9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
80 lines
No EOL
3.6 KiB
Python
80 lines
No EOL
3.6 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, transformers
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import sys, os, time
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import intel_extension_for_pytorch as ipex
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import argparse
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from transformers import LlamaTokenizer, AutoModelForCausalLM
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from ipex_llm import optimize_model
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# Refer to https://huggingface.co/IEITYuan/Yuan2-2B-hf#Usage
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YUAN2_PROMPT_FORMAT = """
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{prompt}
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Generate text using Yuan2-2B model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="IEITYuan/Yuan2-2B-hf",
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help='The huggingface repo id for the Yuan2 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 for the model')
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parser.add_argument('--n-predict', type=int, default=100,
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help='Number of tokens to generate')
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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 tokenizer
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print("Creating tokenizer...")
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tokenizer = LlamaTokenizer.from_pretrained(model_path, add_eos_token=False, add_bos_token=False, eos_token='<eod>')
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tokenizer.add_tokens(['<sep>', '<pad>', '<mask>', '<predict>', '<FIM_SUFFIX>', '<FIM_PREFIX>', '<FIM_MIDDLE>','<commit_before>',
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'<commit_msg>','<commit_after>','<jupyter_start>','<jupyter_text>','<jupyter_code>','<jupyter_output>','<empty_output>'], special_tokens=True)
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# Load model
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print("Creating model...")
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model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype='auto', low_cpu_mem_usage=True).eval()
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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 optimize_model 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(model)
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# Convert the model to xpu
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model = model.to('xpu')
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prompt = YUAN2_PROMPT_FORMAT.format(prompt=args.prompt)
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inputs = tokenizer(prompt, return_tensors="pt")["input_ids"]
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# Convert the inputs to xpu
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inputs = inputs.to('xpu')
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# Default warmup since the first generate() is slow
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outputs = model.generate(inputs, do_sample=True, top_k=5, max_length=args.n_predict)
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print('Finish warmup')
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# Measure the inference time
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start_time = 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 BigDL-LLM INT4 optimizations
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outputs = model.generate(inputs, do_sample=True, top_k=5, max_length=args.n_predict)
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end_time = time.time()
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output_str = tokenizer.decode(outputs[0])
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print(f'Inference time: {end_time - start_time} seconds')
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print('-'*20, 'Output', '-'*20)
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print(output_str) |