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Quickstart

Before running the evaluation script, you need to configure the VLMs and set the model_paths properly.

After that, you can use a single script run.py to inference and evaluate multiple VLMs and benchmarks at a same time.

Step 0. Installation & Setup essential keys

Installation.

git clone https://github.com/open-compass/VLMEvalKit.git
cd VLMEvalKit
pip install -e .

Setup Keys.

To infer with API models (GPT-4v, Gemini-Pro-V, etc.) or use LLM APIs as the judge or choice extractor, you need to first setup API keys. VLMEvalKit will use an judge LLM to extract answer from the output if you set the key, otherwise it uses the exact matching mode (find "Yes", "No", "A", "B", "C"... in the output strings). The exact matching can only be applied to the Yes-or-No tasks and the Multi-choice tasks.

  • You can place the required keys in $VLMEvalKit/.env or directly set them as the environment variable. If you choose to create a .env file, its content will look like:

    # The .env file, place it under $VLMEvalKit
# API Keys of Proprietary VLMs
# QwenVL APIs
DASHSCOPE_API_KEY=
# Gemini w. Google Cloud Backends
GOOGLE_API_KEY=
# OpenAI API
OPENAI_API_KEY=
OPENAI_API_BASE=
# StepAI API
STEPAI_API_KEY=
# REKA API
REKA_API_KEY=
# GLMV API
GLMV_API_KEY=
# CongRong API
CW_API_BASE=
CW_API_KEY=
# SenseChat-V API
SENSECHAT_AK=
SENSECHAT_SK=
# Hunyuan-Vision API
HUNYUAN_SECRET_KEY=
HUNYUAN_SECRET_ID=
# LMDeploy API
LMDEPLOY_API_BASE=
# You can also set a proxy for calling api models during the evaluation stage
EVAL_PROXY=

  • Fill the blanks with your API keys (if necessary). Those API keys will be automatically loaded when doing the inference and evaluation.

Step 1. Configuration

VLM Configuration: All VLMs are configured in vlmeval/config.py. Few legacy VLMs (like MiniGPT-4, LLaVA-v1-7B) requires additional configuration (configuring the code / model_weight root in the config file). During evaluation, you should use the model name specified in supported_VLM in vlmeval/config.py to select the VLM. Make sure you can successfully infer with the VLM before starting the evaluation with the following command vlmutil check {MODEL_NAME}.

Step 2. Evaluation

New!!! We integrated a new config system to enable more flexible evaluation settings. Check the Document or run python run.py --help for more details 🔥🔥🔥

We use run.py for evaluation. To use the script, you can use $VLMEvalKit/run.py or create a soft-link of the script (to use the script anywhere):

Arguments

  • --data (list[str]): Set the dataset names that are supported in VLMEvalKit (names can be found in the codebase README).
  • --model (list[str]): Set the VLM names that are supported in VLMEvalKit (defined in supported_VLM in vlmeval/config.py).
  • --mode (str, default to 'all', choices are ['all', 'infer']): When mode set to "all", will perform both inference and evaluation; when set to "infer", will only perform the inference.
  • --api-nproc (int, default to 4): The number of threads for OpenAI API calling.
  • --work-dir (str, default to '.'): The directory to save evaluation results.

**Command for Evaluating Image Benchmarks **

You can run the script with python or torchrun:

# When running with `python`, only one VLM instance is instantiated, and it might use multiple GPUs (depending on its default behavior).
# That is recommended for evaluating very large VLMs (like IDEFICS-80B-Instruct).

# IDEFICS-80B-Instruct on MMBench_DEV_EN, MME, and SEEDBench_IMG, Inference and Evalution
python run.py --data MMBench_DEV_EN MME SEEDBench_IMG --model idefics_80b_instruct --verbose
# IDEFICS-80B-Instruct on MMBench_DEV_EN, MME, and SEEDBench_IMG, Inference only
python run.py --data MMBench_DEV_EN MME SEEDBench_IMG --model idefics_80b_instruct --verbose --mode infer

# When running with `torchrun`, one VLM instance is instantiated on each GPU. It can speed up the inference.
# However, that is only suitable for VLMs that consume small amounts of GPU memory.

# IDEFICS-9B-Instruct, Qwen-VL-Chat, mPLUG-Owl2 on MMBench_DEV_EN, MME, and SEEDBench_IMG. On a node with 8 GPU. Inference and Evaluation.
torchrun --nproc-per-node=8 run.py --data MMBench_DEV_EN MME SEEDBench_IMG --model idefics_80b_instruct qwen_chat mPLUG-Owl2 --verbose
# Qwen-VL-Chat on MME. On a node with 2 GPU. Inference and Evaluation.
torchrun --nproc-per-node=2 run.py --data MME --model qwen_chat --verbose

Command for Evaluating Video Benchmarks

# When running with `python`, only one VLM instance is instantiated, and it might use multiple GPUs (depending on its default behavior).
# That is recommended for evaluating very large VLMs (like IDEFICS-80B-Instruct).

# IDEFICS2-8B on MMBench-Video, with 8 frames as inputs and vanilla evaluation. On a node with 8 GPUs. MMBench_Video_8frame_nopack is a defined dataset setting in `vlmeval/dataset/video_dataset_config.py`.
torchrun --nproc-per-node=8 run.py --data MMBench_Video_8frame_nopack --model idefics2_8
# GPT-4o (API model) on MMBench-Video, with 1 frame per second as inputs and pack evaluation (all questions of a video in a single query).
python run.py --data MMBench_Video_1fps_pack --model GPT4o

The evaluation results will be printed as logs, besides. Result Files will also be generated in the directory $YOUR_WORKING_DIRECTORY/{model_name}. Files ending with .csv contain the evaluated metrics.

Frequently Asked Questions

Constructing Input Prompt: The build_prompt() Function

If you find that the model's output does not match the expected results when evaluating a specific benchmark, it could be due to the model not constructing the input prompt correctly.

In VLMEvalKit, each dataset class includes a function named build_prompt(), which is responsible for formatting input questions. Different benchmarks can either customize their own build_prompt() function or use the default implementation.

For instance, when handling the default Multiple-Choice QA, the ImageMCQDataset.build_prompt() method combines elements such as hint, question, and options (if present in the dataset) into a complete question format, as shown below:

HINT
QUESTION
Options:
A. Option A
B. Option B
···
Please select the correct answer from the options above.

Additionally, since different models may have varying evaluation requirements, VLMEvalKit also supports customizing the prompt construction method at the model level through model.build_prompt(). For an example, you can refer to InternVL.

Note: If both model.build_prompt() and dataset.build_prompt() are defined, model.build_prompt() will take precedence over dataset.build_prompt(), effectively overriding it.

Some models, such as Qwen2VL and InternVL, define extensive prompt-building methods for various types of benchmarks. To provide more flexibility in adapting to different benchmarks, VLMEvalKit allows users to customize the model.use_custom_prompt() function within the model. By adding or modifying the use_custom_prompt() function, you can decide which benchmarks should utilize the model's custom prompt logic. Below is an example:

def use_custom_prompt(self, dataset: str) -> bool:
    from vlmeval.dataset import DATASET_TYPE, DATASET_MODALITY
    dataset_type = DATASET_TYPE(dataset, default=None)
    if not self._use_custom_prompt:
        return False
    if listinstr(['MMVet'], dataset):
        return True
    if dataset_type == 'MCQ':
        return True
    if DATASET_MODALITY(dataset) == 'VIDEO':
        return False
    return False

Only when the use_custom_prompt() function returns True will VLMEvalKit call the model's build_prompt() function for the current benchmark. With this approach, you can flexibly control which benchmarks use the model's custom prompt logic based on your specific needs, thereby better adapting to different models and tasks.

Model Splitting

For large models with substantial parameter counts, such as InternVL2-78B, a single GPU may not be able to accommodate the entire model during inference. In such cases, you can define the environment variable AUTO_SPLIT=1. For models that support the split_model() function, the model will automatically be split and distributed across multiple GPUs.

For example, on a machine equipped with 8 GPUs, you can run the model using the following command:

# For an 8-GPU machine
AUTO_SPLIT=1 torchrun --nproc-per-node=1 run.py --data MMBench_DEV_EN --model InternVL2-76B --verbose

This command will automatically split the InternVL2-76B model into 8 parts and run each part on a separate GPU.

Performance Discrepancies

Model performance may vary across different environments. As a result, you might observe discrepancies between your evaluation results and those listed on the official VLMEvalKit leaderboard. These differences could be attributed to variations in versions of libraries such as transformers, cuda, and torch.

Besides, if you encounter unexpected performance, we recommend first reviewing the local generation records ({model}_{dataset}.xlsx) or the evaluation records ({model}_{dataset}_{judge_model}.xlsx). This may help you better understand the evaluation outcomes and identify potential issues.

Deploy a local language model as the judge / choice extractor

The default setting mentioned above uses OpenAI's GPT as the judge LLM. However, you can also deploy a local judge LLM with LMDeploy.

First install:

pip install lmdeploy openai

And then deploy a local judge LLM with the single line of code. LMDeploy will automatically download the model from Huggingface. Assuming we use internlm2-chat-1_8b as the judge, port 23333, and the key sk-123456 (the key must start with "sk-" and follow with any number you like):

lmdeploy serve api_server internlm/internlm2-chat-1_8b --server-port 23333

You need to get the model name registered by LMDeploy with the following python code:

from openai import OpenAI
client = OpenAI(
    api_key='sk-123456',
    base_url="http://0.0.0.0:23333/v1"
)
model_name = client.models.list().data[0].id

Now set some environment variables to tell VLMEvalKit how to use the local judge LLM. As mentioned above, you can also set them in $VLMEvalKit/.env file:

OPENAI_API_KEY=sk-123456
OPENAI_API_BASE=http://0.0.0.0:23333/v1/chat/completions
LOCAL_LLM=<model_name you get>

Finally, you can run the commands in step 2 to evaluate your VLM with the local judge LLM.

Note that

  • If you hope to deploy the judge LLM in a single GPU and evaluate your VLM on other GPUs because of limited GPU memory, try CUDA_VISIBLE_DEVICES=x like
CUDA_VISIBLE_DEVICES=0 lmdeploy serve api_server internlm/internlm2-chat-1_8b --server-port 23333
CUDA_VISIBLE_DEVICES=1,2,3 torchrun --nproc-per-node=3 run.py --data HallusionBench  --model qwen_chat --verbose
  • If the local judge LLM is not good enough in following the instructions, the evaluation may fail. Please report such failures (e.g., by issues).
  • It's possible to deploy the judge LLM in different ways, e.g., use a private LLM (not from HuggingFace) or use a quantized LLM. Please refer to the LMDeploy doc. You can use any other deployment framework if they support OpenAI API.

Using LMDeploy to Accelerate Evaluation and Inference

You can refer this doc