9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
66 lines
2.9 KiB
Python
66 lines
2.9 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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from transformers import FuyuProcessor
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import torch
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import argparse
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import time
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from PIL import Image
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from ipex_llm.transformers import AutoModelForCausalLM
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Fuyu model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="adept/fuyu-8b",
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help='The huggingface repo id for the Fuyu 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="Generate a coco-style caption.",
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help='Prompt to infer')
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parser.add_argument('--image-path', type=str, required=True,
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help='Image path for the input image that the chat will focus on')
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parser.add_argument('--n-predict', type=int, default=512, 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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prompt = args.prompt
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image = Image.open(args.image_path)
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# Load model
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# For successful BigDL-LLM optimization on Fuyu, skip the 'vision_embed_tokens' module during optimization
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map='cpu',
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load_in_4bit = True,
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trust_remote_code=True,
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modules_to_not_convert=['vision_embed_tokens'])
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# Load processor
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processor = FuyuProcessor.from_pretrained(model_path)
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# Generate predicted tokens
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with torch.inference_mode():
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inputs = processor(text=prompt, images=image, return_tensors="pt")
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st = time.time()
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generation_outputs = model.generate(**inputs,
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max_new_tokens=args.n_predict)
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end = time.time()
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outputs = processor.batch_decode(generation_outputs[:, -args.n_predict:], skip_special_tokens=True)
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print(f'Inference time: {end-st} s')
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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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for output in outputs:
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# '\x04' is the "beginning of answer" token
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# See https://huggingface.co/adept/fuyu-8b#how-to-use
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answer = output.split('\x04 ', 1)[1] if '\x04' in output else ''
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print(answer)
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