101 lines
3.9 KiB
Python
101 lines
3.9 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 os
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import time
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import argparse
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import requests
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import torch
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from PIL import Image
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer, CLIPImageProcessor
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for OpenGVLab/InternVL2-4B model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="OpenGVLab/InternVL2-4B",
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help='The huggingface repo id for the OpenGVLab/InternVL2-4B 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='https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg',
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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="What is in the image?",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=64, 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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n_predict = args.n_predict
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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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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 = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True,
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load_in_low_bit="sym_int4",
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modules_to_not_convert=["vision_model"])
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model = model.half().to('xpu')
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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model.eval()
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query = args.prompt
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image_processor = CLIPImageProcessor.from_pretrained(model_path)
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if os.path.exists(image_path):
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image = Image.open(image_path).convert('RGB')
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else:
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image = Image.open(requests.get(image_path, stream=True).raw).convert('RGB')
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pixel_values = image_processor(images=[image], return_tensors='pt').pixel_values
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pixel_values = pixel_values.to('xpu')
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question = "<image>" + query
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generation_config = {
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"max_new_tokens": n_predict,
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"do_sample": False,
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}
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with torch.inference_mode():
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# ipex_llm model needs a warmup, then inference time can be accurate
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model.chat(
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pixel_values=None,
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question=question,
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generation_config=generation_config,
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tokenizer=tokenizer,
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)
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st = time.time()
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res = model.chat(
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tokenizer=tokenizer,
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pixel_values=pixel_values,
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question=question,
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generation_config=generation_config,
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history=[]
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)
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torch.xpu.synchronize()
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end = time.time()
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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, 'Input Prompt', '-'*20)
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print(args.prompt)
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print('-'*20, 'Chat Output', '-'*20)
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print(res)
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