440cfe18ed
* modify aquila * modify aquila2 * add baichuan * modify baichuan2 * modify blue-lm * modify chatglm3 * modify chinese-llama2 * modiy codellama * modify distil-whisper * modify dolly-v1 * modify dolly-v2 * modify falcon * modify flan-t5 * modify gpt-j * modify internlm * modify llama2 * modify mistral * modify mixtral * modify mpt * modify phi-1_5 * modify qwen * modify qwen-vl * modify replit * modify solar * modify starcoder * modify vicuna * modify voiceassistant * modify whisper * modify yi * modify aquila2 * modify baichuan * modify baichuan2 * modify blue-lm * modify chatglm2 * modify chatglm3 * modify codellama * modify distil-whisper * modify dolly-v1 * modify dolly-v2 * modify flan-t5 * modify llama2 * modify llava * modify mistral * modify mixtral * modify phi-1_5 * modify qwen-vl * modify replit * modify solar * modify starcoder * modify yi * correct the comments * remove cpu_embedding in code for whisper and distil-whisper * remove comment * remove cpu_embedding for voice assistant * revert modify voice assistant * modify for voice assistant * add comment for voice assistant * fix comments * fix comments
74 lines
3.1 KiB
Python
74 lines
3.1 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 bigdl.llm import optimize_model
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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='Predict Tokens using `generate()` API for Baichuan model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="baichuan-inc/Baichuan-13B-Chat",
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help='The huggingface repo id for the Baichuan model 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="AI是什么？",
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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(model_path,
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trust_remote_code=True,
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torch_dtype='auto',
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low_cpu_mem_usage=True)
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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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model = model.to('xpu')
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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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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# start inference
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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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