d11f257ee7
* Add init example for omni mode * Small fix * Small fix * Add chat example * Remove lagecy link * Further update link * Add readme * Small fix * Update main readme link * Update based on comments * Small fix * Small fix * Small fix
119 lines
4.4 KiB
Python
119 lines
4.4 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 torch
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import librosa
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import argparse
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from PIL import Image
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from transformers import AutoTokenizer
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from ipex_llm.transformers import AutoModel
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Chat with MiniCPM-o-2_6 with text/audio/image')
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parser.add_argument('--repo-id-or-model-path', type=str, default="openbmb/MiniCPM-o-2_6",
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help='The Hugging Face or ModelScope repo id for the MiniCPM-o-2_6 model to be downloaded'
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', or the path to the checkpoint folder')
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parser.add_argument('--image-path', type=str,
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help='The path to the image for inference.')
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parser.add_argument('--audio-path', type=str,
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help='The path to the audio for inference.')
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parser.add_argument('--prompt', type=str,
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help='Prompt for inference.')
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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_path
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audio_path = args.audio_path
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modules_to_not_convert = []
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init_vision = False
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init_audio = False
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if image_path is not None and os.path.exists(image_path):
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init_vision = True
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modules_to_not_convert += ["vpm", "resampler"]
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if audio_path is not None and os.path.exists(audio_path):
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init_audio = True
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modules_to_not_convert += ["apm"]
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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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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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attn_implementation='sdpa',
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use_cache=True,
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init_vision=init_vision,
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init_audio=init_audio,
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init_tts=False,
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modules_to_not_convert=modules_to_not_convert)
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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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# The following code for generation is adapted from
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# https://huggingface.co/openbmb/MiniCPM-o-2_6#addressing-various-audio-understanding-tasks and
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# https://huggingface.co/openbmb/MiniCPM-o-2_6#chat-with-single-image
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content = []
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if init_vision:
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image_input = Image.open(image_path).convert('RGB')
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content.append(image_input)
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if args.prompt is not None:
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content.append(args.prompt)
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if init_audio:
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audio_input, _ = librosa.load(audio_path, sr=16000, mono=True)
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content.append(audio_input)
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messages = [{'role': 'user', 'content': content}]
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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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msgs=messages,
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tokenizer=tokenizer,
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sampling=True,
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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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response = model.chat(
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msgs=messages,
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tokenizer=tokenizer,
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sampling=True,
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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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print(f'Inference time: {end-st} s')
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print('-'*20, 'Input Image Path', '-'*20)
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print(image_path)
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print('-'*20, 'Input Audio Path', '-'*20)
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print(audio_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(response)
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