lmms-eval 0.0.2

A framework for evaluating large multi-modality language models

Large-scale Multi-modality Models Evaluation Suite

Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval

🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets

Annoucement

v0.1.0 Released

The first version of the lmms-eval is released. We are working on providing an one-command evaluation API for accelerating the development of LMMs.

In LLaVA Next development, we internally utilize this API to evaluate the model's performance on various model versions and datasets. It significantly accelerates the model development cycle for it's easy integration and fast evaluation speed. The main feature includes:

One-command evaluation, with detailed logs and samples.

You can evaluate the models on multiple datasets with a single command. No model/data preparation is needed, just one command line, few minutes, and get the results. Not just a result number, but also the detailed logs and samples, including the model args, input question, model response, and ground truth answer.

Accelerator support and Tasks grouping.

We support the usage of accelerate to wrap the model for distributed evaluation, supporting multi-gpu and tensor parallelism. With Task Grouping, all instances from all tasks are grouped and evaluated in parallel, which significantly improves the throughput of the evaluation.

Below are the total runtime on different datasets using 4 x A100 40G.

Dataset (#num)	LLaVA-v1.5-7b	LLaVA-v1.5-13b
mme (2374)	2 mins 43 seconds	3 mins 27 seconds
gqa (12578)	10 mins 43 seconds	14 mins 23 seconds
scienceqa_img (2017)	1 mins 58 seconds	2 mins 52 seconds
ai2d (3088)	3 mins 17 seconds	4 mins 12 seconds
coco2017_cap_val (5000)	14 mins 13 seconds	19 mins 58 seconds

Prepared HF datasets.

We are hosting more than 40 (and increasing) datasets on huggingface/lmms-lab, we carefully converted these datasets from original sources and included all variants, versions and splits. Now they can be directly accessed without any burden of data preprocessing. They also serve for the purpose of visualizing the data and grasping the sense of evaluation tasks distribution.

Detailed YAML task configuration

Including prompt pre-processing, output post-processing, answer extraction, model specific args and more.

Reproducible results (for LLaVA series models) and Logging Utilites.

We provide a set of pre-defined configurations & environments for llava-1.5, which can be directly used to reproduce the results in the paper.

You can refer to the repr_scripts.sh we provide to see how to build and set-up the enviroments to reproduce the results from the paper. However, this environment is not recommended when you try to evaluating your own model or other models since it only install packages necessary to run llava and has a lower pytorch version that may results in a lower speed.

With lmms-eval, all evaluation details will be recorded including log samples and results, generating report tables to terminal output and to Weights & Biases Runs/Tables.

Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub.

Installation

For formal usage, you can install the package from PyPI by running the following command:

pip install lmms-eval

For development, you can install the package by cloning the repository and running the following command:

git clone https://github.com/EvolvingLMMs-Lab/lmms-eval
cd lmms-eval
pip install -e .

If you wanted to test llava, you will have to clone their repo from LLaVA and

git clone https://github.com/haotian-liu/LLaVA
cd LLaVA
pip install -e .

You can check the environment install script and torch environment info to reproduce LLaVA-1.5's paper results. We found torch/cuda versions difference would cause small variations in the results, we provide the results check with different environments.

If you want to test on caption dataset such as coco, refcoco, and nocaps, you will need to have java==1.8.0 to let pycocoeval api to work. If you don't have it, you can install by using conda

conda install openjdk=8

you can then check your java version by java -version

Usage

# Evaluating LLaVA on MME
accelerate launch --num_processes=8 -m lmms_eval --model llava   --model_args pretrained="liuhaotian/llava-v1.5-7b"   --tasks mme  --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme --output_path ./logs/ 

# Evaluating LLaVA on multiple datasets
accelerate launch --num_processes=8 -m lmms_eval --model llava   --model_args pretrained="liuhaotian/llava-v1.5-7b"   --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ #

# From a predefined configuration, supporting evaluation of multiple models and datasets
accelerate launch --num_processes=8 -m lmms_eval --config example_eval.yaml 

Supported models

• GPT4V (API, only generation-based evaluation)
• LLaVA-v1.5/v1.6-7B/13B/34B (ppl-based, generation-based)
• Qwen-VL series (ppl-based, generation-based)
• Fuyu series (ppl-based, generation-based)
• InstructBLIP series (generation-based)

Supported datasets

() indicates the task name in the lmms_eval. The task name is also used to specify the dataset in the configuration file.

• AI2D (ai2d)
• ChartQA (chartqa)
• CMMMU (cmmmu)
  • CMMMU Validation (cmmmu_val)
  • CMMMU Test (cmmmu_test)
• COCO Caption (coco_cap)
  • COCO 2014 Caption (coco2014_cap)
    • COCO 2014 Caption Validation (coco2014_cap_val)
    • COCO 2014 Caption Test (coco2014_cap_test)
  • COCO 2017 Caption (coco2017_cap)
    • COCO 2017 Caption MiniVal (coco2017_cap_val)
    • COCO 2017 Caption MiniTest (coco2017_cap_test)
• DOCVQA (docvqa)
  • DOCVQA Validation (docvqa_val)
  • DOCVQA Test (docvqa_test)
• Ferret (ferret)
• Flickr30K (flickr30k)
  • Ferret Test (ferret_test)
• GQA (gqa)
• HallusionBenchmark (hallusion_bench_image)
• Infographic VQA (info_vqa)
  • Infographic VQA Validation (info_vqa_val)
  • Infographic VQA Test (info_vqa_test)
• LLaVA-Bench (llava_bench_wild)
• LLaVA-Bench-COCO (llava_bench_coco)
• MathVista (mathvista)
  • MathVista Validation (mathvista_testmini)
  • MathVista Test (mathvista_test)
• MMBench (mmbench)
  • MMBench English (mmbench_en)
    • MMBench English Dev (mmbench_en_dev)
    • MMBench English Test (mmbench_en_test)
  • MMBench Chinese (mmbench_cn)
    • MMBench Chinese Dev (mmbench_cn_dev)
    • MMBench Chinese Test (mmbench_cn_test)
• MME (mme)
• MMMU (mmmu)
  • MMMU Validation (mmmu_val)
  • MMMU Test (mmmu_test)
• MMVet (mmvet)
• Multi-DocVQA (multidocvqa)
  • Multi-DocVQA Validation (multidocvqa_val)
  • Multi-DocVQA Test (multidocvqa_test)
• NoCaps (nocaps)
  • NoCaps Validation (nocaps_val)
  • NoCaps Test (nocaps_test)
• OKVQA (ok_vqa)
  • OKVQA Validation 2014 (ok_vqa_val2014)
• POPE (pope)
• RefCOCO (refcoco)
  • refcoco_seg_test
  • refcoco_seg_val
  • refcoco_seg_testA
  • refcoco_seg_testB
  • refcoco_bbox_test
  • refcoco_bbox_val
  • refcoco_bbox_testA
  • refcoco_bbox_testB
• RefCOCO+ (refcoco+)
  • refcoco+_seg
    • refcoco+_seg_val
    • refcoco+_seg_testA
    • refcoco+_seg_testB
  • refcoco+_bbox
    • refcoco+_bbox_val
    • refcoco+_bbox_testA
    • refcoco+_bbox_testB
• RefCOCOg (refcocog)
  • refcocog_seg_test
  • refcocog_seg_val
  • refcocog_bbox_test
  • refcocog_bbox_val
• ScienceQA (scienceqa_full)
  • ScienceQA Full (scienceqa)
  • ScienceQA IMG (scienceqa_img)
• SeedBench (seedbench)
• SeedBench 2 (seedbench_2)
• ST-VQA (stvqa)
• TextCaps (textcaps)
  • TextCaps Validation (textcaps_val)
  • TextCaps Test (textcaps_test)
• TextVQA (textvqa)
  • TextVQA Validation (textvqa_val)
  • TextVQA Test (textvqa_test)
• VizWizVQA (vizwiz_vqa)
  • VizWizVQA Validation (vizwiz_vqa_val)
  • VizWizVQA Test (vizwiz_vqa_test)
• VQAv2 (vqav2)
  • VQAv2 Validation (vqav2_val)
  • VQAv2 Test (vqav2_test)

Datasets to be added and tested

• TallyQA (tallyqa)
• VSR (vsr)
• Winoground (winoground)
• NLVR2 (nlvr2)
• RavenIQ-Test (raveniq)
• IconQA (iconqa)
• VistBench (vistbench)

Add Customized Model and Dataset

Please refer to our documentation.

Acknowledgement

lmms_eval is a fork of lm-eval-harness. We recommend you to read through the docs of lm-eval-harness for relevant information.

Below are the changes we made to the original API:

• Build context now only pass in idx and process image and doc during the model responding phase. This is due to the fact that dataset now contains lots of images and we can't store them in the doc like the original lm-eval-harness other wise the cpu memory would explode.
• Instance.args (lmms_eval/api/instance.py) now contains a list of images to be inputted to lmms.
• lm-eval-harness supports all HF language models as single model class. Currently this is not possible of lmms because the input/output format of lmms in HF are not yet unified. Thererfore, we have to create a new class for each lmms model. This is not ideal and we will try to unify them in the future.