0284801fbd
* Set BIGDL_IMPORT_IPEX default to true, i.e., auto import IPEX for XPU. * Remove import intel_extension_for_pytorch as ipex from GPU example. * Add support for bigdl-core-xe-21.
99 lines
3.8 KiB
Python
99 lines
3.8 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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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation import GenerationConfig
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from bigdl.llm import optimize_model
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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 Qwen-VL 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 model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--n-predict', type=int, default=32, help='Max tokens to predict')
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current_path = os.path.dirname(os.path.abspath(__file__))
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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# Load model
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map="cpu", trust_remote_code=True)
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# With only one line to enable BigDL-LLM optimization on model
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# For successful BigDL-LLM optimization on Qwen-VL-Chat, skip the 'c_fc' and 'out_proj' modules during optimization
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model = optimize_model(model,
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low_bit='sym_int4',
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modules_to_not_convert=['c_fc', 'out_proj'])
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model = model.to('xpu')
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# Due to issue https://github.com/intel/intel-extension-for-pytorch/issues/454,
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# currently put interpolation execution into cpu
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def to_cpu(module, input, output):
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return output.to("cpu")
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def to_xpu(module, input):
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return (input[0].to("xpu"),)
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model.transformer.visual.ln_pre.register_forward_hook(to_cpu)
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model.transformer.visual.transformer.register_forward_pre_hook(to_xpu)
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# Specify hyperparameters for generation (No need to do this if you are using transformers>=4.32.0)
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model.generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# Session ID
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session_id = 1
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while True:
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print('-'*20, 'Session %d' % session_id, '-'*20)
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image_input = input(f' Please input a picture: ')
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if image_input.lower() == 'exit' : # type 'exit' to quit the dialouge
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break
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text_input = input(f' Please enter the text: ')
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if text_input.lower() == 'exit' : # type 'exit' to quit the dialouge
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break
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if session_id == 1:
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history = None
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all_input = [{'image': image_input}, {'text': text_input}]
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input_list = [_input for _input in all_input if list(_input.values())[0] != '']
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if len(input_list) == 0:
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print("Input list should not be empty. Please try again with valid input.")
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continue
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query = tokenizer.from_list_format(input_list)
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response, history = model.chat(tokenizer, query = query, history = history)
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torch.xpu.synchronize()
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print('-'*10, 'Response', '-'*10)
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print(response, '\n')
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image = tokenizer.draw_bbox_on_latest_picture(response, history)
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if image is not None:
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image.save(os.path.join(current_path, f'Session_{session_id}.png'), )
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session_id += 1
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