350fae285d
* Add qwen2-vl example * complete generate.py & readme * improve lint style * update 1-6 * update main readme * Format and other small fixes --------- Co-authored-by: Yuwen Hu <yuwen.hu@intel.com>
126 lines
4.9 KiB
Python
126 lines
4.9 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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import numpy as np
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from ipex_llm.transformers import Qwen2VLForConditionalGeneration
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from qwen_vl_utils import process_vision_info
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using generate() API for Qwen2-VL model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen2-VL-7B-Instruct",
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help='The huggingface repo id for the Qwen2-VL 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="图片里有什么？",
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help='Prompt to infer')
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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('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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parser.add_argument('--modelscope', action="store_true", default=False,
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help="Use models from modelscope")
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args = parser.parse_args()
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if args.modelscope:
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from modelscope import AutoProcessor
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model_hub = 'modelscope'
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else:
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from transformers import AutoProcessor
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model_hub = 'huggingface'
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model_path = args.repo_id_or_model_path
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model = Qwen2VLForConditionalGeneration.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=True,
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trust_remote_code=True,
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modules_to_not_convert=["vision"],
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use_cache=True,
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model_hub=model_hub)
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# Use .float() for better output, and use .half() for better speed
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model = model.half().to("xpu")
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# The following code for generation is adapted from https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct#quickstart
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# The default range for the number of visual tokens per image in the model is 4-16384.
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# You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280,
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# to balance speed and memory usage.
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min_pixels = 256*28*28
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max_pixels = 1280*28*28
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processor = AutoProcessor.from_pretrained(model_path, min_pixels=min_pixels, max_pixels=max_pixels)
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prompt = args.prompt
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image_path = args.image_url_or_path
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with torch.inference_mode():
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": image_path,
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},
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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(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to('xpu')
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# ipex_llm model needs a warmup, then inference time can be accurate
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generated_ids = model.generate(
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**inputs,
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max_new_tokens=args.n_predict
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)
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st = time.time()
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generated_ids = model.generate(
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**inputs,
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max_new_tokens=args.n_predict
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)
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torch.xpu.synchronize()
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end = time.time()
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generated_ids = generated_ids.cpu()
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
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]
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response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(f'Inference time: {end-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(response)
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