93 lines
4.1 KiB
Python
93 lines
4.1 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 argparse
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import requests
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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 THUDM/glm-4v-9b model')
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parser.add_argument('--repo-id-or-model-path', type=str,
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help='The Hugging Face or ModelScope repo id for the glm-4v model to be downloaded'
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', or the path to the checkpoint folder')
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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('--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 AutoTokenizer
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model_hub = 'modelscope'
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else:
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from transformers import AutoTokenizer
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model_hub = 'huggingface'
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model_path = args.repo_id_or_model_path if args.repo_id_or_model_path else \
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("ZhipuAI/glm-4v-9b" if args.modelscope else "THUDM/glm-4v-9b")
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image_path = args.image_url_or_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 = AutoModelForCausalLM.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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use_cache=True,
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model_hub=model_hub)
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model = model.to('xpu')
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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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)
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else:
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image = Image.open(requests.get(image_path, stream=True).raw)
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# here the prompt tuning refers to https://huggingface.co/THUDM/glm-4v-9b/blob/main/README.md
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inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}],
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add_generation_prompt=True,
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tokenize=True,
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return_tensors="pt",
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return_dict=True) # chat mode
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inputs = inputs.to('xpu')
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# Generate predicted tokens
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with torch.inference_mode():
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gen_kwargs = {"max_length": args.n_predict, "do_sample": True, "top_k": 1}
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st = time.time()
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outputs = model.generate(**inputs, **gen_kwargs)
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outputs = outputs[:, inputs['input_ids'].shape[1]:]
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end = time.time()
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print(f'Inference time: {end-st} s')
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output_str = tokenizer.decode(outputs[0])
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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