internvl_chat.py

import torch
from transformers import AutoTokenizer, AutoConfig, AutoModel, CLIPImageProcessor
import warnings
from PIL import Image
from .base import BaseModel
from ..smp import *
from ..dataset import DATASET_TYPE, DATASET_MODALITY
import pandas as pd
import string
import torch.distributed as dist
import torchvision.transforms as T
import transformers

from torchvision.transforms.functional import InterpolationMode
import re


IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)


def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform


def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio


def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images


def load_image(image_file, input_size=448, max_num=6, upscale=False):
    image = Image.open(image_file).convert('RGB')
    if upscale:
        image = image.resize((image.width * 2, image.height * 2), Image.BILINEAR)
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values


# This function is used to split InternVL2-Llama3-76B
def split_model(model_name):
    import math
    device_map = {}
    num_gpus = torch.cuda.device_count()
    rank, world_size = get_rank_and_world_size()
    num_gpus = num_gpus // world_size

    num_layers_map = {
        'InternVL2-8B': 32,
        'InternVL2-26B': 48,
        'InternVL2-40B': 60,
        'InternVL2-Llama3-76B': 80
    }

    if model_name not in num_layers_map:
        return 'cuda'
    num_layers = num_layers_map[model_name]
    # Since the first GPU will be used for ViT, treat it as 0.5 GPU.
    num_layers_per_gpu = math.ceil(num_layers / (num_gpus - 0.5))
    num_layers_per_gpu = [num_layers_per_gpu] * num_gpus
    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
    layer_cnt = 0
    for i, num_layer in enumerate(num_layers_per_gpu):
        for j in range(num_layer):
            device_map[f'language_model.model.layers.{layer_cnt}'] = rank + world_size * i
            layer_cnt += 1
    device_map['vision_model'] = rank
    device_map['mlp1'] = rank
    device_map['language_model.model.tok_embeddings'] = rank
    device_map['language_model.model.embed_tokens'] = rank
    device_map['language_model.output'] = rank
    device_map['language_model.model.norm'] = rank
    device_map['language_model.lm_head'] = rank
    device_map['language_model.model.rotary_emb'] = rank
    device_map[f'language_model.model.layers.{num_layers - 1}'] = rank
    return device_map


def load_image_mmniah(image_file, dynamic_image_size=True, input_size=448, max_num=6):
    image = Image.open(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    if dynamic_image_size:
        images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    else:
        images = [image]
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values


def split_model_mmniah(model_path):
    num_gpus_per_rank = 8
    num_gpus = torch.cuda.device_count()
    # rank = int(os.getenv('SLURM_PROCID', '0'))
    # local_rank = rank % (num_gpus // num_gpus_per_rank)
    # world_size = int(os.getenv('SLURM_NTASKS', '1'))
    local_rank = 0
    local_world_size = num_gpus // num_gpus_per_rank
    visible_devices = [i for i in range(local_rank, num_gpus, local_world_size)]
    device_map = {}
    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
    num_gpus_for_vit = 1
    num_gpus_for_llm = len(visible_devices) - num_gpus_for_vit
    num_layers = config.llm_config.num_hidden_layers
    num_layers_per_gpu = num_layers // num_gpus_for_llm + 1
    for i in range(num_layers):
        device_idx = min(i // num_layers_per_gpu + num_gpus_for_vit, len(visible_devices) - 1)
        device_map[f'language_model.model.layers.{i}'] = visible_devices[device_idx]
    num_layers = config.vision_config.num_hidden_layers
    num_layers_per_gpu = num_layers // num_gpus_for_vit + 1
    for i in range(num_layers):
        device_idx = min(i // num_layers_per_gpu, num_gpus_for_vit - 1)
        device_map[f'vision_model.encoder.layers.{i}'] = visible_devices[device_idx]
    device_map['vision_model.embeddings'] = visible_devices[0]
    device_map['mlp1'] = visible_devices[num_gpus_for_vit - 1]
    # InternLM2
    device_map['language_model.model.tok_embeddings'] = visible_devices[num_gpus_for_vit]
    device_map['language_model.model.norm'] = visible_devices[-1]
    device_map['language_model.output'] = visible_devices[-1]
    # Qwen2
    device_map['language_model.model.embed_tokens'] = visible_devices[num_gpus_for_vit]
    device_map['language_model.model.norm'] = visible_devices[-1]
    device_map['language_model.lm_head'] = visible_devices[-1]
    return device_map


def extract_answer(text):
    match = re.search(r'(Final answer:|Answer:)\s*(.*)', text, re.IGNORECASE)
    if match:
        return match.group(2).strip()
    return text


class InternVLChat(BaseModel):

    INSTALL_REQ = False
    INTERLEAVE = True

    def __init__(self,
                 model_path='OpenGVLab/InternVL-Chat-V1-5',
                 load_in_8bit=False,
                 cot_prompt=False,
                 version='V1.0',
                 **kwargs):

        assert model_path is not None
        assert version_cmp(transformers.__version__, '4.36.2', 'ge')

        self.cot_prompt = cot_prompt
        self.model_path = model_path
        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)

        # Regular expression to match the pattern 'Image' followed by a number, e.g. Image1
        self.pattern = r'Image(\d+)'
        # Replacement pattern to insert a hyphen between 'Image' and the number, e.g. Image-1
        self.replacement = r'Image-\1'

        # Convert InternVL2 response to dataset format
        # e.g. Image1 -> Image-1

        # Regular expression to match the pattern 'Image-' followed by a number
        self.reverse_pattern = r'Image-(\d+)'
        # Replacement pattern to remove the hyphen (Image-1 -> Image1)
        self.reverse_replacement = r'Image\1'
        self.device = 'cuda'

        if auto_split_flag() and listinstr(['InternVL2-8B', 'InternVL2-26B', 'InternVL2-40B'], model_path):
            device_map = split_model(model_path.split('/')[-1])
            self.model = AutoModel.from_pretrained(
                model_path,
                torch_dtype=torch.bfloat16,
                load_in_8bit=load_in_8bit,
                trust_remote_code=True,
                low_cpu_mem_usage=True,
                device_map=device_map).eval()

        elif listinstr(['InternVL2-Llama3-76B'], model_path):
            device_map = split_model(model_path.split('/')[-1])
            breakpoint()
            self.model = AutoModel.from_pretrained(
                model_path,
                torch_dtype=torch.bfloat16,
                load_in_8bit=load_in_8bit,
                trust_remote_code=True,
                low_cpu_mem_usage=True,
                device_map=device_map).eval()

        elif listinstr(['InternVL-Chat-V1-5'], model_path) and version == "mmniah":
            device_map = split_model_mmniah(model_path)
            self.model = AutoModel.from_pretrained(
                model_path,
                torch_dtype=torch.bfloat16,
                low_cpu_mem_usage=True,
                use_flash_attn=True,
                trust_remote_code=True,
                device_map=device_map).eval()

        else:
            self.model = AutoModel.from_pretrained(
                model_path,
                torch_dtype=torch.bfloat16,
                load_in_8bit=load_in_8bit,
                trust_remote_code=True).eval()
            if not load_in_8bit:
                self.model = self.model.to('cuda')

        self.image_size = self.model.config.vision_config.image_size
        self.version = version
        kwargs_default = dict(do_sample=False, max_new_tokens=2048, top_p=None, num_beams=1)
        kwargs_default.update(kwargs)
        self.kwargs = kwargs_default

        warnings.warn(f'Following kwargs received: {self.kwargs}, will use as generation config. ')

    def use_custom_prompt(self, dataset):
        assert dataset is not None
        if listinstr(['MMDU', 'MME-RealWorld', 'MME-RealWorld-CN', "NIAH"], dataset):
            # For Multi-Turn we don't have custom prompt
            return False
        if DATASET_MODALITY(dataset) == 'VIDEO':
            # For Video benchmarks we don't have custom prompt at here
            return False
        else:
            return True

    def build_multi_choice_prompt(self, line, dataset=None):
        question = line['question']
        hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
        if hint is not None:
            question = hint + '\n' + question

        options = {
            cand: line[cand]
            for cand in string.ascii_uppercase
            if cand in line and not pd.isna(line[cand])
        }
        for key, item in options.items():
            question += f'\n{key}. {item}'
        prompt = question

        if len(options):
            prompt += '\n请直接回答选项字母。' if cn_string(
                prompt) else "\nAnswer with the option's letter from the given choices directly."
        else:
            prompt += '\n请直接回答问题。' if cn_string(prompt) else '\nAnswer the question directly.'

        return prompt

    def build_video_prompt(self, prompt, dataset=None, max_frames=64):
        for start in range(0, max_frames, 8):
            images_to_remove = ''.join([f'<Image-{i}>' for i in range(start + 1, start + 9)])
            prompt = prompt.replace(images_to_remove, '')
        for i in range(max_frames):
            prompt = prompt.replace(f'Image-{i + 1}', f'Frame-{i + 1}')
        if listinstr(['MMBench-Video'], dataset):
            prompt = prompt.replace('\nAnswer:', '')
        elif listinstr(['Video-MME'], dataset):
            prompt = prompt.replace('\nAnswer:', '')
            prompt += "\nAnswer with the option's letter from the given choices directly."
        elif listinstr(['MVBench'], dataset):
            prompt = prompt.replace('Best option:(', '')

        return prompt

    def build_prompt(self, line, dataset=None):
        assert self.use_custom_prompt(dataset)
        assert dataset is None or isinstance(dataset, str)
        tgt_path = self.dump_image(line, dataset)

        if self.version == 'V1.1':
            kwargs_default = dict(do_sample=False, max_new_tokens=1024, top_p=None, num_beams=5)
        else:
            kwargs_default = dict(do_sample=False, max_new_tokens=1024, top_p=None, num_beams=1)
        self.kwargs = kwargs_default

        if dataset is not None and DATASET_TYPE(dataset) == 'Y/N':
            question = line['question']
            if listinstr(['MME'], dataset):
                prompt = question + ' Answer the question using a single word or phrase.'
            elif listinstr(['HallusionBench'], dataset):
                prompt = question + ' Please answer yes or no. Answer the question using a single word or phrase.'
            else:
                prompt = question
        elif dataset is not None and DATASET_TYPE(dataset) == 'MCQ':
            prompt = self.build_multi_choice_prompt(line, dataset)
        elif dataset is not None and DATASET_TYPE(dataset) == 'VQA':
            question = line['question']
            if listinstr(['MathVista', 'MathVision', 'VCR', 'MTVQA', 'MMVet', 'MathVerse'], dataset):
                prompt = question
            elif listinstr(['LLaVABench'], dataset):
                prompt = question + '\nAnswer this question in detail.'
            else:
                prompt = question + '\nAnswer the question using a single word or phrase.'
        else:
            prompt = line['question']

        if self.cot_prompt and not listinstr(['LLaVABench'], dataset):
            cot_prompt_with_final_answer = (
                "Your task is to answer the question below. "
                "Give step by step reasoning before you answer, and when you're ready to answer, "
                "please use the format \"Final answer: ..\""
                "\n\n"
                "Question:"
                "\n\n"
                "{question}"
            )
            cot_prompt_wo_final_answer = (
                "Your task is to answer the question below. "
                "Give step by step reasoning. "
                "\n\n"
                "Question:"
                "\n\n"
                "{question}"
            )

            if listinstr(['MMVet'], dataset):
                cot_prompt = cot_prompt_wo_final_answer
            else:
                cot_prompt = cot_prompt_with_final_answer

            question_orig = line['question']
            if listinstr(['MathVerse', 'MathVision'], dataset):
                question_orig = question_orig.split('Question:', 1)[-1].strip()
                question_orig = question_orig.replace('Choices:\n', '').strip()

            options = {
                cand: line[cand]
                for cand in string.ascii_uppercase
                if cand in line and not pd.isna(line[cand])
            }
            options_prompt = ''
            for key, item in options.items():
                options_prompt += f'{key}. {item}\n'

            if options_prompt.strip():
                question_orig = f'{question_orig}\n{options_prompt}'

            prompt = cot_prompt.format(question=question_orig)

        message = [dict(type='text', value=prompt)]
        message.extend([dict(type='image', value=s) for s in tgt_path])
        return message

    def set_max_num(self, dataset):
        if dataset is None:
            self.max_num = 6
            return None
        # res_1_datasets = ['MMBench-Video', 'Video-MME', 'MVBench', 'Video']
        res_12_datasets = ['ChartQA_TEST', 'MMMU_DEV_VAL', 'MMMU_TEST', 'MME-RealWorld',
                           'MME-RealWorld', 'VCR_EN', 'VCR_ZH']
        res_18_datasets = ['DocVQA_VAL', 'DocVQA_TEST']
        res_24_datasets = ['InfoVQA_VAL', 'InfoVQA_TEST', 'OCRBench', 'HRBench4K', 'HRBench8K']
        if DATASET_MODALITY(dataset) == 'VIDEO':
            self.max_num = 1
        elif listinstr(res_12_datasets, dataset):
            self.max_num = 12
        elif listinstr(res_18_datasets, dataset):
            self.max_num = 18
        elif listinstr(res_24_datasets, dataset):
            self.max_num = 24
        else:
            self.max_num = 6

    def generate_v1_2(self, message, dataset=None):
        self.INTERLEAVE = False
        prompt, image_path = self.message_to_promptimg(message, dataset=dataset)
        image = Image.open(image_path).convert('RGB')
        image = image.resize((self.image_size, self.image_size))
        image_processor = CLIPImageProcessor.from_pretrained(self.model_path)
        pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
        pixel_values = pixel_values.to(torch.bfloat16).to(self.device)
        with torch.no_grad():
            response = self.model.chat(self.tokenizer, pixel_values=pixel_values,
                                       question=prompt, generation_config=self.kwargs)
        return response

    def generate_v1_5(self, message, dataset=None):
        image_num = len([x for x in message if x['type'] == 'image'])
        prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])

        if DATASET_MODALITY(dataset) == 'VIDEO':
            prompt = self.build_video_prompt(prompt, dataset)

        if image_num > 1:
            image_path = [x['value'] for x in message if x['type'] == 'image']
            pixel_values_list = []
            for file_name in image_path:
                pixel_values_list.append(load_image(file_name, max_num=self.max_num).to(self.device).to(torch.bfloat16))
            pixel_values = torch.cat(pixel_values_list, dim=0)
        elif image_num == 1:
            image_path = [x['value'] for x in message if x['type'] == 'image'][0]
            pixel_values = load_image(image_path, max_num=self.max_num).to(self.device).to(torch.bfloat16)
        else:
            pixel_values = None
        with torch.no_grad():
            response = self.model.chat(
                self.tokenizer,
                pixel_values=pixel_values,
                question=prompt,
                generation_config=self.kwargs,
                verbose=False)
        return response

    def generate_mmniah(self, message, dataset=None):
        self.tokenizer.model_max_length = 256000
        image_num = len([x for x in message if x['type'] == 'image'])
        prompt = '\n'.join([x['value'] for x in message if x['type'] == 'text'])
        num_patches_list = []
        if image_num > 1:
            image_path = [x['value'] for x in message if x['type'] == 'image']
            pixel_values_list = []
            for file_name in image_path:
                curr_pixel_values = load_image_mmniah(file_name, max_num=6, dynamic_image_size=False)
                curr_pixel_values = curr_pixel_values.to(self.device).to(torch.bfloat16)
                pixel_values_list.append(curr_pixel_values)
                num_patches_list.append(len(curr_pixel_values))
            pixel_values = torch.cat(pixel_values_list, dim=0)
        elif image_num == 1:
            image_path = [x['value'] for x in message if x['type'] == 'image'][0]
            pixel_values = load_image_mmniah(image_path, max_num=6, dynamic_image_size=False)
            pixel_values = pixel_values.to(self.device).to(torch.bfloat16)
            num_patches_list.append(len(pixel_values))
        else:
            pixel_values = None
        with torch.no_grad():
            response = self.model.chat(
                self.tokenizer,
                pixel_values=pixel_values,
                question=prompt,
                generation_config=dict(
                    do_sample=False,
                    num_beams=1,
                    max_new_tokens=32,
                ),
                num_patches_list=num_patches_list,
                history=None,
                return_history=False,
                verbose=False)
        return response

    def generate_v2(self, message, dataset=None):
        image_num = len([x for x in message if x['type'] == 'image'])
        if image_num == 1:
            prompt = '<image>\n' + '\n'.join([x['value'] for x in message if x['type'] == 'text'])
        else:
            prompt, image_idx = '', 1
            for x in message:
                if x['type'] == 'text':
                    prompt += x['value']
                elif x['type'] == 'image':
                    prompt += f'<Image-{image_idx}>'
                    image_idx += 1
            prompt = '\n'.join([f'Image-{i + 1}: <image>' for i in range(image_num)]) + '\n' + prompt

        if dataset is not None and DATASET_MODALITY(dataset) == 'VIDEO':
            prompt = self.build_video_prompt(prompt, dataset)

        if image_num > 1:
            image_path = [x['value'] for x in message if x['type'] == 'image']
            num_patches_list = []
            pixel_values_list = []
            for image_idx, file_name in enumerate(image_path):
                upscale_flag = image_idx == 0 and dataset is not None and listinstr(['MMMU_DEV_VAL'], dataset)
                curr_pixel_values = load_image(
                    file_name, max_num=self.max_num, upscale=upscale_flag).to(self.device).to(torch.bfloat16)
                num_patches_list.append(curr_pixel_values.size(0))
                pixel_values_list.append(curr_pixel_values)
            pixel_values = torch.cat(pixel_values_list, dim=0)
        elif image_num == 1:
            image_path = [x['value'] for x in message if x['type'] == 'image'][0]
            upscale_flag = dataset is not None and listinstr(['MMMU_DEV_VAL'], dataset)
            pixel_values = load_image(
                image_path, max_num=self.max_num, upscale=upscale_flag).to(self.device).to(torch.bfloat16)
            num_patches_list = [pixel_values.size(0)]
        else:
            pixel_values = None
            num_patches_list = []

        with torch.no_grad():
            response = self.model.chat(
                self.tokenizer,
                pixel_values=pixel_values,
                num_patches_list=num_patches_list,
                question=prompt,
                generation_config=self.kwargs,
                verbose=False
            )

        if (
            self.cot_prompt
            and dataset is not None
            and (
                DATASET_TYPE(dataset) in ['Y/N', 'MCQ']
                or listinstr(['CRPE'], dataset)
            )
        ):
            response = extract_answer(response).strip()

        return response

    def generate_inner(self, message, dataset=None):
        self.set_max_num(dataset)
        print(f'InternVL model version: {self.version}')
        if self.version in ['V1.1', 'V1.2']:
            return self.generate_v1_2(message, dataset)
        elif self.version == 'V1.5':
            return self.generate_v1_5(message, dataset)
        elif self.version == 'V2.0':
            return self.generate_v2(message, dataset)
        elif self.version == 'mmniah':
            return self.generate_mmniah(message, dataset)
        else:
            raise ValueError(f'Unsupported version: {self.version}')

    def build_history(self, message):
        # Global Variables
        image_path = []
        image_cnt = 0

        def concat_tilist(tilist):
            nonlocal image_cnt  # Declare image_cnt as nonlocal to modify it
            prompt = ''
            for item in tilist:
                # Substitute the pattern in the text
                if item['type'] == 'text':
                    prompt += re.sub(self.pattern, self.replacement, item['value'])
                elif item['type'] == 'image':
                    image_cnt += 1
                    prompt += '<image>\n'
                    image_path.append(item['value'])
            return prompt

        # Only previous messages
        assert len(message) % 2 == 0
        history = []
        for i in range(len(message) // 2):
            m1, m2 = message[2 * i], message[2 * i + 1]
            assert m1['role'] == 'user' and m2['role'] == 'assistant'
            history.append((concat_tilist(m1['content']), concat_tilist(m2['content'])))

        return history, image_path, image_cnt

    def chat_inner_v2(self, message, dataset=None):

        image_cnt = 0
        if len(message) > 1:
            history, image_path, image_cnt = self.build_history(message[:-1])
        else:
            history, image_path, image_cnt = None, [], 1
        current_msg = message[-1]
        question = ''

        # If message is just text in the conversation
        if len(current_msg['content']) == 1 and current_msg['content'][0]['type'] == 'text':
            question = current_msg['content'][0]['value']
            question = re.sub(self.pattern, self.replacement, question)  # Fix pattern as per InternVL
        else:
            for msg in current_msg['content']:
                if msg['type'] == 'text':
                    question += re.sub(self.pattern, self.replacement, msg['value'])
                elif msg['type'] == 'image':
                    image_cnt += 1
                    question += '<image>\n'
                    image_path.append(msg['value'])

        if image_cnt > 1:
            num_patches_list = []
            pixel_values_list = []
            for image_idx, file_name in enumerate(image_path):
                upscale_flag = image_idx == 0 and dataset is not None and listinstr(['MMMU_DEV_VAL'], dataset)
                curr_pixel_values = load_image(
                    file_name, max_num=self.max_num, upscale=upscale_flag).to(self.device).to(torch.bfloat16)
                num_patches_list.append(curr_pixel_values.size(0))
                pixel_values_list.append(curr_pixel_values)
            pixel_values = torch.cat(pixel_values_list, dim=0)
        elif image_cnt == 1:
            upscale_flag = listinstr(['MMMU_DEV_VAL'], dataset)
            pixel_values = load_image(
                image_path, max_num=self.max_num, upscale=upscale_flag).to(self.device).to(torch.bfloat16)
            num_patches_list = [pixel_values.size(0)]
        else:
            pixel_values = None
            num_patches_list = []

        response, history = self.model.chat(
            self.tokenizer,
            pixel_values=pixel_values,
            num_patches_list=num_patches_list,
            question=question,
            generation_config=self.kwargs,
            history=history,
            return_history=True
        )

        response = re.sub(self.reverse_pattern, self.reverse_replacement, response)

        return response

    def chat_inner(self, message, dataset=None):
        self.set_max_num(dataset)

        if self.version in ['V1.1', 'V1.2']:
            raise ValueError(f'Unsupported version for Multi-Turn: {self.version}')
        elif self.version == 'V1.5':
            raise ValueError(f'Unsupported version for Multi-Turn: {self.version}')
        elif self.version == 'V2.0':
            kwargs_default = dict(do_sample=False, max_new_tokens=512, top_p=None, num_beams=1)
            self.kwargs = kwargs_default
            return self.chat_inner_v2(message, dataset)
        else:
            raise ValueError(f'Unsupported version for Multi-Turn: {self.version}')