9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
79 lines
3 KiB
Python
79 lines
3 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 AutoModelForCausalLM, AutoTokenizer
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from ipex_llm import optimize_model
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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/Deci/DeciLM-7B-instruct#prompt-template
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SYSTEM_PROMPT_TEMPLATE ="""
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### System:
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You are an AI assistant that follows instruction extremely well. Help as much as you can.
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### User:
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{prompt}
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### Assistant:
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for DeciLM-7B model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="Deci/DeciLM-7B-instruct",
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help='The huggingface repo id for the DeciLM-7B (e.g. `Deci/DeciLM-7B-instruct`) 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
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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trust_remote_code=True,
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)
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# With only one line to enable BigDL-LLM optimization on model
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# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
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# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
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model = optimize_model(
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model,
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cpu_embedding=True
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)
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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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tokenizer.pad_token = tokenizer.eos_token
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = SYSTEM_PROMPT_TEMPLATE.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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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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