61 lines
2.8 KiB
Python
61 lines
2.8 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 os
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import time
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import argparse
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from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
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from transformers import LlamaTokenizer, AutoTokenizer
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Transformer INT4 example')
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parser.add_argument('--repo-id-or-model-path', type=str, default="decapoda-research/llama-7b-hf",
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choices=['decapoda-research/llama-7b-hf', 'THUDM/chatglm-6b'],
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help='The huggingface repo id for the larga language model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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if model_path == 'decapoda-research/llama-7b-hf':
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# load_in_4bit=True in bigdl.llm.transformers will convert
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# the relevant layers in the model into int4 format
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model = AutoModelForCausalLM.from_pretrained(model_path, load_in_4bit=True)
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tokenizer = LlamaTokenizer.from_pretrained(model_path)
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input_str = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
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with torch.inference_mode():
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st = time.time()
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input_ids = tokenizer.encode(input_str, return_tensors="pt")
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output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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end = time.time()
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print(output_str)
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print(f'Inference time: {end-st} s')
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elif model_path == 'THUDM/chatglm-6b':
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model = AutoModel.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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input_str = "晚上睡不着应该怎么办"
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with torch.inference_mode():
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st = time.time()
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input_ids = tokenizer.encode(input_str, return_tensors="pt")
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output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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end = time.time()
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print(output_str)
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print(f'Inference time: {end-st} s')
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