cogagent-vqa-hf

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, LlamaTokenizer
import argparse

parser = argparse.ArgumentParser()
parser.add_argument("--quant", choices=[4], type=int, default=None, help='quantization bits')
parser.add_argument("--from_pretrained", type=str, default="THUDM/cogagent-chat-hf", help='pretrained ckpt')
parser.add_argument("--local_tokenizer", type=str, default="lmsys/vicuna-7b-v1.5", help='tokenizer path')
parser.add_argument("--fp16", action="store_true")
parser.add_argument("--bf16", action="store_true")

args = parser.parse_args()
MODEL_PATH = args.from_pretrained
TOKENIZER_PATH = args.local_tokenizer
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'

tokenizer = LlamaTokenizer.from_pretrained(TOKENIZER_PATH)
if args.bf16:
    torch_type = torch.bfloat16
else:
    torch_type = torch.float16

print("========Use torch type as:{} with device:{}========\n\n".format(torch_type, DEVICE))

if args.quant:
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_PATH,
        torch_dtype=torch_type,
        low_cpu_mem_usage=True,
        load_in_4bit=True,
        trust_remote_code=True
    ).eval()
else:
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_PATH,
        torch_dtype=torch_type,
        low_cpu_mem_usage=True,
        load_in_4bit=args.quant is not None,
        trust_remote_code=True
    ).to(DEVICE).eval()

while True:
    image_path = input("image path >>>>> ")
    if image_path == "stop":
        break

    image = Image.open(image_path).convert('RGB')
    history = []
    while True:
        query = input("Human:")
        if query == "clear":
            break
        input_by_model = model.build_conversation_input_ids(tokenizer, query=query, history=history, images=[image])
        inputs = {
            'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE),
            'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE),
            'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE),
            'images': [[input_by_model['images'][0].to(DEVICE).to(torch_type)]],
        }
        if 'cross_images' in input_by_model and input_by_model['cross_images']:
            inputs['cross_images'] = [[input_by_model['cross_images'][0].to(DEVICE).to(torch_type)]]

        # add any transformers params here.
        gen_kwargs = {"max_length": 2048,
                      "temperature": 0.9,
                      "do_sample": False}
        with torch.no_grad():
            outputs = model.generate(**inputs, **gen_kwargs)
            outputs = outputs[:, inputs['input_ids'].shape[1]:]
            response = tokenizer.decode(outputs[0])
            response = response.split("</s>")[0]
            print("\nCog:", response)
        history.append((query, response))

python cli_demo_hf.py --bf16

@misc{hong2023cogagent,
      title={CogAgent: A Visual Language Model for GUI Agents}, 
      author={Wenyi Hong and Weihan Wang and Qingsong Lv and Jiazheng Xu and Wenmeng Yu and Junhui Ji and Yan Wang and Zihan Wang and Yuxiao Dong and Ming Ding and Jie Tang},
      year={2023},
      eprint={2312.08914},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{wang2023cogvlm,
      title={CogVLM: Visual Expert for Pretrained Language Models}, 
      author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
      year={2023},
      eprint={2311.03079},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}