76 lines
3.5 KiB
Python
76 lines
3.5 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 argparse
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import os
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import torch
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import time
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from transformers import AutoTokenizer
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from ipex_llm.transformers import AutoModelForCausalLM, init_pipeline_parallel
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init_pipeline_parallel()
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torch.manual_seed(1234)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for large vision language model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen-VL-Chat",
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help='The huggingface repo id for the Qwen-VL-Chat 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="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="这是什么？",
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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('--low-bit', type=str, default='sym_int4', help='The quantization type the model will convert to.')
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parser.add_argument('--gpu-num', type=int, default=2, help='GPU number to use')
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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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# Load model
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# For successful IPEX-LLM optimization on Qwen-VL-Chat, skip the 'c_fc' and 'out_proj' modules during optimization
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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_low_bit=args.low_bit,
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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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torch_dtype=torch.float32,
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modules_to_not_convert=['c_fc', 'out_proj'],
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pipeline_parallel_stages=args.gpu_num)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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local_rank = torch.distributed.get_rank()
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all_input = [{'image': args.image_url_or_path}, {'text': args.prompt}]
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input_list = [_input for _input in all_input if list(_input.values())[0] != '']
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query = tokenizer.from_list_format(input_list)
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with torch.inference_mode():
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response, _ = model.chat(tokenizer, query=query, history=[])
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torch.xpu.synchronize()
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if local_rank == args.gpu_num - 1:
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print('-'*20, 'Input', '-'*20)
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print(f'Message: {all_input}')
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print('-'*20, 'Output', '-'*20)
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print(response)
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