chat.py

import json

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 tokenizer_image_token, process_images, get_model_name_from_path
from PIL import Image
import base64
import io
import os
from omnilmm.model.omnilmm import OmniLMMForCausalLM
from omnilmm.model.utils import build_transform
from omnilmm.train.train_utils import omni_preprocess
from transformers import AutoTokenizer, AutoModel
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"


def init_omni_lmm(model_path):
    torch.backends.cuda.matmul.allow_tf32 = True
    disable_torch_init()
    model_name = os.path.expanduser(model_path)
    print(f'Load RLAIF-V-12B model and tokenizer from {model_name}')
    tokenizer = AutoTokenizer.from_pretrained(
        model_name, model_max_length=2048)

    if False:
        # model on multiple devices for small size gpu memory (Nvidia 3090 24G x2)
        with init_empty_weights():
            model = OmniLMMForCausalLM.from_pretrained(model_name, tune_clip=True, torch_dtype=torch.bfloat16)
        model = load_checkpoint_and_dispatch(model, model_name, dtype=torch.bfloat16,
                    device_map="auto",  no_split_module_classes=['Eva','MistralDecoderLayer', 'ModuleList', 'Resampler']
        )
    else:
        model = OmniLMMForCausalLM.from_pretrained(
            model_name, tune_clip=True, torch_dtype=torch.bfloat16
        ).to(device='cuda', dtype=torch.bfloat16)

    image_processor = build_transform(
        is_train=False, input_size=model.model.config.image_size, std_mode='OPENAI_CLIP')

    mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
    assert mm_use_im_start_end

    tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN,
                         DEFAULT_IM_END_TOKEN], special_tokens=True)


    vision_config = model.model.vision_config
    vision_config.im_patch_token = tokenizer.convert_tokens_to_ids(
        [DEFAULT_IMAGE_PATCH_TOKEN])[0]
    vision_config.use_im_start_end = mm_use_im_start_end
    vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids(
        [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
    image_token_len = model.model.config.num_query

    return model, image_processor, image_token_len, tokenizer

def expand_question_into_multimodal(question_text, image_token_len, im_st_token, im_ed_token, im_patch_token):
    if '<image>' in question_text[0]['content']:
        question_text[0]['content'] = question_text[0]['content'].replace(
            '<image>', im_st_token + im_patch_token * image_token_len + im_ed_token)
    else:
        question_text[0]['content'] = im_st_token + im_patch_token * \
            image_token_len + im_ed_token + '\n' + question_text[0]['content']
    return question_text

def wrap_question_for_omni_lmm(question, image_token_len, tokenizer):
    if isinstance(question, str):
        question = [{"role": "user", "content": question}]

    question = expand_question_into_multimodal(
        question, image_token_len, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_IMAGE_PATCH_TOKEN)

    conversation = question
    data_dict = omni_preprocess(sources=[conversation],
                                  tokenizer=tokenizer,
                                  generation=True)

    data_dict = dict(input_ids=data_dict["input_ids"][0],
                     labels=data_dict["labels"][0])
    return data_dict


class RLAIFV12B:
    def __init__(self, model_path) -> None:
        model, img_processor, image_token_len, tokenizer = init_omni_lmm(model_path)
        self.model = model
        self.image_token_len = image_token_len
        self.image_transform = img_processor
        self.tokenizer = tokenizer
        self.model.eval()

    def decode(self, image, input_ids):
        with torch.inference_mode():
            output = self.model.generate_vllm(
                input_ids=input_ids.unsqueeze(0).cuda(),
                images=image.unsqueeze(0).half().cuda(),
                temperature=0.6,
                max_new_tokens=1024,
                num_beams=3,
                do_sample=True,
                output_scores=True,
                return_dict_in_generate=True,
                repetition_penalty=1.1,
                top_k=30,
                top_p=0.9,
            )

            response = self.tokenizer.decode(
                output.sequences[0], skip_special_tokens=True)
            response = response.strip()
            return response

    def chat(self, input):
        im_64 = img2base64(input['image'])
        msgs=json.dumps([{"role": "user", "content": input['question']}])

        try:
            image = Image.open(io.BytesIO(base64.b64decode(im_64))).convert('RGB')
        except Exception as e:
            return "Image decode error"

        msgs = json.loads(msgs)
        input_ids = wrap_question_for_omni_lmm(
            msgs, self.image_token_len, self.tokenizer)['input_ids']
        input_ids = torch.as_tensor(input_ids)
        image = self.image_transform(image)

        out = self.decode(image, input_ids)

        return out

def img2base64(file_name):
    with open(file_name, 'rb') as f:
        encoded_string = base64.b64encode(f.read())
        return encoded_string

class RLAIFV7B:
    def __init__(self, model_path) -> None:
        disable_torch_init()
        model_name='llava-v1.5-7b'
        tokenizer, model, image_processor, context_len = load_pretrained_model(
        model_path, model_base=None,model_name=model_name, device_map={"": 'cuda'})
        self.tokenizer=tokenizer
        self.model=model
        self.image_processor=image_processor
        self.context_len=context_len

    def chat(self, input):
        msgs = input['question']
        if self.model.config.mm_use_im_start_end:
            msgs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + msgs
        else:
            msgs = DEFAULT_IMAGE_TOKEN + '\n' + msgs

        image = Image.open(input['image']).convert('RGB')
        conv = conv_templates["llava_v1"].copy()
        conv.append_message(conv.roles[0], msgs)
        conv.append_message(conv.roles[1], None)
        prompt = conv.get_prompt()

        input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
        image_tensor = process_images([image], self.image_processor, self.model.config)[0]
        with torch.inference_mode():
            output_ids = self.model.generate(
                input_ids,
                images=image_tensor.unsqueeze(0).half().cuda(),
                image_sizes=[image.size],
                do_sample=False,
                temperature=0,
                num_beams=3,
                max_new_tokens=1024,
                use_cache=True)
        outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
        return outputs


class RLAIFVChat:
    def __init__(self, model_path) -> None:
        if '12B' in model_path:
            self.model = RLAIFV12B(model_path)
        else:
            self.model = RLAIFV7B(model_path)

    def chat(self, input):
        return self.model.chat(input)


if __name__ == '__main__':

    chat_model = RLAIFVChat('RLAIF-V/RLAIF-V-7B')  # or 'HaoyeZhang/RLAIF-V-12B'
    image_path="./examples/test.jpeg"
    msgs = "Why did the car in the picture stop?"
    inputs = {"image": image_path, "question": msgs}
    answer = chat_model.chat(inputs)
    print(answer)