cli.py

import argparse
import torch

from llava.constants import (
    IMAGE_TOKEN_INDEX,
    DEFAULT_IMAGE_TOKEN,
    DEFAULT_IM_START_TOKEN,
    DEFAULT_IM_END_TOKEN,
)
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import (
    process_images,
    tokenizer_image_token,
    get_model_name_from_path,
)

from PIL import Image

import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer


def load_image(image_file):
    if image_file.startswith("http://") or image_file.startswith("https://"):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert("RGB")
    else:
        image = Image.open(image_file).convert("RGB")
    return image


def main(args):
    # Model
    disable_torch_init()

    model_name = get_model_name_from_path(args.model_path)
    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,
    )

    if "llama-2" in model_name.lower():
        conv_mode = "llava_llama_2"
    elif "mistral" in model_name.lower():
        conv_mode = "mistral_instruct"
    elif "v1.6-34b" in model_name.lower():
        conv_mode = "chatml_direct"
    elif "v1" in model_name.lower():
        conv_mode = "llava_v1"
    elif "mpt" in model_name.lower():
        conv_mode = "mpt"
    else:
        conv_mode = "llava_v0"

    if args.conv_mode is not None and conv_mode != args.conv_mode:
        print(
            "[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format(
                conv_mode, args.conv_mode, args.conv_mode
            )
        )
    else:
        args.conv_mode = conv_mode

    conv = conv_templates[args.conv_mode].copy()
    if "mpt" in model_name.lower():
        roles = ("user", "assistant")
    else:
        roles = conv.roles

    if args.image_file is not None:
        image = load_image(args.image_file)
        image_size = image.size
        # Similar operation in model_worker.py
        image_tensor = process_images([image], image_processor, model.config)
        if type(image_tensor) is list:
            image_tensor = [
                image.to(model.device, dtype=torch.float16) for image in image_tensor
            ]
        else:
            image_tensor = image_tensor.to(model.device, dtype=torch.float16)
    else:
        image = True
        image_size = (1024, 1024)
        image_tensor = torch.zeros(1, 5, 3, 336, 336)
        image_tensor = image_tensor.to(model.device, dtype=torch.float16)

    while True:
        try:
            inp = input(f"{roles[0]}: ")
        except EOFError:
            inp = ""
        if not inp:
            print("exit...")
            break

        print(f"{roles[1]}: ", end="")

        if image is not None:
            # first message
            if model.config.mm_use_im_start_end:
                inp = (
                    DEFAULT_IM_START_TOKEN
                    + DEFAULT_IMAGE_TOKEN
                    + DEFAULT_IM_END_TOKEN
                    + "\n"
                    + inp
                )
            else:
                inp = inp.replace(DEFAULT_IMAGE_TOKEN, "").strip()
                inp = DEFAULT_IMAGE_TOKEN + "\n" + inp
                inp = inp.strip()
            image = None

        conv.append_message(conv.roles[0], inp)
        conv.append_message(conv.roles[1], None)
        prompt = conv.get_prompt()

        input_ids = (
            tokenizer_image_token(
                prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
            )
            .unsqueeze(0)
            .to(model.device)
        )
        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
        keywords = [stop_str]
        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

        with torch.inference_mode():
            output_ids = model.generate(
                input_ids,
                images=image_tensor,
                image_sizes=[image_size],
                do_sample=True if args.temperature > 0 else False,
                temperature=args.temperature,
                max_new_tokens=args.max_new_tokens,
                streamer=streamer,
                use_cache=True,
            )

        outputs = tokenizer.decode(output_ids[0]).strip()
        conv.messages[-1][-1] = outputs

        if args.debug:
            print("\n", {"prompt": prompt, "outputs": outputs}, "\n")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    parser.add_argument("--model-base", type=str, default=None)
    parser.add_argument("--image-file", type=str, default=None)
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--conv-mode", type=str, default=None)
    parser.add_argument("--temperature", type=float, default=0.2)
    parser.add_argument("--max-new-tokens", type=int, default=512)
    parser.add_argument("--load-8bit", action="store_true")
    parser.add_argument("--load-4bit", action="store_true")
    parser.add_argument("--debug", action="store_true")
    args = parser.parse_args()
    main(args)