.base_model_path)
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data = []
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with open(args.jsonl_file, 'r', encoding='utf-8') as file:
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for line in file:
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json_obj = json.loads(line)
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data.append(json_obj)
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total_time=0
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total_token=0
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speeds=[]
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for datapoint in data:
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qid=datapoint["question_id"]
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answer=datapoint["choices"][0]['turns']
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tokens = 0
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for i in answer:
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tokens += (len(tokenizer(i).input_ids) - 1)
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times = sum(datapoint["choices"][0]['wall_time'])
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speeds.append(tokens / times)
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total_time+=times
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total_token+=tokens
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print(f'total time: {total_time} s')
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print('total token',total_token)
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print(f'speed: {np.array(speeds).mean()} tps')
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