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# https://git-scm.com/docs/gitattributes
# Set the default behavior, in case people don't have core.autocrlf set.
# https://git-scm.com/docs/gitattributes#_end_of_line_conversion
* text=auto
# common python attributes, taken from https://github.com/alexkaratarakis/gitattributes/blob/710900479a2bedeec7003d381719521ffbb18bf8/Python.gitattributes
# Source files
# ============
*.pxd text diff=python
*.py text diff=python
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# Python
__pycache__
*.pyc
*.egg-info
dist
# Log
*.log
*.log.*
*.json
*.jsonl
# Data
!**/alpaca-data-conversation.json
# Editor
.idea
*.swp
# Other
.DS_Store
wandb
output
checkpoints
ckpts*
.ipynb_checkpoints
*.ipynb
# DevContainer
!.devcontainer/*
# Demo
serve_images/

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<div align="center">
<img src="images/image_no_back.png" width="200" height="200">
<h1> 🔥 LLaVA-MORE 🔥
Enhancing Visual Instruction Tuning with LLaMA 3.1
</h1>
[![HuggingFace](https://img.shields.io/badge/🤗_LLaVA_MORE-1d8c0a)](https://huggingface.co/collections/aimagelab/llava-more-66aa6c49167e190bf27e7be4)
[![HuggingFace](https://img.shields.io/badge/🤗_AImageLab_-white)](https://huggingface.co/aimagelab)
[![Website](https://img.shields.io/badge/AImageLab-red)](https://aimagelab.ing.unimore.it/imagelab)
</div>
<div align='center'>
#### [Federico Cocchi](https://federico1-creator.github.io/Federico_Cocchi/), [Nicholas Moratelli](https://github.com/NicholasMoratelli), [Davide Caffagni](https://github.com/dcaffo98), [Sara Sarto](https://github.com/sarasarto),
[Marcella Cornia](), [Lorenzo Baraldi](), and [Rita Cucchiara]()
</div>
## Citation
If you make use of our work, please cite our repo:
```bibtex
@misc{cocchi2024llavamore,
title={{LLaVA-MORE: Enhancing Visual Instruction Tuning with LLaMA 3.1}},
author={Federico, Cocchi and Nicholas, Moratelli and Davide, Caffagni and Sara, Sarto and Marcella, Cornia and Lorenzo, Baraldi and Rita, Cucchiara},
url={https://github.com/aimagelab/LLaVA-MORE},
year={2024}
}
```
## 📢 Latest Updates
- [2024/08] 🔥 First release of our LLaVA-MORE 8B model.
- [2024/08] 🔎 If you are interested in this area of research, check out [our survey](https://arxiv.org/abs/2402.12451),
recently published in ACL (Findings).
- [2024/08] 📚 Check out the latest researches from [AImageLab](https://aimagelab.ing.unimore.it/imagelab/).
## Table of Contents
1. [Overview](#overview)
2. [Performance](#performance)
3. [Checkpoints](#checkpoints)
4. [Installation](#installation)
5. [Training](#training)
6. [Inference](#inference)
7. [Acknowledgments](#acknowledgments)
## Overview
```LLaVA-MORE``` enhances the well-known LLaVA architecture by integrating for the first time the use of LLaMA 3.1 as the language model. We are publicly releasing the checkpoints for stages one and two for the first model with 8B parameters.
To further support the research community in enhancing Multimodal LLM performance, we are also releasing the training code and scripts for distributed training.
Remember to star the repository to stay updated on future releases 🤗 and try our models [here](http://www.more.unimore.it/)!
## Performance
In this section, we present the performance of our model compared to other versions of LLaVA across different multimodal datasets.
<div align="center">
<img src="images/radar_plot.png" width="500"">
</div>
### Benchmarks and Comparisons on Instrucion Multimodal Datasets in the Literature
<div align="center">
| Model Name | Text-VQA* | Science-QA | AI2D | SEED-vid | SEED-all | SEED-img | MMMU | MMBench-Cn | MMBench-En | POPE | GQA | MME-P | MME-C |
|----------------------|:----------: |:------------:|:------:|:----------:|:----------:|:----------:|:------:|:------------:|:------------:|:------:|:-----:|:--------:|:-------:|
| LLaVA-v1.5-7B | 58.2 | 69.0 | 56.4 | 42.0 | 61.6 | 66.8 | 34.2 | 56.5 | 65.3 | **85.6** | 62.4 | 1474.3 | 314.6 |
| LLaVA-v1.5-LLaMA3-8B | 57.6 | 74.2 | 60.7 | 42.0 | **64.3** | **70.1** | 37.3 | 65.4 | 70.3 | 85.4 | 63.5 | **1544.4** | 330.3 |
| **LLaVA-MORE-8B** | **58.4** | **76.3** | **61.8** | **42.4** | 64.1 | 69.8 | **39.4** | **68.2** | **72.4** | 85.1 | **63.6** | 1531.5 | **353.3** |
</div>
*\* the results of TextVQA are calculated with OCR token in the input prompt.*
## Checkpoints
In the table below, you can find links to ours 🤗 Hugging Face models.
| Model Name | 🤗 Hugging Face | Summary |
|---------------------------|:-------------------------:|------------------------------------------------|
| LLaVA_MORE-llama_3_1-8B-pretrain | [Hugging Face Model](https://huggingface.co/aimagelab/LLaVA_MORE-llama_3_1-8B-pretrain) | Pretrained on [LCS-558K](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) and using [LLaMA 3.1 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) as LLM backbone |
| LLaVA_MORE-llama_3_1-8B-finetuning | [Hugging Face Model](https://huggingface.co/aimagelab/LLaVA_MORE-llama_3_1-8B-finetuning) | Finetuned on [LLaVA-Instruct-665K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) and using [LLaMA 3.1 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) as LLM backbone |
## Installation
To create the conda environment named ```more``` use the following instructions.
With this environment you will have all the packages to run the code in this repo.
```
conda create -n more python==3.8.16
conda activate more
pip install -r requirements.txt
```
Note that the requirements are heavily inspired by the original [LLaVA](https://github.com/haotian-liu/LLaVA.git) repo.
## Training
To help the community in training complex systems in distributed scenarios, we are publicly releasing not only the source code but also the bash scripts needed to train ```LLaVA-MORE``` on HPC facilities with a SLURM scheduler.
To further extend the reproducibility of our approach, we are also releasing the [wandb logs](https://api.wandb.ai/links/aimagelab/kq668y5l) of the training runs.
**Pretraining**
``` bash
sbatch scripts/more/11_pretrain_llama_31_acc_st_1.sh
```
**Finetuning**
``` bash
sbatch scripts/more/12_finetuning_llama_31_acc_st_1.sh
```
### Visual Backbones
As mentioned before, ```LLaVA-MORE``` introduces the use of LLaMA 3.1 within the LLaVA architecture for the first time. However, this repository goes beyond that single enhancement.
We have also incorporated the ability to use different visual backbones, such as SigLIP, and various methods for managing image resolutions (S2). Additionally, we have experimented with different data mixtures to stress data quality during the LLaVA training stages.
Considering that, you can view this repos as an effort to expand the study of Multimodal LLMs in multiple directions and as a
starting point for enhancing new features to improve the connection between images and language.
You can find more references in this folder: ```scripts/more```
## Inference
You can try our ```LLaVA-MORE``` in the Image-To-Text task by running the following script.
``` python
python -u llava/eval/run_llava.py
```
If you get out-of-memory problems, consider loading the model weights in 8 bit (```load_in_8bit=True```).
## Acknowledgments
We are thankful to the [LLaVA](https://github.com/haotian-liu/LLaVA.git) team for open-sourcing a modular codebase to extend and train different models within the LLaVA family.
We are also happy users of the [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval.git) library , which has significantly reduced the evaluation time of our checkpoints across different datasets.
Furthermore, we extend our gratitude to [CINECA](https://www.hpc.cineca.it/systems/hardware/leonardo/) for providing the HPC facilities used to train ```LLaVA-MORE```.
In case you face any issues or have any questions, please feel free to create an issue.
Additionally, we welcome you to open a pull request to integrate new features and contribute to our project.

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# Customize Components in LLaVA
This is an initial guide on how to replace the LLMs, visual encoders, etc. with your choice of components.
## LLM
It is quite simple to swap out LLaMA to any other LLMs. You can refer to our implementation of [`llava_llama.py`](https://raw.githubusercontent.com/haotian-liu/LLaVA/main/llava/model/language_model/llava_llama.py) for an example of how to replace the LLM.
Although it may seem that it still needs ~100 lines of code, most of them are copied from the original `llama.py` from HF. The only part that is different is to insert some lines for processing the multimodal inputs.
In `forward` function, you can see that we call `self.prepare_inputs_labels_for_multimodal` to process the multimodal inputs. This function is defined in `LlavaMetaForCausalLM` and you just need to insert it into the `forward` function of your LLM.
In `prepare_inputs_for_generation` function, you can see that we add `images` to the `model_inputs`. This is because we need to pass the images to the LLM during generation.
These are basically all the changes you need to make to replace the LLM.
## Visual Encoder
You can check out [`clip_encoder.py`](https://github.com/haotian-liu/LLaVA/blob/main/llava/model/multimodal_encoder/clip_encoder.py) on how we implement the CLIP visual encoder.

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## Data
| Data file name | Size |
| --- | ---: |
| [llava_instruct_150k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_instruct_150k.json) | 229 MB |
| [llava_instruct_80k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_instruct_80k.json) | 229 MB |
| [conversation_58k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/conversation_58k.json) | 126 MB |
| [detail_23k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/detail_23k.json) | 20.5 MB |
| [complex_reasoning_77k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/complex_reasoning_77k.json) | 79.6 MB |
### Pretraining Dataset
The pretraining dataset used in this release is a subset of CC-3M dataset, filtered with a more balanced concept coverage distribution. Please see [here](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K) for a detailed description of the dataset structure and how to download the images.
If you already have CC-3M dataset on your disk, the image names follow this format: `GCC_train_000000000.jpg`. You may edit the `image` field correspondingly if necessary.
| Data | Chat File | Meta Data | Size |
| --- | --- | --- | ---: |
| CC-3M Concept-balanced 595K | [chat.json](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/chat.json) | [metadata.json](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/metadata.json) | 211 MB
| LAION/CC/SBU BLIP-Caption Concept-balanced 558K | [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/blip_laion_cc_sbu_558k.json) | [metadata.json](#) | 181 MB
**Important notice**: Upon the request from the community, as ~15% images of the original CC-3M dataset are no longer accessible, we upload [`images.zip`](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/images.zip) for better reproducing our work in research community. It must not be used for any other purposes. The use of these images must comply with the CC-3M license. This may be taken down at any time when requested by the original CC-3M dataset owner or owners of the referenced images.
### GPT-4 Prompts
We provide our prompts and few-shot samples for GPT-4 queries, to better facilitate research in this domain. Please check out the [`prompts`](https://github.com/haotian-liu/LLaVA/tree/main/playground/data/prompts) folder for three kinds of questions: conversation, detail description, and complex reasoning.
They are organized in a format of `system_message.txt` for system message, pairs of `abc_caps.txt` for few-shot sample user input, and `abc_conv.txt` for few-shot sample reference output.
Note that you may find them in different format. For example, `conversation` is in `jsonl`, and detail description is answer-only. The selected format in our preliminary experiments works slightly better than a limited set of alternatives that we tried: `jsonl`, more natural format, answer-only. If interested, you may try other variants or conduct more careful study in this. Contributions are welcomed!

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# Evaluation
In LLaVA-1.5, we evaluate models on a diverse set of 12 benchmarks. To ensure the reproducibility, we evaluate the models with greedy decoding. We do not evaluate using beam search to make the inference process consistent with the chat demo of real-time outputs.
Currently, we mostly utilize the official toolkit or server for the evaluation.
## Evaluate on Custom Datasets
You can evaluate LLaVA on your custom datasets by converting your dataset to LLaVA's jsonl format, and evaluate using [`model_vqa.py`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/model_vqa.py).
Below we provide a general guideline for evaluating datasets with some common formats.
1. Short-answer (e.g. VQAv2, MME).
```
<question>
Answer the question using a single word or phrase.
```
2. Option-only for multiple-choice (e.g. MMBench, SEED-Bench).
```
<question>
A. <option_1>
B. <option_2>
C. <option_3>
D. <option_4>
Answer with the option's letter from the given choices directly.
```
3. Natural QA (e.g. LLaVA-Bench, MM-Vet).
No postprocessing is needed.
## Scripts
Before preparing task-specific data, **you MUST first download [eval.zip](https://drive.google.com/file/d/1atZSBBrAX54yYpxtVVW33zFvcnaHeFPy/view?usp=sharing)**. It contains custom annotations, scripts, and the prediction files with LLaVA v1.5. Extract to `./playground/data/eval`. This also provides a general structure for all datasets.
### VQAv2
1. Download [`test2015`](http://images.cocodataset.org/zips/test2015.zip) and put it under `./playground/data/eval/vqav2`.
2. Multi-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/vqav2.sh
```
3. Submit the results to the [evaluation server](https://eval.ai/web/challenges/challenge-page/830/my-submission): `./playground/data/eval/vqav2/answers_upload`.
### GQA
1. Download the [data](https://cs.stanford.edu/people/dorarad/gqa/download.html) and [evaluation scripts](https://cs.stanford.edu/people/dorarad/gqa/evaluate.html) following the official instructions and put under `./playground/data/eval/gqa/data`. You may need to modify `eval.py` as [this](https://gist.github.com/haotian-liu/db6eddc2a984b4cbcc8a7f26fd523187) due to the missing assets in the GQA v1.2 release.
2. Multi-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/gqa.sh
```
### VisWiz
1. Download [`test.json`](https://vizwiz.cs.colorado.edu/VizWiz_final/vqa_data/Annotations.zip) and extract [`test.zip`](https://vizwiz.cs.colorado.edu/VizWiz_final/images/test.zip) to `test`. Put them under `./playground/data/eval/vizwiz`.
2. Single-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/vizwiz.sh
```
3. Submit the results to the [evaluation server](https://eval.ai/web/challenges/challenge-page/2185/my-submission): `./playground/data/eval/vizwiz/answers_upload`.
### ScienceQA
1. Under `./playground/data/eval/scienceqa`, download `images`, `pid_splits.json`, `problems.json` from the `data/scienceqa` folder of the ScienceQA [repo](https://github.com/lupantech/ScienceQA).
2. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/sqa.sh
```
### TextVQA
1. Download [`TextVQA_0.5.1_val.json`](https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json) and [images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip) and extract to `./playground/data/eval/textvqa`.
2. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/textvqa.sh
```
### POPE
1. Download `coco` from [POPE](https://github.com/AoiDragon/POPE/tree/e3e39262c85a6a83f26cf5094022a782cb0df58d/output/coco) and put under `./playground/data/eval/pope`.
2. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/pope.sh
```
### MME
1. Download the data following the official instructions [here](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation).
2. Downloaded images to `MME_Benchmark_release_version`.
3. put the official `eval_tool` and `MME_Benchmark_release_version` under `./playground/data/eval/MME`.
4. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mme.sh
```
### MMBench
1. Download [`mmbench_dev_20230712.tsv`](https://download.openmmlab.com/mmclassification/datasets/mmbench/mmbench_dev_20230712.tsv) and put under `./playground/data/eval/mmbench`.
2. Single-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mmbench.sh
```
3. Submit the results to the [evaluation server](https://opencompass.org.cn/leaderboard-multimodal): `./playground/data/eval/mmbench/answers_upload/mmbench_dev_20230712`.
### MMBench-CN
1. Download [`mmbench_dev_cn_20231003.tsv`](https://download.openmmlab.com/mmclassification/datasets/mmbench/mmbench_dev_cn_20231003.tsv) and put under `./playground/data/eval/mmbench`.
2. Single-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mmbench_cn.sh
```
3. Submit the results to the evaluation server: `./playground/data/eval/mmbench/answers_upload/mmbench_dev_cn_20231003`.
### SEED-Bench
1. Following the official [instructions](https://github.com/AILab-CVC/SEED-Bench/blob/main/DATASET.md) to download the images and the videos. Put images under `./playground/data/eval/seed_bench/SEED-Bench-image`.
2. Extract the video frame in the middle from the downloaded videos, and put them under `./playground/data/eval/seed_bench/SEED-Bench-video-image`. We provide our script `extract_video_frames.py` modified from the official one.
3. Multiple-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/seed.sh
```
4. Optionally, submit the results to the leaderboard: `./playground/data/eval/seed_bench/answers_upload` using the official jupyter notebook.
### LLaVA-Bench-in-the-Wild
1. Extract contents of [`llava-bench-in-the-wild`](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild) to `./playground/data/eval/llava-bench-in-the-wild`.
2. Single-GPU inference and evaluate.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/llavabench.sh
```
### MM-Vet
1. Extract [`mm-vet.zip`](https://github.com/yuweihao/MM-Vet/releases/download/v1/mm-vet.zip) to `./playground/data/eval/mmvet`.
2. Single-GPU inference.
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mmvet.sh
```
3. Evaluate the predictions in `./playground/data/eval/mmvet/results` using the official jupyter notebook.
## More Benchmarks
Below are awesome benchmarks for multimodal understanding from the research community, that are not initially included in the LLaVA-1.5 release.
### Q-Bench
1. Download [`llvisionqa_dev.json`](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/llvisionqa_dev.json) (for `dev`-subset) and [`llvisionqa_test.json`](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/llvisionqa_test.json) (for `test`-subset). Put them under `./playground/data/eval/qbench`.
2. Download and extract [images](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/images_llvisionqa.tar) and put all the images directly under `./playground/data/eval/qbench/images_llviqionqa`.
3. Single-GPU inference (change `dev` to `test` for evaluation on test set).
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/qbench.sh dev
```
4. Submit the results by instruction [here](https://github.com/VQAssessment/Q-Bench#option-1-submit-results): `./playground/data/eval/qbench/llvisionqa_dev_answers.jsonl`.
### Chinese-Q-Bench
1. Download [`质衡-问答-验证集.json`](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/%E8%B4%A8%E8%A1%A1-%E9%97%AE%E7%AD%94-%E9%AA%8C%E8%AF%81%E9%9B%86.json) (for `dev`-subset) and [`质衡-问答-测试集.json`](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/%E8%B4%A8%E8%A1%A1-%E9%97%AE%E7%AD%94-%E6%B5%8B%E8%AF%95%E9%9B%86.json) (for `test`-subset). Put them under `./playground/data/eval/qbench`.
2. Download and extract [images](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/images_llvisionqa.tar) and put all the images directly under `./playground/data/eval/qbench/images_llviqionqa`.
3. Single-GPU inference (change `dev` to `test` for evaluation on test set).
```Shell
CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/qbench_zh.sh dev
```
4. Submit the results by instruction [here](https://github.com/VQAssessment/Q-Bench#option-1-submit-results): `./playground/data/eval/qbench/llvisionqa_zh_dev_answers.jsonl`.

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# Finetune LLaVA on Custom Datasets
## Dataset Format
Convert your data to a JSON file of a List of all samples. Sample metadata should contain `id` (a unique identifier), `image` (the path to the image), and `conversations` (the conversation data between human and AI).
A sample JSON for finetuning LLaVA for generating tag-style captions for Stable Diffusion:
```json
[
{
"id": "997bb945-628d-4724-b370-b84de974a19f",
"image": "part-000001/997bb945-628d-4724-b370-b84de974a19f.jpg",
"conversations": [
{
"from": "human",
"value": "<image>\nWrite a prompt for Stable Diffusion to generate this image."
},
{
"from": "gpt",
"value": "a beautiful painting of chernobyl by nekro, pascal blanche, john harris, greg rutkowski, sin jong hun, moebius, simon stalenhag. in style of cg art. ray tracing. cel shading. hyper detailed. realistic. ue 5. maya. octane render. "
},
]
},
...
]
```
## Command
If you have a limited task-specific data, we recommend finetuning from LLaVA checkpoints with LoRA following this [script](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune_task_lora.sh).
If the amount of the task-specific data is sufficient, you can also finetune from LLaVA checkpoints with full-model finetuning following this [script](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune_task.sh).
You may need to adjust the hyperparameters to fit each specific dataset and your hardware constraint.

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# Intel Platforms
* Support [Intel GPU Max Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/max-series.html)
* Support [Intel CPU Sapphire Rapides](https://ark.intel.com/content/www/us/en/ark/products/codename/126212/products-formerly-sapphire-rapids.html)
* Based on [Intel Extension for Pytorch](https://intel.github.io/intel-extension-for-pytorch)
More details in [**intel branch**](https://github.com/haotian-liu/LLaVA/tree/intel/docs/intel)

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# LLaVA-Bench [[Download](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild)]
**-Introduction-** Large commercial multimodal chatbots have been released in this week, including
- [Multimodal Bing-Chat by Microsoft](https://blogs.bing.com/search/july-2023/Bing-Chat-Enterprise-announced,-multimodal-Visual-Search-rolling-out-to-Bing-Chat) (July 18, 2023)
- [Multimodal Bard by Google](https://bard.google.com/).
These chatbots are presumably supported by proprietary large multimodal models (LMM). Compared with the open-source LMM such as LLaVA, proprietary LMM represent the scaling success upperbound of the current SoTA techniques. They share the goal of developing multimodal chatbots that follow human intents to complete various daily-life visual tasks in the wild. While it remains less explored how to evaluate multimodal chat ability, it provides useful feedback to study open-source LMMs against the commercial multimodal chatbots. In addition to the *LLaVA-Bench (COCO)* dataset we used to develop the early versions of LLaVA, we are releasing [*LLaVA-Bench (In-the-Wild)*](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild) to the community for the public use.
## LLaVA-Bench (In-the-Wild *[Ongoing work]*)
To evaluate the model's capability in more challenging tasks and generalizability to novel domains, we collect a diverse set of 24 images with 60 questions in total, including indoor and outdoor scenes, memes, paintings, sketches, etc, and associate each image with a highly-detailed and manually-curated description and a proper selection of questions. Such design also assesses the model's robustness to different prompts. In this release, we also categorize questions into three categories: conversation (simple QA), detailed description, and complex reasoning. We continue to expand and improve the diversity of the LLaVA-Bench (In-the-Wild). We manually query Bing-Chat and Bard to get the responses.
### Results
The score is measured by comparing against a reference answer generated by text-only GPT-4. It is generated by feeding the question, along with the ground truth image annotations as the context. A text-only GPT-4 evaluator rates both answers. We query GPT-4 by putting the reference answer first, and then the answer generated by the candidate model. We upload images at their original resolution to Bard and Bing-Chat to obtain the results.
| Approach | Conversation | Detail | Reasoning | Overall |
|----------------|--------------|--------|-----------|---------|
| Bard-0718 | 83.7 | 69.7 | 78.7 | 77.8 |
| Bing-Chat-0629 | 59.6 | 52.2 | 90.1 | 71.5 |
| LLaVA-13B-v1-336px-0719 (beam=1) | 64.3 | 55.9 | 81.7 | 70.1 |
| LLaVA-13B-v1-336px-0719 (beam=5) | 68.4 | 59.9 | 84.3 | 73.5 |
Note that Bard sometimes refuses to answer questions about images containing humans, and Bing-Chat blurs the human faces in the images. We also provide the benchmark score for the subset without humans.
| Approach | Conversation | Detail | Reasoning | Overall |
|----------------|--------------|--------|-----------|---------|
| Bard-0718 | 94.9 | 74.3 | 84.3 | 84.6 |
| Bing-Chat-0629 | 55.8 | 53.6 | 93.5 | 72.6 |
| LLaVA-13B-v1-336px-0719 (beam=1) | 62.2 | 56.4 | 82.2 | 70.0 |
| LLaVA-13B-v1-336px-0719 (beam=5) | 65.6 | 61.7 | 85.0 | 73.6 |

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# LLaVA (based on Llama 2 LLM, Preview)
*NOTE: This is a technical preview. We are still running hyperparameter search, and will release the final model soon. If you'd like to contribute to this, please contact us.*
:llama: **-Introduction-** [Llama 2 is an open-source LLM released by Meta AI](https://about.fb.com/news/2023/07/llama-2/) today (July 18, 2023). Compared with its early version [Llama 1](https://ai.meta.com/blog/large-language-model-llama-meta-ai/), Llama 2 is more favored in ***stronger language performance***, ***longer context window***, and importantly ***commercially usable***! While Llama 2 is changing the LLM market landscape in the language space, its multimodal ability remains unknown. We quickly develop the LLaVA variant based on the latest Llama 2 checkpoints, and release it to the community for the public use.
You need to apply for and download the latest Llama 2 checkpoints to start your own training (apply [here](https://ai.meta.com/resources/models-and-libraries/llama-downloads/))
## Training
Please checkout [`pretrain.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/pretrain.sh), [`finetune.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune.sh), [`finetune_lora.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_lora.sh).
## LLaVA (based on Llama 2), What is different?
:volcano: How is the new LLaVA based on Llama 2 different from Llama 1? The comparisons of the training process are described:
- **Pre-training**. The pre-trained base LLM is changed from Llama 1 to Llama 2
- **Language instruction-tuning**. The previous LLaVA model starts with Vicuna, which is instruct tuned on ShareGPT data from Llama 1; The new LLaVA model starts with Llama 2 Chat, which is an instruct tuned checkpoint on dialogue data from Llama 2.
- **Multimodal instruction-tuning**. The same LLaVA-Lighting process is applied.
### Results
- Llama 2 is better at following the instructions of role playing; Llama 2 fails in following the instructions of translation
- The quantitative evaluation on [LLaVA-Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) demonstrates on-par performance between Llama 2 and Llama 1 in LLaVA's multimodal chat ability.
<img src="../images/llava_example_cmp.png" width="100%">

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# LLaVA (LoRA, Preview)
NOTE: This is a technical preview, and is not yet ready for production use. We are still running hyperparameter search for the LoRA model, and will release the final model soon. If you'd like to contribute to this, please contact us.
You need latest code base for LoRA support (instructions [here](https://github.com/haotian-liu/LLaVA#upgrade-to-latest-code-base))
## Demo (Web UI)
Please execute each of the commands below one by one (after the previous one has finished). The commands are the same as launching other demos except for an additional `--model-base` flag to specify the base model to use. Please make sure the base model corresponds to the LoRA checkpoint that you are using. For this technical preview, you need Vicuna v1.1 (7B) checkpoint (if you do not have that already, follow the instructions [here](https://github.com/lm-sys/FastChat#vicuna-weights)).
#### Launch a controller
```Shell
python -m llava.serve.controller --host 0.0.0.0 --port 10000
```
#### Launch a gradio web server.
```Shell
python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload
```
You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker.
#### Launch a model worker
```Shell
python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-vicuna-7b-v1.1-lcs_558k-instruct_80k_3e-lora-preview-alpha --model-base /path/to/vicuna-v1.1
```
Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list.
You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the `--controller` the same, and modify the `--port` and `--worker` to a different port number for each worker.
## Training
Please see sample training scripts for [LoRA](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_lora.sh) and [QLoRA](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_qlora.sh).
We provide sample DeepSpeed configs, [`zero3.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero3.json) is more like PyTorch FSDP, and [`zero3_offload.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero3_offload.json) can further save memory consumption by offloading parameters to CPU. `zero3.json` is usually faster than `zero3_offload.json` but requires more GPU memory, therefore, we recommend trying `zero3.json` first, and if you run out of GPU memory, try `zero3_offload.json`. You can also tweak the `per_device_train_batch_size` and `gradient_accumulation_steps` in the config to save memory, and just to make sure that `per_device_train_batch_size` and `gradient_accumulation_steps` remains the same.
If you are having issues with ZeRO-3 configs, and there are enough VRAM, you may try [`zero2.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero2.json). This consumes slightly more memory than ZeRO-3, and behaves more similar to PyTorch FSDP, while still supporting parameter-efficient tuning.
## Create Merged Checkpoints
```Shell
python scripts/merge_lora_weights.py \
--model-path /path/to/lora_model \
--model-base /path/to/base_model \
--save-model-path /path/to/merge_model
```

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# Model Zoo
**To Use LLaVA-1.6 checkpoints, your llava package version must be newer than 1.2.0. [Instructions](https://github.com/haotian-liu/LLaVA#upgrade-to-latest-code-base) on how to upgrade.**
If you are interested in including any other details in Model Zoo, please open an issue :)
The model weights below are *merged* weights. You do not need to apply delta. The usage of LLaVA checkpoints should comply with the base LLM's model license.
## LLaVA-v1.6
| Version | LLM | Schedule | Checkpoint | MMMU | MathVista | VQAv2 | GQA | VizWiz | SQA | TextVQA | POPE | MME | MM-Bench | MM-Bench-CN | SEED-IMG | LLaVA-Bench-Wild | MM-Vet |
|----------|----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LLaVA-1.6 | Vicuna-7B | full_ft-1e | [liuhaotian/llava-v1.6-vicuna-7b](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b) | 35.8 | 34.6 | 81.8 | 64.2 | 57.6 | 70.1 | 64.9 | 86.5 | 1519/332 | 67.4 | 60.6 | 70.2 | 81.6 | 43.9 |
| LLaVA-1.6 | Vicuna-13B | full_ft-1e | [liuhaotian/llava-v1.6-vicuna-13b](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-13b) | 36.2 | 35.3 | 82.8 | 65.4 | 60.5 | 73.6 | 67.1 | 86.2 | 1575/326 | 70 | 64.4 | 71.9 | 87.3 | 48.4 |
| LLaVA-1.6 | Mistral-7B | full_ft-1e | [liuhaotian/llava-v1.6-mistral-7b](https://huggingface.co/liuhaotian/llava-v1.6-mistral-7b) | 35.3 | 37.7 | 82.2 | 64.8 | 60.0 | 72.8 | 65.7 | 86.7 | 1498/321 | 68.7 | 61.2 | 72.2 | 83.2 | 47.3 |
| LLaVA-1.6 | Hermes-Yi-34B | full_ft-1e | [liuhaotian/llava-v1.6-34b](https://huggingface.co/liuhaotian/llava-v1.6-34b) | 51.1 | 46.5 | 83.7 | 67.1 | 63.8 | 81.8 | 69.5 | 87.7 | 1631/397 | 79.3 | 79 | 75.9 | 89.6 | 57.4 |
*LLaVA-1.6-34B outperforms Gemini Pro on benchmarks like MMMU and MathVista.*
## LLaVA-v1.5
| Version | Size | Schedule | Checkpoint | VQAv2 | GQA | VizWiz | SQA | TextVQA | POPE | MME | MM-Bench | MM-Bench-CN | SEED | LLaVA-Bench-Wild | MM-Vet |
|----------|----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---|---|---|
| LLaVA-1.5 | 7B | full_ft-1e | [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 78.5 | 62.0 | 50.0 | 66.8 | 58.2 | 85.9 | 1510.7 | 64.3 | 58.3 | 58.6 | 65.4 | 31.1 |
| LLaVA-1.5 | 13B | full_ft-1e | [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) | 80.0 | 63.3 | 53.6 | 71.6 | 61.3 | 85.9 | 1531.3 | 67.7 | 63.6 | 61.6 | 72.5 | 36.1 |
| LLaVA-1.5 | 7B | lora-1e | [liuhaotian/llava-v1.5-7b-lora](https://huggingface.co/liuhaotian/llava-v1.5-7b-lora) | 79.1 | 63.0 | 47.8 | 68.4 | 58.2 | 86.4 | 1476.9 | 66.1 | 58.9 | 60.1 | 67.9 | 30.2 |
| LLaVA-1.5 | 13B | lora-1e | [liuhaotian/llava-v1.5-13b-lora](https://huggingface.co/liuhaotian/llava-v1.5-13b-lora) | 80.0 | 63.3 | 58.9 | 71.2 | 60.2 | 86.7 | 1541.7 | 68.5 | 61.5 | 61.3 | 69.5 | 38.3 |
Base model: Vicuna v1.5. Training logs: [wandb](https://api.wandb.ai/links/lht/6orh56wc).
<p align="center">
<img src="../images/llava_v1_5_radar.jpg" width="500px"> <br>
LLaVA-1.5 achieves SoTA performance across 11 benchmarks.
</p>
## LLaVA-v1
*Note: We recommend using the most capable LLaVA-v1.6 series above for the best performance.*
| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | LLaVA-Bench-Conv | LLaVA-Bench-Detail | LLaVA-Bench-Complex | LLaVA-Bench-Overall | Download |
|----------|----------------|---------------|----------------------|-----------------|--------------------|------------------|--------------------|---------------------|---------------------|---------------------|
| Vicuna-13B-v1.3 | CLIP-L-336px | LCS-558K | 1e | LLaVA-Instruct-80K | proj-1e, lora-1e | 64.3 | 55.9 | 81.7 | 70.1 | [LoRA](https://huggingface.co/liuhaotian/llava-v1-0719-336px-lora-vicuna-13b-v1.3) [LoRA-Merged](https://huggingface.co/liuhaotian/llava-v1-0719-336px-lora-merge-vicuna-13b-v1.3) |
| LLaMA-2-13B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | 56.7 | 58.6 | 80.0 | 67.9 | [ckpt](https://huggingface.co/liuhaotian/llava-llama-2-13b-chat-lightning-preview) |
| LLaMA-2-7B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | lora-1e | 51.2 | 58.9 | 71.6 | 62.8 | [LoRA](https://huggingface.co/liuhaotian/llava-llama-2-7b-chat-lightning-lora-preview) |
## Projector weights
These are projector weights we have pretrained. You can use these projector weights for visual instruction tuning. They are just pretrained on image-text pairs and are NOT instruction-tuned, which means they do NOT follow instructions as well as our official models and can output repetitive, lengthy, and garbled outputs. If you want to have nice conversations with LLaVA, use the checkpoints above (LLaVA v1.6).
NOTE: These projector weights are only compatible with `llava>=1.0.0`. Please check out the latest codebase if your local code version is below v1.0.0.
NOTE: When you use our pretrained projector for visual instruction tuning, it is very important to use the same base LLM and vision encoder as the one we used for pretraining the projector. Otherwise, the performance will be very poor.
When using these projector weights to instruction-tune your LMM, please make sure that these options are correctly set as follows,
```Shell
--mm_use_im_start_end False
--mm_use_im_patch_token False
```
| Base LLM | Vision Encoder | Projection | Pretrain Data | Pretraining schedule | Download |
|----------|----------------|---------------|----------------------|----------|----------|
| Vicuna-13B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-v1.5-mlp2x-336px-pretrain-vicuna-13b-v1.5) |
| Vicuna-7B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-v1.5-mlp2x-336px-pretrain-vicuna-7b-v1.5) |
| LLaMA-2-13B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-13b-chat) |
| LLaMA-2-7B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-7b-chat) |
| LLaMA-2-13B-Chat | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-13b-chat) |
| LLaMA-2-7B-Chat | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-7b-chat) |
| Vicuna-13B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-13b-v1.3) |
| Vicuna-7B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-7b-v1.3) |
| Vicuna-13B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-13b-v1.3) |
| Vicuna-7B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-7b-v1.3) |
## Science QA Checkpoints
| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download |
|----------|----------------|---------------|----------------------|-----------------|--------------------|---------------------|
| Vicuna-13B-v1.3 | CLIP-L | LCS-558K | 1e | ScienceQA | full_ft-12e | [ckpt](https://huggingface.co/liuhaotian/llava-lcs558k-scienceqa-vicuna-13b-v1.3) |
## Legacy Models (merged weights)
The model weights below are *merged* weights. You do not need to apply delta. The usage of LLaVA checkpoints should comply with the base LLM's model license.
| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download |
|----------|----------------|---------------|----------------------|-----------------|--------------------|------------------|
| MPT-7B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | [preview](https://huggingface.co/liuhaotian/LLaVA-Lightning-MPT-7B-preview) |
## Legacy Models (delta weights)
The model weights below are *delta* weights. The usage of LLaVA checkpoints should comply with the base LLM's model license: [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md).
You can add our delta to the original LLaMA weights to obtain the LLaVA weights.
Instructions:
1. Get the original LLaMA weights in the huggingface format by following the instructions [here](https://huggingface.co/docs/transformers/main/model_doc/llama).
2. Use the following scripts to get LLaVA weights by applying our delta. It will automatically download delta weights from our Hugging Face account. In the script below, we use the delta weights of [`liuhaotian/LLaVA-7b-delta-v0`](https://huggingface.co/liuhaotian/LLaVA-7b-delta-v0) as an example. It can be adapted for other delta weights by changing the `--delta` argument (and base/target accordingly).
```bash
python3 -m llava.model.apply_delta \
--base /path/to/llama-7b \
--target /output/path/to/LLaVA-7B-v0 \
--delta liuhaotian/LLaVA-7b-delta-v0
```
| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download |
|----------|----------------|---------------|----------------------|-----------------|--------------------|------------------|
| Vicuna-13B-v1.1 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1) |
| Vicuna-7B-v1.1 | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-Lightning-7B-delta-v1-1) |
| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v0) |
| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | ScienceQA | full_ft-12e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v0-science_qa) |
| Vicuna-7B-v0 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-7b-delta-v0) |
## Legacy Projector weights
The following projector weights are deprecated, and the support for them may be removed in the future. They do not support zero-shot inference. Please use the projector weights in the [table above](#projector-weights) if possible.
**NOTE**: When you use our pretrained projector for visual instruction tuning, it is very important to **use the same base LLM and vision encoder** as the one we used for pretraining the projector. Otherwise, the performance will be very bad.
When using these projector weights to instruction tune your LMM, please make sure that these options are correctly set as follows,
```Shell
--mm_use_im_start_end True
--mm_use_im_patch_token False
```
| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Download |
|----------|----------------|---------------|----------------------|----------|
| Vicuna-7B-v1.1 | CLIP-L | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-7b-pretrain-projector-v1-1-LCS-558K-blip_caption.bin) |
| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-13b-pretrain-projector-v0-CC3M-595K-original_caption.bin) |
| Vicuna-7B-v0 | CLIP-L | CC-595K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-7b-pretrain-projector-v0-CC3M-595K-original_caption.bin) |
When using these projector weights to instruction tune your LMM, please make sure that these options are correctly set as follows,
```Shell
--mm_use_im_start_end False
--mm_use_im_patch_token False
```
| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Download |
|----------|----------------|---------------|----------------------|----------|
| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-13b-pretrain-projector-v0-CC3M-595K-original_caption-no_im_token.bin) |

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### ScienceQA
#### Prepare Data
1. Please see ScienceQA [repo](https://github.com/lupantech/ScienceQA) for setting up the dataset.
2. Generate ScienceQA dataset for LLaVA conversation-style format.
```Shell
python scripts/convert_sqa_to_llava.py \
convert_to_llava \
--base-dir /path/to/ScienceQA/data/scienceqa \
--prompt-format "QCM-LEA" \
--split {train,val,minival,test,minitest}
```
#### Training
1. Pretraining
You can download our pretrained projector weights from our [Model Zoo](), or train your own projector weights using [`pretrain.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/pretrain.sh).
2. Finetuning
See [`finetune_sqa.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_sqa.sh).
#### Evaluation
1. Multiple-GPU inference
You may evaluate this with multiple GPUs, and concatenate the generated jsonl files. Please refer to our script for [batch evaluation](https://github.com/haotian-liu/LLaVA/blob/main/scripts/sqa_eval_batch.sh) and [results gathering](https://github.com/haotian-liu/LLaVA/blob/main/scripts/sqa_eval_gather.sh).
2. Single-GPU inference
(a) Generate LLaVA responses on ScienceQA dataset
```Shell
python -m llava.eval.model_vqa_science \
--model-path liuhaotian/llava-lcs558k-scienceqa-vicuna-13b-v1.3 \
--question-file /path/to/ScienceQA/data/scienceqa/llava_test_QCM-LEA.json \
--image-folder /path/to/ScienceQA/data/scienceqa/images/test \
--answers-file vqa/results/ScienceQA/test_llava-13b.jsonl \
--conv-mode llava_v1
```
(b) Evaluate the generated responses
```Shell
python eval_science_qa.py \
--base-dir /path/to/ScienceQA/data/scienceqa \
--result-file vqa/results/ScienceQA/test_llava-13b.jsonl \
--output-file vqa/results/ScienceQA/test_llava-13b_output.json \
--output-result vqa/results/ScienceQA/test_llava-13b_result.json \
```
For reference, we attach our prediction file [`test_sqa_llava_lcs_558k_sqa_12e_vicuna_v1_3_13b.json`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/table/results/test_sqa_llava_lcs_558k_sqa_12e_vicuna_v1_3_13b.json) and [`test_sqa_llava_13b_v0.json`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/table/results/test_sqa_llava_13b_v0.json) for comparison when reproducing our results, as well as for further analysis in detail.

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# Run LLaVA on Windows
*NOTE: LLaVA on Windows is not fully supported. Currently we only support 16-bit inference. For a more complete support, please use [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) for now. More functionalities on Windows is to be added soon, stay tuned.*
## Installation
1. Clone this repository and navigate to LLaVA folder
```bash
git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA
```
2. Install Package
```Shell
conda create -n llava python=3.10 -y
conda activate llava
python -m pip install --upgrade pip # enable PEP 660 support
pip install torch==2.0.1+cu117 torchvision==0.15.2+cu117 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu117
pip install -e .
pip uninstall bitsandbytes
```
## Run demo
See instructions [here](https://github.com/haotian-liu/LLaVA#demo).
Note that quantization (4-bit, 8-bit) is *NOT* supported on Windows. Stay tuned for the 4-bit support on Windows!

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# Run LLaVA on macOS
*NOTE: LLaVA on macOS is not fully supported. Currently we only support 16-bit inference. More functionalities on macOS is to be added soon, stay tuned.*
## Installation
1. Clone this repository and navigate to LLaVA folder
```bash
git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA
```
2. Install Package
```Shell
conda create -n llava python=3.10 -y
conda activate llava
python -mpip install --upgrade pip # enable PEP 660 support
pip install -e .
pip install torch==2.1.0 torchvision==0.16.0
pip uninstall bitsandbytes
```
## Run demo
Specify `--device mps` when launching model worker or CLI.
See instructions [here](https://github.com/haotian-liu/LLaVA#demo).
Note that quantization (4-bit, 8-bit) is *NOT* supported on macOS. Stay tuned for the 4-bit support on macOS!

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from .model import LlavaLlamaForCausalLM

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CONTROLLER_HEART_BEAT_EXPIRATION = 30
WORKER_HEART_BEAT_INTERVAL = 15
LOGDIR = "."
# Model Constants
IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"
IMAGE_PLACEHOLDER = "<image-placeholder>"

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import os
import dataclasses
from enum import auto, Enum
from typing import List, Any, Dict, Union, Tuple
import base64
from io import BytesIO
from PIL import Image
from transformers import AutoTokenizer
import utils
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
MPT = auto()
PLAIN = auto()
LLAMA_2 = auto()
LLAMA_3 = auto()
LLAMA_3_1 = auto()
@dataclasses.dataclass
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: List[str]
messages: List[List[str]]
offset: int
sep_style: SeparatorStyle = SeparatorStyle.SINGLE
sep: str = "###"
sep2: str = None
version: str = "Unknown"
tokenizer: Any = None
# Stop criteria (the default one is EOS token)
stop_str: Union[str, List[str]] = None
# Stops generation if meeting any token in this list
stop_token_ids: List[int] = None
skip_next: bool = False
def get_prompt(self):
messages = self.messages
if len(messages) > 0 and type(messages[0][1]) is tuple:
messages = self.messages.copy()
init_role, init_msg = messages[0].copy()
init_msg = init_msg[0].replace("<image>", "").strip()
if 'mmtag' in self.version:
messages[0] = (init_role, init_msg)
messages.insert(0, (self.roles[0], "<Image><image></Image>"))
messages.insert(1, (self.roles[1], "Received."))
else:
messages[0] = (init_role, "<image>\n" + init_msg)
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system + self.sep
for role, message in messages:
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + ": " + message + self.sep
else:
ret += role + ":"
elif self.sep_style == SeparatorStyle.TWO:
seps = [self.sep, self.sep2]
ret = self.system + seps[0]
for i, (role, message) in enumerate(messages):
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + ": " + message + seps[i % 2]
else:
ret += role + ":"
elif self.sep_style == SeparatorStyle.MPT:
ret = self.system + self.sep
for role, message in messages:
if message:
if type(message) is tuple:
message, _, _ = message
ret += role + message + self.sep
else:
ret += role
elif self.sep_style == SeparatorStyle.LLAMA_2:
wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n" if len(msg) > 0 else msg
wrap_inst = lambda msg: f"[INST] {msg} [/INST]"
ret = ""
for i, (role, message) in enumerate(messages):
if i == 0:
assert message, "first message should not be none"
assert role == self.roles[0], "first message should come from user"
if message:
if type(message) is tuple:
message, _, _ = message
if i == 0: message = wrap_sys(self.system) + message
if i % 2 == 0:
message = wrap_inst(message)
ret += self.sep + message
else:
ret += " " + message + " " + self.sep2
else:
ret += ""
ret = ret.lstrip(self.sep)
elif self.sep_style == SeparatorStyle.LLAMA_3:
chat_template_messages = [{"role": "system", "content": self.system}]
for role, message in messages:
if message:
if type(message) is tuple:
message, images = message
message = "<image>" * len(images) + message
chat_template_messages.append({"role": role, "content": message})
# print(chat_template_messages)
return self.tokenizer.apply_chat_template(chat_template_messages, tokenize=False, add_generation_prompt=False)
elif self.sep_style == SeparatorStyle.LLAMA_3_1:
chat_template_messages = [{"role": "system", "content": self.system}]
for role, message in messages:
if message:
if type(message) is tuple:
message, images = message
message = "<image>" * len(images) + message
chat_template_messages.append({"role": role, "content": message})
return self.tokenizer.apply_chat_template(chat_template_messages, tokenize=False, add_generation_prompt=False)
elif self.sep_style == SeparatorStyle.PLAIN:
seps = [self.sep, self.sep2]
ret = self.system
for i, (role, message) in enumerate(messages):
if message:
if type(message) is tuple:
message, _, _ = message
ret += message + seps[i % 2]
else:
ret += ""
else:
raise ValueError(f"Invalid style: {self.sep_style}")
return ret
def append_message(self, role, message):
self.messages.append([role, message])
def process_image(self, image, image_process_mode, return_pil=False, image_format='PNG', max_len=1344, min_len=672):
if image_process_mode == "Pad":
def expand2square(pil_img, background_color=(122, 116, 104)):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
image = expand2square(image)
elif image_process_mode in ["Default", "Crop"]:
pass
elif image_process_mode == "Resize":
image = image.resize((336, 336))
else:
raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
if max(image.size) > max_len:
max_hw, min_hw = max(image.size), min(image.size)
aspect_ratio = max_hw / min_hw
shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
longest_edge = int(shortest_edge * aspect_ratio)
W, H = image.size
if H > W:
H, W = longest_edge, shortest_edge
else:
H, W = shortest_edge, longest_edge
image = image.resize((W, H))
if return_pil:
return image
else:
buffered = BytesIO()
image.save(buffered, format=image_format)
img_b64_str = base64.b64encode(buffered.getvalue()).decode()
return img_b64_str
def get_images(self, return_pil=False):
images = []
for i, (role, msg) in enumerate(self.messages[self.offset:]):
if i % 2 == 0:
if type(msg) is tuple:
msg, image, image_process_mode = msg
image = self.process_image(image, image_process_mode, return_pil=return_pil)
images.append(image)
return images
def to_gradio_chatbot(self):
ret = []
for i, (role, msg) in enumerate(self.messages[self.offset:]):
if i % 2 == 0:
if type(msg) is tuple:
msg, image, image_process_mode = msg
img_b64_str = self.process_image(
image, "Default", return_pil=False,
image_format='JPEG')
img_str = f'<img src="data:image/jpeg;base64,{img_b64_str}" alt="user upload image" />'
msg = img_str + msg.replace('<image>', '').strip()
ret.append([msg, None])
else:
ret.append([msg, None])
else:
ret[-1][-1] = msg
return ret
def copy(self):
return Conversation(
system=self.system,
roles=self.roles,
messages=[[x, y] for x, y in self.messages],
offset=self.offset,
sep_style=self.sep_style,
sep=self.sep,
sep2=self.sep2,
version=self.version,
tokenizer=self.tokenizer,
stop_str=self.stop_str,
stop_token_ids=self.stop_token_ids,
skip_next=self.skip_next
)
def dict(self):
if len(self.get_images()) > 0:
return {
"system": self.system,
"roles": self.roles,
"messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages],
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
}
return {
"system": self.system,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
}
conv_vicuna_v0 = Conversation(
system="A chat between a curious human and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the human's questions.",
roles=("Human", "Assistant"),
messages=(
("Human", "What are the key differences between renewable and non-renewable energy sources?"),
("Assistant",
"Renewable energy sources are those that can be replenished naturally in a relatively "
"short amount of time, such as solar, wind, hydro, geothermal, and biomass. "
"Non-renewable energy sources, on the other hand, are finite and will eventually be "
"depleted, such as coal, oil, and natural gas. Here are some key differences between "
"renewable and non-renewable energy sources:\n"
"1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable "
"energy sources are finite and will eventually run out.\n"
"2. Environmental impact: Renewable energy sources have a much lower environmental impact "
"than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, "
"and other negative effects.\n"
"3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically "
"have lower operational costs than non-renewable sources.\n"
"4. Reliability: Renewable energy sources are often more reliable and can be used in more remote "
"locations than non-renewable sources.\n"
"5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different "
"situations and needs, while non-renewable sources are more rigid and inflexible.\n"
"6. Sustainability: Renewable energy sources are more sustainable over the long term, while "
"non-renewable sources are not, and their depletion can lead to economic and social instability.\n")
),
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
conv_vicuna_v1 = Conversation(
system="A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions.",
roles=("USER", "ASSISTANT"),
version="v1",
messages=(),
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
conv_llama_2 = Conversation(
system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""",
roles=("USER", "ASSISTANT"),
version="llama_v2",
messages=(),
offset=0,
sep_style=SeparatorStyle.LLAMA_2,
sep="<s>",
sep2="</s>",
)
conv_llava_llama_2 = Conversation(
system="You are a helpful language and vision assistant. "
"You are able to understand the visual content that the user provides, "
"and assist the user with a variety of tasks using natural language.",
roles=("USER", "ASSISTANT"),
version="llama_v2",
messages=(),
offset=0,
sep_style=SeparatorStyle.LLAMA_2,
sep="<s>",
sep2="</s>",
)
conv_mpt = Conversation(
system="""<|im_start|>system
A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
version="mpt",
messages=(),
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
)
conv_llava_plain = Conversation(
system="",
roles=("", ""),
messages=(
),
offset=0,
sep_style=SeparatorStyle.PLAIN,
sep="\n",
)
conv_llava_v0 = Conversation(
system="A chat between a curious human and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the human's questions.",
roles=("Human", "Assistant"),
messages=(
),
offset=0,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
conv_llava_v0_mmtag = Conversation(
system="A chat between a curious user and an artificial intelligence assistant. "
"The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
"The visual content will be provided with the following format: <Image>visual content</Image>.",
roles=("Human", "Assistant"),
messages=(
),
offset=0,
sep_style=SeparatorStyle.SINGLE,
sep="###",
version="v0_mmtag",
)
conv_llava_v1 = Conversation(
system="A chat between a curious human and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the human's questions.",
roles=("USER", "ASSISTANT"),
version="v1",
messages=(),
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
)
conv_llava_v1_mmtag = Conversation(
system="A chat between a curious user and an artificial intelligence assistant. "
"The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
"The visual content will be provided with the following format: <Image>visual content</Image>.",
roles=("USER", "ASSISTANT"),
messages=(),
offset=0,
sep_style=SeparatorStyle.TWO,
sep=" ",
sep2="</s>",
version="v1_mmtag",
)
conv_mistral_instruct = Conversation(
system="",
roles=("USER", "ASSISTANT"),
version="llama_v2",
messages=(),
offset=0,
sep_style=SeparatorStyle.LLAMA_2,
sep="",
sep2="</s>",
)
conv_chatml_direct = Conversation(
system="""<|im_start|>system
Answer the questions.""",
roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
version="mpt",
messages=(),
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|im_end|>",
)
# define the correct tokenizer path
tokenizer_path= os.getenv("TOKENIZER_PATH")
llama_tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
conv_llava_llama_3 = Conversation(
system="You are a helpful language and vision assistant. " "You are able to understand the visual content that the user provides, " "and assist the user with a variety of tasks using natural language.",
#roles=("<|start_header_id|>user", "<|start_header_id|>assistant"),
roles=("user", "assistant"),
version="llama_3",
messages=[],
offset=0,
sep_style=SeparatorStyle.LLAMA_3,
tokenizer=llama_tokenizer,
stop_token_ids=[128009],
)
conv_llava_llama_3_1 = Conversation(
system="You are a helpful language and vision assistant. " "You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.",
#roles=("<|start_header_id|>user", "<|start_header_id|>assistant"),
roles=("user", "assistant"),
version="llama_3_1",
messages=[],
offset=0,
sep_style=SeparatorStyle.LLAMA_3_1,
tokenizer=llama_tokenizer,
stop_token_ids=[128009, 128008, 128001],
)
default_conversation = conv_vicuna_v1
conv_templates = {
"default": conv_vicuna_v0,
"v0": conv_vicuna_v0,
"v1": conv_vicuna_v1,
"vicuna_v1": conv_vicuna_v1,
"llama_2": conv_llama_2,
"mistral_instruct": conv_mistral_instruct,
"chatml_direct": conv_chatml_direct,
"mistral_direct": conv_chatml_direct,
"plain": conv_llava_plain,
"v0_plain": conv_llava_plain,
"llava_v0": conv_llava_v0,
"v0_mmtag": conv_llava_v0_mmtag,
"llava_v1": conv_llava_v1,
"v1_mmtag": conv_llava_v1_mmtag,
"llava_llama_2": conv_llava_llama_2,
"mpt": conv_mpt,
"llama_3": conv_llava_llama_3,
"llama_3_1": conv_llava_llama_3_1,
}
if __name__ == "__main__":
print(default_conversation.get_prompt())

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import argparse
import json
import os
import openai
import tqdm
import ray
import time
NUM_SECONDS_TO_SLEEP = 3
@ray.remote(num_cpus=4)
def get_eval(content: str, max_tokens: int):
while True:
try:
response = openai.ChatCompletion.create(
model='gpt-4',
messages=[{
'role': 'system',
'content': 'You are a helpful and precise assistant for checking the quality of the answer.'
}, {
'role': 'user',
'content': content,
}],
temperature=0.2, # TODO: figure out which temperature is best for evaluation
max_tokens=max_tokens,
)
break
except openai.error.RateLimitError:
pass
except Exception as e:
print(e)
time.sleep(NUM_SECONDS_TO_SLEEP)
print('success!')
return response['choices'][0]['message']['content']
def parse_score(review):
try:
score_pair = review.split('\n')[0]
score_pair = score_pair.replace(',', ' ')
sp = score_pair.split(' ')
if len(sp) == 2:
return [float(sp[0]), float(sp[1])]
else:
print('error', review)
return [-1, -1]
except Exception as e:
print(e)
print('error', review)
return [-1, -1]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
parser.add_argument('-q', '--question')
# parser.add_argument('-a', '--answer')
parser.add_argument('-a', '--answer-list', nargs='+', default=[])
parser.add_argument('-r', '--rule')
parser.add_argument('-o', '--output')
parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output')
args = parser.parse_args()
ray.init()
f_q = open(os.path.expanduser(args.question))
f_ans1 = open(os.path.expanduser(args.answer_list[0]))
f_ans2 = open(os.path.expanduser(args.answer_list[1]))
rule_dict = json.load(open(os.path.expanduser(args.rule), 'r'))
review_file = open(f'{args.output}', 'w')
js_list = []
handles = []
idx = 0
for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2):
# if idx == 1:
# break
ques = json.loads(ques_js)
ans1 = json.loads(ans1_js)
ans2 = json.loads(ans2_js)
category = json.loads(ques_js)['category']
if category in rule_dict:
rule = rule_dict[category]
else:
rule = rule_dict['default']
prompt = rule['prompt']
role = rule['role']
content = (f'[Question]\n{ques["text"]}\n\n'
f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n'
f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n'
f'[System]\n{prompt}\n\n')
js_list.append({
'id': idx+1,
'question_id': ques['question_id'],
'answer1_id': ans1['answer_id'],
'answer2_id': ans2['answer_id'],
'category': category})
idx += 1
handles.append(get_eval.remote(content, args.max_tokens))
# To avoid the rate limit set by OpenAI
time.sleep(NUM_SECONDS_TO_SLEEP)
reviews = ray.get(handles)
for idx, review in enumerate(reviews):
scores = parse_score(review)
js_list[idx]['content'] = review
js_list[idx]['tuple'] = scores
review_file.write(json.dumps(js_list[idx]) + '\n')
review_file.close()

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import argparse
import json
import os
import openai
import time
NUM_SECONDS_TO_SLEEP = 0.5
def get_eval(content: str, max_tokens: int):
while True:
try:
response = openai.ChatCompletion.create(
model='gpt-4-0314',
messages=[{
'role': 'system',
'content': 'You are a helpful and precise assistant for checking the quality of the answer.'
}, {
'role': 'user',
'content': content,
}],
temperature=0.2, # TODO: figure out which temperature is best for evaluation
max_tokens=max_tokens,
)
break
except openai.error.RateLimitError:
pass
except Exception as e:
print(e)
time.sleep(NUM_SECONDS_TO_SLEEP)
return response['choices'][0]['message']['content']
def parse_score(review):
try:
score_pair = review.split('\n')[0]
score_pair = score_pair.replace(',', ' ')
sp = score_pair.split(' ')
if len(sp) == 2:
return [float(sp[0]), float(sp[1])]
else:
print('error', review)
return [-1, -1]
except Exception as e:
print(e)
print('error', review)
return [-1, -1]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
parser.add_argument('-q', '--question')
parser.add_argument('-c', '--context')
parser.add_argument('-a', '--answer-list', nargs='+', default=[])
parser.add_argument('-r', '--rule')
parser.add_argument('-o', '--output')
parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output')
args = parser.parse_args()
f_q = open(os.path.expanduser(args.question))
f_ans1 = open(os.path.expanduser(args.answer_list[0]))
f_ans2 = open(os.path.expanduser(args.answer_list[1]))
rule_dict = json.load(open(os.path.expanduser(args.rule), 'r'))
if os.path.isfile(os.path.expanduser(args.output)):
cur_reviews = [json.loads(line) for line in open(os.path.expanduser(args.output))]
else:
cur_reviews = []
review_file = open(f'{args.output}', 'a')
context_list = [json.loads(line) for line in open(os.path.expanduser(args.context))]
image_to_context = {context['image']: context for context in context_list}
handles = []
idx = 0
for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2):
ques = json.loads(ques_js)
ans1 = json.loads(ans1_js)
ans2 = json.loads(ans2_js)
inst = image_to_context[ques['image']]
if isinstance(inst['caption'], list):
cap_str = '\n'.join(inst['caption'])
else:
cap_str = inst['caption']
category = 'llava_bench_' + json.loads(ques_js)['category']
if category in rule_dict:
rule = rule_dict[category]
else:
assert False, f"Visual QA category not found in rule file: {category}."
prompt = rule['prompt']
role = rule['role']
content = (f'[Context]\n{cap_str}\n\n'
f'[Question]\n{ques["text"]}\n\n'
f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n'
f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n'
f'[System]\n{prompt}\n\n')
cur_js = {
'id': idx+1,
'question_id': ques['question_id'],
'answer1_id': ans1.get('answer_id', ans1['question_id']),
'answer2_id': ans2.get('answer_id', ans2['answer_id']),
'category': category
}
if idx >= len(cur_reviews):
review = get_eval(content, args.max_tokens)
scores = parse_score(review)
cur_js['content'] = review
cur_js['tuple'] = scores
review_file.write(json.dumps(cur_js) + '\n')
review_file.flush()
else:
print(f'Skipping {idx} as we already have it.')
idx += 1
print(idx)
review_file.close()

118
llava/eval/eval_gpt_review_visual.py Normal file
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import argparse
import json
import os
import openai
import time
NUM_SECONDS_TO_SLEEP = 0.5
def get_eval(content: str, max_tokens: int):
while True:
try:
response = openai.ChatCompletion.create(
model='gpt-4-0314',
messages=[{
'role': 'system',
'content': 'You are a helpful and precise assistant for checking the quality of the answer.'
}, {
'role': 'user',
'content': content,
}],
temperature=0.2, # TODO: figure out which temperature is best for evaluation
max_tokens=max_tokens,
)
break
except openai.error.RateLimitError:
pass
except Exception as e:
print(e)
time.sleep(NUM_SECONDS_TO_SLEEP)
return response['choices'][0]['message']['content']
def parse_score(review):
try:
score_pair = review.split('\n')[0]
score_pair = score_pair.replace(',', ' ')
sp = score_pair.split(' ')
if len(sp) == 2:
return [float(sp[0]), float(sp[1])]
else:
print('error', review)
return [-1, -1]
except Exception as e:
print(e)
print('error', review)
return [-1, -1]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
parser.add_argument('-q', '--question')
parser.add_argument('-c', '--context')
parser.add_argument('-a', '--answer-list', nargs='+', default=[])
parser.add_argument('-r', '--rule')
parser.add_argument('-o', '--output')
parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output')
args = parser.parse_args()
f_q = open(os.path.expanduser(args.question))
f_ans1 = open(os.path.expanduser(args.answer_list[0]))
f_ans2 = open(os.path.expanduser(args.answer_list[1]))
rule_dict = json.load(open(os.path.expanduser(args.rule), 'r'))
if os.path.isfile(os.path.expanduser(args.output)):
cur_reviews = [json.loads(line) for line in open(os.path.expanduser(args.output))]
else:
cur_reviews = []
review_file = open(f'{args.output}', 'a')
context_list = [json.loads(line) for line in open(os.path.expanduser(args.context))]
image_to_context = {context['image']: context for context in context_list}
handles = []
idx = 0
for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2):
ques = json.loads(ques_js)
ans1 = json.loads(ans1_js)
ans2 = json.loads(ans2_js)
inst = image_to_context[ques['image']]
cap_str = '\n'.join(inst['captions'])
box_str = '\n'.join([f'{instance["category"]}: {instance["bbox"]}' for instance in inst['instances']])
category = json.loads(ques_js)['category']
if category in rule_dict:
rule = rule_dict[category]
else:
assert False, f"Visual QA category not found in rule file: {category}."
prompt = rule['prompt']
role = rule['role']
content = (f'[Context]\n{cap_str}\n\n{box_str}\n\n'
f'[Question]\n{ques["text"]}\n\n'
f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n'
f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n'
f'[System]\n{prompt}\n\n')
cur_js = {
'id': idx+1,
'question_id': ques['question_id'],
'answer1_id': ans1.get('answer_id', ans1['question_id']),
'answer2_id': ans2.get('answer_id', ans2['answer_id']),
'category': category
}
if idx >= len(cur_reviews):
review = get_eval(content, args.max_tokens)
scores = parse_score(review)
cur_js['content'] = review
cur_js['tuple'] = scores
review_file.write(json.dumps(cur_js) + '\n')
review_file.flush()
else:
print(f'Skipping {idx} as we already have it.')
idx += 1
print(idx)
review_file.close()

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llava/eval/eval_pope.py Normal file
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import os
import json
import argparse
def eval_pope(answers, label_file):
label_list = [json.loads(q)['label'] for q in open(label_file, 'r')]
for answer in answers:
text = answer['text']
# Only keep the first sentence
if text.find('.') != -1:
text = text.split('.')[0]
text = text.replace(',', '')
words = text.split(' ')
if 'No' in words or 'not' in words or 'no' in words:
answer['text'] = 'no'
else:
answer['text'] = 'yes'
for i in range(len(label_list)):
if label_list[i] == 'no':
label_list[i] = 0
else:
label_list[i] = 1
pred_list = []
for answer in answers:
if answer['text'] == 'no':
pred_list.append(0)
else:
pred_list.append(1)
pos = 1
neg = 0
yes_ratio = pred_list.count(1) / len(pred_list)
TP, TN, FP, FN = 0, 0, 0, 0
for pred, label in zip(pred_list, label_list):
if pred == pos and label == pos:
TP += 1
elif pred == pos and label == neg:
FP += 1
elif pred == neg and label == neg:
TN += 1
elif pred == neg and label == pos:
FN += 1
print('TP\tFP\tTN\tFN\t')
print('{}\t{}\t{}\t{}'.format(TP, FP, TN, FN))
precision = float(TP) / float(TP + FP)
recall = float(TP) / float(TP + FN)
f1 = 2*precision*recall / (precision + recall)
acc = (TP + TN) / (TP + TN + FP + FN)
print('Accuracy: {}'.format(acc))
print('Precision: {}'.format(precision))
print('Recall: {}'.format(recall))
print('F1 score: {}'.format(f1))
print('Yes ratio: {}'.format(yes_ratio))
print('%.3f, %.3f, %.3f, %.3f, %.3f' % (f1, acc, precision, recall, yes_ratio) )
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--annotation-dir", type=str)
parser.add_argument("--question-file", type=str)
parser.add_argument("--result-file", type=str)
args = parser.parse_args()
questions = [json.loads(line) for line in open(args.question_file)]
questions = {question['question_id']: question for question in questions}
answers = [json.loads(q) for q in open(args.result_file)]
for file in os.listdir(args.annotation_dir):
assert file.startswith('coco_pope_')
assert file.endswith('.json')
category = file[10:-5]
cur_answers = [x for x in answers if questions[x['question_id']]['category'] == category]
print('Category: {}, # samples: {}'.format(category, len(cur_answers)))
eval_pope(cur_answers, os.path.join(args.annotation_dir, file))
print("====================================")

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llava/eval/eval_science_qa.py Normal file
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import argparse
import json
import os
import re
import random
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base-dir', type=str)
parser.add_argument('--result-file', type=str)
parser.add_argument('--output-file', type=str)
parser.add_argument('--output-result', type=str)
parser.add_argument('--split', type=str, default='test')
parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
return parser.parse_args()
def convert_caps(results):
fakecaps = []
for result in results:
image_id = result['question_id']
caption = result['text']
fakecaps.append({"image_id": int(image_id), "caption": caption})
return fakecaps
def get_pred_idx(prediction, choices, options):
"""
Get the index (e.g. 2) from the prediction (e.g. 'C')
"""
if prediction in options[:len(choices)]:
return options.index(prediction)
else:
return -1
return random.choice(range(len(choices)))
if __name__ == "__main__":
args = get_args()
base_dir = args.base_dir
split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split]
problems = json.load(open(os.path.join(base_dir, "problems.json")))
predictions = [json.loads(line) for line in open(args.result_file)]
predictions = {pred['question_id']: pred for pred in predictions}
split_problems = {idx: problems[idx] for idx in split_indices}
results = {'correct': [], 'incorrect': []}
sqa_results = {}
sqa_results['acc'] = None
sqa_results['correct'] = None
sqa_results['count'] = None
sqa_results['results'] = {}
sqa_results['outputs'] = {}
for prob_id, prob in split_problems.items():
if prob_id not in predictions:
pred = {'text': 'FAILED', 'prompt': 'Unknown'}
pred_text = 'FAILED'
else:
pred = predictions[prob_id]
pred_text = pred['text']
if pred_text in args.options:
answer = pred_text
elif len(pred_text) >= 3 and pred_text[0] in args.options and pred_text[1:3] == ". ":
answer = pred_text[0]
else:
pattern = re.compile(r'The answer is ([A-Z]).')
res = pattern.findall(pred_text)
if len(res) == 1:
answer = res[0] # 'A', 'B', ...
else:
answer = "FAILED"
pred_idx = get_pred_idx(answer, prob['choices'], args.options)
analysis = {
'question_id': prob_id,
'parsed_ans': answer,
'ground_truth': args.options[prob['answer']],
'question': pred['prompt'],
'pred': pred_text,
'is_multimodal': '<image>' in pred['prompt'],
}
sqa_results['results'][prob_id] = get_pred_idx(answer, prob['choices'], args.options)
sqa_results['outputs'][prob_id] = pred_text
if pred_idx == prob['answer']:
results['correct'].append(analysis)
else:
results['incorrect'].append(analysis)
correct = len(results['correct'])
total = len(results['correct']) + len(results['incorrect'])
###### IMG ######
multimodal_correct = len([x for x in results['correct'] if x['is_multimodal']])
multimodal_incorrect = len([x for x in results['incorrect'] if x['is_multimodal']])
multimodal_total = multimodal_correct + multimodal_incorrect
###### IMG ######
print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%, IMG-Accuracy: {multimodal_correct / multimodal_total * 100:.2f}%')
sqa_results['acc'] = correct / total * 100
sqa_results['correct'] = correct
sqa_results['count'] = total
with open(args.output_file, 'w') as f:
json.dump(results, f, indent=2)
with open(args.output_result, 'w') as f:
json.dump(sqa_results, f, indent=2)

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llava/eval/eval_science_qa_gpt4.py Normal file
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import argparse
import json
import os
import re
import random
from collections import defaultdict
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base-dir', type=str)
parser.add_argument('--gpt4-result', type=str)
parser.add_argument('--our-result', type=str)
parser.add_argument('--split', type=str, default='test')
parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
return parser.parse_args()
def convert_caps(results):
fakecaps = []
for result in results:
image_id = result['question_id']
caption = result['text']
fakecaps.append({"image_id": int(image_id), "caption": caption})
return fakecaps
def get_pred_idx(prediction, choices, options):
"""
Get the index (e.g. 2) from the prediction (e.g. 'C')
"""
if prediction in options[:len(choices)]:
return options.index(prediction)
else:
return random.choice(range(len(choices)))
if __name__ == "__main__":
args = get_args()
base_dir = args.base_dir
split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split]
problems = json.load(open(os.path.join(base_dir, "problems.json")))
our_predictions = [json.loads(line) for line in open(args.our_result)]
our_predictions = {pred['question_id']: pred for pred in our_predictions}
split_problems = {idx: problems[idx] for idx in split_indices}
gpt4_predictions = json.load(open(args.gpt4_result))['outputs']
results = defaultdict(lambda: 0)
for prob_id, prob in split_problems.items():
if prob_id not in our_predictions:
continue
if prob_id not in gpt4_predictions:
continue
our_pred = our_predictions[prob_id]['text']
gpt4_pred = gpt4_predictions[prob_id]
pattern = re.compile(r'The answer is ([A-Z]).')
our_res = pattern.findall(our_pred)
if len(our_res) == 1:
our_answer = our_res[0] # 'A', 'B', ...
else:
our_answer = "FAILED"
gpt4_res = pattern.findall(gpt4_pred)
if len(gpt4_res) == 1:
gpt4_answer = gpt4_res[0] # 'A', 'B', ...
else:
gpt4_answer = "FAILED"
our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options)
gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options)
if gpt4_answer == 'FAILED':
results['gpt4_failed'] += 1
# continue
gpt4_pred_idx = our_pred_idx
# if our_pred_idx != prob['answer']:
# print(our_predictions[prob_id]['prompt'])
# print('-----------------')
# print(f'LECTURE: {prob["lecture"]}')
# print(f'SOLUTION: {prob["solution"]}')
# print('=====================')
else:
# continue
pass
# gpt4_pred_idx = our_pred_idx
if gpt4_pred_idx == prob['answer']:
results['correct'] += 1
else:
results['incorrect'] += 1
if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']:
results['correct_upperbound'] += 1
correct = results['correct']
total = results['correct'] + results['incorrect']
print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%')
print(f'Total: {total}, Correct (upper): {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%')
print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%')

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import argparse
import json
import os
import re
import random
from collections import defaultdict
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--base-dir', type=str)
parser.add_argument('--gpt4-result', type=str)
parser.add_argument('--requery-result', type=str)
parser.add_argument('--our-result', type=str)
parser.add_argument('--output-result', type=str)
parser.add_argument('--split', type=str, default='test')
parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"])
return parser.parse_args()
def convert_caps(results):
fakecaps = []
for result in results:
image_id = result['question_id']
caption = result['text']
fakecaps.append({"image_id": int(image_id), "caption": caption})
return fakecaps
def get_pred_idx(prediction, choices, options):
"""
Get the index (e.g. 2) from the prediction (e.g. 'C')
"""
if prediction in options[:len(choices)]:
return options.index(prediction)
else:
return random.choice(range(len(choices)))
if __name__ == "__main__":
args = get_args()
base_dir = args.base_dir
split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split]
problems = json.load(open(os.path.join(base_dir, "problems.json")))
our_predictions = [json.loads(line) for line in open(args.our_result)]
our_predictions = {pred['question_id']: pred for pred in our_predictions}
split_problems = {idx: problems[idx] for idx in split_indices}
requery_predictions = [json.loads(line) for line in open(args.requery_result)]
requery_predictions = {pred['question_id']: pred for pred in requery_predictions}
gpt4_predictions = json.load(open(args.gpt4_result))['outputs']
results = defaultdict(lambda: 0)
sqa_results = {}
sqa_results['acc'] = None
sqa_results['correct'] = None
sqa_results['count'] = None
sqa_results['results'] = {}
sqa_results['outputs'] = {}
for prob_id, prob in split_problems.items():
if prob_id not in our_predictions:
assert False
if prob_id not in gpt4_predictions:
assert False
our_pred = our_predictions[prob_id]['text']
gpt4_pred = gpt4_predictions[prob_id]
if prob_id not in requery_predictions:
results['missing_requery'] += 1
requery_pred = "MISSING"
else:
requery_pred = requery_predictions[prob_id]['text']
pattern = re.compile(r'The answer is ([A-Z]).')
our_res = pattern.findall(our_pred)
if len(our_res) == 1:
our_answer = our_res[0] # 'A', 'B', ...
else:
our_answer = "FAILED"
requery_res = pattern.findall(requery_pred)
if len(requery_res) == 1:
requery_answer = requery_res[0] # 'A', 'B', ...
else:
requery_answer = "FAILED"
gpt4_res = pattern.findall(gpt4_pred)
if len(gpt4_res) == 1:
gpt4_answer = gpt4_res[0] # 'A', 'B', ...
else:
gpt4_answer = "FAILED"
our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options)
gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options)
requery_pred_idx = get_pred_idx(requery_answer, prob['choices'], args.options)
results['total'] += 1
if gpt4_answer == 'FAILED':
results['gpt4_failed'] += 1
if gpt4_pred_idx == prob['answer']:
results['gpt4_correct'] += 1
if our_pred_idx == prob['answer']:
results['gpt4_ourvisual_correct'] += 1
elif gpt4_pred_idx == prob['answer']:
results['gpt4_correct'] += 1
results['gpt4_ourvisual_correct'] += 1
if our_pred_idx == prob['answer']:
results['our_correct'] += 1
if requery_answer == 'FAILED':
sqa_results['results'][prob_id] = our_pred_idx
if our_pred_idx == prob['answer']:
results['requery_correct'] += 1
else:
sqa_results['results'][prob_id] = requery_pred_idx
if requery_pred_idx == prob['answer']:
results['requery_correct'] += 1
else:
print(f"""
Question ({args.options[prob['answer']]}): {our_predictions[prob_id]['prompt']}
Our ({our_answer}): {our_pred}
GPT-4 ({gpt4_answer}): {gpt4_pred}
Requery ({requery_answer}): {requery_pred}
print("=====================================")
""")
if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']:
results['correct_upperbound'] += 1
total = results['total']
print(f'Total: {total}, Our-Correct: {results["our_correct"]}, Accuracy: {results["our_correct"] / total * 100:.2f}%')
print(f'Total: {total}, GPT-4-Correct: {results["gpt4_correct"]}, Accuracy: {results["gpt4_correct"] / total * 100:.2f}%')
print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%')
print(f'Total: {total}, GPT-4-OursVisual-Correct: {results["gpt4_ourvisual_correct"]}, Accuracy: {results["gpt4_ourvisual_correct"] / total * 100:.2f}%')
print(f'Total: {total}, Requery-Correct: {results["requery_correct"]}, Accuracy: {results["requery_correct"] / total * 100:.2f}%')
print(f'Total: {total}, Correct upper: {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%')
sqa_results['acc'] = results["requery_correct"] / total * 100
sqa_results['correct'] = results["requery_correct"]
sqa_results['count'] = total
with open(args.output_result, 'w') as f:
json.dump(sqa_results, f, indent=2)

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import os
import argparse
import json
import re
from llava.eval.m4c_evaluator import TextVQAAccuracyEvaluator
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--annotation-file', type=str)
parser.add_argument('--result-file', type=str)
parser.add_argument('--result-dir', type=str)
return parser.parse_args()
def prompt_processor(prompt):
if prompt.startswith('OCR tokens: '):
pattern = r"Question: (.*?) Short answer:"
match = re.search(pattern, prompt, re.DOTALL)
question = match.group(1)
elif 'Reference OCR token: ' in prompt and len(prompt.split('\n')) == 3:
if prompt.startswith('Reference OCR token:'):
question = prompt.split('\n')[1]
else:
question = prompt.split('\n')[0]
elif len(prompt.split('\n')) == 2:
question = prompt.split('\n')[0]
else:
assert False
return question.lower()
def eval_single(annotation_file, result_file):
experiment_name = os.path.splitext(os.path.basename(result_file))[0]
print(experiment_name)
annotations = json.load(open(annotation_file))['data']
annotations = {(annotation['image_id'], annotation['question'].lower()): annotation for annotation in annotations}
results = [json.loads(line) for line in open(result_file)]
pred_list = []
for result in results:
annotation = annotations[(result['question_id'], prompt_processor(result['prompt']))]
pred_list.append({
"pred_answer": result['text'],
"gt_answers": annotation['answers'],
})
evaluator = TextVQAAccuracyEvaluator()
print('Samples: {}\nAccuracy: {:.2f}%\n'.format(len(pred_list), 100. * evaluator.eval_pred_list(pred_list)))
if __name__ == "__main__":
args = get_args()
if args.result_file is not None:
eval_single(args.annotation_file, args.result_file)
if args.result_dir is not None:
for result_file in sorted(os.listdir(args.result_dir)):
if not result_file.endswith('.jsonl'):
print(f'Skipping {result_file}')
continue
eval_single(args.annotation_file, os.path.join(args.result_dir, result_file))

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"""Generate json file for webpage."""
import json
import os
import re
# models = ['llama', 'alpaca', 'gpt35', 'bard']
models = ['vicuna']
def read_jsonl(path: str, key: str=None):
data = []
with open(os.path.expanduser(path)) as f:
for line in f:
if not line:
continue
data.append(json.loads(line))
if key is not None:
data.sort(key=lambda x: x[key])
data = {item[key]: item for item in data}
return data
def trim_hanging_lines(s: str, n: int) -> str:
s = s.strip()
for _ in range(n):
s = s.split('\n', 1)[1].strip()
return s
if __name__ == '__main__':
questions = read_jsonl('table/question.jsonl', key='question_id')
# alpaca_answers = read_jsonl('table/answer/answer_alpaca-13b.jsonl', key='question_id')
# bard_answers = read_jsonl('table/answer/answer_bard.jsonl', key='question_id')
# gpt35_answers = read_jsonl('table/answer/answer_gpt35.jsonl', key='question_id')
# llama_answers = read_jsonl('table/answer/answer_llama-13b.jsonl', key='question_id')
vicuna_answers = read_jsonl('table/answer/answer_vicuna-13b.jsonl', key='question_id')
ours_answers = read_jsonl('table/results/llama-13b-hf-alpaca.jsonl', key='question_id')
review_vicuna = read_jsonl('table/review/review_vicuna-13b_llama-13b-hf-alpaca.jsonl', key='question_id')
# review_alpaca = read_jsonl('table/review/review_alpaca-13b_vicuna-13b.jsonl', key='question_id')
# review_bard = read_jsonl('table/review/review_bard_vicuna-13b.jsonl', key='question_id')
# review_gpt35 = read_jsonl('table/review/review_gpt35_vicuna-13b.jsonl', key='question_id')
# review_llama = read_jsonl('table/review/review_llama-13b_vicuna-13b.jsonl', key='question_id')
records = []
for qid in questions.keys():
r = {
'id': qid,
'category': questions[qid]['category'],
'question': questions[qid]['text'],
'answers': {
# 'alpaca': alpaca_answers[qid]['text'],
# 'llama': llama_answers[qid]['text'],
# 'bard': bard_answers[qid]['text'],
# 'gpt35': gpt35_answers[qid]['text'],
'vicuna': vicuna_answers[qid]['text'],
'ours': ours_answers[qid]['text'],
},
'evaluations': {
# 'alpaca': review_alpaca[qid]['text'],
# 'llama': review_llama[qid]['text'],
# 'bard': review_bard[qid]['text'],
'vicuna': review_vicuna[qid]['content'],
# 'gpt35': review_gpt35[qid]['text'],
},
'scores': {
'vicuna': review_vicuna[qid]['tuple'],
# 'alpaca': review_alpaca[qid]['score'],
# 'llama': review_llama[qid]['score'],
# 'bard': review_bard[qid]['score'],
# 'gpt35': review_gpt35[qid]['score'],
},
}
# cleanup data
cleaned_evals = {}
for k, v in r['evaluations'].items():
v = v.strip()
lines = v.split('\n')
# trim the first line if it's a pair of numbers
if re.match(r'\d+[, ]+\d+', lines[0]):
lines = lines[1:]
v = '\n'.join(lines)
cleaned_evals[k] = v.replace('Assistant 1', "**Assistant 1**").replace('Assistant 2', '**Assistant 2**')
r['evaluations'] = cleaned_evals
records.append(r)
# Reorder the records, this is optional
for r in records:
if r['id'] <= 20:
r['id'] += 60
else:
r['id'] -= 20
for r in records:
if r['id'] <= 50:
r['id'] += 10
elif 50 < r['id'] <= 60:
r['id'] -= 50
for r in records:
if r['id'] == 7:
r['id'] = 1
elif r['id'] < 7:
r['id'] += 1
records.sort(key=lambda x: x['id'])
# Write to file
with open('webpage/data.json', 'w') as f:
json.dump({'questions': records, 'models': models}, f, indent=2)

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# Copyright (c) Facebook, Inc. and its affiliates.
import re
from tqdm import tqdm
class EvalAIAnswerProcessor:
"""
Processes an answer similar to Eval AI
copied from
https://github.com/facebookresearch/mmf/blob/c46b3b3391275b4181567db80943473a89ab98ab/pythia/tasks/processors.py#L897
"""
CONTRACTIONS = {
"aint": "ain't",
"arent": "aren't",
"cant": "can't",
"couldve": "could've",
"couldnt": "couldn't",
"couldn'tve": "couldn't've",
"couldnt've": "couldn't've",
"didnt": "didn't",
"doesnt": "doesn't",
"dont": "don't",
"hadnt": "hadn't",
"hadnt've": "hadn't've",
"hadn'tve": "hadn't've",
"hasnt": "hasn't",
"havent": "haven't",
"hed": "he'd",
"hed've": "he'd've",
"he'dve": "he'd've",
"hes": "he's",
"howd": "how'd",
"howll": "how'll",
"hows": "how's",
"Id've": "I'd've",
"I'dve": "I'd've",
"Im": "I'm",
"Ive": "I've",
"isnt": "isn't",
"itd": "it'd",
"itd've": "it'd've",
"it'dve": "it'd've",
"itll": "it'll",
"let's": "let's",
"maam": "ma'am",
"mightnt": "mightn't",
"mightnt've": "mightn't've",
"mightn'tve": "mightn't've",
"mightve": "might've",
"mustnt": "mustn't",
"mustve": "must've",
"neednt": "needn't",
"notve": "not've",
"oclock": "o'clock",
"oughtnt": "oughtn't",
"ow's'at": "'ow's'at",
"'ows'at": "'ow's'at",
"'ow'sat": "'ow's'at",
"shant": "shan't",
"shed've": "she'd've",
"she'dve": "she'd've",
"she's": "she's",
"shouldve": "should've",
"shouldnt": "shouldn't",
"shouldnt've": "shouldn't've",
"shouldn'tve": "shouldn't've",
"somebody'd": "somebodyd",
"somebodyd've": "somebody'd've",
"somebody'dve": "somebody'd've",
"somebodyll": "somebody'll",
"somebodys": "somebody's",
"someoned": "someone'd",
"someoned've": "someone'd've",
"someone'dve": "someone'd've",
"someonell": "someone'll",
"someones": "someone's",
"somethingd": "something'd",
"somethingd've": "something'd've",
"something'dve": "something'd've",
"somethingll": "something'll",
"thats": "that's",
"thered": "there'd",
"thered've": "there'd've",
"there'dve": "there'd've",
"therere": "there're",
"theres": "there's",
"theyd": "they'd",
"theyd've": "they'd've",
"they'dve": "they'd've",
"theyll": "they'll",
"theyre": "they're",
"theyve": "they've",
"twas": "'twas",
"wasnt": "wasn't",
"wed've": "we'd've",
"we'dve": "we'd've",
"weve": "we've",
"werent": "weren't",
"whatll": "what'll",
"whatre": "what're",
"whats": "what's",
"whatve": "what've",
"whens": "when's",
"whered": "where'd",
"wheres": "where's",
"whereve": "where've",
"whod": "who'd",
"whod've": "who'd've",
"who'dve": "who'd've",
"wholl": "who'll",
"whos": "who's",
"whove": "who've",
"whyll": "why'll",
"whyre": "why're",
"whys": "why's",
"wont": "won't",
"wouldve": "would've",
"wouldnt": "wouldn't",
"wouldnt've": "wouldn't've",
"wouldn'tve": "wouldn't've",
"yall": "y'all",
"yall'll": "y'all'll",
"y'allll": "y'all'll",
"yall'd've": "y'all'd've",
"y'alld've": "y'all'd've",
"y'all'dve": "y'all'd've",
"youd": "you'd",
"youd've": "you'd've",
"you'dve": "you'd've",
"youll": "you'll",
"youre": "you're",
"youve": "you've",
}
NUMBER_MAP = {
"none": "0",
"zero": "0",
"one": "1",
"two": "2",
"three": "3",
"four": "4",
"five": "5",
"six": "6",
"seven": "7",
"eight": "8",
"nine": "9",
"ten": "10",
}
ARTICLES = ["a", "an", "the"]
PERIOD_STRIP = re.compile(r"(?!<=\d)(\.)(?!\d)")
COMMA_STRIP = re.compile(r"(?<=\d)(\,)+(?=\d)")
PUNCTUATIONS = [
";",
r"/",
"[",
"]",
'"',
"{",
"}",
"(",
")",
"=",
"+",
"\\",
"_",
"-",
">",
"<",
"@",
"`",
",",
"?",
"!",
]
def __init__(self, *args, **kwargs):
pass
def word_tokenize(self, word):
word = word.lower()
word = word.replace(",", "").replace("?", "").replace("'s", " 's")
return word.strip()
def process_punctuation(self, in_text):
out_text = in_text
for p in self.PUNCTUATIONS:
if (p + " " in in_text or " " + p in in_text) or (
re.search(self.COMMA_STRIP, in_text) is not None
):
out_text = out_text.replace(p, "")
else:
out_text = out_text.replace(p, " ")
out_text = self.PERIOD_STRIP.sub("", out_text, re.UNICODE)
return out_text
def process_digit_article(self, in_text):
out_text = []
temp_text = in_text.lower().split()
for word in temp_text:
word = self.NUMBER_MAP.setdefault(word, word)
if word not in self.ARTICLES:
out_text.append(word)
else:
pass
for word_id, word in enumerate(out_text):
if word in self.CONTRACTIONS:
out_text[word_id] = self.CONTRACTIONS[word]
out_text = " ".join(out_text)
return out_text
def __call__(self, item):
item = self.word_tokenize(item)
item = item.replace("\n", " ").replace("\t", " ").strip()
item = self.process_punctuation(item)
item = self.process_digit_article(item)
return item
class TextVQAAccuracyEvaluator:
def __init__(self):
self.answer_processor = EvalAIAnswerProcessor()
def _compute_answer_scores(self, raw_answers):
"""
compute the accuracy (soft score) of human answers
"""
answers = [self.answer_processor(a) for a in raw_answers]
assert len(answers) == 10
gt_answers = list(enumerate(answers))
unique_answers = set(answers)
unique_answer_scores = {}
for unique_answer in unique_answers:
accs = []
for gt_answer in gt_answers:
other_answers = [item for item in gt_answers if item != gt_answer]
matching_answers = [
item for item in other_answers if item[1] == unique_answer
]
acc = min(1, float(len(matching_answers)) / 3)
accs.append(acc)
unique_answer_scores[unique_answer] = sum(accs) / len(accs)
return unique_answer_scores
def eval_pred_list(self, pred_list):
pred_scores = []
for entry in tqdm(pred_list):
pred_answer = self.answer_processor(entry["pred_answer"])
unique_answer_scores = self._compute_answer_scores(entry["gt_answers"])
score = unique_answer_scores.get(pred_answer, 0.0)
pred_scores.append(score)
accuracy = sum(pred_scores) / len(pred_scores)
return accuracy
class STVQAAccuracyEvaluator:
def __init__(self):
self.answer_processor = EvalAIAnswerProcessor()
def eval_pred_list(self, pred_list):
pred_scores = []
for entry in pred_list:
pred_answer = self.answer_processor(entry["pred_answer"])
gts = [self.answer_processor(a) for a in entry["gt_answers"]]
score = 1.0 if pred_answer in gts else 0.0
pred_scores.append(score)
accuracy = sum(pred_scores) / len(pred_scores)
return accuracy
class STVQAANLSEvaluator:
def __init__(self):
import editdistance # install with `pip install editdistance`
self.get_edit_distance = editdistance.eval
def get_anls(self, s1, s2):
s1 = s1.lower().strip()
s2 = s2.lower().strip()
iou = 1 - self.get_edit_distance(s1, s2) / max(len(s1), len(s2))
anls = iou if iou >= 0.5 else 0.0
return anls
def eval_pred_list(self, pred_list):
pred_scores = []
for entry in pred_list:
anls = max(
self.get_anls(entry["pred_answer"], gt) for gt in entry["gt_answers"]
)
pred_scores.append(anls)
accuracy = sum(pred_scores) / len(pred_scores)
return accuracy
class TextCapsBleu4Evaluator:
def __init__(self):
# The following script requires Java 1.8.0 and pycocotools installed.
# The pycocoevalcap can be installed with pip as
# pip install git+https://github.com/ronghanghu/coco-caption.git@python23
# Original pycocoevalcap code is at https://github.com/tylin/coco-caption
# but has no python3 support yet.
try:
from pycocoevalcap.bleu.bleu import Bleu
from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
except ModuleNotFoundError:
print(
"Please install pycocoevalcap module using "
"pip install git+https://github.com/ronghanghu/coco-caption.git@python23" # noqa
)
raise
self.tokenizer = PTBTokenizer()
self.scorer = Bleu(4)
def eval_pred_list(self, pred_list):
# Create reference and hypotheses captions.
gts = {}
res = {}
for idx, entry in enumerate(pred_list):
gts[idx] = [{"caption": a} for a in entry["gt_answers"]]
res[idx] = [{"caption": entry["pred_answer"]}]
gts = self.tokenizer.tokenize(gts)
res = self.tokenizer.tokenize(res)
score, _ = self.scorer.compute_score(gts, res)
bleu4 = score[3] # score is (Bleu-1, Bleu-2, Bleu-3, Bleu-4)
return bleu4

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import argparse
from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from llava.conversation import default_conversation
from llava.utils import disable_torch_init
@torch.inference_mode()
def eval_model(model_name, questions_file, answers_file):
# Model
disable_torch_init()
model_name = os.path.expanduser(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name,
torch_dtype=torch.float16).cuda()
ques_file = open(os.path.expanduser(questions_file), "r")
ans_file = open(os.path.expanduser(answers_file), "w")
for i, line in enumerate(tqdm(ques_file)):
idx = json.loads(line)["question_id"]
qs = json.loads(line)["text"]
cat = json.loads(line)["category"]
conv = default_conversation.copy()
conv.append_message(conv.roles[0], qs)
prompt = conv.get_prompt()
inputs = tokenizer([prompt])
input_ids = torch.as_tensor(inputs.input_ids).cuda()
output_ids = model.generate(
input_ids,
do_sample=True,
use_cache=True,
temperature=0.7,
max_new_tokens=1024,)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
try:
index = outputs.index(conv.sep, len(prompt))
except ValueError:
outputs += conv.sep
index = outputs.index(conv.sep, len(prompt))
outputs = outputs[len(prompt) + len(conv.roles[1]) + 2:index].strip()
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({"question_id": idx,
"text": outputs,
"answer_id": ans_id,
"model_id": model_name,
"metadata": {}}) + "\n")
ans_file.flush()
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
args = parser.parse_args()
eval_model(args.model_name, args.question_file, args.answers_file)

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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from PIL import Image
import math
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def eval_model(args):
# Model
disable_torch_init()
model_path = os.path.expanduser(args.model_path)
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
ans_file = open(answers_file, "w")
for line in tqdm(questions):
idx = line["question_id"]
image_file = line["image"]
qs = line["text"]
cur_prompt = qs
if model.config.mm_use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
image = Image.open(os.path.join(args.image_folder, image_file)).convert('RGB')
image_tensor = process_images([image], image_processor, model.config)[0]
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor.unsqueeze(0).half().cuda(),
image_sizes=[image.size],
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
# no_repeat_ngram_size=3,
max_new_tokens=1024,
use_cache=True)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({"question_id": idx,
"prompt": cur_prompt,
"text": outputs,
"answer_id": ans_id,
"model_id": model_name,
"metadata": {}}) + "\n")
ans_file.flush()
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-folder", type=str, default="")
parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--conv-mode", type=str, default="llava_v1")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
args = parser.parse_args()
eval_model(args)

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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import math
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
# Custom dataset class
class CustomDataset(Dataset):
def __init__(self, questions, image_folder, tokenizer, image_processor, model_config):
self.questions = questions
self.image_folder = image_folder
self.tokenizer = tokenizer
self.image_processor = image_processor
self.model_config = model_config
def __getitem__(self, index):
line = self.questions[index]
image_file = line["image"]
qs = line["text"]
if self.model_config.mm_use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
image = Image.open(os.path.join(self.image_folder, image_file)).convert('RGB')
image_tensor = process_images([image], self.image_processor, self.model_config)[0]
input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')
return input_ids, image_tensor, image.size
def __len__(self):
return len(self.questions)
def collate_fn(batch):
input_ids, image_tensors, image_sizes = zip(*batch)
input_ids = torch.stack(input_ids, dim=0)
image_tensors = torch.stack(image_tensors, dim=0)
return input_ids, image_tensors, image_sizes
# DataLoader
def create_data_loader(questions, image_folder, tokenizer, image_processor, model_config, batch_size=1, num_workers=4):
assert batch_size == 1, "batch_size must be 1"
dataset = CustomDataset(questions, image_folder, tokenizer, image_processor, model_config)
data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False, collate_fn=collate_fn)
return data_loader
def eval_model(args):
# Model
disable_torch_init()
model_path = os.path.expanduser(args.model_path)
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
ans_file = open(answers_file, "w")
if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode:
args.conv_mode = args.conv_mode + '_mmtag'
print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.')
data_loader = create_data_loader(questions, args.image_folder, tokenizer, image_processor, model.config)
for (input_ids, image_tensor, image_sizes), line in tqdm(zip(data_loader, questions), total=len(questions)):
idx = line["question_id"]
cur_prompt = line["text"]
input_ids = input_ids.to(device='cuda', non_blocking=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True),
image_sizes=image_sizes,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
max_new_tokens=args.max_new_tokens,
use_cache=True)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({"question_id": idx,
"prompt": cur_prompt,
"text": outputs,
"answer_id": ans_id,
"model_id": model_name,
"metadata": {}}) + "\n")
# ans_file.flush()
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-folder", type=str, default="")
parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--conv-mode", type=str, default="llava_v1")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--max_new_tokens", type=int, default=128)
args = parser.parse_args()
eval_model(args)

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import argparse
import torch
import os
import json
import pandas as pd
from tqdm import tqdm
import shortuuid
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_image_token, process_images, load_image_from_base64, get_model_name_from_path
from PIL import Image
import math
all_options = ['A', 'B', 'C', 'D']
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def is_none(value):
if value is None:
return True
if type(value) is float and math.isnan(value):
return True
if type(value) is str and value.lower() == 'nan':
return True
if type(value) is str and value.lower() == 'none':
return True
return False
def get_options(row, options):
parsed_options = []
for option in options:
option_value = row[option]
if is_none(option_value):
break
parsed_options.append(option_value)
return parsed_options
def eval_model(args):
# Model
disable_torch_init()
model_path = os.path.expanduser(args.model_path)
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
questions = pd.read_table(os.path.expanduser(args.question_file))
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
ans_file = open(answers_file, "w")
if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode:
args.conv_mode = args.conv_mode + '_mmtag'
print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.')
for index, row in tqdm(questions.iterrows(), total=len(questions)):
options = get_options(row, all_options)
cur_option_char = all_options[:len(options)]
if args.all_rounds:
num_rounds = len(options)
else:
num_rounds = 1
for round_idx in range(num_rounds):
idx = row['index']
question = row['question']
hint = row['hint']
image = load_image_from_base64(row['image'])
if not is_none(hint):
question = hint + '\n' + question
for option_char, option in zip(all_options[:len(options)], options):
question = question + '\n' + option_char + '. ' + option
qs = cur_prompt = question
if model.config.mm_use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
if args.single_pred_prompt:
if args.lang == 'cn':
qs = qs + '\n' + "请直接回答选项字母。"
else:
qs = qs + '\n' + "Answer with the option's letter from the given choices directly."
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
image_tensor = process_images([image], image_processor, model.config)[0]
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor.unsqueeze(0).half().cuda(),
image_sizes=[image.size],
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
# no_repeat_ngram_size=3,
max_new_tokens=1024,
use_cache=True)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({"question_id": idx,
"round_id": round_idx,
"prompt": cur_prompt,
"text": outputs,
"options": options,
"option_char": cur_option_char,
"answer_id": ans_id,
"model_id": model_name,
"metadata": {}}) + "\n")
ans_file.flush()
# rotate options
options = options[1:] + options[:1]
cur_option_char = cur_option_char[1:] + cur_option_char[:1]
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-folder", type=str, default="")
parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--conv-mode", type=str, default="llava_v1")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--all-rounds", action="store_true")
parser.add_argument("--single-pred-prompt", action="store_true")
parser.add_argument("--lang", type=str, default="en")
args = parser.parse_args()
eval_model(args)

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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from PIL import Image
import math
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def eval_model(args):
# Model
disable_torch_init()
model_path = os.path.expanduser(args.model_path)
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
questions = json.load(open(os.path.expanduser(args.question_file), "r"))
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
ans_file = open(answers_file, "w")
for i, line in enumerate(tqdm(questions)):
idx = line["id"]
question = line['conversations'][0]
qs = question['value'].replace('<image>', '').strip()
cur_prompt = qs
if 'image' in line:
image_file = line["image"]
image = Image.open(os.path.join(args.image_folder, image_file))
image_tensor = process_images([image], image_processor, model.config)[0]
images = image_tensor.unsqueeze(0).half().cuda()
image_sizes = [image.size]
if getattr(model.config, 'mm_use_im_start_end', False):
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
cur_prompt = '<image>' + '\n' + cur_prompt
else:
images = None
image_sizes = None
if args.single_pred_prompt:
qs = qs + '\n' + "Answer with the option's letter from the given choices directly."
cur_prompt = cur_prompt + '\n' + "Answer with the option's letter from the given choices directly."
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=images,
image_sizes=image_sizes,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
max_new_tokens=1024,
use_cache=True,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({"question_id": idx,
"prompt": cur_prompt,
"text": outputs,
"answer_id": ans_id,
"model_id": model_name,
"metadata": {}}) + "\n")
ans_file.flush()
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-folder", type=str, default="")
parser.add_argument("--question-file", type=str, default="tables/question.json")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--conv-mode", type=str, default="llava_v0")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--answer-prompter", action="store_true")
parser.add_argument("--single-pred-prompt", action="store_true")
args = parser.parse_args()
eval_model(args)

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llava/eval/qa_baseline_gpt35.py Normal file
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"""Generate answers with GPT-3.5"""
# Note: you need to be using OpenAI Python v0.27.0 for the code below to work
import argparse
import json
import os
import time
import concurrent.futures
import openai
import tqdm
import shortuuid
MODEL = 'gpt-3.5-turbo'
MODEL_ID = 'gpt-3.5-turbo:20230327'
def get_answer(question_id: int, question: str, max_tokens: int):
ans = {
'answer_id': shortuuid.uuid(),
'question_id': question_id,
'model_id': MODEL_ID,
}
for _ in range(3):
try:
response = openai.ChatCompletion.create(
model=MODEL,
messages=[{
'role': 'system',
'content': 'You are a helpful assistant.'
}, {
'role': 'user',
'content': question,
}],
max_tokens=max_tokens,
)
ans['text'] = response['choices'][0]['message']['content']
return ans
except Exception as e:
print('[ERROR]', e)
ans['text'] = '#ERROR#'
time.sleep(1)
return ans
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='ChatGPT answer generation.')
parser.add_argument('-q', '--question')
parser.add_argument('-o', '--output')
parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output')
args = parser.parse_args()
questions_dict = {}
with open(os.path.expanduser(args.question)) as f:
for line in f:
if not line:
continue
q = json.loads(line)
questions_dict[q['question_id']] = q['text']
answers = []
with concurrent.futures.ThreadPoolExecutor(max_workers=32) as executor:
futures = []
for qid, question in questions_dict.items():
future = executor.submit(get_answer, qid, question, args.max_tokens)
futures.append(future)
for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
answers.append(future.result())
answers.sort(key=lambda x: x['question_id'])
with open(os.path.expanduser(args.output), 'w') as f:
table = [json.dumps(ans) for ans in answers]
f.write('\n'.join(table))

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llava/eval/run_llava.py Normal file
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import argparse
import torch
from llava.constants import (
IMAGE_TOKEN_INDEX,
DEFAULT_IMAGE_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN,
IMAGE_PLACEHOLDER,
)
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import (
process_images,
tokenizer_image_token,
get_model_name_from_path,
)
from PIL import Image
import requests
from PIL import Image
from io import BytesIO
import re
def image_parser(args):
out = args.image_file.split(args.sep)
return out
def load_image(image_file):
if image_file.startswith("http") or image_file.startswith("https"):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert("RGB")
else:
image = Image.open(image_file).convert("RGB")
return image
def load_images(image_files):
out = []
for image_file in image_files:
image = load_image(image_file)
out.append(image)
return out
def eval_model(args):
# Model
disable_torch_init()
model_name = get_model_name_from_path(args.model_path)
model_name= 'llava'
tokenizer, model, image_processor, context_len = load_pretrained_model(
args.model_path, args.model_base, model_name
)
qs = args.query
image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
if IMAGE_PLACEHOLDER in qs:
if model.config.mm_use_im_start_end:
qs = re.sub(IMAGE_PLACEHOLDER, image_token_se, qs)
else:
qs = re.sub(IMAGE_PLACEHOLDER, DEFAULT_IMAGE_TOKEN, qs)
else:
if model.config.mm_use_im_start_end:
qs = image_token_se + "\n" + qs
else:
qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
if "llama-2" in model_name.lower():
conv_mode = "llava_llama_2"
elif "mistral" in model_name.lower():
conv_mode = "mistral_instruct"
elif "v1.6-34b" in model_name.lower():
conv_mode = "chatml_direct"
elif "v1" in model_name.lower():
conv_mode = "llava_v1"
elif "mpt" in model_name.lower():
conv_mode = "mpt"
else:
conv_mode = "llava_v0"
if args.conv_mode is not None and conv_mode != args.conv_mode:
print(
"[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format(
conv_mode, args.conv_mode, args.conv_mode
)
)
else:
args.conv_mode = conv_mode
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
conv.tokenizer = tokenizer
image_files = image_parser(args)
images = load_images(image_files)
image_sizes = [x.size for x in images]
images_tensor = process_images(
images,
image_processor,
model.config
).to(model.device, dtype=torch.float16)
input_ids = (
tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
.unsqueeze(0)
.cuda()
)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=images_tensor,
image_sizes=image_sizes,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
max_new_tokens=args.max_new_tokens,
use_cache=True,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
print(outputs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="aimagelab/LLaVA_MORE-llama_3_1-8B-finetuning")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-file", type=str, default='https://farm2.staticflickr.com/1168/4723652147_ae14813f08_z.jpg')
parser.add_argument("--query", type=str, default="Describe this image.")
parser.add_argument("--conv-mode", type=str, default=None)
parser.add_argument("--sep", type=str, default=",")
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--max_new_tokens", type=int, default=128)
args = parser.parse_args()
# args.conv_mode= 'vicuna_v1'
# args.conv_mode= 'llama_3'
args.conv_mode= 'llama_3_1'
print(f"conversation mode: {args.conv_mode}")
print(f"model name: {args.model_path}")
eval_model(args)

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import json
import os
from collections import defaultdict
import numpy as np
import argparse
def parse_args():
parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
parser.add_argument('-d', '--dir', default=None)
parser.add_argument('-v', '--version', default=None)
parser.add_argument('-s', '--select', nargs='*', default=None)
parser.add_argument('-f', '--files', nargs='*', default=[])
parser.add_argument('-i', '--ignore', nargs='*', default=[])
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
if args.ignore is not None:
args.ignore = [int(x) for x in args.ignore]
if len(args.files) > 0:
review_files = args.files
else:
review_files = [x for x in os.listdir(args.dir) if x.endswith('.jsonl') and (x.startswith('gpt4_text') or x.startswith('reviews_') or x.startswith('review_') or 'review' in args.dir)]
for review_file in sorted(review_files):
config = os.path.basename(review_file).replace('gpt4_text_', '').replace('.jsonl', '')
if args.select is not None and any(x not in config for x in args.select):
continue
if '0613' in config:
version = '0613'
else:
version = '0314'
if args.version is not None and args.version != version:
continue
scores = defaultdict(list)
print(config)
with open(os.path.join(args.dir, review_file) if args.dir is not None else review_file) as f:
for review_str in f:
review = json.loads(review_str)
if review['question_id'] in args.ignore:
continue
if 'category' in review:
scores[review['category']].append(review['tuple'])
scores['all'].append(review['tuple'])
else:
if 'tuple' in review:
scores['all'].append(review['tuple'])
else:
scores['all'].append(review['score'])
for k, v in sorted(scores.items()):
stats = np.asarray(v).mean(0).tolist()
stats = [round(x, 3) for x in stats]
# print(k, stats, round(stats[1]/stats[0]*100, 1))
print(k, round(stats[1]/stats[0]*100, 1), round(stats[0] * 10, 1), round(stats[1] * 10, 1))
print('=================================')

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llava/eval/webpage/index.html Normal file
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Who's GPT-4's favorite? Battles between State-of-the-Art Chatbots</title>
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
<link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
<link rel="stylesheet" href="styles.css">
</head>
<body>
<nav class="navbar navbar-expand-lg navbar-dark bg-dark">
<a class="navbar-brand" href="#">🏔️ Vicuna Evaluation Examples</a>
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav mr-auto">
<li class="nav-item">
<a class="nav-link" href="https://chat.lmsys.org/">Demo</a>
</li>
<li class="nav-item">
<a class="nav-link" href="https://vicuna.lmsys.org">Blog</a>
</li>
<li class="nav-item">
<a class="nav-link" href="https://github.com/lm-sys/FastChat">Github</a>
</li>
</ul>
</div>
</nav>
<div class="container mt-5">
<h2 class="text-center mb-5">Who's GPT-4's favorite? Battles between State-of-the-Art Chatbots</h2>
<!-- Selection -->
<div class="form-row">
<div class="form-group col-md-2">
<label for="category-select">Category</label>
<select class="form-control" id="category-select"></select>
</div>
<div class="form-group col-md-8">
<label for="question-select">Question</label>
<select class="form-control" id="question-select"></select>
</div>
<div class="form-group col-md-2">
<div class="col-md-2"><label>&nbsp;</label></div>
<div class="btn-group" role="group" aria-label="Left and Right Controller">
<button type="button" class="form-control btn btn-primary" id="prev-question"><i class="material-icons">keyboard_arrow_left</i></button>
<button type="button" class="form-control btn btn-primary" id="next-question"><i class="material-icons">keyboard_arrow_right</i></button>
</div>
</div>
</div>
<!-- "Battle" -->
<div class="row mb-4" style="justify-content: center;">
<div class="col" style="display: flex; justify-content: center; align-items: center;">
<label class="adjustable-font-size" id="other-score-label">*/10</label>
</div>
<div class="col">
<div class="vertical-flex-layout">
<img class="shadow figure-img img-fluid" src="" alt="other logo" width="150" id="other-model-figure">
</div>
</div>
<div class="col">
<div class="vertical-flex-layout">
<!-- from: https://fonts.google.com/icons?icon.query=battle&selected=Material+Symbols+Outlined:swords:FILL@0;wght@300;GRAD@0;opsz@48&icon.style=Outlined -->
<img class="figure-img img-fluid" src="figures/swords_FILL0_wght300_GRAD0_opsz48.svg" width="60" height="60">
</div>
</div>
<div class="col">
<div class="vertical-flex-layout">
<img class="shadow figure-img img-fluid" src="figures/vicuna.jpeg" alt="vicuna logo" width="150" id="our-model-figure">
</div>
</div>
<div class="col" style="display: flex; justify-content: center; align-items: center;">
<label class="adjustable-font-size" id="our-score-label">*/10</label>
</div>
</div>
<!-- Question Card -->
<div class="card mb-4">
<div class="card-body" id="selected-question"></div>
</div>
<!-- Answer Cards -->
<div class="row">
<div class="col-md-6">
<div class="card mb-4 expandable-card">
<div class="card-header" style="padding-bottom: 0.2rem" id="other-model-header-bg">
<div class="row">
<div class="col-md-5" style="align-items: center; display: flex;">
<label id="other-model-header">Assistant #1</label>
</div>
<div class="col-md-7">
<select class="form-control" id="model-select" style="height: fit-content; margin-top: -0.3rem;"></select>
</div>
</div>
</div>
<div class="card-body">
<div class="card-text-container">
<div class="card-text" id="other-model-answer"></div>
</div>
<div class="btn btn-primary expand-btn" style="display:flex;"></div>
</div>
</div>
</div>
<div class="col-md-6">
<div class="card mb-4 expandable-card">
<div class="card-header" id="our-model-header">
Assistant #2 (Vicuna, our model)
</div>
<div class="card-body">
<div class="card-text-container">
<div class="card-text" id="our-model-answer"></div>
</div>
<div class="btn btn-primary expand-btn" style="display:flex;"></div>
</div>
</div>
</div>
</div>
<!-- Evaluation -->
<div class="card expandable-card">
<div class="card-header" style="background-color: #c9c9f2;" id="evaluation-header">GPT-4 Evaluation</div>
<div class="card-body">
<div class="card-text-container">
<div class="card-text" id="evaluation-result"></div>
</div>
<div class="btn btn-primary expand-btn" style="display:flex;"></div>
</div>
</div>
</div>
<div class="container-fluid bg-light py-2">
<div class="text-center">
<small class="text-muted">This website is co-authored with <a href="https://openai.com" target="_blank">GPT-4</a>.</small>
</div>
</div>
<!-- Marked.js -->
<script src="https://cdn.jsdelivr.net/npm/marked@4.3.0/lib/marked.umd.min.js"></script>
<!-- Bootstrap and Popper.js JavaScript dependencies -->
<script src="https://code.jquery.com/jquery-3.5.1.slim.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.6/dist/umd/popper.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"></script>
<script src="script.js"></script>
<script>
// Fetch the JSON file
fetch('data.json')
.then(response => response.json())
.then(json_data => {
// Populate the models and questions.
populateModels(json_data.models);
populateQuestions(json_data.questions);
displayQuestion(currentQuestionIndex);
}).catch(error => console.error(error));
</script>
</body>
</html>

245
llava/eval/webpage/script.js Normal file
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// Description: Script for the evaluation webpage.
let currentQuestionIndex = 1;
// Store the model name mapping for later use.
modelNameMapping = {
"gpt35": "ChatGPT-3.5",
"gpt4": "GPT-4",
"alpaca": "Alpaca-13b",
"vicuna": "Vicuna-13b",
"llama": "LLaMA-13b",
"bard": "Bard",
};
modelFigureMapping = {
"vicuna": "figures/vicuna.jpeg",
// Image from: https://commons.wikimedia.org/wiki/File:ChatGPT_logo.svg
"gpt35": "figures/chatgpt.svg",
// Image from: https://www.reddit.com/r/logodesign/comments/1128aat/google_ai_bard_logo_design/
"bard": "figures/bard.jpg",
// Image from: https://crfm.stanford.edu/2023/03/13/alpaca.html
"alpaca": "figures/alpaca.png",
// Image adapted from https://commons.wikimedia.org/wiki/File:Llama_on_Machu_Picchu.jpg
"llama": "figures/llama.jpg",
}
// Store the question data in a mapping for later use.
questionMapping = {};
// Store the question ids in a mapping for later use.
categoryMapping = {};
// Store the number of questions for later use.
questionsCount = 0;
function text2Markdown(text) {
// Normalize the text for markdown rendering.
text = text.trim().replaceAll('\n\n', '\n').replaceAll('\n', '\n\n');
return marked.parse(text);
}
function capitalizeFirstChar(str) {
if (!str || str.length === 0) {
return str;
}
return str.charAt(0).toUpperCase() + str.slice(1);
}
function updateQuestionSelect(question_id) {
const select = document.getElementById('question-select');
// Clear the question select.
select.innerHTML = '';
// Populate the question select.
category = questionMapping[question_id].category;
categoryMapping[category].forEach(question_id => {
const question = questionMapping[question_id];
const option = document.createElement('option');
option.value = question_id;
option.textContent = 'Q' + question_id.toString() + ': ' + question.question;
select.appendChild(option);
});
select.value = question_id;
}
function updateModelSelect() {
const select = document.getElementById('model-select');
img_path = modelFigureMapping[select.value];
document.getElementById('other-model-figure').src = img_path;
}
function populateModels(models) {
const select = document.getElementById('model-select');
models.forEach(model => {
const option = document.createElement('option');
option.value = model;
option.textContent = modelNameMapping[model];
select.appendChild(option);
});
updateModelSelect();
}
function populateQuestions(questions) {
const category_select = document.getElementById('category-select');
questionsCount = questions.length;
questions.forEach(question => {
const option = document.createElement('option');
// Store the question data in a mapping for later use.
questionMapping[question.id] = {
category: question.category,
question: question.question,
answers: question.answers,
evaluations: question.evaluations,
scores: question.scores,
};
// Store the question id in the category mapping.
if (question.category in categoryMapping) {
categoryMapping[question.category].push(question.id);
} else {
categoryMapping[question.category] = [question.id];
const category_option = document.createElement('option');
category_option.value = question.category;
category_option.textContent = capitalizeFirstChar(question.category);
category_select.appendChild(category_option);
}
});
// Set the default category.
updateQuestionSelect(currentQuestionIndex);
}
function displayQuestion(index) {
const question = questionMapping[index].question;
document.getElementById('selected-question').innerHTML = text2Markdown('**Question:** ' + question);
displayAnswers(index);
}
function displayAnswers(index) {
const question = questionMapping[index];
const otherModel = document.getElementById('model-select').value;
// render the answers with markdown
document.getElementById('other-model-answer').innerHTML = text2Markdown(question.answers[otherModel]);
document.getElementById('our-model-answer').innerHTML = text2Markdown(question.answers.vicuna);
// Display evaluation
score = question.scores[otherModel];
score_text = modelNameMapping[otherModel] + " " + score[0] + "/10, Vicuna-13b " + score[1] + "/10";
document.getElementById('evaluation-header').textContent = "GPT-4 Evaluation" + " (Score: " + score_text + ")";
document.getElementById('evaluation-result').innerHTML = text2Markdown(question.evaluations[otherModel]);
// Update model names
let assistant1_title = "Assistant #1"; // (" + modelNameMapping[otherModel] + ")";
let assistant2_title = "Assistant #2 (Vicuna-13b, our model)";
// Update scores/labels.
let assistant1_score_label = score[0].toString() + '/10';
let assistant2_score_label = score[1].toString() + '/10';
const colorRed ='#fa9'; // '#eb978d';
// const colorGreen = '#c9f2c9';
const colorBlue = '#8ef'; // '#71dbf9';
const colorYellow = '#fe7'; // '#fada57';
let otherModelHeaderColor = '';
let ourModelHeaderColor = '';
// Update the winner.
if (score[0] == score[1]) {
assistant1_title = '🏆 ' + assistant1_title;
assistant1_score_label = '🏆 ' + assistant1_score_label;
assistant2_title = '🏆 ' + assistant2_title;
assistant2_score_label = '🏆 ' + assistant2_score_label;
otherModelHeaderColor = colorYellow;
ourModelHeaderColor = colorYellow;
} else if (score[0] > score[1]) {
assistant1_title = '🏆 ' + assistant1_title;
assistant1_score_label = '🏆 ' + assistant1_score_label;
otherModelHeaderColor = colorBlue;
ourModelHeaderColor = colorRed;
} else if (score[0] < score[1]) {
assistant2_title = '🏆 ' + assistant2_title;
assistant2_score_label = '🏆 ' + assistant2_score_label;
otherModelHeaderColor = colorRed;
ourModelHeaderColor = colorBlue;
}
document.getElementById('other-model-header-bg').style.backgroundColor = otherModelHeaderColor;
document.getElementById('our-model-header').style.backgroundColor = ourModelHeaderColor;
document.getElementById('other-model-header').textContent = assistant1_title;
document.getElementById('our-model-header').textContent = assistant2_title;
document.getElementById('other-score-label').textContent = assistant1_score_label;
document.getElementById('our-score-label').textContent = assistant2_score_label;
// Update expand buttons visibility for both cards after displaying answers
// Reset the expanded state and update expand buttons visibility for both cards after displaying answers
document.querySelectorAll('.expandable-card').forEach(card => {
card.classList.remove('expanded');
updateExpandButtonVisibility(card);
const expandBtn = card.querySelector('.expand-btn');
expandBtn.innerHTML = '<i class="material-icons" style="pointer-events: none">keyboard_arrow_down</i> Show more'; // .textContent = 'Show more';
});
}
document.getElementById('question-select').addEventListener('change', e => {
currentQuestionIndex = parseInt(e.target.value);
displayQuestion(currentQuestionIndex);
});
document.getElementById('category-select').addEventListener('change', e => {
let currentCategory = e.target.value;
const questionIds = categoryMapping[currentCategory];
currentQuestionIndex = questionIds[0];
updateQuestionSelect(currentQuestionIndex);
displayQuestion(currentQuestionIndex);
});
// Update expand buttons whenever the model is changed
document.getElementById('model-select').addEventListener('change', () => {
displayAnswers(currentQuestionIndex);
document.querySelectorAll('.expandable-card').forEach(card => {
updateExpandButtonVisibility(card);
});
updateModelSelect();
});
function switchQuestionAndCategory() {
document.getElementById('question-select').value = currentQuestionIndex;
old_category = document.getElementById('category-select').value;
new_category = questionMapping[currentQuestionIndex].category;
if (old_category != new_category) {
document.getElementById('category-select').value = new_category;
updateQuestionSelect(currentQuestionIndex);
}
displayQuestion(currentQuestionIndex);
}
document.getElementById('prev-question').addEventListener('click', () => {
// Question index starts from 1.
currentQuestionIndex = Math.max(1, currentQuestionIndex - 1);
switchQuestionAndCategory();
});
document.getElementById('next-question').addEventListener('click', () => {
// Question index starts from 1.
currentQuestionIndex = Math.min(questionsCount, currentQuestionIndex + 1);
switchQuestionAndCategory();
});
function updateExpandButtonVisibility(card) {
const cardTextContainer = card.querySelector('.card-text-container');
const expandBtn = card.querySelector('.expand-btn');
if (cardTextContainer.scrollHeight > cardTextContainer.offsetHeight) {
expandBtn.style.display = 'flex';
} else {
expandBtn.style.display = 'none';
card.classList.add('expanded');
}
}
document.querySelectorAll('.expand-btn').forEach(btn => {
btn.addEventListener('click', e => {
const card = e.target.closest('.expandable-card');
card.classList.toggle('expanded');
const more = '<i class="material-icons" style="pointer-events: none">keyboard_arrow_down</i> Show more';
const less = '<i class="material-icons" style="pointer-events: none">keyboard_arrow_up</i> Show less';
e.target.innerHTML = card.classList.contains('expanded') ? less : more;
});
});

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body {
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
background-color: #f8f9fa;
}
.navbar-dark .navbar-nav .nav-link {
color: #f1cf68;
font-size: 1.1rem;
padding: 0.5rem 0.6rem;
}
.card-header {
font-weight: bold;
}
.card {
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
transition: 0.3s;
}
.card:hover {
box-shadow: 0 8px 16px rgba(0, 0, 0, 0.2);
}
button {
transition: background-color 0.3s;
}
button:hover {
background-color: #007bff;
}
@media (max-width: 767px) {
.form-row .form-group {
margin-bottom: 10px;
}
}
/* Extra styles */
.expandable-card .card-text-container {
max-height: 200px;
overflow-y: hidden;
position: relative;
}
.expandable-card.expanded .card-text-container {
max-height: none;
}
.expand-btn {
position: relative;
display: none;
background-color: rgba(255, 255, 255, 0.8);
color: #510c75;
border-color: transparent;
}
.expand-btn:hover {
background-color: rgba(200, 200, 200, 0.8);
text-decoration: none;
border-color: transparent;
color: #510c75;
}
.expand-btn:focus {
outline: none;
text-decoration: none;
}
.expandable-card:not(.expanded) .card-text-container:after {
content: "";
position: absolute;
bottom: 0;
left: 0;
width: 100%;
height: 90px;
background: linear-gradient(rgba(255, 255, 255, 0.2), rgba(255, 255, 255, 1));
}
.expandable-card:not(.expanded) .expand-btn {
margin-top: -40px;
}
.card-body {
padding-bottom: 5px;
}
.vertical-flex-layout {
justify-content: center;
align-items: center;
height: 100%;
display: flex;
flex-direction: column;
gap: 5px;
}
.figure-img {
max-width: 100%;
height: auto;
}
.adjustable-font-size {
font-size: calc(0.5rem + 2vw);
}

251
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from PIL import Image
from io import BytesIO
import base64
import torch
import math
import ast
from transformers import StoppingCriteria
from llava.constants import IMAGE_TOKEN_INDEX
def select_best_resolution(original_size, possible_resolutions):
"""
Selects the best resolution from a list of possible resolutions based on the original size.
Args:
original_size (tuple): The original size of the image in the format (width, height).
possible_resolutions (list): A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
Returns:
tuple: The best fit resolution in the format (width, height).
"""
original_width, original_height = original_size
best_fit = None
max_effective_resolution = 0
min_wasted_resolution = float('inf')
for width, height in possible_resolutions:
scale = min(width / original_width, height / original_height)
downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale)
effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height)
wasted_resolution = (width * height) - effective_resolution
if effective_resolution > max_effective_resolution or (effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution):
max_effective_resolution = effective_resolution
min_wasted_resolution = wasted_resolution
best_fit = (width, height)
return best_fit
def resize_and_pad_image(image, target_resolution):
"""
Resize and pad an image to a target resolution while maintaining aspect ratio.
Args:
image (PIL.Image.Image): The input image.
target_resolution (tuple): The target resolution (width, height) of the image.
Returns:
PIL.Image.Image: The resized and padded image.
"""
original_width, original_height = image.size
target_width, target_height = target_resolution
scale_w = target_width / original_width
scale_h = target_height / original_height
if scale_w < scale_h:
new_width = target_width
new_height = min(math.ceil(original_height * scale_w), target_height)
else:
new_height = target_height
new_width = min(math.ceil(original_width * scale_h), target_width)
# Resize the image
resized_image = image.resize((new_width, new_height))
new_image = Image.new('RGB', (target_width, target_height), (0, 0, 0))
paste_x = (target_width - new_width) // 2
paste_y = (target_height - new_height) // 2
new_image.paste(resized_image, (paste_x, paste_y))
return new_image
def divide_to_patches(image, patch_size):
"""
Divides an image into patches of a specified size.
Args:
image (PIL.Image.Image): The input image.
patch_size (int): The size of each patch.
Returns:
list: A list of PIL.Image.Image objects representing the patches.
"""
patches = []
width, height = image.size
for i in range(0, height, patch_size):
for j in range(0, width, patch_size):
box = (j, i, j + patch_size, i + patch_size)
patch = image.crop(box)
patches.append(patch)
return patches
def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
"""
Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
Args:
image_size (tuple): The size of the input image in the format (width, height).
grid_pinpoints (str): A string representation of a list of possible resolutions.
patch_size (int): The size of each image patch.
Returns:
tuple: The shape of the image patch grid in the format (width, height).
"""
if type(grid_pinpoints) is list:
possible_resolutions = grid_pinpoints
else:
possible_resolutions = ast.literal_eval(grid_pinpoints)
width, height = select_best_resolution(image_size, possible_resolutions)
return width // patch_size, height // patch_size
def process_anyres_image(image, processor, grid_pinpoints, siglip=False):
"""
Process an image with variable resolutions.
Args:
image (PIL.Image.Image): The input image to be processed.
processor: The image processor object.
grid_pinpoints (str): A string representation of a list of possible resolutions.
Returns:
torch.Tensor: A tensor containing the processed image patches.
"""
if type(grid_pinpoints) is list:
possible_resolutions = grid_pinpoints
else:
possible_resolutions = ast.literal_eval(grid_pinpoints)
best_resolution = select_best_resolution(image.size, possible_resolutions)
image_padded = resize_and_pad_image(image, best_resolution)
if siglip:
patches = divide_to_patches(image_padded, processor.size['height'])
image_original_resize = image.resize((processor.size['height'], processor.size['width']))
else:
patches = divide_to_patches(image_padded, processor.crop_size['height'])
image_original_resize = image.resize((processor.size['shortest_edge'], processor.size['shortest_edge']))
# print(f"Considering {len(patches)} patches.")
image_patches = [image_original_resize] + patches
image_patches = [processor.preprocess(image_patch, return_tensors='pt')['pixel_values'][0]
for image_patch in image_patches]
return torch.stack(image_patches, dim=0)
def load_image_from_base64(image):
return Image.open(BytesIO(base64.b64decode(image)))
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
def process_images(images, image_processor, model_cfg):
image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
new_images = []
if image_aspect_ratio == 'pad':
for image in images:
image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
new_images.append(image)
elif image_aspect_ratio == "anyres":
for image in images:
image = process_anyres_image(image, image_processor, model_cfg.image_grid_pinpoints)
new_images.append(image)
else:
return image_processor(images, return_tensors='pt')['pixel_values']
if all(x.shape == new_images[0].shape for x in new_images):
new_images = torch.stack(new_images, dim=0)
return new_images
def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
def insert_separator(X, sep):
return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
input_ids = []
offset = 0
if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
offset = 1
input_ids.append(prompt_chunks[0][0])
for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
input_ids.extend(x[offset:])
if return_tensors is not None:
if return_tensors == 'pt':
return torch.tensor(input_ids, dtype=torch.long)
raise ValueError(f'Unsupported tensor type: {return_tensors}')
return input_ids
def get_model_name_from_path(model_path):
model_path = model_path.strip("/")
model_paths = model_path.split("/")
if model_paths[-1].startswith('checkpoint-'):
return model_paths[-2] + "_" + model_paths[-1]
else:
return model_paths[-1]
class KeywordsStoppingCriteria(StoppingCriteria):
def __init__(self, keywords, tokenizer, input_ids):
self.keywords = keywords
self.keyword_ids = []
self.max_keyword_len = 0
for keyword in keywords:
cur_keyword_ids = tokenizer(keyword).input_ids
if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
cur_keyword_ids = cur_keyword_ids[1:]
if len(cur_keyword_ids) > self.max_keyword_len:
self.max_keyword_len = len(cur_keyword_ids)
self.keyword_ids.append(torch.tensor(cur_keyword_ids))
self.tokenizer = tokenizer
self.start_len = input_ids.shape[1]
def call_for_batch(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
for keyword_id in self.keyword_ids:
truncated_output_ids = output_ids[0, -keyword_id.shape[0]:]
if torch.equal(truncated_output_ids, keyword_id):
return True
outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
for keyword in self.keywords:
if keyword in outputs:
return True
return False
def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
outputs = []
for i in range(output_ids.shape[0]):
outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores))
return all(outputs)

6
llava/model/__init__.py Normal file
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# try:
from .language_model.llava_llama import LlavaLlamaForCausalLM, LlavaConfig
from .language_model.llava_mpt import LlavaMptForCausalLM, LlavaMptConfig
from .language_model.llava_mistral import LlavaMistralForCausalLM, LlavaMistralConfig
# except:
# pass

48
llava/model/apply_delta.py Normal file
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"""
Usage:
python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta
"""
import argparse
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
from llava import LlavaLlamaForCausalLM
def apply_delta(base_model_path, target_model_path, delta_path):
print("Loading base model")
base = AutoModelForCausalLM.from_pretrained(
base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
print("Loading delta")
delta = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
delta_tokenizer = AutoTokenizer.from_pretrained(delta_path)
print("Applying delta")
for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"):
if name not in base.state_dict():
assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model'
continue
if param.data.shape == base.state_dict()[name].shape:
param.data += base.state_dict()[name]
else:
assert name in ['model.embed_tokens.weight', 'lm_head.weight'], \
f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}'
bparam = base.state_dict()[name]
param.data[:bparam.shape[0], :bparam.shape[1]] += bparam
print("Saving target model")
delta.save_pretrained(target_model_path)
delta_tokenizer.save_pretrained(target_model_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--base-model-path", type=str, required=True)
parser.add_argument("--target-model-path", type=str, required=True)
parser.add_argument("--delta-path", type=str, required=True)
args = parser.parse_args()
apply_delta(args.base_model_path, args.target_model_path, args.delta_path)

210
llava/model/builder.py Normal file
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# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import json
import warnings
import shutil
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
import torch
from llava.model import *
from llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda",
use_flash_attn=False, mlp_path=None, **kwargs):
kwargs = {"device_map": device_map, **kwargs}
if device != "cuda":
kwargs['device_map'] = {"": device}
if load_8bit:
kwargs['load_in_8bit'] = True
elif load_4bit:
kwargs['load_in_4bit'] = True
kwargs['quantization_config'] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
)
else:
kwargs['torch_dtype'] = torch.float16
if use_flash_attn:
kwargs['attn_implementation'] = 'flash_attention_2'
if 'llava' in model_name.lower():
# Load LLaVA model
if 'lora' in model_name.lower() and model_base is None:
warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
config_path = os.path.join(model_path, 'config.json')
try:
with open(config_path) as f:
configuration= json.load(f)
model_base = configuration['_name_or_path']
except:
raise ValueError('Cannot find the model name in the configuration file. Please provide the `model_base` argument.')
if 'lora' in model_name.lower() and model_base is not None:
from llava.model.language_model.llava_llama import LlavaConfig
lora_cfg_pretrained = LlavaConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
print('Loading LLaVA from base model...')
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
if model.lm_head.weight.shape[0] != token_num:
model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
print('Loading additional LLaVA weights...')
if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
else:
# this is probably from HF Hub
from huggingface_hub import hf_hub_download
def load_from_hf(repo_id, filename, subfolder=None):
cache_file = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder)
return torch.load(cache_file, map_location='cpu')
non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
if any(k.startswith('model.model.') for k in non_lora_trainables):
non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
model.load_state_dict(non_lora_trainables, strict=False)
from peft import PeftModel
print('Loading LoRA weights...')
model = PeftModel.from_pretrained(model, model_path)
print('Merging LoRA weights...')
model = model.merge_and_unload()
print('Model is loaded...')
elif model_base is not None:
# this may be mm projector only
print('Loading LLaVA from base model...')
if 'mpt' in model_name.lower():
if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')):
shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(model_path, 'configuration_mpt.py'))
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = LlavaMptForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
model.load_state_dict(mm_projector_weights, strict=False)
else:
if 'mpt' in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = LlavaMptForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
elif 'mistral' in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = LlavaMistralForCausalLM.from_pretrained(
model_path,
low_cpu_mem_usage=True,
**kwargs
)
else:
# some old checkpoints may not have the siglip parameter in configuration file
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = LlavaLlamaForCausalLM.from_pretrained(
model_path,
low_cpu_mem_usage=True,
**kwargs
)
else:
# Load language model
if model_base is not None:
# PEFT model
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **kwargs)
print(f"Loading LoRA weights from {model_path}")
model = PeftModel.from_pretrained(model, model_path)
print(f"Merging weights")
model = model.merge_and_unload()
print('Convert to FP16...')
model.to(torch.float16)
else:
use_fast = False
if 'mpt' in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
image_processor = None
if 'llava' in model_name.lower() or mlp_path is not None:
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
# # RE-CONTROL THE IF STATEMENT select the correct class, considering also S2
# if 'siglip' in model.config.mm_vision_tower and hasattr(model.config, 's2'):
# # change the args for the new class
# from llava.model.multimodal_encoder.builder import SigLIPVisionTowerS2
# vision_tower = SigLIPVisionTowerS2('google/siglip-so400m-patch14-384', args=model.config)
# model.model.vision_tower = vision_tower.vision_tower
# elif 'siglip' in model.config.mm_vision_tower:
# from llava.model.multimodal_encoder.builder import SigLIPVisionTower
# vision_tower= SigLIPVisionTower('google/siglip-so400m-patch14-384', args=model.config)
# vision_tower.to("cuda", dtype=torch.float16)
# model.model.vision_tower = vision_tower.vision_tower
# else:
# if hasattr(model.config, 's2'): # and on work
# from llava.model.multimodal_encoder.builder import CLIPVisionTowerS2
# vision_tower= CLIPVisionTowerS2('google/siglip-so400m-patch14-384', args=model.config)
# model.model.vision_tower = vision_tower.vision_tower
# else:
# vision_tower = model.get_vision_tower()
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model(device_map=device_map)
if device_map != 'auto':
vision_tower.to(device=device_map, dtype=torch.float16)
image_processor = vision_tower.image_processor
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
if mlp_path is not None:
print('Loading mm projector weights...')
mm_projector_weights = torch.load(mlp_path)
new_dict= {}
new_keys= ['0.weight', '0.bias', '2.weight', '2.bias']
for el, key in enumerate(new_keys):
new_dict[key] = mm_projector_weights[list(mm_projector_weights.keys())[el]]
model.model.mm_projector.load_state_dict(new_dict)
# model.model.mm_projector.to(device=device_map, dtype=torch.float16)
return tokenizer, model, image_processor, context_len

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llava/model/consolidate.py Normal file
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"""
Usage:
python3 -m llava.model.consolidate --src ~/model_weights/llava-7b --dst ~/model_weights/llava-7b_consolidate
"""
import argparse
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from llava.model import *
from llava.model.utils import auto_upgrade
def consolidate_ckpt(src_path, dst_path):
print("Loading model")
auto_upgrade(src_path)
src_model = AutoModelForCausalLM.from_pretrained(src_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
src_tokenizer = AutoTokenizer.from_pretrained(src_path, use_fast=False)
src_model.save_pretrained(dst_path)
src_tokenizer.save_pretrained(dst_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--src", type=str, required=True)
parser.add_argument("--dst", type=str, required=True)
args = parser.parse_args()
consolidate_ckpt(args.src, args.dst)

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llava/model/language_model/llava_llama.py Normal file
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# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModelForCausalLM, \
LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
import sys
import os
sys.path.append(os.path.abspath("."))
sys.path.append(os.path.abspath("../.."))
import utils
logger= utils.get_logger(__name__)
class LlavaConfig(LlamaConfig):
model_type = "llava_llama"
class LlavaLlamaModel(LlavaMetaModel, LlamaModel):
config_class = LlavaConfig
def __init__(self, config: LlamaConfig):
super(LlavaLlamaModel, self).__init__(config)
class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaConfig
def __init__(self, config):
super(LlamaForCausalLM, self).__init__(config)
self.model = LlavaLlamaModel(config)
self.pretraining_tp = config.pretraining_tp
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
cache_position=None # warning: this is required for inference and last versions of transformers
) -> Union[Tuple, CausalLMOutputWithPast]:
# if self.cache_position: self.cache_position = None
if inputs_embeds is None:
(
input_ids,
position_ids,
attention_mask,
past_key_values,
inputs_embeds,
labels
) = self.prepare_inputs_labels_for_multimodal(
input_ids,
position_ids,
attention_mask,
past_key_values,
labels,
images,
image_sizes
)
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
kwargs.pop("cache_position", None)
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(
inputs,
position_ids,
attention_mask,
_,
inputs_embeds,
_
) = self.prepare_inputs_labels_for_multimodal(
inputs,
position_ids,
attention_mask,
None,
None,
images,
image_sizes=image_sizes
)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
**kwargs
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(
input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
)
if images is not None:
inputs['images'] = images
if image_sizes is not None:
inputs['image_sizes'] = image_sizes
return inputs
AutoConfig.register("llava_llama", LlavaConfig)
AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)

158
llava/model/language_model/llava_mistral.py Normal file
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# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, \
MistralConfig, MistralModel, MistralForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaMistralConfig(MistralConfig):
model_type = "llava_mistral"
class LlavaMistralModel(LlavaMetaModel, MistralModel):
config_class = LlavaMistralConfig
def __init__(self, config: MistralConfig):
super(LlavaMistralModel, self).__init__(config)
class LlavaMistralForCausalLM(MistralForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMistralConfig
def __init__(self, config):
super(MistralForCausalLM, self).__init__(config)
self.model = LlavaMistralModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
if inputs_embeds is None:
(
input_ids,
position_ids,
attention_mask,
past_key_values,
inputs_embeds,
labels
) = self.prepare_inputs_labels_for_multimodal(
input_ids,
position_ids,
attention_mask,
past_key_values,
labels,
images,
image_sizes
)
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("`inputs_embeds` is not supported")
if images is not None:
(
inputs,
position_ids,
attention_mask,
_,
inputs_embeds,
_
) = self.prepare_inputs_labels_for_multimodal(
inputs,
position_ids,
attention_mask,
None,
None,
images,
image_sizes=image_sizes
)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
**kwargs
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(
input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
)
if images is not None:
inputs['images'] = images
if image_sizes is not None:
inputs['image_sizes'] = image_sizes
return inputs
AutoConfig.register("llava_mistral", LlavaMistralConfig)
AutoModelForCausalLM.register(LlavaMistralConfig, LlavaMistralForCausalLM)

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llava/model/language_model/llava_mpt.py Normal file
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# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple
import torch
from transformers import AutoConfig, AutoModelForCausalLM, \
MptConfig, MptForCausalLM, MptModel
from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
class LlavaMptConfig(MptConfig):
model_type = "llava_mpt"
class LlavaMptModel(LlavaMetaModel, MptModel):
config_class = LlavaMptConfig
def __init__(self, config: MptConfig):
config.hidden_size = config.d_model
super(LlavaMptModel, self).__init__(config)
def embed_tokens(self, x):
return self.wte(x)
class LlavaMptForCausalLM(MptForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMptConfig
supports_gradient_checkpointing = True
def __init__(self, config):
super(MptForCausalLM, self).__init__(config)
self.transformer = LlavaMptModel(config)
self.lm_head = torch.nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.transformer
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, LlavaMptModel):
module.gradient_checkpointing = value
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
images=None):
input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
return super().forward(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
images = kwargs.pop("images", None)
_inputs = super().prepare_inputs_for_generation(
input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
)
_inputs['images'] = images
return _inputs
AutoConfig.register("llava_mpt", LlavaMptConfig)
AutoModelForCausalLM.register(LlavaMptConfig, LlavaMptForCausalLM)

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# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from .multimodal_encoder.builder import build_vision_tower
from .multimodal_projector.builder import build_vision_projector
from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.mm_utils import get_anyres_image_grid_shape
class LlavaMetaModel:
def __init__(self, config):
super(LlavaMetaModel, self).__init__(config)
if hasattr(config, "mm_vision_tower"):
self.vision_tower = build_vision_tower(config, delay_load=True)
self.mm_projector = build_vision_projector(config)
if 'unpad' in getattr(config, 'mm_patch_merge_type', ''):
self.image_newline = nn.Parameter(
torch.empty(config.hidden_size, dtype=self.dtype)
)
def get_vision_tower(self):
vision_tower = getattr(self, 'vision_tower', None)
if type(vision_tower) is list:
vision_tower = vision_tower[0]
return vision_tower
def initialize_vision_modules(self, model_args, fsdp=None):
vision_tower = model_args.vision_tower
mm_vision_select_layer = model_args.mm_vision_select_layer
mm_vision_select_feature = model_args.mm_vision_select_feature
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
mm_patch_merge_type = model_args.mm_patch_merge_type
self.config.mm_vision_tower = vision_tower
if self.get_vision_tower() is None:
vision_tower = build_vision_tower(model_args)
if fsdp is not None and len(fsdp) > 0:
self.vision_tower = [vision_tower]
else:
self.vision_tower = vision_tower
else:
if fsdp is not None and len(fsdp) > 0:
vision_tower = self.vision_tower[0]
else:
vision_tower = self.vision_tower
vision_tower.load_model()
self.config.use_mm_proj = True
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
self.config.mm_vision_select_layer = mm_vision_select_layer
self.config.mm_vision_select_feature = mm_vision_select_feature
self.config.mm_patch_merge_type = mm_patch_merge_type
if model_args.siglip and not model_args.s2:
self.config.mm_hidden_size = vision_tower.config.hidden_size
else:
self.config.mm_hidden_size = vision_tower.hidden_size
if getattr(self, 'mm_projector', None) is None:
self.mm_projector = build_vision_projector(self.config)
if 'unpad' in mm_patch_merge_type:
embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
self.image_newline = nn.Parameter(
torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std
)
else:
# In case it is frozen by LoRA
for p in self.mm_projector.parameters():
p.requires_grad = True
if pretrain_mm_mlp_adapter is not None:
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
def get_w(weights, keyword):
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
def unpad_image(tensor, original_size):
"""
Unpads a PyTorch tensor of a padded and resized image.
Args:
tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
original_size (tuple): The original size of PIL image (width, height).
Returns:
torch.Tensor: The unpadded image tensor.
"""
original_width, original_height = original_size
current_height, current_width = tensor.shape[1:]
original_aspect_ratio = original_width / original_height
current_aspect_ratio = current_width / current_height
if original_aspect_ratio > current_aspect_ratio:
scale_factor = current_width / original_width
new_height = int(original_height * scale_factor)
padding = (current_height - new_height) // 2
unpadded_tensor = tensor[:, padding:current_height - padding, :]
else:
scale_factor = current_height / original_height
new_width = int(original_width * scale_factor)
padding = (current_width - new_width) // 2
unpadded_tensor = tensor[:, :, padding:current_width - padding]
return unpadded_tensor
class LlavaMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def encode_images(self, images):
image_features = self.get_model().get_vision_tower()(images) # batch, 576 / 729, vision_model hidden_size
image_features = self.get_model().mm_projector(image_features)
return image_features
def prepare_inputs_labels_for_multimodal(
self, input_ids, position_ids, attention_mask, past_key_values, labels,
images, image_sizes=None
):
vision_tower = self.get_vision_tower()
if vision_tower is None or images is None or input_ids.shape[1] == 1:
return input_ids, position_ids, attention_mask, past_key_values, None, labels
# enter in this if from llava 1.6
if type(images) is list or images.ndim == 5:
if type(images) is list:
images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
concat_images = torch.cat([image for image in images], dim=0)
image_features = self.encode_images(concat_images)
split_sizes = [image.shape[0] for image in images]
image_features = torch.split(image_features, split_sizes, dim=0)
mm_patch_merge_type = getattr(self.config, 'mm_patch_merge_type', 'flat')
image_aspect_ratio = getattr(self.config, 'image_aspect_ratio', 'square')
if mm_patch_merge_type == 'flat':
image_features = [x.flatten(0, 1) for x in image_features]
elif mm_patch_merge_type.startswith('spatial'):
new_image_features = []
for image_idx, image_feature in enumerate(image_features):
if image_feature.shape[0] > 1:
tmp_image_shape = images[image_idx].shape
base_image_feature = image_feature[0]
image_feature = image_feature[1:]
if hasattr(self.get_vision_tower(), 'num_patches_per_side'):
height = width = self.get_vision_tower().num_patches_per_side
else:
height = width = self.get_vision_tower().config.image_size // self.get_vision_tower().config.patch_size
assert height * width == base_image_feature.shape[0]
if image_aspect_ratio == 'anyres':
num_patch_width, num_patch_height = get_anyres_image_grid_shape((images[image_idx].shape[-1],
images[image_idx].shape[-2]),
self.config.image_grid_pinpoints,
self.get_vision_tower().config.image_size)
# num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, self.get_vision_tower().config.image_size)
if num_patch_height + num_patch_width != tmp_image_shape[0]:
image_feature = image_feature.view(num_patch_height+1, num_patch_width, height, width, -1)
else:
image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
else:
raise NotImplementedError
if 'unpad' in mm_patch_merge_type:
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature= unpad_image(image_feature, (images[image_idx].shape[-1], images[image_idx].shape[-2]))
# image_feature = unpad_image(image_feature, image_sizes[image_idx])
# here the first dimension should be 4096
image_feature = torch.cat(( # 6144, 24, 24
image_feature,
self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
), dim=-1)
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
else:
image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
image_feature = image_feature.flatten(0, 3)
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
else:
image_feature = image_feature[0]
if 'unpad' in mm_patch_merge_type:
image_feature = torch.cat((
image_feature,
self.model.image_newline[None].to(image_feature.device)
), dim=0)
new_image_features.append(image_feature)
image_features = new_image_features
else:
raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}")
else:
image_features = self.encode_images(images)
# TODO: image start / end is not implemented here to support pretraining.
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
raise NotImplementedError
# Let's just add dummy tensors if they do not exist,
# it is a headache to deal with None all the time.
# But it is not ideal, and if you have a better idea,
# please open an issue / submit a PR, thanks.
_labels = labels
_position_ids = position_ids
_attention_mask = attention_mask
if attention_mask is None:
attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
else:
attention_mask = attention_mask.bool()
if position_ids is None:
position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
if labels is None:
labels = torch.full_like(input_ids, IGNORE_INDEX)
# remove the padding using attention_mask -- FIXME
_input_ids = input_ids
input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
new_input_embeds = []
new_labels = []
cur_image_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
if num_images == 0:
cur_image_features = image_features[cur_image_idx]
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
new_input_embeds.append(cur_input_embeds)
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
cur_input_ids_noim = []
cur_labels = labels[batch_idx]
cur_labels_noim = []
for i in range(len(image_token_indices) - 1):
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
split_sizes = [x.shape[0] for x in cur_labels_noim]
cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
cur_new_input_embeds = []
cur_new_labels = []
for i in range(num_images + 1):
cur_new_input_embeds.append(cur_input_embeds_no_im[i])
cur_new_labels.append(cur_labels_noim[i])
if i < num_images:
cur_image_features = image_features[cur_image_idx]
cur_image_idx += 1
cur_new_input_embeds.append(cur_image_features)
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]
cur_new_input_embeds = torch.cat(cur_new_input_embeds)
cur_new_labels = torch.cat(cur_new_labels)
new_input_embeds.append(cur_new_input_embeds)
new_labels.append(cur_new_labels)
# Truncate sequences to max length as image embeddings can make the sequence longer
tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
if tokenizer_model_max_length is not None:
new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
# Combine them
max_len = max(x.shape[0] for x in new_input_embeds)
batch_size = len(new_input_embeds)
new_input_embeds_padded = []
new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
cur_len = cur_new_embed.shape[0]
if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
new_input_embeds_padded.append(torch.cat((
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
cur_new_embed
), dim=0))
if cur_len > 0:
new_labels_padded[i, -cur_len:] = cur_new_labels
attention_mask[i, -cur_len:] = True
position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
else:
new_input_embeds_padded.append(torch.cat((
cur_new_embed,
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
), dim=0))
if cur_len > 0:
new_labels_padded[i, :cur_len] = cur_new_labels
attention_mask[i, :cur_len] = True
position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
if _labels is None:
new_labels = None
else:
new_labels = new_labels_padded
if _attention_mask is None:
attention_mask = None
else:
attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
if _position_ids is None:
position_ids = None
return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
def initialize_vision_tokenizer(self, model_args, tokenizer):
if model_args.mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if model_args.mm_use_im_start_end:
num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = self.get_input_embeddings().weight.data
output_embeddings = self.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = True
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
if model_args.pretrain_mm_mlp_adapter:
mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
assert num_new_tokens == 2
if input_embeddings.shape == embed_tokens_weight.shape:
input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
elif embed_tokens_weight.shape[0] == num_new_tokens:
input_embeddings[-num_new_tokens:] = embed_tokens_weight
else:
raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
elif model_args.mm_use_im_patch_token:
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = False
for p in self.get_output_embeddings().parameters():
p.requires_grad = False

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llava/model/make_delta.py Normal file
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"""
Usage:
python3 -m llava.model.make_delta --base ~/model_weights/llama-7b --target ~/model_weights/llava-7b --delta ~/model_weights/llava-7b-delta --hub-repo-id liuhaotian/llava-7b-delta
"""
import argparse
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
from llava.model.utils import auto_upgrade
def make_delta(base_model_path, target_model_path, delta_path, hub_repo_id):
print("Loading base model")
base = AutoModelForCausalLM.from_pretrained(
base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
print("Loading target model")
auto_upgrade(target_model_path)
target = AutoModelForCausalLM.from_pretrained(target_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
print("Calculating delta")
for name, param in tqdm(target.state_dict().items(), desc="Calculating delta"):
if name not in base.state_dict():
assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model'
continue
if param.data.shape == base.state_dict()[name].shape:
param.data -= base.state_dict()[name]
else:
assert name in ['model.embed_tokens.weight', 'lm_head.weight'], f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}'
bparam = base.state_dict()[name]
param.data[:bparam.shape[0], :bparam.shape[1]] -= bparam
print("Saving delta")
if hub_repo_id:
kwargs = {"push_to_hub": True, "repo_id": hub_repo_id}
else:
kwargs = {}
target.save_pretrained(delta_path, **kwargs)
target_tokenizer = AutoTokenizer.from_pretrained(target_model_path)
target_tokenizer.save_pretrained(delta_path, **kwargs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--base-model-path", type=str, required=True)
parser.add_argument("--target-model-path", type=str, required=True)
parser.add_argument("--delta-path", type=str, required=True)
parser.add_argument("--hub-repo-id", type=str, default=None)
args = parser.parse_args()
make_delta(args.base_model_path, args.target_model_path, args.delta_path, args.hub_repo_id)

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import os
from .clip_encoder import CLIPVisionTower, CLIPVisionTowerS2, SigLIPVisionTower, SigLIPVisionTowerS2
def build_vision_tower(vision_tower_cfg, **kwargs):
vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))
is_absolute_path_exists = os.path.exists(vision_tower)
use_s2 = getattr(vision_tower_cfg, 's2', False)
use_siglip = getattr(vision_tower_cfg, 'siglip', False)
if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion") or vision_tower.startswith("timm") or vision_tower.startswith("google") or "ShareGPT4V" in vision_tower:
if use_s2 and use_siglip:
return SigLIPVisionTowerS2(vision_tower, args=vision_tower_cfg, **kwargs)
if use_s2:
return CLIPVisionTowerS2(vision_tower, args=vision_tower_cfg, **kwargs)
if use_siglip:
return SigLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
else:
return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
raise ValueError(f'Unknown vision tower: {vision_tower}')

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llava/model/multimodal_encoder/clip_encoder.py Normal file
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import torch
import torch.nn as nn
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
from transformers import AutoProcessor, AutoModel
from transformers import SiglipImageProcessor, SiglipVisionModel
#from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
class SigLIPVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
if not delay_load:
self.load_model()
elif getattr(args, 'unfreeze_mm_vision_tower', False):
self.load_model()
else:
#model, _ = create_model_from_pretrained('hf-hub:timm/ViT-SO400M-14-SigLIP-384')
model = SiglipVisionModel.from_pretrained(self.vision_tower_name)
self.cfg_only = model.config
def load_model(self, device_map=None):
if self.is_loaded:
print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name))
return
# model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-SO400M-14-SigLIP-384')
# self.image_processor = preprocess
# self.vision_tower = model.to(device_map['device'])
# self.image_processor = AutoProcessor.from_pretrained(self.vision_tower_name)
# self.vision_tower= AutoModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.image_processor = SiglipImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower= SiglipVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer] # len 25, each is (batch, 577, 1024) - (batch, 729, 1152)
if self.select_feature == 'patch':
image_features = image_features[:, :] # not remove the first token --> (batch, 576, 1024) - (batch, 729, 1152)
elif self.select_feature == 'cls_patch':
image_features = image_features
else:
raise ValueError(f'Unexpected select feature: {self.select_feature}')
return image_features
@torch.no_grad()
def forward(self, images): # images tensor of shape (batch, 3, 336, 336)
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
image_feature = self.feature_select(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.config.vision_config.hidden_size, device=self.device, dtype=self.dtype) # V
@property
def dtype(self):
return self.vision_tower.dtype # torch.float16 V
@property
def device(self):
return self.vision_tower.device # device(type='cuda', index=0) V
@property
def config(self):
if self.is_loaded:
return self.vision_tower.config # CLIPVisionConfig - SiglipConfig V
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.vision_config.hidden_size # 1152 V
@property
def num_patches_per_side(self):
return self.config.vision_config.image_size // self.config.vision_config.patch_size # (348 // 14) = 27 V
@property
def num_patches(self):
return (self.config.vision_config.image_size // self.config.vision_config.patch_size) ** 2 # 729 V
class CustomSiglipImageProcessor(SiglipImageProcessor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.crop_size = {'width': 0, 'height': 0}
self.crop_size['height'] = self.crop_size['width'] = self.size['height']
class SigLIPVisionTowerS2(SigLIPVisionTower):
def __init__(self, vision_tower, args, delay_load=False):
#super().__init__(vision_tower, args, delay_load)
self.s2_scales = getattr(args, 's2_scales', '336,672,1008')
self.s2_scales = list(map(int, self.s2_scales.split(',')))
self.s2_scales.sort()
self.s2_split_size = self.s2_scales[0]
self.s2_image_size = self.s2_scales[-1]
super().__init__(vision_tower, args, delay_load)
try:
from s2wrapper import forward as multiscale_forward
except ImportError:
raise ImportError('Package s2wrapper not found! Please install by running: \npip install git+https://github.com/bfshi/scaling_on_scales.git')
self.multiscale_forward = multiscale_forward
# change resize/crop size in preprocessing to the largest image size in s2_scale
# if not delay_load or getattr(args, 'unfreeze_mm_vision_tower', False):
# self.image_processor.size['shortest_edge'] = self.s2_image_size
# self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
def load_model(self, device_map=None):
if self.is_loaded:
print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name))
return
self.image_processor = CustomSiglipImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower= SiglipVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.vision_tower.requires_grad_(False)
#self.image_processor.size['shortest_edge'] = self.s2_image_size
self.image_processor.size['height'] = self.image_processor.size['width'] = self.s2_image_size
self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
self.is_loaded = True
@torch.no_grad()
def forward_feature(self, images):
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_feature = self.multiscale_forward(self.forward_feature, image.unsqueeze(0), img_sizes=self.s2_scales, max_split_size=self.s2_split_size)
image_features.append(image_feature)
else:
image_features = self.multiscale_forward(self.forward_feature, images, img_sizes=self.s2_scales, max_split_size=self.s2_split_size)
return image_features
@property
def hidden_size(self):
return self.config.hidden_size * len(self.s2_scales)
class CLIPVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
if not delay_load:
self.load_model()
elif getattr(args, 'unfreeze_mm_vision_tower', False):
self.load_model()
else:
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
def load_model(self, device_map=None):
if self.is_loaded:
print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name))
return
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer] # len 25, each is (batch, 577, 1024)
if self.select_feature == 'patch':
image_features = image_features[:, 1:] # remove the CLS token --> (batch, 576, 1024)
elif self.select_feature == 'cls_patch':
image_features = image_features
else:
raise ValueError(f'Unexpected select feature: {self.select_feature}')
return image_features
@torch.no_grad()
def forward(self, images): # images tensor of shape (batch, 3, 336, 336)
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
image_feature = self.feature_select(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.vision_tower.dtype # torch.float16
@property
def device(self):
return self.vision_tower.device # device(type='cuda', index=0)
@property
def config(self):
if self.is_loaded:
return self.vision_tower.config # CLIPVisionConfig
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size # 1024
@property
def num_patches_per_side(self):
return self.config.image_size // self.config.patch_size # (336 // 14) = 24
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2 # 576
class CLIPVisionTowerS2(CLIPVisionTower):
def __init__(self, vision_tower, args, delay_load=False):
#super().__init__(vision_tower, args, delay_load)
self.s2_scales = getattr(args, 's2_scales', '336,672,1008')
self.s2_scales = list(map(int, self.s2_scales.split(',')))
self.s2_scales.sort()
self.s2_split_size = self.s2_scales[0]
self.s2_image_size = self.s2_scales[-1]
super().__init__(vision_tower, args, delay_load)
try:
from s2wrapper import forward as multiscale_forward
except ImportError:
raise ImportError('Package s2wrapper not found! Please install by running: \npip install git+https://github.com/bfshi/scaling_on_scales.git')
self.multiscale_forward = multiscale_forward
# change resize/crop size in preprocessing to the largest image size in s2_scale
# if not delay_load or getattr(args, 'unfreeze_mm_vision_tower', False):
# self.image_processor.size['shortest_edge'] = self.s2_image_size
# self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
def load_model(self, device_map=None):
if self.is_loaded:
print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name))
return
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.vision_tower.requires_grad_(False)
self.image_processor.size['shortest_edge'] = self.s2_image_size
self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
self.is_loaded = True
@torch.no_grad()
def forward_feature(self, images):
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_feature = self.multiscale_forward(self.forward_feature, image.unsqueeze(0), img_sizes=self.s2_scales, max_split_size=self.s2_split_size)
image_features.append(image_feature)
else:
image_features = self.multiscale_forward(self.forward_feature, images, img_sizes=self.s2_scales, max_split_size=self.s2_split_size)
return image_features
@property
def hidden_size(self):
return self.config.hidden_size * len(self.s2_scales)

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import torch
import torch.nn as nn
import re
class IdentityMap(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, *args, **kwargs):
return x
@property
def config(self):
return {"mm_projector_type": 'identity'}
class SimpleResBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.pre_norm = nn.LayerNorm(channels)
self.proj = nn.Sequential(
nn.Linear(channels, channels),
nn.GELU(),
nn.Linear(channels, channels)
)
def forward(self, x):
x = self.pre_norm(x)
return x + self.proj(x)
def build_vision_projector(config, delay_load=False, **kwargs):
projector_type = getattr(config, 'mm_projector_type', 'linear')
if projector_type == 'linear':
return nn.Linear(config.mm_hidden_size, config.hidden_size)
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
return nn.Sequential(*modules)
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')

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from transformers import AutoConfig
def auto_upgrade(config):
cfg = AutoConfig.from_pretrained(config)
if 'llava' in config and 'llava' not in cfg.model_type:
assert cfg.model_type == 'llama'
print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.")
print("You must upgrade the checkpoint to the new code base (this can be done automatically).")
confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]")
if confirm.lower() in ["y", "yes"]:
print("Upgrading checkpoint...")
assert len(cfg.architectures) == 1
setattr(cfg.__class__, "model_type", "llava")
cfg.architectures[0] = 'LlavaLlamaForCausalLM'
cfg.save_pretrained(config)
print("Checkpoint upgraded.")
else:
print("Checkpoint upgrade aborted.")
exit(1)

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import argparse
import torch
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path
from PIL import Image
import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer
def load_image(image_file):
if image_file.startswith('http://') or image_file.startswith('https://'):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert('RGB')
else:
image = Image.open(image_file).convert('RGB')
return image
def main(args):
# Model
disable_torch_init()
model_name = get_model_name_from_path(args.model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device)
if "llama-2" in model_name.lower():
conv_mode = "llava_llama_2"
elif "mistral" in model_name.lower():
conv_mode = "mistral_instruct"
elif "v1.6-34b" in model_name.lower():
conv_mode = "chatml_direct"
elif "v1" in model_name.lower():
conv_mode = "llava_v1"
elif "mpt" in model_name.lower():
conv_mode = "mpt"
else:
conv_mode = "llava_v0"
if args.conv_mode is not None and conv_mode != args.conv_mode:
print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
else:
args.conv_mode = conv_mode
conv = conv_templates[args.conv_mode].copy()
if "mpt" in model_name.lower():
roles = ('user', 'assistant')
else:
roles = conv.roles
image = load_image(args.image_file)
image_size = image.size
# Similar operation in model_worker.py
image_tensor = process_images([image], image_processor, model.config)
if type(image_tensor) is list:
image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
else:
image_tensor = image_tensor.to(model.device, dtype=torch.float16)
while True:
try:
inp = input(f"{roles[0]}: ")
except EOFError:
inp = ""
if not inp:
print("exit...")
break
print(f"{roles[1]}: ", end="")
if image is not None:
# first message
if model.config.mm_use_im_start_end:
inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
else:
inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
image = None
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
image_sizes=[image_size],
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True)
outputs = tokenizer.decode(output_ids[0]).strip()
conv.messages[-1][-1] = outputs
if args.debug:
print("\n", {"prompt": prompt, "outputs": outputs}, "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-file", type=str, required=True)
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--conv-mode", type=str, default=None)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--max-new-tokens", type=int, default=512)
parser.add_argument("--load-8bit", action="store_true")
parser.add_argument("--load-4bit", action="store_true")
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
main(args)

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"""
A controller manages distributed workers.
It sends worker addresses to clients.
"""
import argparse
import asyncio
import dataclasses
from enum import Enum, auto
import json
import logging
import time
from typing import List, Union
import threading
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
import numpy as np
import requests
import uvicorn
from llava.constants import CONTROLLER_HEART_BEAT_EXPIRATION
from llava.utils import build_logger, server_error_msg
logger = build_logger("controller", "controller.log")
class DispatchMethod(Enum):
LOTTERY = auto()
SHORTEST_QUEUE = auto()
@classmethod
def from_str(cls, name):
if name == "lottery":
return cls.LOTTERY
elif name == "shortest_queue":
return cls.SHORTEST_QUEUE
else:
raise ValueError(f"Invalid dispatch method")
@dataclasses.dataclass
class WorkerInfo:
model_names: List[str]
speed: int
queue_length: int
check_heart_beat: bool
last_heart_beat: str
def heart_beat_controller(controller):
while True:
time.sleep(CONTROLLER_HEART_BEAT_EXPIRATION)
controller.remove_stable_workers_by_expiration()
class Controller:
def __init__(self, dispatch_method: str):
# Dict[str -> WorkerInfo]
self.worker_info = {}
self.dispatch_method = DispatchMethod.from_str(dispatch_method)
self.heart_beat_thread = threading.Thread(
target=heart_beat_controller, args=(self,), daemon=True)
self.heart_beat_thread.start()
logger.info("Init controller")
def register_worker(self, worker_name: str, check_heart_beat: bool,
worker_status: dict):
if worker_name not in self.worker_info:
logger.info(f"Register a new worker: {worker_name}")
else:
logger.info(f"Register an existing worker: {worker_name}")
if not worker_status:
worker_status = self.get_worker_status(worker_name)
if not worker_status:
return False
self.worker_info[worker_name] = WorkerInfo(
worker_status["model_names"], worker_status["speed"], worker_status["queue_length"],
check_heart_beat, time.time())
logger.info(f"Register done: {worker_name}, {worker_status}")
return True
def get_worker_status(self, worker_name: str):
try:
r = requests.post(worker_name + "/worker_get_status", timeout=5)
except requests.exceptions.RequestException as e:
logger.error(f"Get status fails: {worker_name}, {e}")
return None
if r.status_code != 200:
logger.error(f"Get status fails: {worker_name}, {r}")
return None
return r.json()
def remove_worker(self, worker_name: str):
del self.worker_info[worker_name]
def refresh_all_workers(self):
old_info = dict(self.worker_info)
self.worker_info = {}
for w_name, w_info in old_info.items():
if not self.register_worker(w_name, w_info.check_heart_beat, None):
logger.info(f"Remove stale worker: {w_name}")
def list_models(self):
model_names = set()
for w_name, w_info in self.worker_info.items():
model_names.update(w_info.model_names)
return list(model_names)
def get_worker_address(self, model_name: str):
if self.dispatch_method == DispatchMethod.LOTTERY:
worker_names = []
worker_speeds = []
for w_name, w_info in self.worker_info.items():
if model_name in w_info.model_names:
worker_names.append(w_name)
worker_speeds.append(w_info.speed)
worker_speeds = np.array(worker_speeds, dtype=np.float32)
norm = np.sum(worker_speeds)
if norm < 1e-4:
return ""
worker_speeds = worker_speeds / norm
if True: # Directly return address
pt = np.random.choice(np.arange(len(worker_names)),
p=worker_speeds)
worker_name = worker_names[pt]
return worker_name
# Check status before returning
while True:
pt = np.random.choice(np.arange(len(worker_names)),
p=worker_speeds)
worker_name = worker_names[pt]
if self.get_worker_status(worker_name):
break
else:
self.remove_worker(worker_name)
worker_speeds[pt] = 0
norm = np.sum(worker_speeds)
if norm < 1e-4:
return ""
worker_speeds = worker_speeds / norm
continue
return worker_name
elif self.dispatch_method == DispatchMethod.SHORTEST_QUEUE:
worker_names = []
worker_qlen = []
for w_name, w_info in self.worker_info.items():
if model_name in w_info.model_names:
worker_names.append(w_name)
worker_qlen.append(w_info.queue_length / w_info.speed)
if len(worker_names) == 0:
return ""
min_index = np.argmin(worker_qlen)
w_name = worker_names[min_index]
self.worker_info[w_name].queue_length += 1
logger.info(f"names: {worker_names}, queue_lens: {worker_qlen}, ret: {w_name}")
return w_name
else:
raise ValueError(f"Invalid dispatch method: {self.dispatch_method}")
def receive_heart_beat(self, worker_name: str, queue_length: int):
if worker_name not in self.worker_info:
logger.info(f"Receive unknown heart beat. {worker_name}")
return False
self.worker_info[worker_name].queue_length = queue_length
self.worker_info[worker_name].last_heart_beat = time.time()
logger.info(f"Receive heart beat. {worker_name}")
return True
def remove_stable_workers_by_expiration(self):
expire = time.time() - CONTROLLER_HEART_BEAT_EXPIRATION
to_delete = []
for worker_name, w_info in self.worker_info.items():
if w_info.check_heart_beat and w_info.last_heart_beat < expire:
to_delete.append(worker_name)
for worker_name in to_delete:
self.remove_worker(worker_name)
def worker_api_generate_stream(self, params):
worker_addr = self.get_worker_address(params["model"])
if not worker_addr:
logger.info(f"no worker: {params['model']}")
ret = {
"text": server_error_msg,
"error_code": 2,
}
yield json.dumps(ret).encode() + b"\0"
try:
response = requests.post(worker_addr + "/worker_generate_stream",
json=params, stream=True, timeout=5)
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
if chunk:
yield chunk + b"\0"
except requests.exceptions.RequestException as e:
logger.info(f"worker timeout: {worker_addr}")
ret = {
"text": server_error_msg,
"error_code": 3,
}
yield json.dumps(ret).encode() + b"\0"
# Let the controller act as a worker to achieve hierarchical
# management. This can be used to connect isolated sub networks.
def worker_api_get_status(self):
model_names = set()
speed = 0
queue_length = 0
for w_name in self.worker_info:
worker_status = self.get_worker_status(w_name)
if worker_status is not None:
model_names.update(worker_status["model_names"])
speed += worker_status["speed"]
queue_length += worker_status["queue_length"]
return {
"model_names": list(model_names),
"speed": speed,
"queue_length": queue_length,
}
app = FastAPI()
@app.post("/register_worker")
async def register_worker(request: Request):
data = await request.json()
controller.register_worker(
data["worker_name"], data["check_heart_beat"],
data.get("worker_status", None))
@app.post("/refresh_all_workers")
async def refresh_all_workers():
models = controller.refresh_all_workers()
@app.post("/list_models")
async def list_models():
models = controller.list_models()
return {"models": models}
@app.post("/get_worker_address")
async def get_worker_address(request: Request):
data = await request.json()
addr = controller.get_worker_address(data["model"])
return {"address": addr}
@app.post("/receive_heart_beat")
async def receive_heart_beat(request: Request):
data = await request.json()
exist = controller.receive_heart_beat(
data["worker_name"], data["queue_length"])
return {"exist": exist}
@app.post("/worker_generate_stream")
async def worker_api_generate_stream(request: Request):
params = await request.json()
generator = controller.worker_api_generate_stream(params)
return StreamingResponse(generator)
@app.post("/worker_get_status")
async def worker_api_get_status(request: Request):
return controller.worker_api_get_status()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--port", type=int, default=21001)
parser.add_argument("--dispatch-method", type=str, choices=[
"lottery", "shortest_queue"], default="shortest_queue")
args = parser.parse_args()
logger.info(f"args: {args}")
controller = Controller(args.dispatch_method)
uvicorn.run(app, host=args.host, port=args.port, log_level="info")

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import argparse
import datetime
import json
import os
import time
import gradio as gr
import requests
from llava.conversation import (default_conversation, conv_templates,
SeparatorStyle)
from llava.constants import LOGDIR
from llava.utils import (build_logger, server_error_msg,
violates_moderation, moderation_msg)
import hashlib
logger = build_logger("gradio_web_server", "gradio_web_server.log")
headers = {"User-Agent": "LLaVA Client"}
no_change_btn = gr.Button()
enable_btn = gr.Button(interactive=True)
disable_btn = gr.Button(interactive=False)
priority = {
"vicuna-13b": "aaaaaaa",
"koala-13b": "aaaaaab",
}
def get_conv_log_filename():
t = datetime.datetime.now()
name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
return name
def get_model_list():
ret = requests.post(args.controller_url + "/refresh_all_workers")
assert ret.status_code == 200
ret = requests.post(args.controller_url + "/list_models")
models = ret.json()["models"]
models.sort(key=lambda x: priority.get(x, x))
logger.info(f"Models: {models}")
return models
get_window_url_params = """
function() {
const params = new URLSearchParams(window.location.search);
url_params = Object.fromEntries(params);
console.log(url_params);
return url_params;
}
"""
def load_demo(url_params, request: gr.Request):
logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")
dropdown_update = gr.Dropdown(visible=True)
if "model" in url_params:
model = url_params["model"]
if model in models:
dropdown_update = gr.Dropdown(value=model, visible=True)
state = default_conversation.copy()
return state, dropdown_update
def load_demo_refresh_model_list(request: gr.Request):
logger.info(f"load_demo. ip: {request.client.host}")
models = get_model_list()
state = default_conversation.copy()
dropdown_update = gr.Dropdown(
choices=models,
value=models[0] if len(models) > 0 else ""
)
return state, dropdown_update
def vote_last_response(state, vote_type, model_selector, request: gr.Request):
with open(get_conv_log_filename(), "a") as fout:
data = {
"tstamp": round(time.time(), 4),
"type": vote_type,
"model": model_selector,
"state": state.dict(),
"ip": request.client.host,
}
fout.write(json.dumps(data) + "\n")
def upvote_last_response(state, model_selector, request: gr.Request):
logger.info(f"upvote. ip: {request.client.host}")
vote_last_response(state, "upvote", model_selector, request)
return ("",) + (disable_btn,) * 3
def downvote_last_response(state, model_selector, request: gr.Request):
logger.info(f"downvote. ip: {request.client.host}")
vote_last_response(state, "downvote", model_selector, request)
return ("",) + (disable_btn,) * 3
def flag_last_response(state, model_selector, request: gr.Request):
logger.info(f"flag. ip: {request.client.host}")
vote_last_response(state, "flag", model_selector, request)
return ("",) + (disable_btn,) * 3
def regenerate(state, image_process_mode, request: gr.Request):
logger.info(f"regenerate. ip: {request.client.host}")
state.messages[-1][-1] = None
prev_human_msg = state.messages[-2]
if type(prev_human_msg[1]) in (tuple, list):
prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode)
state.skip_next = False
return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
def clear_history(request: gr.Request):
logger.info(f"clear_history. ip: {request.client.host}")
state = default_conversation.copy()
return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
def add_text(state, text, image, image_process_mode, request: gr.Request):
logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")
if len(text) <= 0 and image is None:
state.skip_next = True
return (state, state.to_gradio_chatbot(), "", None) + (no_change_btn,) * 5
if args.moderate:
flagged = violates_moderation(text)
if flagged:
state.skip_next = True
return (state, state.to_gradio_chatbot(), moderation_msg, None) + (
no_change_btn,) * 5
text = text[:1536] # Hard cut-off
if image is not None:
text = text[:1200] # Hard cut-off for images
if '<image>' not in text:
# text = '<Image><image></Image>' + text
text = text + '\n<image>'
text = (text, image, image_process_mode)
state = default_conversation.copy()
state.append_message(state.roles[0], text)
state.append_message(state.roles[1], None)
state.skip_next = False
return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
def http_bot(state, model_selector, temperature, top_p, max_new_tokens, request: gr.Request):
logger.info(f"http_bot. ip: {request.client.host}")
start_tstamp = time.time()
model_name = model_selector
if state.skip_next:
# This generate call is skipped due to invalid inputs
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
return
if len(state.messages) == state.offset + 2:
# First round of conversation
if "llava" in model_name.lower():
if 'llama-2' in model_name.lower():
template_name = "llava_llama_2"
elif "mistral" in model_name.lower() or "mixtral" in model_name.lower():
if 'orca' in model_name.lower():
template_name = "mistral_orca"
elif 'hermes' in model_name.lower():
template_name = "chatml_direct"
else:
template_name = "mistral_instruct"
elif 'llava-v1.6-34b' in model_name.lower():
template_name = "chatml_direct"
elif "v1" in model_name.lower():
if 'mmtag' in model_name.lower():
template_name = "v1_mmtag"
elif 'plain' in model_name.lower() and 'finetune' not in model_name.lower():
template_name = "v1_mmtag"
else:
template_name = "llava_v1"
elif "mpt" in model_name.lower():
template_name = "mpt"
else:
if 'mmtag' in model_name.lower():
template_name = "v0_mmtag"
elif 'plain' in model_name.lower() and 'finetune' not in model_name.lower():
template_name = "v0_mmtag"
else:
template_name = "llava_v0"
elif "mpt" in model_name:
template_name = "mpt_text"
elif "llama-2" in model_name:
template_name = "llama_2"
else:
template_name = "vicuna_v1"
new_state = conv_templates[template_name].copy()
new_state.append_message(new_state.roles[0], state.messages[-2][1])
new_state.append_message(new_state.roles[1], None)
state = new_state
# Query worker address
controller_url = args.controller_url
ret = requests.post(controller_url + "/get_worker_address",
json={"model": model_name})
worker_addr = ret.json()["address"]
logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}")
# No available worker
if worker_addr == "":
state.messages[-1][-1] = server_error_msg
yield (state, state.to_gradio_chatbot(), disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
return
# Construct prompt
prompt = state.get_prompt()
all_images = state.get_images(return_pil=True)
all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images]
for image, hash in zip(all_images, all_image_hash):
t = datetime.datetime.now()
filename = os.path.join(LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{hash}.jpg")
if not os.path.isfile(filename):
os.makedirs(os.path.dirname(filename), exist_ok=True)
image.save(filename)
# Make requests
pload = {
"model": model_name,
"prompt": prompt,
"temperature": float(temperature),
"top_p": float(top_p),
"max_new_tokens": min(int(max_new_tokens), 1536),
"stop": state.sep if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT] else state.sep2,
"images": f'List of {len(state.get_images())} images: {all_image_hash}',
}
logger.info(f"==== request ====\n{pload}")
pload['images'] = state.get_images()
state.messages[-1][-1] = "▌"
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
try:
# Stream output
response = requests.post(worker_addr + "/worker_generate_stream",
headers=headers, json=pload, stream=True, timeout=10)
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
if chunk:
data = json.loads(chunk.decode())
if data["error_code"] == 0:
output = data["text"][len(prompt):].strip()
state.messages[-1][-1] = output + "▌"
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
else:
output = data["text"] + f" (error_code: {data['error_code']})"
state.messages[-1][-1] = output
yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
return
time.sleep(0.03)
except requests.exceptions.RequestException as e:
state.messages[-1][-1] = server_error_msg
yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
return
state.messages[-1][-1] = state.messages[-1][-1][:-1]
yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
finish_tstamp = time.time()
logger.info(f"{output}")
with open(get_conv_log_filename(), "a") as fout:
data = {
"tstamp": round(finish_tstamp, 4),
"type": "chat",
"model": model_name,
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"state": state.dict(),
"images": all_image_hash,
"ip": request.client.host,
}
fout.write(json.dumps(data) + "\n")
title_markdown = ("""
# 🌋 LLaVA: Large Language and Vision Assistant
[[Project Page](https://llava-vl.github.io)] [[Code](https://github.com/haotian-liu/LLaVA)] [[Model](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)] | 📚 [[LLaVA](https://arxiv.org/abs/2304.08485)] [[LLaVA-v1.5](https://arxiv.org/abs/2310.03744)] [[LLaVA-v1.6](https://llava-vl.github.io/blog/2024-01-30-llava-1-6/)]
""")
tos_markdown = ("""
### Terms of use
By using this service, users are required to agree to the following terms:
The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
""")
learn_more_markdown = ("""
### License
The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
""")
block_css = """
#buttons button {
min-width: min(120px,100%);
}
"""
def build_demo(embed_mode, cur_dir=None, concurrency_count=10):
textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False)
with gr.Blocks(title="LLaVA", theme=gr.themes.Default(), css=block_css) as demo:
state = gr.State()
if not embed_mode:
gr.Markdown(title_markdown)
with gr.Row():
with gr.Column(scale=3):
with gr.Row(elem_id="model_selector_row"):
model_selector = gr.Dropdown(
choices=models,
value=models[0] if len(models) > 0 else "",
interactive=True,
show_label=False,
container=False)
imagebox = gr.Image(type="pil")
image_process_mode = gr.Radio(
["Crop", "Resize", "Pad", "Default"],
value="Default",
label="Preprocess for non-square image", visible=False)
if cur_dir is None:
cur_dir = os.path.dirname(os.path.abspath(__file__))
gr.Examples(examples=[
[f"{cur_dir}/examples/extreme_ironing.jpg", "What is unusual about this image?"],
[f"{cur_dir}/examples/waterview.jpg", "What are the things I should be cautious about when I visit here?"],
], inputs=[imagebox, textbox])
with gr.Accordion("Parameters", open=False) as parameter_row:
temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, label="Temperature",)
top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P",)
max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, label="Max output tokens",)
with gr.Column(scale=8):
chatbot = gr.Chatbot(
elem_id="chatbot",
label="LLaVA Chatbot",
height=650,
layout="panel",
)
with gr.Row():
with gr.Column(scale=8):
textbox.render()
with gr.Column(scale=1, min_width=50):
submit_btn = gr.Button(value="Send", variant="primary")
with gr.Row(elem_id="buttons") as button_row:
upvote_btn = gr.Button(value="👍 Upvote", interactive=False)
downvote_btn = gr.Button(value="👎 Downvote", interactive=False)
flag_btn = gr.Button(value="⚠️ Flag", interactive=False)
#stop_btn = gr.Button(value="⏹️ Stop Generation", interactive=False)
regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False)
clear_btn = gr.Button(value="🗑️ Clear", interactive=False)
if not embed_mode:
gr.Markdown(tos_markdown)
gr.Markdown(learn_more_markdown)
url_params = gr.JSON(visible=False)
# Register listeners
btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn]
upvote_btn.click(
upvote_last_response,
[state, model_selector],
[textbox, upvote_btn, downvote_btn, flag_btn]
)
downvote_btn.click(
downvote_last_response,
[state, model_selector],
[textbox, upvote_btn, downvote_btn, flag_btn]
)
flag_btn.click(
flag_last_response,
[state, model_selector],
[textbox, upvote_btn, downvote_btn, flag_btn]
)
regenerate_btn.click(
regenerate,
[state, image_process_mode],
[state, chatbot, textbox, imagebox] + btn_list
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens],
[state, chatbot] + btn_list,
concurrency_limit=concurrency_count
)
clear_btn.click(
clear_history,
None,
[state, chatbot, textbox, imagebox] + btn_list,
queue=False
)
textbox.submit(
add_text,
[state, textbox, imagebox, image_process_mode],
[state, chatbot, textbox, imagebox] + btn_list,
queue=False
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens],
[state, chatbot] + btn_list,
concurrency_limit=concurrency_count
)
submit_btn.click(
add_text,
[state, textbox, imagebox, image_process_mode],
[state, chatbot, textbox, imagebox] + btn_list
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens],
[state, chatbot] + btn_list,
concurrency_limit=concurrency_count
)
if args.model_list_mode == "once":
demo.load(
load_demo,
[url_params],
[state, model_selector],
js=get_window_url_params
)
elif args.model_list_mode == "reload":
demo.load(
load_demo_refresh_model_list,
None,
[state, model_selector],
queue=False
)
else:
raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
return demo
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="0.0.0.0")
parser.add_argument("--port", type=int)
parser.add_argument("--controller-url", type=str, default="http://localhost:21001")
parser.add_argument("--concurrency-count", type=int, default=16)
parser.add_argument("--model-list-mode", type=str, default="once",
choices=["once", "reload"])
parser.add_argument("--share", action="store_true")
parser.add_argument("--moderate", action="store_true")
parser.add_argument("--embed", action="store_true")
args = parser.parse_args()
logger.info(f"args: {args}")
models = get_model_list()
logger.info(args)
demo = build_demo(args.embed, concurrency_count=args.concurrency_count)
demo.queue(
api_open=False
).launch(
server_name=args.host,
server_port=args.port,
share=args.share
)

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llava/serve/model_worker.py Normal file
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"""
A model worker executes the model.
"""
import argparse
import asyncio
import json
import time
import threading
import uuid
from fastapi import FastAPI, Request, BackgroundTasks
from fastapi.responses import StreamingResponse
import requests
import torch
import uvicorn
from functools import partial
from llava.constants import WORKER_HEART_BEAT_INTERVAL
from llava.utils import (build_logger, server_error_msg,
pretty_print_semaphore)
from llava.model.builder import load_pretrained_model
from llava.mm_utils import process_images, load_image_from_base64, tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from transformers import TextIteratorStreamer
from threading import Thread
GB = 1 << 30
worker_id = str(uuid.uuid4())[:6]
logger = build_logger("model_worker", f"model_worker_{worker_id}.log")
global_counter = 0
model_semaphore = None
def heart_beat_worker(controller):
while True:
time.sleep(WORKER_HEART_BEAT_INTERVAL)
controller.send_heart_beat()
class ModelWorker:
def __init__(self, controller_addr, worker_addr,
worker_id, no_register,
model_path, model_base, model_name,
load_8bit, load_4bit, device, use_flash_attn=False):
self.controller_addr = controller_addr
self.worker_addr = worker_addr
self.worker_id = worker_id
if model_path.endswith("/"):
model_path = model_path[:-1]
if model_name is None:
model_paths = model_path.split("/")
if model_paths[-1].startswith('checkpoint-'):
self.model_name = model_paths[-2] + "_" + model_paths[-1]
else:
self.model_name = model_paths[-1]
else:
self.model_name = model_name
self.device = device
logger.info(f"Loading the model {self.model_name} on worker {worker_id} ...")
self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model(
model_path, model_base, self.model_name, load_8bit, load_4bit, device=self.device, use_flash_attn=use_flash_attn)
self.is_multimodal = 'llava' in self.model_name.lower()
if not no_register:
self.register_to_controller()
self.heart_beat_thread = threading.Thread(
target=heart_beat_worker, args=(self,), daemon=True)
self.heart_beat_thread.start()
def register_to_controller(self):
logger.info("Register to controller")
url = self.controller_addr + "/register_worker"
data = {
"worker_name": self.worker_addr,
"check_heart_beat": True,
"worker_status": self.get_status()
}
r = requests.post(url, json=data)
assert r.status_code == 200
def send_heart_beat(self):
logger.info(f"Send heart beat. Models: {[self.model_name]}. "
f"Semaphore: {pretty_print_semaphore(model_semaphore)}. "
f"global_counter: {global_counter}")
url = self.controller_addr + "/receive_heart_beat"
while True:
try:
ret = requests.post(url, json={
"worker_name": self.worker_addr,
"queue_length": self.get_queue_length()}, timeout=5)
exist = ret.json()["exist"]
break
except requests.exceptions.RequestException as e:
logger.error(f"heart beat error: {e}")
time.sleep(5)
if not exist:
self.register_to_controller()
def get_queue_length(self):
if model_semaphore is None:
return 0
else:
return args.limit_model_concurrency - model_semaphore._value + (len(
model_semaphore._waiters) if model_semaphore._waiters is not None else 0)
def get_status(self):
return {
"model_names": [self.model_name],
"speed": 1,
"queue_length": self.get_queue_length(),
}
@torch.inference_mode()
def generate_stream(self, params):
tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor
prompt = params["prompt"]
ori_prompt = prompt
images = params.get("images", None)
num_image_tokens = 0
if images is not None and len(images) > 0 and self.is_multimodal:
if len(images) > 0:
if len(images) != prompt.count(DEFAULT_IMAGE_TOKEN):
raise ValueError("Number of images does not match number of <image> tokens in prompt")
images = [load_image_from_base64(image) for image in images]
image_sizes = [image.size for image in images]
images = process_images(images, image_processor, model.config)
if type(images) is list:
images = [image.to(self.model.device, dtype=torch.float16) for image in images]
else:
images = images.to(self.model.device, dtype=torch.float16)
replace_token = DEFAULT_IMAGE_TOKEN
if getattr(self.model.config, 'mm_use_im_start_end', False):
replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token)
num_image_tokens = prompt.count(replace_token) * model.get_vision_tower().num_patches
else:
images = None
image_sizes = None
image_args = {"images": images, "image_sizes": image_sizes}
else:
images = None
image_args = {}
temperature = float(params.get("temperature", 1.0))
top_p = float(params.get("top_p", 1.0))
max_context_length = getattr(model.config, 'max_position_embeddings', 2048)
max_new_tokens = min(int(params.get("max_new_tokens", 256)), 1024)
stop_str = params.get("stop", None)
do_sample = True if temperature > 0.001 else False
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
keywords = [stop_str]
# stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15)
max_new_tokens = min(max_new_tokens, max_context_length - input_ids.shape[-1] - num_image_tokens)
if max_new_tokens < 1:
yield json.dumps({"text": ori_prompt + "Exceeds max token length. Please start a new conversation, thanks.", "error_code": 0}).encode() + b"\0"
return
thread = Thread(target=model.generate, kwargs=dict(
inputs=input_ids,
do_sample=do_sample,
temperature=temperature,
top_p=top_p,
max_new_tokens=max_new_tokens,
streamer=streamer,
use_cache=True,
**image_args
))
thread.start()
generated_text = ori_prompt
for new_text in streamer:
generated_text += new_text
if generated_text.endswith(stop_str):
generated_text = generated_text[:-len(stop_str)]
yield json.dumps({"text": generated_text, "error_code": 0}).encode() + b"\0"
def generate_stream_gate(self, params):
try:
for x in self.generate_stream(params):
yield x
except ValueError as e:
print("Caught ValueError:", e)
ret = {
"text": server_error_msg,
"error_code": 1,
}
yield json.dumps(ret).encode() + b"\0"
except torch.cuda.CudaError as e:
print("Caught torch.cuda.CudaError:", e)
ret = {
"text": server_error_msg,
"error_code": 1,
}
yield json.dumps(ret).encode() + b"\0"
except Exception as e:
print("Caught Unknown Error", e)
ret = {
"text": server_error_msg,
"error_code": 1,
}
yield json.dumps(ret).encode() + b"\0"
app = FastAPI()
def release_model_semaphore(fn=None):
model_semaphore.release()
if fn is not None:
fn()
@app.post("/worker_generate_stream")
async def generate_stream(request: Request):
global model_semaphore, global_counter
global_counter += 1
params = await request.json()
if model_semaphore is None:
model_semaphore = asyncio.Semaphore(args.limit_model_concurrency)
await model_semaphore.acquire()
worker.send_heart_beat()
generator = worker.generate_stream_gate(params)
background_tasks = BackgroundTasks()
background_tasks.add_task(partial(release_model_semaphore, fn=worker.send_heart_beat))
return StreamingResponse(generator, background=background_tasks)
@app.post("/worker_get_status")
async def get_status(request: Request):
return worker.get_status()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--port", type=int, default=21002)
parser.add_argument("--worker-address", type=str,
default="http://localhost:21002")
parser.add_argument("--controller-address", type=str,
default="http://localhost:21001")
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--model-name", type=str)
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--multi-modal", action="store_true", help="Multimodal mode is automatically detected with model name, please make sure `llava` is included in the model path.")
parser.add_argument("--limit-model-concurrency", type=int, default=5)
parser.add_argument("--stream-interval", type=int, default=1)
parser.add_argument("--no-register", action="store_true")
parser.add_argument("--load-8bit", action="store_true")
parser.add_argument("--load-4bit", action="store_true")
parser.add_argument("--use-flash-attn", action="store_true")
args = parser.parse_args()
logger.info(f"args: {args}")
if args.multi_modal:
logger.warning("Multimodal mode is automatically detected with model name, please make sure `llava` is included in the model path.")
worker = ModelWorker(args.controller_address,
args.worker_address,
worker_id,
args.no_register,
args.model_path,
args.model_base,
args.model_name,
args.load_8bit,
args.load_4bit,
args.device,
use_flash_attn=args.use_flash_attn)
uvicorn.run(app, host=args.host, port=args.port, log_level="info")

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"""
Manually register workers.
Usage:
python3 -m fastchat.serve.register_worker --controller http://localhost:21001 --worker-name http://localhost:21002
"""
import argparse
import requests
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--controller-address", type=str)
parser.add_argument("--worker-name", type=str)
parser.add_argument("--check-heart-beat", action="store_true")
args = parser.parse_args()
url = args.controller_address + "/register_worker"
data = {
"worker_name": args.worker_name,
"check_heart_beat": args.check_heart_beat,
"worker_status": None,
}
r = requests.post(url, json=data)
assert r.status_code == 200

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"""
A model worker executes the model.
"""
import argparse
import asyncio
from concurrent.futures import ThreadPoolExecutor
import json
import time
import threading
import uuid
from fastapi import FastAPI, Request, BackgroundTasks
from fastapi.responses import StreamingResponse
import requests
import re
import uvicorn
from functools import partial
from llava.constants import WORKER_HEART_BEAT_INTERVAL
from llava.utils import (build_logger, server_error_msg,
pretty_print_semaphore)
from llava.mm_utils import process_images, load_image_from_base64, tokenizer_image_token, expand2square
from llava.constants import DEFAULT_IMAGE_TOKEN
import sglang as sgl
from sglang.backend.runtime_endpoint import RuntimeEndpoint
GB = 1 << 30
worker_id = str(uuid.uuid4())[:6]
logger = build_logger("model_worker", f"model_worker_{worker_id}.log")
global_counter = 0
model_semaphore = None
def heart_beat_worker(controller):
while True:
time.sleep(WORKER_HEART_BEAT_INTERVAL)
controller.send_heart_beat()
@sgl.function
def pipeline(s, prompt, max_tokens):
for p in prompt:
if type(p) is str:
s += p
else:
s += sgl.image(p)
s += sgl.gen("response", max_tokens=max_tokens)
class ModelWorker:
def __init__(self, controller_addr, worker_addr, sgl_endpoint,
worker_id, no_register, model_name):
self.controller_addr = controller_addr
self.worker_addr = worker_addr
self.worker_id = worker_id
# Select backend
backend = RuntimeEndpoint(sgl_endpoint)
sgl.set_default_backend(backend)
model_path = backend.model_info["model_path"]
if model_path.endswith("/"):
model_path = model_path[:-1]
if model_name is None:
model_paths = model_path.split("/")
if model_paths[-1].startswith('checkpoint-'):
self.model_name = model_paths[-2] + "_" + model_paths[-1]
else:
self.model_name = model_paths[-1]
else:
self.model_name = model_name
logger.info(f"Loading the SGLANG model {self.model_name} on worker {worker_id} ...")
if not no_register:
self.register_to_controller()
self.heart_beat_thread = threading.Thread(
target=heart_beat_worker, args=(self,), daemon=True)
self.heart_beat_thread.start()
def register_to_controller(self):
logger.info("Register to controller")
url = self.controller_addr + "/register_worker"
data = {
"worker_name": self.worker_addr,
"check_heart_beat": True,
"worker_status": self.get_status()
}
r = requests.post(url, json=data)
assert r.status_code == 200
def send_heart_beat(self):
logger.info(f"Send heart beat. Models: {[self.model_name]}. "
f"Semaphore: {pretty_print_semaphore(model_semaphore)}. "
f"global_counter: {global_counter}")
url = self.controller_addr + "/receive_heart_beat"
while True:
try:
ret = requests.post(url, json={
"worker_name": self.worker_addr,
"queue_length": self.get_queue_length()}, timeout=5)
exist = ret.json()["exist"]
break
except requests.exceptions.RequestException as e:
logger.error(f"heart beat error: {e}")
time.sleep(5)
if not exist:
self.register_to_controller()
def get_queue_length(self):
if model_semaphore is None:
return 0
else:
return args.limit_model_concurrency - model_semaphore._value + (len(
model_semaphore._waiters) if model_semaphore._waiters is not None else 0)
def get_status(self):
return {
"model_names": [self.model_name],
"speed": 1,
"queue_length": self.get_queue_length(),
}
async def generate_stream(self, params):
ori_prompt = prompt = params["prompt"]
images = params.get("images", None)
if images is not None and len(images) > 0:
if len(images) > 0:
if len(images) != prompt.count(DEFAULT_IMAGE_TOKEN):
raise ValueError("Number of images does not match number of <image> tokens in prompt")
images = [load_image_from_base64(image) for image in images]
# FIXME: for image-start/end token
# replace_token = DEFAULT_IMAGE_TOKEN
# if getattr(self.model.config, 'mm_use_im_start_end', False):
# replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
# prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token)
prompt = prompt.replace(' ' + DEFAULT_IMAGE_TOKEN + '\n', DEFAULT_IMAGE_TOKEN)
prompt_split = prompt.split(DEFAULT_IMAGE_TOKEN)
prompt = []
for i in range(len(prompt_split)):
prompt.append(prompt_split[i])
if i < len(images):
prompt.append(images[i])
else:
prompt = [prompt]
temperature = float(params.get("temperature", 1.0))
top_p = float(params.get("top_p", 1.0))
# max_context_length = getattr(model.config, 'max_position_embeddings', 2048)
max_new_tokens = min(int(params.get("max_new_tokens", 256)), 1024)
stop_str = params.get("stop", None)
stop_str = [stop_str] if stop_str is not None else None
print({'prompt': prompt, 'max_new_tokens': max_new_tokens, 'temperature': temperature, 'top_p': top_p})
state = pipeline.run(prompt, max_new_tokens, temperature=temperature, top_p=top_p, stream=True)
generated_text = ori_prompt
async for text_outputs in state.text_async_iter(var_name="response"):
generated_text += text_outputs
yield json.dumps({"text": generated_text, "error_code": 0}).encode() + b"\0"
async def generate_stream_gate(self, params):
try:
async for x in self.generate_stream(params):
yield x
except ValueError as e:
print("Caught ValueError:", e)
ret = {
"text": server_error_msg,
"error_code": 1,
}
yield json.dumps(ret).encode() + b"\0"
except Exception as e:
print("Caught Unknown Error", e)
ret = {
"text": server_error_msg,
"error_code": 1,
}
yield json.dumps(ret).encode() + b"\0"
app = FastAPI()
def release_model_semaphore(fn=None):
model_semaphore.release()
if fn is not None:
fn()
@app.post("/worker_generate_stream")
async def generate_stream(request: Request):
global model_semaphore, global_counter
global_counter += 1
params = await request.json()
if model_semaphore is None:
model_semaphore = asyncio.Semaphore(args.limit_model_concurrency)
await model_semaphore.acquire()
worker.send_heart_beat()
generator = worker.generate_stream_gate(params)
background_tasks = BackgroundTasks()
background_tasks.add_task(partial(release_model_semaphore, fn=worker.send_heart_beat))
return StreamingResponse(generator, background=background_tasks)
@app.post("/worker_get_status")
async def get_status(request: Request):
return worker.get_status()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--port", type=int, default=21002)
parser.add_argument("--worker-address", type=str,
default="http://localhost:21002")
parser.add_argument("--controller-address", type=str,
default="http://localhost:21001")
parser.add_argument("--model-name", type=str)
parser.add_argument("--sgl-endpoint", type=str)
parser.add_argument("--limit-model-concurrency", type=int, default=5)
parser.add_argument("--stream-interval", type=int, default=1)
parser.add_argument("--no-register", action="store_true")
args = parser.parse_args()
logger.info(f"args: {args}")
worker = ModelWorker(args.controller_address,
args.worker_address,
args.sgl_endpoint,
worker_id,
args.no_register,
args.model_name)
uvicorn.run(app, host=args.host, port=args.port, log_level="info")

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import argparse
import json
import requests
from llava.conversation import default_conversation
def main():
if args.worker_address:
worker_addr = args.worker_address
else:
controller_addr = args.controller_address
ret = requests.post(controller_addr + "/refresh_all_workers")
ret = requests.post(controller_addr + "/list_models")
models = ret.json()["models"]
models.sort()
print(f"Models: {models}")
ret = requests.post(controller_addr + "/get_worker_address",
json={"model": args.model_name})
worker_addr = ret.json()["address"]
print(f"worker_addr: {worker_addr}")
if worker_addr == "":
return
conv = default_conversation.copy()
conv.append_message(conv.roles[0], args.message)
prompt = conv.get_prompt()
headers = {"User-Agent": "LLaVA Client"}
pload = {
"model": args.model_name,
"prompt": prompt,
"max_new_tokens": args.max_new_tokens,
"temperature": 0.7,
"stop": conv.sep,
}
response = requests.post(worker_addr + "/worker_generate_stream", headers=headers,
json=pload, stream=True)
print(prompt.replace(conv.sep, "\n"), end="")
for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b"\0"):
if chunk:
data = json.loads(chunk.decode("utf-8"))
output = data["text"].split(conv.sep)[-1]
print(output, end="\r")
print("")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--controller-address", type=str, default="http://localhost:21001")
parser.add_argument("--worker-address", type=str)
parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
parser.add_argument("--max-new-tokens", type=int, default=32)
parser.add_argument("--message", type=str, default=
"Tell me a story with more than 1000 words.")
args = parser.parse_args()
main()

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llava/train/llama_flash_attn_monkey_patch.py Normal file
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from typing import Optional, Tuple
import warnings
import torch
import transformers
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
try:
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
except ImportError:
from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
from flash_attn.bert_padding import unpad_input, pad_input
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if output_attentions:
warnings.warn(
"Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
)
bsz, q_len, _ = hidden_states.size()
query_states = (
self.q_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
key_states = (
self.k_proj(hidden_states)
.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
.transpose(1, 2)
)
value_states = (
self.v_proj(hidden_states)
.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
.transpose(1, 2)
) # shape: (b, num_heads, s, head_dim)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
if past_key_value is not None:
# reuse k, v
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
# Transform the data into the format required by flash attention
qkv = torch.stack([query_states, key_states, value_states], dim=2)
qkv = qkv.transpose(1, 3) # shape: [b, s, 3, num_heads, head_dim]
key_padding_mask = attention_mask
if key_padding_mask is None:
qkv = qkv.reshape(-1, 3, self.num_heads, self.head_dim)
cu_q_lens = torch.arange(
0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device
)
max_s = q_len
output = flash_attn_unpadded_qkvpacked_func(
qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
)
output = output.view(bsz, q_len, -1)
else:
qkv = qkv.reshape(bsz, q_len, -1)
qkv, indices, cu_q_lens, max_s = unpad_input(qkv, key_padding_mask)
qkv = qkv.view(-1, 3, self.num_heads, self.head_dim)
output_unpad = flash_attn_unpadded_qkvpacked_func(
qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
)
output_unpad = output_unpad.reshape(-1, self.num_heads * self.head_dim)
output = pad_input(output_unpad, indices, bsz, q_len)
return self.o_proj(output), None, past_key_value
# Disable the transformation of the attention mask in LlamaModel as the flash attention
# requires the attention mask to be the same as the key_padding_mask
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# [bsz, seq_len]
return attention_mask
def replace_llama_attn_with_flash_attn():
cuda_major, cuda_minor = torch.cuda.get_device_capability()
if cuda_major < 8:
warnings.warn(
"Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward."
"ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593"
)
transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (
_prepare_decoder_attention_mask
)
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward

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llava/train/llama_xformers_attn_monkey_patch.py Normal file
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"""
Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments
"""
import logging
import math
from typing import Optional, Tuple
import torch
import transformers.models.llama.modeling_llama
from torch import nn
try:
import xformers.ops
except ImportError:
logging.error("xformers not found! Please install it before trying to use it.")
def replace_llama_attn_with_xformers_attn():
transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
def xformers_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# pylint: disable=duplicate-code
bsz, q_len, _ = hidden_states.size()
query_states = (
self.q_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
key_states = (
self.k_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
value_states = (
self.v_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
(
query_states,
key_states,
) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
# We only apply xformers optimizations if we don't need to output the whole attention matrix
if not output_attentions:
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
# This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
# We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
# input and output should be of form (bsz, q_len, num_heads, head_dim)
attn_output = xformers.ops.memory_efficient_attention(
query_states, key_states, value_states, attn_bias=None
)
else:
# input and output should be of form (bsz, q_len, num_heads, head_dim)
attn_output = xformers.ops.memory_efficient_attention(
query_states,
key_states,
value_states,
attn_bias=xformers.ops.LowerTriangularMask(),
)
attn_weights = None
else:
attn_weights = torch.matmul(
query_states, key_states.transpose(2, 3)
) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
attn_weights = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
)
# upcast attention to fp32
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights, past_key_value

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import os
import torch
import torch.nn as nn
from torch.utils.data import Sampler
from transformers import Trainer
from transformers.trainer import (
is_sagemaker_mp_enabled,
get_parameter_names,
has_length,
ALL_LAYERNORM_LAYERS,
logger,
)
from typing import List, Optional
def maybe_zero_3(param, ignore_status=False, name=None):
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
if hasattr(param, "ds_id"):
if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
if not ignore_status:
print(name, 'no ignore status')
with zero.GatheredParameters([param]):
param = param.data.detach().cpu().clone()
else:
param = param.detach().cpu().clone()
return param
def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()}
return to_return
def split_to_even_chunks(indices, lengths, num_chunks):
"""
Split a list of indices into `chunks` chunks of roughly equal lengths.
"""
if len(indices) % num_chunks != 0:
return [indices[i::num_chunks] for i in range(num_chunks)]
num_indices_per_chunk = len(indices) // num_chunks
chunks = [[] for _ in range(num_chunks)]
chunks_lengths = [0 for _ in range(num_chunks)]
for index in indices:
shortest_chunk = chunks_lengths.index(min(chunks_lengths))
chunks[shortest_chunk].append(index)
chunks_lengths[shortest_chunk] += lengths[index]
if len(chunks[shortest_chunk]) == num_indices_per_chunk:
chunks_lengths[shortest_chunk] = float("inf")
return chunks
def get_modality_length_grouped_indices(lengths, batch_size, world_size, generator=None):
# We need to use torch for the random part as a distributed sampler will set the random seed for torch.
assert all(l != 0 for l in lengths), "Should not have zero length."
if all(l > 0 for l in lengths) or all(l < 0 for l in lengths):
# all samples are in the same modality
return get_length_grouped_indices(lengths, batch_size, world_size, generator=generator)
mm_indices, mm_lengths = zip(*[(i, l) for i, l in enumerate(lengths) if l > 0])
lang_indices, lang_lengths = zip(*[(i, -l) for i, l in enumerate(lengths) if l < 0])
mm_shuffle = [mm_indices[i] for i in get_length_grouped_indices(mm_lengths, batch_size, world_size, generator=None)]
lang_shuffle = [lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)]
megabatch_size = world_size * batch_size
mm_megabatches = [mm_shuffle[i : i + megabatch_size] for i in range(0, len(mm_shuffle), megabatch_size)]
lang_megabatches = [lang_shuffle[i : i + megabatch_size] for i in range(0, len(lang_shuffle), megabatch_size)]
last_mm = mm_megabatches[-1]
last_lang = lang_megabatches[-1]
additional_batch = last_mm + last_lang
megabatches = mm_megabatches[:-1] + lang_megabatches[:-1]
megabatch_indices = torch.randperm(len(megabatches), generator=generator)
megabatches = [megabatches[i] for i in megabatch_indices]
if len(additional_batch) > 0:
megabatches.append(sorted(additional_batch))
return [i for megabatch in megabatches for i in megabatch]
def get_length_grouped_indices(lengths, batch_size, world_size, generator=None, merge=True):
# We need to use torch for the random part as a distributed sampler will set the random seed for torch.
indices = torch.randperm(len(lengths), generator=generator)
megabatch_size = world_size * batch_size
megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)]
megabatches = [sorted(megabatch, key=lambda i: lengths[i], reverse=True) for megabatch in megabatches]
megabatches = [split_to_even_chunks(megabatch, lengths, world_size) for megabatch in megabatches]
return [i for megabatch in megabatches for batch in megabatch for i in batch]
class LengthGroupedSampler(Sampler):
r"""
Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while
keeping a bit of randomness.
"""
def __init__(
self,
batch_size: int,
world_size: int,
lengths: Optional[List[int]] = None,
generator=None,
group_by_modality: bool = False,
):
if lengths is None:
raise ValueError("Lengths must be provided.")
self.batch_size = batch_size
self.world_size = world_size
self.lengths = lengths
self.generator = generator
self.group_by_modality = group_by_modality
def __len__(self):
return len(self.lengths)
def __iter__(self):
if self.group_by_modality:
indices = get_modality_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
else:
indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
return iter(indices)
class LLaVATrainer(Trainer):
def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
if self.train_dataset is None or not has_length(self.train_dataset):
return None
if self.args.group_by_modality_length:
lengths = self.train_dataset.modality_lengths
return LengthGroupedSampler(
self.args.train_batch_size,
world_size=self.args.world_size * self.args.gradient_accumulation_steps,
lengths=lengths,
group_by_modality=True,
)
else:
return super()._get_train_sampler()
def create_optimizer(self):
"""
Setup the optimizer.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method in a subclass.
"""
if is_sagemaker_mp_enabled():
return super().create_optimizer()
opt_model = self.model
if self.optimizer is None:
decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
decay_parameters = [name for name in decay_parameters if "bias" not in name]
if self.args.mm_projector_lr is not None:
projector_parameters = [name for name, _ in opt_model.named_parameters() if "mm_projector" in name]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and n not in projector_parameters and p.requires_grad)
],
"weight_decay": self.args.weight_decay,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n not in projector_parameters and p.requires_grad)
],
"weight_decay": 0.0,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and n in projector_parameters and p.requires_grad)
],
"weight_decay": self.args.weight_decay,
"lr": self.args.mm_projector_lr,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n in projector_parameters and p.requires_grad)
],
"weight_decay": 0.0,
"lr": self.args.mm_projector_lr,
},
]
else:
optimizer_grouped_parameters = [
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
],
"weight_decay": self.args.weight_decay,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
],
"weight_decay": 0.0,
},
]
optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)
self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
if optimizer_cls.__name__ == "Adam8bit":
import bitsandbytes
manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
skipped = 0
for module in opt_model.modules():
if isinstance(module, nn.Embedding):
skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())
logger.info(f"skipped {module}: {skipped/2**20}M params")
manager.register_module_override(module, "weight", {"optim_bits": 32})
logger.debug(f"bitsandbytes: will optimize {module} in fp32")
logger.info(f"skipped: {skipped/2**20}M params")
return self.optimizer
def _save_checkpoint(self, model, trial, metrics=None):
if getattr(self.args, 'tune_mm_mlp_adapter', False):
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
run_dir = self._get_output_dir(trial=trial)
output_dir = os.path.join(run_dir, checkpoint_folder)
# Only save Adapter
keys_to_match = ['mm_projector', 'vision_resampler']
if getattr(self.args, "use_im_start_end", False):
keys_to_match.extend(['embed_tokens', 'embed_in'])
weight_to_save = get_mm_adapter_state_maybe_zero_3(self.model.named_parameters(), keys_to_match)
if self.args.local_rank == 0 or self.args.local_rank == -1:
self.model.config.save_pretrained(output_dir)
torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
else:
super(LLaVATrainer, self)._save_checkpoint(model, trial, metrics)
def _save(self, output_dir: Optional[str] = None, state_dict=None):
if getattr(self.args, 'tune_mm_mlp_adapter', False):
pass
else:
super(LLaVATrainer, self)._save(output_dir, state_dict)

1262
llava/train/train.py Normal file
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14
llava/train/train_mem.py Normal file
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import sys
import os
sys.path.insert(0, os.path.abspath(".."))
sys.path.insert(0, os.path.abspath("."))
from llava.train.train import train
def is_debug():
return int(os.environ.get('DEBUG', 0))
if __name__ == "__main__":
if is_debug():
train(attn_implementation=None)
else:
train(attn_implementation="flash_attention_2")

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llava/train/train_xformers.py Normal file
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# Make it more memory efficient by monkey patching the LLaMA model with xformers attention.
# Need to call this before importing transformers.
from llava.train.llama_xformers_attn_monkey_patch import (
replace_llama_attn_with_xformers_attn,
)
replace_llama_attn_with_xformers_attn()
from llava.train.train import train
if __name__ == "__main__":
train()

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llava/utils.py Normal file
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import datetime
import logging
import logging.handlers
import os
import sys
import requests
from llava.constants import LOGDIR
server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN."
handler = None
def build_logger(logger_name, logger_filename):
global handler
formatter = logging.Formatter(
fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
# Set the format of root handlers
if not logging.getLogger().handlers:
logging.basicConfig(level=logging.INFO)
logging.getLogger().handlers[0].setFormatter(formatter)
# Redirect stdout and stderr to loggers
stdout_logger = logging.getLogger("stdout")
stdout_logger.setLevel(logging.INFO)
sl = StreamToLogger(stdout_logger, logging.INFO)
sys.stdout = sl
stderr_logger = logging.getLogger("stderr")
stderr_logger.setLevel(logging.ERROR)
sl = StreamToLogger(stderr_logger, logging.ERROR)
sys.stderr = sl
# Get logger
logger = logging.getLogger(logger_name)
logger.setLevel(logging.INFO)
# Add a file handler for all loggers
if handler is None:
os.makedirs(LOGDIR, exist_ok=True)
filename = os.path.join(LOGDIR, logger_filename)
handler = logging.handlers.TimedRotatingFileHandler(
filename, when='D', utc=True, encoding='UTF-8')
handler.setFormatter(formatter)
for name, item in logging.root.manager.loggerDict.items():
if isinstance(item, logging.Logger):
item.addHandler(handler)
return logger
class StreamToLogger(object):
"""
Fake file-like stream object that redirects writes to a logger instance.
"""
def __init__(self, logger, log_level=logging.INFO):
self.terminal = sys.stdout
self.logger = logger
self.log_level = log_level
self.linebuf = ''
def __getattr__(self, attr):
return getattr(self.terminal, attr)
def write(self, buf):
temp_linebuf = self.linebuf + buf
self.linebuf = ''
for line in temp_linebuf.splitlines(True):
# From the io.TextIOWrapper docs:
# On output, if newline is None, any '\n' characters written
# are translated to the system default line separator.
# By default sys.stdout.write() expects '\n' newlines and then
# translates them so this is still cross platform.
if line[-1] == '\n':
self.logger.log(self.log_level, line.rstrip())
else:
self.linebuf += line
def flush(self):
if self.linebuf != '':
self.logger.log(self.log_level, self.linebuf.rstrip())
self.linebuf = ''
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
import torch
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
def violates_moderation(text):
"""
Check whether the text violates OpenAI moderation API.
"""
url = "https://api.openai.com/v1/moderations"
headers = {"Content-Type": "application/json",
"Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]}
text = text.replace("\n", "")
data = "{" + '"input": ' + f'"{text}"' + "}"
data = data.encode("utf-8")
try:
ret = requests.post(url, headers=headers, data=data, timeout=5)
flagged = ret.json()["results"][0]["flagged"]
except requests.exceptions.RequestException as e:
flagged = False
except KeyError as e:
flagged = False
return flagged
def pretty_print_semaphore(semaphore):
if semaphore is None:
return "None"
return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})"

213
requirements.txt Normal file
View File

@@ -0,0 +1,213 @@
accelerate==0.33.0
aiohttp==3.9.5
aiosignal==1.3.1
altair==5.3.0
annotated-types==0.6.0
antlr4-python3-runtime==4.9.3
appdirs==1.4.4
asttokens==2.4.1
async-timeout==4.0.3
attrs==23.2.0
autofaiss==2.17.0
backcall==0.2.0
bleach==6.1.0
blinker==1.7.0
blis==0.7.11
braceexpand==0.1.7
cachetools==5.3.3
cairocffi==1.6.1
CairoSVG==2.7.1
catalogue==2.0.10
certifi==2024.2.2
cffi==1.16.0
cfgv==3.4.0
chardet==5.2.0
charset-normalizer==2.1.1
click==8.1.7
cloudpathlib==0.16.0
cmake==3.25.0
colorama==0.4.6
confection==0.1.4
contexttimer==0.3.3
contourpy==1.1.1
cssselect2==0.7.0
cycler==0.12.1
cymem==2.0.8
DataProperty==1.0.1
datasets==2.19.1
decorator==5.1.1
decord==0.6.0
deepspeed==0.12.6
defusedxml==0.7.1
dill==0.3.8
distlib==0.3.8
docker-pycreds==0.4.0
einops==0.7.0
embedding-reader==1.7.0
en-core-web-lg @ https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.7.1/en_core_web_lg-3.7.1-py3-none-any.whl#sha256=ab70aeb6172cde82508f7739f35ebc9918a3d07debeed637403c8f794ba3d3dc
evaluate==0.4.2
executing==2.0.1
fairscale==0.4.4
faiss-cpu==1.7.4
filelock==3.13.3
fire==0.5.0
flash-attn==2.6.2
fonttools==4.50.0
frozenlist==1.4.1
fsspec==2024.2.0
ftfy==6.2.0
gitdb==4.0.11
GitPython==3.1.42
hf_transfer==0.1.6
hjson==3.1.0
huggingface-hub==0.23.3
identify==2.5.35
idna==3.4
imageio==2.34.0
importlib_resources==6.4.0
iopath==0.1.10
ipython==8.12.3
jedi==0.19.1
Jinja2==3.1.2
joblib==1.4.2
jsonschema==4.21.1
jsonschema-specifications==2023.12.1
kaggle==1.6.8
kiwisolver==1.4.5
langcodes==3.3.0
lazy_loader==0.3
lit==15.0.7
lxml==5.2.2
markdown-it-py==3.0.0
MarkupSafe==2.1.3
matplotlib==3.7.5
matplotlib-inline==0.1.6
mbstrdecoder==1.1.3
mdurl==0.1.2
mpmath==1.3.0
multidict==6.0.5
multiprocess==0.70.16
murmurhash==1.0.10
natsort==8.4.0
networkx==3.0
ninja==1.11.1.1
nodeenv==1.8.0
numpy==1.24.1
nvidia-cublas-cu11==11.11.3.6
nvidia-cuda-cupti-cu11==11.8.87
nvidia-cuda-nvrtc-cu11==11.8.89
nvidia-cuda-runtime-cu11==11.8.89
nvidia-cudnn-cu11==8.7.0.84
nvidia-cufft-cu11==10.9.0.58
nvidia-curand-cu11==10.3.0.86
nvidia-cusolver-cu11==11.4.1.48
nvidia-cusparse-cu11==11.7.5.86
nvidia-nccl-cu11==2.20.5
nvidia-nvtx-cu11==11.8.86
omegaconf==2.3.0
opencv-python-headless==4.5.5.64
opendatasets==0.1.22
packaging==23.2
pandas==2.0.3
parso==0.8.3
pathvalidate==3.2.0
peft==0.12.0
pexpect==4.9.0
pickleshare==0.7.5
pillow==10.2.0
pkgutil_resolve_name==1.3.10
platformdirs==4.2.0
plotly==5.20.0
portalocker==2.8.2
pre-commit==3.5.0
preshed==3.0.9
prompt-toolkit==3.0.43
protobuf==4.25.3
psutil==5.9.8
ptyprocess==0.7.0
pure-eval==0.2.2
py-cpuinfo==9.0.0
pyarrow==15.0.0
pyarrow-hotfix==0.6
pycocoevalcap==1.2
pycocotools==2.0.7
pycparser==2.21
pydantic==2.6.4
pydantic_core==2.16.3
pydeck==0.8.1b0
Pygments==2.17.2
pynvml==11.5.0
pyparsing==3.1.2
pytablewriter==1.2.0
python-dateutil==2.8.2
python-magic==0.4.27
python-slugify==8.0.4
pytz==2024.1
PyWavelets==1.4.1
PyYAML==6.0.1
referencing==0.34.0
regex==2023.12.25
requests==2.32.3
rich==13.7.1
rpds-py==0.18.0
s2wrapper @ git+https://github.com/bfshi/scaling_on_scales.git@3bbee376f98fcd0e670ef6814416e35d5454e803
sacrebleu==2.4.2
safetensors==0.4.2
salesforce-lavis==1.0.2
scikit-image==0.21.0
scikit-learn==1.3.2
scipy==1.10.1
sentencepiece==0.2.0
sentry-sdk==1.42.0
setproctitle==1.3.3
six==1.16.0
smart-open==6.4.0
smmap==5.0.1
spacy==3.7.4
spacy-legacy==3.0.12
spacy-loggers==1.0.5
sqlitedict==2.1.0
srsly==2.4.8
stack-data==0.6.3
streamlit==1.32.2
sympy==1.12
tabledata==1.3.3
tabulate==0.9.0
tcolorpy==0.1.6
tenacity==8.2.3
termcolor==2.4.0
text-unidecode==1.3
thinc==8.2.3
threadpoolctl==3.5.0
tifffile==2023.7.10
timm==0.4.12
tinycss2==1.2.1
tokenizers==0.19.1
toml==0.10.2
toolz==0.12.1
torch==2.3.0+cu118
torchaudio==2.2.2+cu118
torchvision==0.18.0+cu118
tornado==6.4
tqdm==4.66.2
traitlets==5.14.2
transformers==4.43.1
triton==2.3.0
typepy==1.3.2
typer==0.9.4
typing_extensions==4.10.0
tzdata==2024.1
ujson==5.9.0
urllib3==1.26.13
virtualenv==20.25.1
wandb==0.16.4
wasabi==1.1.2
watchdog==4.0.0
wcwidth==0.2.13
weasel==0.3.4
webdataset==0.2.86
webencodings==0.5.1
wget==3.2
xxhash==3.4.1
yarl==1.9.4
zipp==3.18.1

18
scripts/convert_gqa_for_eval.py Normal file
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import os
import json
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--src", type=str)
parser.add_argument("--dst", type=str)
args = parser.parse_args()
all_answers = []
for line_idx, line in enumerate(open(args.src)):
res = json.loads(line)
question_id = res['question_id']
text = res['text'].rstrip('.').lower()
all_answers.append({"questionId": question_id, "prediction": text})
with open(args.dst, 'w') as f:
json.dump(all_answers, f)

27
scripts/convert_mmbench_for_submission.py Normal file
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import os
import json
import argparse
import pandas as pd
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--annotation-file", type=str, required=True)
parser.add_argument("--result-dir", type=str, required=True)
parser.add_argument("--upload-dir", type=str, required=True)
parser.add_argument("--experiment", type=str, required=True)
return parser.parse_args()
if __name__ == "__main__":
args = get_args()
df = pd.read_table(args.annotation_file)
cur_df = df.copy()
cur_df = cur_df.drop(columns=['hint', 'category', 'source', 'image', 'comment', 'l2-category'])
cur_df.insert(6, 'prediction', None)
for pred in open(os.path.join(args.result_dir, f"{args.experiment}.jsonl")):
pred = json.loads(pred)
cur_df.loc[df['index'] == pred['question_id'], 'prediction'] = pred['text']
cur_df.to_excel(os.path.join(args.upload_dir, f"{args.experiment}.xlsx"), index=False, engine='openpyxl')

18
scripts/convert_mmvet_for_eval.py Normal file
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import os
import json
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--src", type=str)
parser.add_argument("--dst", type=str)
args = parser.parse_args()
cur_result = {}
for line in open(args.src):
data = json.loads(line)
qid = data['question_id']
cur_result[f'v1_{qid}'] = data['text']
with open(args.dst, 'w') as f:
json.dump(cur_result, f, indent=2)

74
scripts/convert_seed_for_submission.py Normal file
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import os
import json
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--annotation-file", type=str)
parser.add_argument("--result-file", type=str)
parser.add_argument("--result-upload-file", type=str)
return parser.parse_args()
def eval_single(result_file, eval_only_type=None):
results = {}
for line in open(result_file):
row = json.loads(line)
results[row['question_id']] = row
type_counts = {}
correct_counts = {}
for question_data in data['questions']:
if eval_only_type is not None and question_data['data_type'] != eval_only_type: continue
data_type = question_data['question_type_id']
type_counts[data_type] = type_counts.get(data_type, 0) + 1
try:
question_id = int(question_data['question_id'])
except:
question_id = question_data['question_id']
if question_id not in results:
correct_counts[data_type] = correct_counts.get(data_type, 0)
continue
row = results[question_id]
if row['text'] == question_data['answer']:
correct_counts[data_type] = correct_counts.get(data_type, 0) + 1
total_count = 0
total_correct = 0
for data_type in sorted(type_counts.keys()):
accuracy = correct_counts[data_type] / type_counts[data_type] * 100
if eval_only_type is None:
print(f"{ques_type_id_to_name[data_type]}: {accuracy:.2f}%")
total_count += type_counts[data_type]
total_correct += correct_counts[data_type]
total_accuracy = total_correct / total_count * 100
if eval_only_type is None:
print(f"Total accuracy: {total_accuracy:.2f}%")
else:
print(f"{eval_only_type} accuracy: {total_accuracy:.2f}%")
return results
if __name__ == "__main__":
args = get_args()
data = json.load(open(args.annotation_file))
ques_type_id_to_name = {id:n for n,id in data['question_type'].items()}
results = eval_single(args.result_file)
eval_single(args.result_file, eval_only_type='image')
eval_single(args.result_file, eval_only_type='video')
with open(args.result_upload_file, 'w') as fp:
for question in data['questions']:
qid = question['question_id']
if qid in results:
result = results[qid]
else:
result = results[int(qid)]
fp.write(json.dumps({
'question_id': qid,
'prediction': result['text']
}) + '\n')

88
scripts/convert_sqa_to_llava.py Normal file
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import json
import os
import fire
import re
from convert_sqa_to_llava_base_prompt import build_prompt_chatbot
def convert_to_llava(base_dir, split, prompt_format="QCM-LEA"):
split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[split]
problems = json.load(open(os.path.join(base_dir, "problems.json")))
split_problems = build_prompt_chatbot(
problems, split_indices, prompt_format,
use_caption=False, is_test=False)
target_format = []
for prob_id, (input, output) in split_problems.items():
if input.startswith('Question: '):
input = input.replace('Question: ', '')
if output.startswith('Answer: '):
output = output.replace('Answer: ', '')
raw_prob_data = problems[prob_id]
if raw_prob_data['image'] is None:
target_format.append({
"id": prob_id,
"conversations": [
{'from': 'human', 'value': f"{input}"},
{'from': 'gpt', 'value': f"{output}"},
],
})
else:
target_format.append({
"id": prob_id,
"image": os.path.join(prob_id, raw_prob_data['image']),
"conversations": [
{'from': 'human', 'value': f"{input}\n<image>"},
{'from': 'gpt', 'value': f"{output}"},
],
})
print(f'Number of samples: {len(target_format)}')
with open(os.path.join(base_dir, f"llava_{split}_{prompt_format}.json"), "w") as f:
json.dump(target_format, f, indent=2)
def convert_to_jsonl(base_dir, split, prompt_format="QCM-LEPA"):
split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[split]
problems = json.load(open(os.path.join(base_dir, "problems.json")))
split_problems = build_prompt_chatbot(
problems, split_indices, prompt_format,
use_caption=False, is_test=False)
writer = open(os.path.join(base_dir, f"scienceqa_{split}_{prompt_format}.jsonl"), "w")
for prob_id, (input, output) in split_problems.items():
if input.startswith('Question: '):
input = input.replace('Question: ', '')
if output.startswith('Answer: '):
output = output.replace('Answer: ', '')
raw_prob_data = problems[prob_id]
if raw_prob_data['image'] is None:
data = {
"id": prob_id,
"instruction": f"{input}",
"output": f"{output}",
}
else:
data = {
"id": prob_id,
"image": os.path.join(prob_id, raw_prob_data['image']),
"instruction": f"{input}\n<image>",
"output": f"{output}",
}
writer.write(json.dumps(data) + '\n')
writer.close()
def main(task, **kwargs):
globals()[task](**kwargs)
if __name__ == "__main__":
fire.Fire(main)

334
scripts/convert_sqa_to_llava_base_prompt.py Normal file
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def get_question_text(problem):
question = problem['question']
return question
def get_context_text(problem, use_caption):
txt_context = problem['hint']
img_context = problem['caption'] if use_caption else ""
context = " ".join([txt_context, img_context]).strip()
if context == "":
context = "N/A"
return context
def get_choice_text(probelm, options):
choices = probelm['choices']
choice_list = []
for i, c in enumerate(choices):
choice_list.append("({}) {}".format(options[i], c))
choice_txt = " ".join(choice_list)
#print(choice_txt)
return choice_txt
def get_answer(problem, options):
return options[problem['answer']]
def get_lecture_text(problem):
# \\n: GPT-3 can generate the lecture with more tokens.
lecture = problem['lecture'].replace("\n", "\\n")
return lecture
def get_solution_text(problem):
# \\n: GPT-3 can generate the solution with more tokens
solution = problem['solution'].replace("\n", "\\n")
return solution
def create_one_example_chatbot(format, question, context, choice, answer, lecture, solution, test_example=True):
input_format, output_format = format.split("-")
## Inputs
if input_format == "CQM":
input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
elif input_format == "QCM":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
# upper bound experiment
elif input_format == "QCML":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
elif input_format == "QCME":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
elif input_format == "QCMLE":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
elif input_format == "QCLM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
elif input_format == "QCEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
elif input_format == "QCLEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
# Outputs
if test_example:
output = "Answer:"
elif output_format == 'A':
output = f"Answer: The answer is {answer}."
elif output_format == 'AL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
elif output_format == 'AE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
elif output_format == 'ALE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
elif output_format == 'AEL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
elif output_format == 'LA':
output = f"Answer: {lecture} The answer is {answer}."
elif output_format == 'EA':
output = f"Answer: {solution} The answer is {answer}."
elif output_format == 'LEA':
output = f"Answer: {lecture} {solution} The answer is {answer}."
elif output_format == 'ELA':
output = f"Answer: {solution} {lecture} The answer is {answer}."
elif output_format == 'LEPA':
output = ''
if len(lecture.strip()) > 0:
output += f"LECTURE: {lecture}\n"
if len(solution.strip()) > 0:
output += f"SOLUTION: {solution}\n"
output += '###\n'
output += f"ANSWER: {answer}."
input = input.replace(" ", " ").strip()
output = output.replace(" ", " ").strip()
if input.endswith("BECAUSE:"):
input = input.replace("BECAUSE:", "").strip()
if output.endswith("BECAUSE:"):
output = output.replace("BECAUSE:", "").strip()
return input, output
def create_one_example(format, question, context, choice, answer, lecture, solution, test_example=True):
input_format, output_format = format.split("-")
## Inputs
if input_format == "CQM":
input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
elif input_format == "QCM":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
# upper bound experiment
elif input_format == "QCML":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
elif input_format == "QCME":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
elif input_format == "QCMLE":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
elif input_format == "QCLM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
elif input_format == "QCEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
elif input_format == "QCLEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
# Outputs
if test_example:
output = "Answer:"
elif output_format == 'A':
output = f"Answer: The answer is {answer}."
elif output_format == 'AL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
elif output_format == 'AE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
elif output_format == 'ALE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
elif output_format == 'AEL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
elif output_format == 'LA':
output = f"Answer: {lecture} The answer is {answer}."
elif output_format == 'EA':
output = f"Answer: {solution} The answer is {answer}."
elif output_format == 'LEA':
output = f"Answer: {lecture} {solution} The answer is {answer}."
elif output_format == 'ELA':
output = f"Answer: {solution} {lecture} The answer is {answer}."
text = input + output
text = text.replace(" ", " ").strip()
if text.endswith("BECAUSE:"):
text = text.replace("BECAUSE:", "").strip()
return text
def create_one_example_gpt4(format, question, context, choice, answer, lecture, solution, test_example=True):
input_format, output_format = format.split("-")
## Inputs
if input_format == "CQM":
input = f"Context: {context}\nQuestion: {question}\nOptions: {choice}\n"
elif input_format == "QCM":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\n"
# upper bound experiment
elif input_format == "QCML":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture}\n"
elif input_format == "QCME":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {solution}\n"
elif input_format == "QCMLE":
input = f"Question: {question}\nContext: {context}\nOptions: {choice}\nBECAUSE: {lecture} {solution}\n"
elif input_format == "QCLM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture}\nOptions: {choice}\n"
elif input_format == "QCEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {solution}\nOptions: {choice}\n"
elif input_format == "QCLEM":
input = f"Question: {question}\nContext: {context}\nBECAUSE: {lecture} {solution}\nOptions: {choice}\n"
# Outputs
if test_example:
output = "Answer:"
elif output_format == 'A':
output = f"Answer: The answer is {answer}."
elif output_format == 'AL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution}"
elif output_format == 'AE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture}"
elif output_format == 'ALE':
output = f"Answer: The answer is {answer}. BECAUSE: {lecture} {solution}"
elif output_format == 'AEL':
output = f"Answer: The answer is {answer}. BECAUSE: {solution} {lecture}"
elif output_format == 'LA':
output = f"Answer: {lecture} The answer is {answer}."
elif output_format == 'EA':
output = f"Answer: {solution} The answer is {answer}."
elif output_format == 'LEA':
output = f"Answer: {lecture} {solution} The answer is {answer}."
elif output_format == 'ELA':
output = f"Answer: {solution} {lecture} The answer is {answer}."
input = input.replace(" ", " ").strip()
output = output.replace(" ", " ").strip()
if output.endswith("BECAUSE:"):
output = output.replace("BECAUSE:", "").strip()
user_prompt = {"role": "user", "content": f"Can you explain {input}?"}
assistant_prompt = {"role": "assistant", "content": f"{output}"}
return user_prompt, assistant_prompt
def build_prompt_chatbot(problems, shot_qids, prompt_format, use_caption=False, options=["A", "B", "C", "D", "E"], is_test=False):
examples = {}
for qid in shot_qids:
question = get_question_text(problems[qid])
context = get_context_text(problems[qid], use_caption)
choice = get_choice_text(problems[qid], options)
answer = get_answer(problems[qid], options)
lecture = get_lecture_text(problems[qid]).replace('\\n', '\n')
solution = get_solution_text(problems[qid]).replace('\\n', '\n')
train_example = create_one_example_chatbot(prompt_format,
question,
context,
choice,
answer,
lecture,
solution,
test_example=is_test)
examples[qid] = train_example
return examples
def build_prompt(problems, shot_qids, test_qid, args):
examples = []
# n-shot training examples
for qid in shot_qids:
question = get_question_text(problems[qid])
context = get_context_text(problems[qid], args.use_caption)
choice = get_choice_text(problems[qid], args.options)
answer = get_answer(problems[qid], args.options)
lecture = get_lecture_text(problems[qid])
solution = get_solution_text(problems[qid])
train_example = create_one_example(args.prompt_format,
question,
context,
choice,
answer,
lecture,
solution,
test_example=False)
examples.append(train_example)
# test example
question = get_question_text(problems[test_qid])
context = get_context_text(problems[test_qid], args.use_caption)
choice = get_choice_text(problems[test_qid], args.options)
answer = get_answer(problems[test_qid], args.options)
lecture = get_lecture_text(problems[test_qid])
solution = get_solution_text(problems[test_qid])
test_example = create_one_example(args.prompt_format,
question,
context,
choice,
answer,
lecture,
solution,
test_example=True)
examples.append(test_example)
# create the prompt input
prompt_input = '\n\n'.join(examples)
return prompt_input
def build_prompt_gpt4(problems, shot_qids, test_qid, args):
prompt_array = [{"role": "system", "content": "You are a helpful assistant."}]
# n-shot training examples
for qid in shot_qids:
question = get_question_text(problems[qid])
context = get_context_text(problems[qid], args.use_caption)
choice = get_choice_text(problems[qid], args.options)
answer = get_answer(problems[qid], args.options)
lecture = get_lecture_text(problems[qid])
solution = get_solution_text(problems[qid])
user_prompt, assistant_prompt = create_one_example_gpt4(args.prompt_format,
question,
context,
choice,
answer,
lecture,
solution,
test_example=False)
prompt_array.append(user_prompt)
prompt_array.append(assistant_prompt)
# test example
question = get_question_text(problems[test_qid])
context = get_context_text(problems[test_qid], args.use_caption)
choice = get_choice_text(problems[test_qid], args.options)
answer = get_answer(problems[test_qid], args.options)
lecture = get_lecture_text(problems[test_qid])
solution = get_solution_text(problems[test_qid])
user_prompt, assistant_prompt = create_one_example_gpt4(args.prompt_format,
question,
context,
choice,
answer,
lecture,
solution,
test_example=True)
prompt_array.append(user_prompt)
prompt_array.append(assistant_prompt)
return prompt_array

47
scripts/convert_vizwiz_for_submission.py Normal file
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import os
import argparse
import json
from llava.eval.m4c_evaluator import EvalAIAnswerProcessor
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--annotation-file', type=str, required=True)
parser.add_argument('--result-file', type=str, required=True)
parser.add_argument('--result-upload-file', type=str, required=True)
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
os.makedirs(os.path.dirname(args.result_upload_file), exist_ok=True)
results = []
error_line = 0
for line_idx, line in enumerate(open(args.result_file)):
try:
results.append(json.loads(line))
except:
error_line += 1
results = {x['question_id']: x['text'] for x in results}
test_split = [json.loads(line) for line in open(args.annotation_file)]
split_ids = set([x['question_id'] for x in test_split])
print(f'total results: {len(results)}, total split: {len(test_split)}, error_line: {error_line}')
all_answers = []
answer_processor = EvalAIAnswerProcessor()
for x in test_split:
assert x['question_id'] in results
all_answers.append({
'image': x['image'],
'answer': answer_processor(results[x['question_id']])
})
with open(args.result_upload_file, 'w') as f:
json.dump(all_answers, f)

56
scripts/convert_vqav2_for_submission.py Normal file
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import os
import argparse
import json
from llava.eval.m4c_evaluator import EvalAIAnswerProcessor
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dir', type=str, default="./playground/data/eval/vqav2")
parser.add_argument('--ckpt', type=str, required=True)
parser.add_argument('--split', type=str, required=True)
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
src = os.path.join(args.dir, 'answers', args.split, args.ckpt, 'merge.jsonl')
test_split = os.path.join(args.dir, 'llava_vqav2_mscoco_test2015.jsonl')
dst = os.path.join(args.dir, 'answers_upload', args.split, f'{args.ckpt}.json')
os.makedirs(os.path.dirname(dst), exist_ok=True)
results = []
error_line = 0
for line_idx, line in enumerate(open(src)):
try:
results.append(json.loads(line))
except:
error_line += 1
results = {x['question_id']: x['text'] for x in results}
test_split = [json.loads(line) for line in open(test_split)]
split_ids = set([x['question_id'] for x in test_split])
print(f'total results: {len(results)}, total split: {len(test_split)}, error_line: {error_line}')
all_answers = []
answer_processor = EvalAIAnswerProcessor()
for x in test_split:
if x['question_id'] not in results:
all_answers.append({
'question_id': x['question_id'],
'answer': ''
})
else:
all_answers.append({
'question_id': x['question_id'],
'answer': answer_processor(results[x['question_id']])
})
with open(dst, 'w') as f:
json.dump(all_answers, open(dst, 'w'))

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"""
This is just a utility that I use to extract the projector for quantized models.
It is NOT necessary at all to train, or run inference/serve demos.
Use this script ONLY if you fully understand its implications.
"""
import os
import argparse
import torch
import json
from collections import defaultdict
def parse_args():
parser = argparse.ArgumentParser(description='Extract MMProjector weights')
parser.add_argument('--model-path', type=str, help='model folder')
parser.add_argument('--output', type=str, help='output file')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
keys_to_match = ['mm_projector']
ckpt_to_key = defaultdict(list)
try:
model_indices = json.load(open(os.path.join(args.model_path, 'pytorch_model.bin.index.json')))
for k, v in model_indices['weight_map'].items():
if any(key_match in k for key_match in keys_to_match):
ckpt_to_key[v].append(k)
except FileNotFoundError:
# Smaller models or model checkpoints saved by DeepSpeed.
v = 'pytorch_model.bin'
for k in torch.load(os.path.join(args.model_path, v), map_location='cpu').keys():
if any(key_match in k for key_match in keys_to_match):
ckpt_to_key[v].append(k)
loaded_weights = {}
for ckpt_name, weight_keys in ckpt_to_key.items():
ckpt = torch.load(os.path.join(args.model_path, ckpt_name), map_location='cpu')
for k in weight_keys:
loaded_weights[k] = ckpt[k]
torch.save(loaded_weights, args.output)

48
scripts/finetune.sh Normal file
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#!/bin/bash
# IMPORTANT: this is the training script for the original LLaVA, NOT FOR LLaVA V1.5!
# Uncomment and set the following variables correspondingly to run this script:
################## VICUNA ##################
# PROMPT_VERSION=v1
# MODEL_VERSION="vicuna-v1-3-7b"
################## VICUNA ##################
################## LLaMA-2 ##################
# PROMPT_VERSION="llava_llama_2"
# MODEL_VERSION="llama-2-7b-chat"
################## LLaMA-2 ##################
deepspeed llava/train/train_mem.py \
--deepspeed ./scripts/zero2.json \
--model_name_or_path ./checkpoints/$MODEL_VERSION \
--version $PROMPT_VERSION \
--data_path ./playground/data/llava_instruct_80k.json \
--image_folder /path/to/coco/train2017 \
--vision_tower openai/clip-vit-large-patch14 \
--pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--bf16 True \
--output_dir ./checkpoints/llava-$MODEL_VERSION-finetune \
--num_train_epochs 1 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 50000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--dataloader_num_workers 4 \
--lazy_preprocess True \
--report_to wandb

48
scripts/finetune_full_schedule.sh Normal file
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#!/bin/bash
# IMPORTANT: this is the training script for the original LLaVA, NOT FOR LLaVA V1.5!
# Uncomment and set the following variables correspondingly to run this script:
################## VICUNA ##################
# PROMPT_VERSION=v1
# MODEL_VERSION="vicuna-v1-3-7b"
################## VICUNA ##################
################## LLaMA-2 ##################
# PROMPT_VERSION="llava_llama_2"
# MODEL_VERSION="llama-2-7b-chat"
################## LLaMA-2 ##################
deepspeed llava/train/train_mem.py \
--deepspeed ./scripts/zero2.json \
--model_name_or_path ./checkpoints/$MODEL_VERSION \
--version $PROMPT_VERSION \
--data_path ./playground/data/llava_instruct_158k.json \
--image_folder /path/to/coco/train2017 \
--vision_tower openai/clip-vit-large-patch14 \
--pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--bf16 True \
--output_dir ./checkpoints/llava-$MODEL_VERSION-finetune \
--num_train_epochs 3 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 50000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--dataloader_num_workers 4 \
--lazy_preprocess True \
--report_to wandb

49
scripts/finetune_lora.sh Normal file
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#!/bin/bash
# IMPORTANT: this is the training script for the original LLaVA, NOT FOR LLaVA V1.5!
# Uncomment and set the following variables correspondingly to run this script:
################## VICUNA ##################
# PROMPT_VERSION=v1
# MODEL_VERSION="vicuna-v1-3-7b"
################## VICUNA ##################
################## LLaMA-2 ##################
# PROMPT_VERSION="llava_llama_2"
# MODEL_VERSION="llama-2-7b-chat"
################## LLaMA-2 ##################
deepspeed llava/train/train_mem.py \
--deepspeed ./scripts/zero2.json \
--lora_enable True \
--model_name_or_path ./checkpoints/$MODEL_VERSION \
--version $PROMPT_VERSION \
--data_path ./playground/data/llava_instruct_80k.json \
--image_folder /path/to/coco/train2017 \
--vision_tower openai/clip-vit-large-patch14 \
--pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--bf16 True \
--output_dir ./checkpoints/llava-$MODEL_VERSION-finetune_lora \
--num_train_epochs 1 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 50000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--lazy_preprocess True \
--dataloader_num_workers 4 \
--report_to wandb

50
scripts/finetune_qlora.sh Normal file
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#!/bin/bash
# IMPORTANT: this is the training script for the original LLaVA, NOT FOR LLaVA V1.5!
# Uncomment and set the following variables correspondingly to run this script:
################## VICUNA ##################
# PROMPT_VERSION=v1
# MODEL_VERSION="vicuna-v1-3-7b"
################## VICUNA ##################
################## LLaMA-2 ##################
# PROMPT_VERSION="llava_llama_2"
# MODEL_VERSION="llama-2-7b-chat"
################## LLaMA-2 ##################
deepspeed llava/train/train_mem.py \
--deepspeed ./scripts/zero2.json \
--lora_enable True \
--bits 4 \
--model_name_or_path ./checkpoints/$MODEL_VERSION \
--version $PROMPT_VERSION \
--data_path ./playground/data/llava_instruct_80k.json \
--image_folder /path/to/coco/train2017 \
--vision_tower openai/clip-vit-large-patch14 \
--pretrain_mm_mlp_adapter ./checkpoints/llava-$MODEL_VERSION-pretrain/mm_projector.bin \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--bf16 True \
--output_dir ./checkpoints/llava-$MODEL_VERSION-finetune_lora \
--num_train_epochs 1 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 50000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--lazy_preprocess True \
--dataloader_num_workers 4 \
--report_to wandb

36
scripts/finetune_sqa.sh Normal file
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#!/bin/bash
# IMPORTANT: this is the training script for the original LLaVA, NOT FOR LLaVA V1.5!
deepspeed llava/train/train_mem.py \
--deepspeed ./scripts/zero2.json \
--model_name_or_path lmsys/vicuna-13b-v1.3 \
--version $PROMPT_VERSION \
--data_path /Data/ScienceQA/data/scienceqa/llava_train_QCM-LEA.json \
--image_folder /Data/ScienceQA/data/scienceqa/images/train \
--vision_tower openai/clip-vit-large-patch14 \
--pretrain_mm_mlp_adapter ./checkpoints/huggingface/liuhaotian/llava-pretrain-vicuna-13b-v1.3/mm_projector.bin \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--bf16 True \
--output_dir ./checkpoints/llava-vicuna-13b-v1.3-pretrain_lcs558k_plain-ScienceQA_QCM_LEA-12e \
--num_train_epochs 12 \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 50000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--dataloader_num_workers 4 \
--lazy_preprocess True \
--report_to wandb

22
scripts/merge_lora_weights.py Normal file
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import argparse
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path
def merge_lora(args):
model_name = get_model_name_from_path(args.model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, device_map='cpu')
model.save_pretrained(args.save_model_path)
tokenizer.save_pretrained(args.save_model_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, required=True)
parser.add_argument("--model-base", type=str, required=True)
parser.add_argument("--save-model-path", type=str, required=True)
args = parser.parse_args()
merge_lora(args)

64
scripts/more/00_pretrain.sh Normal file
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#!/bin/bash
source activate more
cd local/path
export PYTHONPATH=.
export WANDB_ENTITYproject_entity
export WANDB_PROJECT=project_name
export WANDB_MODE=offline
export TOKENIZER_PATH=lmsys/vicuna-7b-v1.5
IFS=',' read -r -a nodelist <<<$SLURM_NODELIST
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
export MASTER_PORT=`comm -23 <(seq 5000 6000 | sort) <(ss -Htan | awk '{print $4}' | cut -d':' -f2 | sort -u) | shuf | head -n 1`
export OMP_NUM_THREADS=1
echo "CPUs: $SLURM_CPUS_PER_TASK"
echo "GPUs: $SLURM_GPUS_PER_NODE"
echo "MASTER ADDR: ${MASTER_ADDR}"
echo "MASTER PORT: ${MASTER_PORT}"
epochs=1
vicuna_path=local/path
images_path=local/path
data_train_path=local/path
vision_tower=local/path
job_name="your/job/name"
echo "job name: $job_name"
deepspeed llava/train/train_mem.py \
--deepspeed ./scripts/zero2.json \
--model_name_or_path $vicuna_path \
--version plain \
--data_path $data_train_path \
--image_folder $images_path \
--vision_tower $vision_tower \
--mm_projector_type mlp2x_gelu \
--tune_mm_mlp_adapter True \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--bf16 True \
--output_dir ./checkpoints/${job_name} \
--num_train_epochs $epochs \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 2 \
--evaluation_strategy no \
--save_strategy steps \
--save_steps 24000 \
--save_total_limit 2 \
--learning_rate 1e-3 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type cosine \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--dataloader_num_workers 8 \
--lazy_preprocess True \
--report_to wandb \
--run_name $job_name \

67
scripts/more/01_finetuning.sh Normal file
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#!/bin/bash
source activate more
cd local/path
export PYTHONPATH=.
export WANDB_ENTITYproject_entity
export WANDB_PROJECT=project_name
export WANDB_MODE=offline
export TOKENIZER_PATH=lmsys/vicuna-7b-v1.5
IFS=',' read -r -a nodelist <<<$SLURM_NODELIST
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
export MASTER_PORT=`comm -23 <(seq 5000 6000 | sort) <(ss -Htan | awk '{print $4}' | cut -d':' -f2 | sort -u) | shuf | head -n 1`
export OMP_NUM_THREADS=1
echo "CPUs: $SLURM_CPUS_PER_TASK"
echo "GPUs: $SLURM_GPUS_PER_NODE"
echo "MASTER ADDR: ${MASTER_ADDR}"
echo "MASTER PORT: ${MASTER_PORT}"
epochs=1
vicuna_path=local/path
images_path=local/path
data_train_path=local/path
vision_tower=local/path
mm_projector_path=local/path/mm_projector.bin
job_name="your/job/name"
echo "job name: $job_name"
deepspeed llava/train/train_mem.py \
--deepspeed ./scripts/zero3.json \
--model_name_or_path $vicuna_path \
--version v1 \
--data_path $data_train_path \
--image_folder $images_path \
--vision_tower $vision_tower \
--pretrain_mm_mlp_adapter $mm_projector_path \
--mm_projector_type mlp2x_gelu \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--image_aspect_ratio pad \
--group_by_modality_length True \
--bf16 True \
--output_dir ./checkpoints/${job_name} \
--num_train_epochs $epochs \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy no \
--save_strategy steps \
--save_steps 24000 \
--save_total_limit 2 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type cosine \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--dataloader_num_workers 8 \
--lazy_preprocess True \
--report_to wandb \
--run_name $job_name \

68
scripts/more/02_finetuning_lora.sh Normal file
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#!/bin/bash
source activate more
cd local/path
export PYTHONPATH=.
export WANDB_ENTITYproject_entity
export WANDB_PROJECT=project_name
export WANDB_MODE=offline
export TOKENIZER_PATH=lmsys/vicuna-7b-v1.5
IFS=',' read -r -a nodelist <<<$SLURM_NODELIST
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
export MASTER_PORT=`comm -23 <(seq 5000 6000 | sort) <(ss -Htan | awk '{print $4}' | cut -d':' -f2 | sort -u) | shuf | head -n 1`
export OMP_NUM_THREADS=1
echo "CPUs: $SLURM_CPUS_PER_TASK"
echo "GPUs: $SLURM_GPUS_PER_NODE"
echo "MASTER ADDR: ${MASTER_ADDR}"
echo "MASTER PORT: ${MASTER_PORT}"
epochs=1
vicuna_path=local/path
images_path=local/path
data_train_path=local/path
vision_tower=local/path
mm_projector_path=local/path/mm_projector.bin
job_name="your/job/name"
echo "job name: $job_name"
deepspeed llava/train/train_mem.py \
--lora_enable True --lora_r 128 --lora_alpha 256 --mm_projector_lr 2e-5 \
--deepspeed ./scripts/zero3.json \
--model_name_or_path $vicuna_path \
--version v1 \
--data_path $data_train_path \
--image_folder $images_path \
--vision_tower $vision_tower \
--pretrain_mm_mlp_adapter $mm_projector_path \
--mm_projector_type mlp2x_gelu \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--image_aspect_ratio pad \
--group_by_modality_length True \
--bf16 True \
--output_dir ./checkpoints/${job_name} \
--num_train_epochs $epochs \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy no \
--save_strategy steps \
--save_steps 24000 \
--save_total_limit 2 \
--learning_rate 2e-4 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type cosine \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--dataloader_num_workers 8 \
--lazy_preprocess True \
--report_to wandb \
--run_name $job_name \

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