136 lines
5.4 KiB
Python
136 lines
5.4 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 AutoModel
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from transformers import AutoTokenizer, AutoProcessor
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for openbmb/MiniCPM-V-2_6 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-V-2_6",
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help='The huggingface repo id for the openbmb/MiniCPM-V-2_6 model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument("--lowbit-path", type=str,
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default="",
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help="The path to the saved model folder with IPEX-LLM low-bit optimization. "
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"Leave it blank if you want to load from the original model. "
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"If the path does not exist, model with low-bit optimization will be saved there."
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"Otherwise, model with low-bit optimization will be loaded from the path.",
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)
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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="What is in the image?",
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help='Prompt to infer')
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parser.add_argument('--stream', action='store_true',
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help='Whether to chat in streaming mode')
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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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lowbit_path = args.lowbit_path
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if not lowbit_path or not os.path.exists(lowbit_path):
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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 = AutoModel.from_pretrained(model_path,
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load_in_low_bit="sym_int4",
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optimize_model=True,
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trust_remote_code=True,
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use_cache=True,
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modules_to_not_convert=["vpm", "resampler"])
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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else:
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model = AutoModel.load_low_bit(lowbit_path,
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optimize_model=True,
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trust_remote_code=True,
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use_cache=True,
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modules_to_not_convert=["vpm", "resampler"])
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tokenizer = AutoTokenizer.from_pretrained(lowbit_path,
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trust_remote_code=True)
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model.eval()
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if lowbit_path and not os.path.exists(lowbit_path):
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processor = AutoProcessor.from_pretrained(model_path,
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trust_remote_code=True)
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model.save_low_bit(lowbit_path)
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tokenizer.save_pretrained(lowbit_path)
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processor.save_pretrained(lowbit_path)
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model = model.half().to('xpu')
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query = args.prompt
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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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# Generate predicted tokens
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# here the prompt tuning refers to https://huggingface.co/openbmb/MiniCPM-V-2_6/blob/main/README.md
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msgs = [{'role': 'user', 'content': [image, args.prompt]}]
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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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image=None,
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msgs=msgs,
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tokenizer=tokenizer,
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)
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if args.stream:
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res = model.chat(
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image=None,
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msgs=msgs,
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tokenizer=tokenizer,
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stream=True
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)
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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, 'Stream Chat Output', '-'*20)
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for new_text in res:
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print(new_text, flush=True, end='')
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else:
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st = time.time()
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res = model.chat(
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image=None,
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msgs=msgs,
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tokenizer=tokenizer,
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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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