9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
78 lines
3.4 KiB
Python
78 lines
3.4 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 ipex_llm.transformers import AutoModelForCausalLM
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from modelscope import AutoTokenizer
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# you could tune the prompt based on your own model,
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BAICHUAN_PROMPT_FORMAT = "<human>{prompt} <bot>"
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Example of saving and loading the optimized model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="baichuan-inc/Baichuan2-7B-Chat",
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help='The ModelScope repo id for the Baichuan model to be downloaded to be downloaded'
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', or the path to the ModelScope checkpoint folder')
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parser.add_argument('--save-path', type=str, default=None,
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help='The path to save the low-bit model.')
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parser.add_argument('--load-path', type=str, default=None,
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help='The path to load the low-bit model.')
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parser.add_argument('--prompt', type=str, default="What is AI?",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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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_path = args.load_path
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if load_path:
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model = AutoModelForCausalLM.load_low_bit(load_path, trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained(load_path, trust_remote_code=True)
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else:
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=True,
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model_hub='modelscope')
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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save_path = args.save_path
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if save_path:
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model.save_low_bit(save_path)
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tokenizer.save_pretrained(save_path)
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print(f"Model and tokenizer are saved to {save_path}")
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# please save/load model before you run it on GPU
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model = model.to('xpu')
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = BAICHUAN_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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# ipex model needs a warmup, then inference time can be accurate
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict)
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st = time.time()
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict)
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torch.xpu.synchronize()
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end = time.time()
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output = output.cpu()
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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