Repository: EvolvingLMMs-Lab/lmms-eval
Issue: Qwen2.5-VL support.
Root cause: Unimplemented Feature Gap

Hi,

[Qwen2.5VL](https://huggingface.co/collections/Qwen/qwen25-vl-6795ffac22b334a837c0f9a5) was released. Can we add evaluation support for this model as well?

Thanks.

The following works as of now.
`pip install git+https://github.com/huggingface/transformers accelerate
`
Register qwen2_5

```import base64
from io import BytesIO
from typing import List, Optional, Tuple, Union

import decord
import numpy as np
import torch
from accelerate import Accelerator, DistributedType
from loguru import logger as eval_logger
from PIL import Image
from tqdm import tqdm
from transformers import AutoProcessor, AutoTokenizer, Qwen2_5_VLForConditionalGeneration

from lmms_eval import utils
from lmms_eval.api.instance import Instance
from lmms_eval.api.model import lmms
from lmms_eval.api.registry import register_model
from lmms_eval.models.model_utils.load_video import load_video_decord

try:
    from qwen_vl_utils import process_vision_info
except ImportError:
    eval_logger.warning("Failed to import qwen_vl_utils; Please install it via `pip install qwen-vl-utils`")


@register_model("qwen2_5_vl")
class Qwen2_5_VL(lmms):
    """
    Qwen2_VL Model
    "https://github.com/QwenLM/Qwen2-VL"
    """

    def __init__(
        self,
        pretrained: str = "Qwen/Qwen2.5-VL-7B-Instruct",
        device: Optional[str] = "cuda",
        device_map: Optional[str] = "cuda",
        batch_size: Optional[Union[int, str]] = 1,
        use_cache=True,
        use_flash_attention_2: Optional[bool] = False,
        max_pixels: int = 201600,
        min_pixels: int = 3136,
        max_num_frames: int = 32,
        **kwargs,
    ) -> None:
        super().__init__()
        # Do not use kwargs for now
        assert kwargs == {}, f"Unexpected kwargs: {kwargs}"

        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 use_flash_attention_2:
            self._model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
                pretrained,
                torch_dtype="auto",
                device_map=self.device_map,
                attn_implementation="flash_attention_2",
            ).eval()
        else:
            self._model = Qwen2_5_VLForConditionalGeneration.from_pretrained(pretrained, torch_dtype="auto", device_map=self.device_map).eval()
        self.processor = AutoProcessor.from_pretrained(pretrained, max_pixels=max_pixels, min_pixels=min_pixels)
        self.max_pixels = max_pixels
        self.min_pixels = min_pixels
        self.max_num_frames = max_num_frames
        self._tokenizer = AutoTokenizer.from_pretrained(pretrained)

        self._config = self.model.config
        self.batch_size_per_gpu = int(batch_size)
        self.use_cache = use_cache

        if accelerator.num_processes > 1:
            assert accelerator.distributed_type in [
                DistributedType.FSDP,
                DistributedType.MULTI_GPU,
            ], "Unsupported distributed type provided. Only DDP and FSDP are supported."
            if accelerator.distributed_type == DistributedType.FSDP:
                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
        else:
            self._rank = 0
            self._word_size = 1

    @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 eot_token_id(self):
        return self.tokenizer.eos_token_id

    @property
    def max_length(self):
        return self._max_length

    @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 loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
        raise NotImplementedError("Loglikelihood is not implemented for Qwen2_VL")

    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[Instance]) -> List[str]:
        res = []

        def _collate(x):
            # the negative sign on len(toks) sorts descending - this has a few advantages:
            # - time estimates will always be over not underestimates, which is more useful for planning
            # - to know the size of a batch when going through the list, you know the first one is always the batch
            #   padded context length. this is useful to simplify the batching logic and more importantly to make
            #   automatic adaptive batches much much easier to implement
            # - any OOMs will happen right away rather than near the end
            toks = self.tokenizer.encode(x[0])
            return -len(toks), x[0]

        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
        # we group requests by their generation_kwargs,
        # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling
        # in the same batch.
        re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True)
        chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None)
        for chunk in chunks:
            contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk)
            task = task[0]
            split = split[0]
            visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id]
            visuals = self.flatten(visuals)

            gen_kwargs = all_gen_kwargs[0]

            # Set default values for until and max_new_tokens
            until = [self.tokenizer.decode(self.eot_token_id)]

            # Update values from gen_kwargs if present
            if "until" in gen_kwargs:
                until = gen_kwargs.pop("until")
                if isinstance(until, str):
                    until = [until]
                elif not isinstance(until, list):
                    raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}")

            if isinstance(contexts, tuple):
                contexts = list(contexts)

            for i in range(len(contexts)):
                if "<image>" in contexts[i]:
                    contexts[i] = contexts[i].replace("<image>", "")

            messages = []
            processed_visuals = []
            for i, context in enumerate(contexts):
                if "<image>" in context:
                    context = context.replace("<image>", "")

                message = [{"role": "system", "content": "You are a helpful assistant."}]

                if len(visuals) > 0:
                    visual = visuals[i] if i < len(visuals) else None
                    if isinstance(visual, str) and visual.endswith((".mp4", ".avi", ".mov")):  # Video file
                        vr = decord.VideoReader(visual)
                        first_frame = vr[0].asnumpy()
                        height, width = first_frame.shape[:2]
                        # max_pixels = height * width
                        message.append({"role": "user", "content": [{"type": "video", "video": visual, "max_pixels": self.max_pixels}, {"type": "text", "text": context}]})
                    elif isinstance(visual, Image.Image):  # Single image
                        base64_image = visual.convert("RGB")
                        buffer = BytesIO()
                        base64_image.save(buffer, format="JPEG")
                        base64_bytes = base64.b64encode(buffer.getvalue())
                        base64_string = base64_bytes.decode("utf-8")
                        message.append({"role": "user", "content": [{"type": "image", "image": f"data:image/jpeg;base64,{base64_string}"}, {"type": "text", "text": context}]})
                    elif isinstance(visual, (list, tuple)) and all(isinstance(v, Image.Image) for v in visual):  # Multiple images
                        image_content = []
                        for v in visual:
                            base64_image = v.convert("RGB")
                            buffer = BytesIO()
                            base64_image.save(buffer, format="JPEG")
                            base64_bytes = base64.b64encode(buffer.getvalue())
                            base64_string = base64_bytes.decode("utf-8")
                            image_content.append({"type": "image", "image": f"data:image/jpeg;base64,{base64_string}"})
                        message.append({"role": "user", "content": image_content + [{"type": "text", "text": context}]})
                    else:
                        message.append({"role": "user", "content": [{"type": "text", "text": context}]})
                else:
                    message.append({"role": "user", "content": [{"type": "text", "text": context}]})

                messages.append(message)

            texts = [self.processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in messages]
            image_inputs, video_inputs = process_vision_info(messages)
            if video_inputs is not None:
                total_frames = video_inputs[0].shape[0]
                indices = np.linspace(0, total_frames - 1, self.max_num_frames, dtype=int)
                # Append the last frame index if not already included
                if total_frames - 1 not in indices:
                    indices = np.append(indices, total_frames - 1)
                video_inputs[0] = video_inputs[0][indices]
            inputs = self.processor(text=texts, images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", fps=32)

            if self.device_map == "auto":
                inputs = inputs.to("cuda")
            else:
                inputs = inputs.to(self.device)

            if "max_new_tokens" not in gen_kwargs:
                gen_kwargs["max_new_tokens"] = 128
            if "temperature" not in gen_kwargs:
                gen_kwargs["temperature"] = 0
            if "top_p" not in gen_kwargs:
                gen_kwargs["top_p"] = None
            if "num_beams" not in gen_kwargs:
                gen_kwargs["num_beams"] = 1

            pad_token_id = self.tokenizer.pad_token_id

            cont = self.model.generate(
                **inputs,
                eos_token_id=self.tokenizer.eos_token_id,
                pad_token_id=pad_token_id,
                do_sample=True if gen_kwargs["temperature"] > 0 else False,
                temperature=gen_kwargs["temperature"],
                top_p=gen_kwargs["top_p"],
                num_beams=gen_kwargs["num_beams"],
                max_new_tokens=gen_kwargs["max_new_tokens"],
                use_cache=self.use_cache,
            )

            generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, cont)]
            answers = self.processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
            for i, ans in enumerate(answers):
                for term in until:
                    if len(term) > 0:
                        ans = ans.split(term)[0]
                answers[i] = ans

            for ans, context in zip(answers, contexts):
                res.append(ans)
                self.cache_hook.add_partial("generate_until", (context, gen_kwargs), ans)
                pbar.update(1)
            # reorder this group of results back to original unsorted form
        res = re_ords.get_original(res)

        pbar.close()
        return res

    def generate_until_multi_round(self, requests) -> List[str]:
        raise NotImplementedError("TODO: Implement multi-round generation")
```

@yogkul2000 hi, nice work, have you tested some benchmarks to check the modified code?

Hi, thanks for the work. Do you mind create a PR so that we can review and add this into the repo?

> [@yogkul2000](https://github.com/yogkul2000) hi, nice work, have you tested some benchmarks to check the modified code?

Just VideoMME but best I can do is 32frames so its 2% lower than the original Qwen2.5VL results.

> Hi, thanks for the work. Do you mind create a PR so that we can review and add this into the repo?

Yes.

Seems like support was added. Closing this issue.