88 lines
No EOL
3.8 KiB
Python
88 lines
No EOL
3.8 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 time
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import argparse
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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# you could tune the prompt based on your own model,
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# here the prompt tuning refers to https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Chat#gpu-inference
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RedPajama_PROMPT_FORMAT = "<human>: {prompt}\n<bot>:"
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Transformer INT4 gpu example for RedPajama model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="togethercomputer/RedPajama-INCITE-7B-Chat",
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help='The huggingface repo id for the RedPajama to be downloaded'
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', or the path to the huggingface checkpoint folder')
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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 model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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model = AutoModelForCausalLM.from_pretrained(model_path,
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trust_remote_code=True,
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load_in_4bit=True,
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optimize_model=True,
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use_cache=True)
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model = model.to('xpu')
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = RedPajama_PROMPT_FORMAT.format(prompt=args.prompt)
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inputs = tokenizer(prompt, return_tensors='pt').to('xpu')
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# ipex_llm model needs a warmup, then inference time can be accurate
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output = model.generate(**inputs,
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max_new_tokens=args.n_predict,
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do_sample=True,
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temperature=0.7,
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top_p=0.7,
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top_k=50,
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return_dict_in_generate=True)
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# start inference
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st = time.time()
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# if your selected model is capable of utilizing previous key/value attentions
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# to enhance decoding speed, but has `"use_cache": false` in its model config,
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# it is important to set `use_cache=True` explicitly in the `generate` function
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# to obtain optimal performance with IPEX-LLM INT4 optimizations
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output = model.generate(**inputs,
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max_new_tokens=args.n_predict,
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do_sample=True,
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temperature=0.7,
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top_p=0.7,
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top_k=50,
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return_dict_in_generate=True)
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torch.xpu.synchronize()
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end = time.time()
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output_str = tokenizer.decode(output.sequences[0])
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Prompt', '-'*20)
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print(prompt)
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print('-'*20, 'Output', '-'*20)
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print(output_str) |