cd5e22cee5
* update-llava-example * add warmup * small fix on llava example * remove space& extra print prompt * renew example * small fix --------- Co-authored-by: Jinhe Tang <jin.tang1337@gmail.com>
86 lines
3.2 KiB
Python
86 lines
3.2 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 argparse
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import os
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import requests
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import time
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import torch
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from PIL import Image
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from transformers import LlavaForConditionalGeneration, AutoProcessor
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from ipex_llm import optimize_model
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for LLaVA model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="llava-hf/llava-1.5-7b-hf",
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help='The huggingface repo id for the LLaVA model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--image-url-or-path', type=str,
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default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg',
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help='The URL or path to the image to infer')
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parser.add_argument('--prompt', type=str, default="Describe image in detail",
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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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image_path = args.image_url_or_path
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prompt = args.prompt
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model = LlavaForConditionalGeneration.from_pretrained(model_path)
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model = optimize_model(model, low_bit='sym_int4').eval()
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model = model.half().to("xpu")
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processor = AutoProcessor.from_pretrained(model_path)
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# here the prompt tuning refers to https://huggingface.co/llava-hf/llava-1.5-7b-hf#using-pure-transformers
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": prompt}
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]
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}
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]
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text = processor.apply_chat_template(messages, add_generation_prompt=True)
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if os.path.exists(image_path):
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image = Image.open(image_path)
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else:
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image = Image.open(requests.get(image_path, stream=True).raw)
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inputs = processor(text=text, images=image, return_tensors="pt").to('xpu')
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with torch.inference_mode():
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# warmup
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output = model.generate(**inputs, do_sample=False, 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(**inputs, do_sample=False, max_new_tokens=args.n_predict)
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et = time.time()
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output_str = processor.decode(output[0])
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print(f'Inference time: {et-st} s')
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print('-'*20, 'Input Image', '-'*20)
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print(image_path)
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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)
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