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import argparse |
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import torch |
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from mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN |
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from mplug_owl2.conversation import conv_templates, SeparatorStyle |
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from mplug_owl2.model.builder import load_pretrained_model |
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from mplug_owl2.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria |
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from PIL import Image |
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import requests |
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from PIL import Image |
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from io import BytesIO |
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from transformers import TextStreamer |
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def disable_torch_init(): |
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""" |
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Disable the redundant torch default initialization to accelerate model creation. |
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""" |
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import torch |
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setattr(torch.nn.Linear, "reset_parameters", lambda self: None) |
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setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) |
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def load_image(image_file): |
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if image_file.startswith('http://') or image_file.startswith('https://'): |
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response = requests.get(image_file) |
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image = Image.open(BytesIO(response.content)).convert('RGB') |
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else: |
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image = Image.open(image_file).convert('RGB') |
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return image |
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def main(args): |
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disable_torch_init() |
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model_name = get_model_name_from_path(args.model_path) |
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tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device) |
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conv_mode = "mplug_owl2" |
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if args.conv_mode is not None and conv_mode != args.conv_mode: |
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print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode)) |
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else: |
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args.conv_mode = conv_mode |
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conv = conv_templates[args.conv_mode].copy() |
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roles = conv.roles |
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image = load_image(args.image_file) |
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image_tensor = process_images([image], image_processor, args) |
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if type(image_tensor) is list: |
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image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor] |
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else: |
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image_tensor = image_tensor.to(model.device, dtype=torch.float16) |
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while True: |
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try: |
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inp = input(f"{roles[0]}: ") |
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except EOFError: |
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inp = "" |
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if not inp: |
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print("exit...") |
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break |
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print(f"{roles[1]}: ", end="") |
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if image is not None: |
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inp = DEFAULT_IMAGE_TOKEN + inp |
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conv.append_message(conv.roles[0], inp) |
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image = None |
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else: |
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conv.append_message(conv.roles[0], inp) |
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conv.append_message(conv.roles[1], None) |
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prompt = conv.get_prompt() |
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input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device) |
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stop_str = conv.sep if conv.sep_style not in [SeparatorStyle.TWO, SeparatorStyle.TWO_NO_SYS] else conv.sep2 |
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keywords = [stop_str] |
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stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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with torch.inference_mode(): |
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output_ids = model.generate( |
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input_ids, |
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images=image_tensor, |
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do_sample=True, |
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temperature=args.temperature, |
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max_new_tokens=args.max_new_tokens, |
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streamer=streamer, |
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use_cache=True, |
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stopping_criteria=[stopping_criteria]) |
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outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip() |
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conv.messages[-1][-1] = outputs |
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if args.debug: |
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print("\n", {"prompt": prompt, "outputs": outputs}, "\n") |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--model-path", type=str, default="facebook/opt-350m") |
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parser.add_argument("--model-base", type=str, default=None) |
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parser.add_argument("--image-file", type=str, required=True) |
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parser.add_argument("--device", type=str, default="cuda") |
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parser.add_argument("--conv-mode", type=str, default=None) |
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parser.add_argument("--temperature", type=float, default=0.2) |
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parser.add_argument("--max-new-tokens", type=int, default=512) |
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parser.add_argument("--load-8bit", action="store_true") |
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parser.add_argument("--load-4bit", action="store_true") |
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parser.add_argument("--debug", action="store_true") |
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parser.add_argument("--image-aspect-ratio", type=str, default='pad') |
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args = parser.parse_args() |
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main(args) |