src/lmms-eval/lmms_eval/models/simple/internvideo2_5.py

import logging
from datetime import timedelta
from typing import List, Tuple

import numpy as np
import torch
import torchvision.transforms as T
from accelerate import Accelerator, DistributedType
from accelerate.state import AcceleratorState
from accelerate.utils import InitProcessGroupKwargs
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from tqdm import tqdm
from transformers import AutoModel, AutoTokenizer

from lmms_eval.api.instance import Instance
from lmms_eval.api.model import lmms
from lmms_eval.api.registry import register_model

eval_logger = logging.getLogger("eval_logger")

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

DEFAULT_GEN_KWARGS = dict(
    num_beams=1,
    max_new_tokens=1024,
    do_sample=False,
)


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, input_size=448, max_num=6):
    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


def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
    if bound:
        start, end = bound[0], bound[1]
    else:
        start, end = -100000, 100000
    start_idx = max(first_idx, round(start * fps))
    end_idx = min(round(end * fps), max_frame)
    seg_size = float(end_idx - start_idx) / num_segments
    frame_indices = [int(start_idx + (seg_size / 2) + np.round(seg_size * idx)) for idx in range(num_segments)]
    return frame_indices


def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32, media_dict=None):
    if type(video_path) == str:
        vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
    else:
        vr = VideoReader(video_path[0], ctx=cpu(0), num_threads=1)

    max_frame = len(vr) - 1
    fps = float(vr.get_avg_fps())

    pixel_values_list, num_patches_list = [], []
    transform = build_transform(input_size=input_size)
    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
    if media_dict is not None and media_dict["video_read_type"] == "decord_last":
        frame_indices = frame_indices + [max_frame for _ in range(4)]  # add last 4 frames

    frame_indices = np.array(frame_indices)

    for frame_index in frame_indices:
        img = Image.fromarray(vr[frame_index].asnumpy()).convert("RGB")
        img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=False, max_num=max_num)
        pixel_values = [transform(tile) for tile in img]
        pixel_values = torch.stack(pixel_values)
        num_patches_list.append(pixel_values.shape[0])
        pixel_values_list.append(pixel_values)
    pixel_values = torch.cat(pixel_values_list)
    return pixel_values, num_patches_list


@register_model("internvideo2_5")
class InternVideo2_5(lmms):
    def __init__(
        self,
        pretrained: str = "OpenGVLab/InternVideo2_5_Chat_8B",
        modality: str = "video",
        device: str = "cuda:0",
        device_map: str = "cuda:0",
        batch_size: str = "1",
        max_frames_num: int = 32,
        **kwargs,
    ):
        super().__init__()
        self.max_frames_num = max_frames_num
        self.path = pretrained
        self._model = AutoModel.from_pretrained(self.path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda()
        self._tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True)

        batch_size = int(batch_size)
        assert batch_size == 1, f"Batch size should be 1 for InternVL2, but got {batch_size}."
        self.batch_size_per_gpu = batch_size

        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
        self.accelerator = accelerator
        if accelerator.num_processes > 1:
            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
            self.device_map = f"cuda:{accelerator.local_process_index}"
        elif accelerator.num_processes == 1 and device_map == "auto":
            self._device = torch.device(device)
            self.device_map = device_map
        else:
            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
            self.device_map = f"cuda:{accelerator.local_process_index}"

        if accelerator.num_processes > 1:
            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
            if accelerator.distributed_type == DistributedType.DEEPSPEED:
                kwargs = {
                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
                }
                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")

            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
                self._model = accelerator.prepare(self.model)
            else:
                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
            self.accelerator = accelerator
            if self.accelerator.is_local_main_process:
                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
            self._rank = self.accelerator.local_process_index
            self._world_size = self.accelerator.num_processes
        elif accelerator.num_processes == 1 and device_map == "auto":
            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
            self._rank = 0
            self._word_size = 1
        else:
            eval_logger.info(f"Using single device: {self._device}")
            self.model.to(self._device)
            self._rank = 0
            self._world_size = 1

        self.modality = modality

    @property
    def config(self):
        # return the associated transformers.AutoConfig for the given pretrained model.
        return self._config

    @property
    def tokenizer(self):
        return self._tokenizer

    @property
    def model(self):
        # returns the model, unwrapping it if using Accelerate
        if hasattr(self, "accelerator"):
            return self.accelerator.unwrap_model(self._model)
        else:
            return self._model

    @property
    def batch_size(self):
        return self.batch_size_per_gpu

    @property
    def device(self):
        return self._device

    @property
    def rank(self):
        return self._rank

    @property
    def world_size(self):
        return self._world_size

    def flatten(self, input):
        new_list = []
        for i in input:
            for j in i:
                new_list.append(j)
        return new_list

    def generate_until(self, requests) -> List[str]:
        res = []
        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")

        for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
            if "until" in gen_kwargs:
                gen_kwargs.pop("until")
            for k, v in DEFAULT_GEN_KWARGS.items():
                if k not in gen_kwargs:
                    gen_kwargs[k] = v

            pop_keys = []
            for k, v in gen_kwargs.items():
                if k not in DEFAULT_GEN_KWARGS:
                    pop_keys.append(k)

            for k in pop_keys:
                gen_kwargs.pop(k)

            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
            visuals = self.flatten(visuals)
            if self.modality == "image":
                if visuals:
                    visuals = [load_image(visual).to(torch.bfloat16).cuda() for visual in visuals]
                    pixel_values = torch.cat(visuals, dim=0)
                    num_patches_list = [visual.size(0) for visual in visuals]
                    image_tokens = ["<image>"] * len(visuals)
                    image_tokens = " ".join(image_tokens)
                    contexts = image_tokens + "\n" + contexts
                else:
                    pixel_values = None
                    num_patch_list = None
                response, history = self.model.chat(self.tokenizer, pixel_values, contexts, gen_kwargs, num_patches_list=num_patches_list, history=None, return_history=True)
            elif self.modality == "video":
                # assert len(visuals) == 1, f"Only one video is supported, but got {len(visuals)} videos. {visuals}"
                video_path = visuals[0]
                if len(visuals) > 1:
                    assert len(visuals) == 2, visuals
                    media_dict = visuals[1]
                else:
                    media_dict = {"video_read_type": "decord"}
                pixel_values, num_patches_list = load_video(video_path, num_segments=self.max_frames_num, max_num=1, media_dict=media_dict)
                pixel_values = pixel_values.to(torch.bfloat16).cuda()
                video_prefix = "".join([f"Frame{i+1}: <image>\n" for i in range(len(num_patches_list))])
                question = video_prefix + contexts
                response, history = self.model.chat(self.tokenizer, pixel_values, question, gen_kwargs, num_patches_list=num_patches_list, history=None, return_history=True)
            res.append(response)
            pbar.update(1)
        pbar.close()
        return res

    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
        assert False, "Not implemented yet."