├── LICENSE ├── README.md ├── docs ├── .jekyll-cache │ └── Jekyll │ │ └── Cache │ │ ├── Jekyll--Cache │ │ └── b7 │ │ │ └── 9606fb3afea5bd1609ed40b622142f1c98125abcfe89a76a661b0e8e343910 │ │ └── Jekyll--Converters--Markdown │ │ └── fd │ │ └── aa4200cef2544ec568b25970b3a49cf9b4f3d7015485506ccda914caf9c5b9 ├── 404.html ├── Gemfile ├── Gemfile.lock ├── LICENSE ├── _config.yml ├── _data │ └── authors.yml ├── _layouts │ └── default.html ├── _posts │ └── 2021-01-16-welcome-to-jekyll.markdown ├── _sass │ └── main.scss ├── _site │ ├── 404.html │ ├── LICENSE │ ├── about │ │ └── index.html │ ├── assets │ │ ├── css │ │ │ ├── styles.css │ │ │ └── styles.css.map │ │ ├── favicon │ │ │ ├── android-chrome-192x192.png │ │ │ ├── android-chrome-512x512.png │ │ │ ├── apple-touch-icon.png │ │ │ ├── favicon-16x16.png │ │ │ ├── favicon-32x32.png │ │ │ ├── favicon.ico │ │ │ └── site.webmanifest │ │ ├── img │ │ │ ├── animation.gif │ │ │ ├── criteria.png │ │ │ ├── datarow.png │ │ │ ├── interface.png │ │ │ ├── jamin.jpg │ │ │ ├── juho.jpg │ │ │ ├── kaist_logo.png │ │ │ ├── kixlab_logo.png │ │ │ ├── naver_logo.png │ │ │ ├── taesoo.jpeg │ │ │ ├── yoonjoo.jpeg │ │ │ └── youngho.jpg │ │ ├── main.css │ │ ├── main.css.map │ │ └── minima-social-icons.svg │ ├── feed.xml │ ├── index.html │ └── jekyll │ │ └── update │ │ └── 2021 │ │ └── 01 │ │ └── 16 │ │ └── welcome-to-jekyll.html ├── about.markdown ├── assets │ ├── css │ │ └── styles.scss │ ├── favicon │ │ ├── android-chrome-192x192.png │ │ ├── android-chrome-512x512.png │ │ ├── apple-touch-icon.png │ │ ├── favicon-16x16.png │ │ ├── favicon-32x32.png │ │ ├── favicon.ico │ │ └── site.webmanifest │ └── img │ │ ├── blue_fire.png │ │ ├── geewook.jpg │ │ ├── human_corr.svg │ │ ├── instructionfollowing_results.png │ │ ├── kaist_logo.png │ │ ├── lklab_logo.jpg │ │ ├── minjoon.jpeg │ │ ├── naver_ai_lab_logo.png │ │ ├── naver_cloud_logo.png │ │ ├── naver_logo.png │ │ ├── pairwise_win_rate.svg │ │ ├── perception_collection_stats.png │ │ ├── prometheus_vision_components.svg │ │ ├── seongyun.jpeg │ │ ├── seungone.jpeg │ │ ├── suehyun.png │ │ └── vlm_as_a_judge.svg └── index.markdown ├── llava ├── __init__.py ├── constants.py ├── conversation.py ├── eval │ ├── eval_gpt_review.py │ ├── eval_gpt_review_bench.py │ ├── eval_gpt_review_visual.py │ ├── eval_pope.py │ ├── eval_science_qa.py │ ├── eval_science_qa_gpt4.py │ ├── eval_science_qa_gpt4_requery.py │ ├── eval_textvqa.py │ ├── generate_webpage_data_from_table.py │ ├── m4c_evaluator.py │ ├── model_qa.py │ ├── model_vqa.py │ ├── model_vqa_loader.py │ ├── model_vqa_mmbench.py │ ├── model_vqa_qbench.py │ ├── model_vqa_science.py │ ├── qa_baseline_gpt35.py │ ├── run_llava.py │ ├── summarize_gpt_review.py │ ├── table │ │ ├── answer │ │ │ ├── answer_alpaca-13b.jsonl │ │ │ ├── answer_bard.jsonl │ │ │ ├── answer_gpt35.jsonl │ │ │ ├── answer_llama-13b.jsonl │ │ │ └── answer_vicuna-13b.jsonl │ │ ├── caps_boxes_coco2014_val_80.jsonl │ │ ├── model.jsonl │ │ ├── prompt.jsonl │ │ ├── question.jsonl │ │ ├── results │ │ │ ├── test_sqa_llava_13b_v0.json │ │ │ └── test_sqa_llava_lcs_558k_sqa_12e_vicuna_v1_3_13b.json │ │ ├── review │ │ │ ├── review_alpaca-13b_vicuna-13b.jsonl │ │ │ ├── review_bard_vicuna-13b.jsonl │ │ │ ├── review_gpt35_vicuna-13b.jsonl │ │ │ └── review_llama-13b_vicuna-13b.jsonl │ │ ├── reviewer.jsonl │ │ └── rule.json │ └── webpage │ │ ├── figures │ │ ├── alpaca.png │ │ ├── bard.jpg │ │ ├── chatgpt.svg │ │ ├── llama.jpg │ │ ├── swords_FILL0_wght300_GRAD0_opsz48.svg │ │ └── vicuna.jpeg │ │ ├── index.html │ │ ├── script.js │ │ └── styles.css ├── mm_utils.py ├── model │ ├── __init__.py │ ├── apply_delta.py │ ├── builder.py │ ├── consolidate.py │ ├── language_model │ │ ├── llava_llama.py │ │ ├── llava_mpt.py │ │ └── mpt │ │ │ ├── adapt_tokenizer.py │ │ │ ├── attention.py │ │ │ ├── blocks.py │ │ │ ├── configuration_mpt.py │ │ │ ├── custom_embedding.py │ │ │ ├── flash_attn_triton.py │ │ │ ├── hf_prefixlm_converter.py │ │ │ ├── meta_init_context.py │ │ │ ├── modeling_mpt.py │ │ │ ├── norm.py │ │ │ └── param_init_fns.py │ ├── llava_arch.py │ ├── make_delta.py │ ├── multimodal_encoder │ │ ├── builder.py │ │ └── clip_encoder.py │ ├── multimodal_projector │ │ └── builder.py │ └── utils.py ├── serve │ ├── __init__.py │ ├── cli.py │ ├── controller.py │ ├── examples │ │ ├── extreme_ironing.jpg │ │ └── waterview.jpg │ ├── gradio_web_server.py │ ├── model_worker.py │ ├── register_worker.py │ └── test_message.py ├── train │ ├── llama_flash_attn_monkey_patch.py │ ├── llama_xformers_attn_monkey_patch.py │ ├── llava_trainer.py │ ├── train.py │ ├── train_mem.py │ └── train_xformers.py └── utils.py ├── mmmu_download.py ├── pyproject.toml ├── sample_eval_data.jsonl └── sample_train_data.json /README.md: -------------------------------------------------------------------------------- 1 | # Prometheus-Vision 2 | [ACL 2024 Findings & ICLR 2024 WS] An Evaluator VLM that is open-source, offers reproducible evaluation, and inexpensive to use. Specifically designed for fine-grained evaluation on customized score rubric, Prometheus-Vision is a good alternative for human evaluation and GPT-4V evaluation. 3 | 4 | Our preprint, which describes our method in detail and provides full experimental results and analyses, can be found here:
5 | > [**Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation**](https://arxiv.org/abs/2401.06591).
6 | > [Seongyun Lee](https://www.linkedin.com/in/seongyun-lee-647753233/)$^\ast$, [Seungone Kim](https://seungonekim.github.io/)$^\ast$, [Sue Hyun Park](https://suehyunpark.github.io/), [Geewook Kim](https://geewook.kim/), [Minjoon Seo](https://seominjoon.github.io/). Work in progress, arXiv preprint. 7 | 8 | ## Setup 9 | 1. Install package 10 | ```bash 11 | conda create -n prometheus-vision python=3.10 -y 12 | conda activate prometheus-vision 13 | pip install --upgrade pip # enable PEP 660 support 14 | pip install -e . 15 | pip install -e ".[train]" 16 | pip install flash-attn --no-build-isolation 17 | ``` 18 | 2. Download data 19 | ``` 20 | python mmmu_donwload.py 21 | wget http://images.cocodataset.org/zips/train2017.zip 22 | unzip train2017 23 | ``` 24 | ## Input and Output Format of Prometheus-Vision 25 | Prometheus-Vision is trained and inferenced using the following input prompt format. Note that you could fill in the instruction, response, reference answer, and score rubrics with your own data. 26 | ```text 27 | ###Task Description: 28 | An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, image and a score rubric representing an evaluation criterion is given. 29 | 1. Write a detailed feedback that assesses the quality of the response strictly based on the given score rubric, not evaluating in general. 30 | 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 31 | 3. The output format should look as follows: Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5) 32 | 4. Please do not generate any other opening, closing, and explanations. 33 | 34 | ###The instruction to evaluate: 35 | {orig_instruction} 36 | 37 | ###Response to evaluate: 38 | {orig_response} 39 | 40 | ###Reference Answer (Score 5): 41 | {orig_reference_answer} 42 | 43 | ###Score Rubrics: 44 | [{orig_criteria}] 45 | Score 1: {orig_score1_description} 46 | Score 2: {orig_score2_description} 47 | Score 3: {orig_score3_description} 48 | Score 4: {orig_score4_description} 49 | Score 5: {orig_score5_description} 50 | 51 | ###Feedback: 52 | ``` 53 | You can check the [Perception Collection](https://huggingface.co/datasets/kaist-ai/Perception-Collection) used for training Prometheus-Vision. 54 | ## Train 55 | We use [LLaVA](https://github.com/haotian-liu/LLaVA) codebase in developing Prometheus-Vision. Therefore, the following training & inference script is tailored to this.
56 | 57 | If you plan to start from a different VLM codebase, you should adapt the format of the data to suit your custom code.
58 | 59 | Note that you can also check the data format at [sample_train_data.json](https://github.com/kaistAI/prometheus-vision/blob/main/sample_train_data.json) and [sample_eval_data.json](https://github.com/kaistAI/prometheus-vision/blob/main/sample_train_data.json) 60 | ```bash 61 | deepspeed --include llava/train/train_mem.py \ 62 | --deepspeed ./scripts/zero2.json \ 63 | --model_name_or_path lmsys/vicuna-13b-v1.5 \ 64 | --version plain \ 65 | --data_path TRAINING_DATA_PATH \ 66 | --vision_tower openai/clip-vit-large-patch14-336 \ 67 | --mm_projector_type mlp2x_gelu \ 68 | --tune_mm_mlp_adapter True \ 69 | --mm_vision_select_layer -2 \ 70 | --mm_use_im_start_end False \ 71 | --mm_use_im_patch_token False \ 72 | --bf16 True \ 73 | --output_dir OUTPUT_DIR \ 74 | --num_train_epochs 1 \ 75 | --per_device_train_batch_size 32 \ 76 | --per_device_eval_batch_size 4 \ 77 | --gradient_accumulation_steps 1 \ 78 | --evaluation_strategy "no" \ 79 | --save_strategy "steps" \ 80 | --save_steps 24000 \ 81 | --save_total_limit 1 \ 82 | --learning_rate 1e-3 \ 83 | --weight_decay 0. \ 84 | --warmup_ratio 0.03 \ 85 | --lr_scheduler_type "cosine" \ 86 | --logging_steps 1 \ 87 | --tf32 True \ 88 | --model_max_length 2048 \ 89 | --gradient_checkpointing True \ 90 | --dataloader_num_workers 4 \ 91 | --lazy_preprocess True \ 92 | --report_to wandb 93 | ``` 94 | ## Inference 95 | You can place the path of your llava-style model in MODEL_PATH, or insert the trained [Prometheus-Vision](https://huggingface.co/kaist-ai/prometheus-vision-13b-v1.0) we provide. 96 | ```bash 97 | python -m llava.eval.model_vqa \ 98 | --model-path MODEL_PATH \ 99 | --question-file EVALUATION_DATA_PATH \ 100 | --answers-file OUTPUT_PATH \ 101 | --temperature 1.0 \ 102 | --top_p 0.9 \ 103 | --conv-mode vicuna_v1 104 | ``` 105 | Additionally, you can use [Perception-Bench](https://huggingface.co/datasets/kaist-ai/Perception-Bench) when evaluating VLM. 106 | 107 | ## Citation 108 | If you find our work useful in your work, please consider citing our paper: 109 | 110 | ``` 111 | @misc{lee2024prometheusvision, 112 | title={Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation}, 113 | author={Seongyun Lee and Seungone Kim and Sue Hyun Park and Geewook Kim and Minjoon Seo}, 114 | year={2024}, 115 | eprint={2401.06591}, 116 | archivePrefix={arXiv}, 117 | primaryClass={cs.CL} 118 | } 119 | ``` 120 | -------------------------------------------------------------------------------- /docs/.jekyll-cache/Jekyll/Cache/Jekyll--Cache/b7/9606fb3afea5bd1609ed40b622142f1c98125abcfe89a76a661b0e8e343910: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/prometheus-eval/prometheus-vision/fe668b5e7e120e5bea8a3821eeb468ddf719c23c/docs/.jekyll-cache/Jekyll/Cache/Jekyll--Cache/b7/9606fb3afea5bd1609ed40b622142f1c98125abcfe89a76a661b0e8e343910 -------------------------------------------------------------------------------- /docs/.jekyll-cache/Jekyll/Cache/Jekyll--Converters--Markdown/fd/aa4200cef2544ec568b25970b3a49cf9b4f3d7015485506ccda914caf9c5b9: -------------------------------------------------------------------------------- 1 | I"9

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2 | 3 |

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Where YEAR is a four-digit number, MONTH and DAY are both two-digit numbers, and MARKUP is the file extension representing the format used in the file. After that, include the necessary front matter. Take a look at the source for this post to get an idea about how it works.

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Check out the Jekyll docs for more info on how to get the most out of Jekyll. File all bugs/feature requests at Jekyll’s GitHub repo. If you have questions, you can ask them on Jekyll Talk.

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IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /docs/_config.yml: -------------------------------------------------------------------------------- 1 | # Welcome to Jekyll! 2 | # 3 | # This config file is meant for settings that affect your whole blog, values 4 | # which you are expected to set up once and rarely edit after that. If you find 5 | # yourself editing this file very often, consider using Jekyll's data files 6 | # feature for the data you need to update frequently. 7 | # 8 | # For technical reasons, this file is *NOT* reloaded automatically when you use 9 | # 'bundle exec jekyll serve'. If you change this file, please restart the server process. 10 | # 11 | # If you need help with YAML syntax, here are some quick references for you: 12 | # https://learn-thse-web.algonquindesign.ca/topics/markdown-yaml-cheat-sheet/#yaml 13 | # https://learnxinyminutes.com/docs/yaml/ 14 | # 15 | # Site settings 16 | # These are used to personalize your new site. If you look in the HTML files, 17 | # you will see them accessed via {{ site.title }}, {{ site.email }}, and so on. 18 | # You can create any custom variable you would like, and they will be accessible 19 | # in the templates via {{ site.myvariable }}. 20 | 21 | title: Prometheus-Vision 22 | theme: minima 23 | description: "Vision-Language Model as a Judge for Fine-Grained Evaluation" 24 | 25 | syscolor: "#555555" 26 | sysfont: Inter 27 | paper: https://arxiv.org/abs/2401.06591 28 | code: https://github.com/kaistAI/prometheus-vision 29 | hf_train_data: https://huggingface.co/datasets/kaist-ai/Perception-Collection 30 | hf_test_data: https://huggingface.co/datasets/kaist-ai/Perception-Bench 31 | hf_data: https://huggingface.co/datasets/kaist-ai/Perception-Collection 32 | hf_model_7b: https://huggingface.co/kaist-ai/prometheus-vision-7b-v1.0 33 | hf_model_13b: https://huggingface.co/kaist-ai/prometheus-vision-13b-v1.0 34 | 35 | plugins: 36 | - jekyll-feed 37 | 38 | # Exclude from processing. 39 | # The following items will not be processed, by default. 40 | # Any item listed under the `exclude:` key here will be automatically added to 41 | # the internal "default list". 42 | # 43 | # Excluded items can be processed by explicitly listing the directories or 44 | # their entries' file path in the `include:` list. 45 | # 46 | # exclude: 47 | # - .sass-cache/ 48 | # - .jekyll-cache/ 49 | # - gemfiles/ 50 | # - Gemfile 51 | # - Gemfile.lock 52 | # - node_modules/ 53 | # - vendor/bundle/ 54 | # - vendor/cache/ 55 | # - vendor/gems/ 56 | # - vendor/ruby/ 57 | -------------------------------------------------------------------------------- /docs/_data/authors.yml: -------------------------------------------------------------------------------- 1 | - name: Seongyun Lee* 2 | affiliation: KAIST AI 3 | website: https://github.com/sylee0520 4 | img: seongyun.jpeg 5 | - name: Seungone Kim* 6 | affiliation: KAIST AI, NAVER AI Lab 7 | website: https://seungonekim.github.io/ 8 | img: seungone.jpeg 9 | - name: Sue Hyun Park 10 | affiliation: KAIST AI 11 | website: https://suehyunpark.github.io/ 12 | img: suehyun.png 13 | - name: Geewook Kim 14 | affiliation: KAIST AI, NAVER Cloud AI 15 | website: https://geewook.kim/ 16 | img: geewook.jpg 17 | - name: Minjoon Seo 18 | affiliation: KAIST AI 19 | website: https://seominjoon.github.io/ 20 | img: minjoon.jpeg 21 | -------------------------------------------------------------------------------- /docs/_posts/2021-01-16-welcome-to-jekyll.markdown: -------------------------------------------------------------------------------- 1 | --- 2 | layout: post 3 | title: "Welcome to Jekyll!" 4 | date: 2021-01-16 20:44:40 +0900 5 | categories: jekyll update 6 | --- 7 | You’ll find this post in your `_posts` directory. Go ahead and edit it and re-build the site to see your changes. You can rebuild the site in many different ways, but the most common way is to run `jekyll serve`, which launches a web server and auto-regenerates your site when a file is updated. 8 | 9 | Jekyll requires blog post files to be named according to the following format: 10 | 11 | `YEAR-MONTH-DAY-title.MARKUP` 12 | 13 | Where `YEAR` is a four-digit number, `MONTH` and `DAY` are both two-digit numbers, and `MARKUP` is the file extension representing the format used in the file. After that, include the necessary front matter. Take a look at the source for this post to get an idea about how it works. 14 | 15 | Jekyll also offers powerful support for code snippets: 16 | 17 | {% highlight ruby %} 18 | def print_hi(name) 19 | puts "Hi, #{name}" 20 | end 21 | print_hi('Tom') 22 | #=> prints 'Hi, Tom' to STDOUT. 23 | {% endhighlight %} 24 | 25 | Check out the [Jekyll docs][jekyll-docs] for more info on how to get the most out of Jekyll. File all bugs/feature requests at [Jekyll’s GitHub repo][jekyll-gh]. If you have questions, you can ask them on [Jekyll Talk][jekyll-talk]. 26 | 27 | [jekyll-docs]: https://jekyllrb.com/docs/home 28 | [jekyll-gh]: https://github.com/jekyll/jekyll 29 | [jekyll-talk]: https://talk.jekyllrb.com/ 30 | -------------------------------------------------------------------------------- /docs/_sass/main.scss: -------------------------------------------------------------------------------- 1 | body { 2 | display: flex; 3 | flex-direction: row; 4 | justify-content: center; 5 | } 6 | 7 | .whitespace { 8 | flex: 1; 9 | } 10 | 11 | .wrapper { 12 | flex: 2 2 50%; 13 | } 14 | -------------------------------------------------------------------------------- /docs/_site/404.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | EvalLM 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 24 | 25 | 26 | 27 |
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175 | Template from EvalLM by tsook. Licensed under MIT License. 176 |
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Jekyll!2021-01-16T20:44:40+09:002021-01-16T20:44:40+09:00/jekyll/update/2021/01/16/welcome-to-jekyll<p>You’ll find this post in your <code class="language-plaintext highlighter-rouge">_posts</code> directory. Go ahead and edit it and re-build the site to see your changes. You can rebuild the site in many different ways, but the most common way is to run <code class="language-plaintext highlighter-rouge">jekyll serve</code>, which launches a web server and auto-regenerates your site when a file is updated.</p> 2 | 3 | <p>Jekyll requires blog post files to be named according to the following format:</p> 4 | 5 | <p><code class="language-plaintext highlighter-rouge">YEAR-MONTH-DAY-title.MARKUP</code></p> 6 | 7 | <p>Where <code class="language-plaintext highlighter-rouge">YEAR</code> is a four-digit number, <code class="language-plaintext highlighter-rouge">MONTH</code> and <code class="language-plaintext highlighter-rouge">DAY</code> are both two-digit numbers, and <code class="language-plaintext highlighter-rouge">MARKUP</code> is the file extension representing the format used in the file. After that, include the necessary front matter. Take a look at the source for this post to get an idea about how it works.</p> 8 | 9 | <p>Jekyll also offers powerful support for code snippets:</p> 10 | 11 | <figure class="highlight"><pre><code class="language-ruby" data-lang="ruby"><span class="k">def</span> <span class="nf">print_hi</span><span class="p">(</span><span class="nb">name</span><span class="p">)</span> 12 | <span class="nb">puts</span> <span class="s2">"Hi, </span><span class="si">#{</span><span class="nb">name</span><span class="si">}</span><span class="s2">"</span> 13 | <span class="k">end</span> 14 | <span class="n">print_hi</span><span class="p">(</span><span class="s1">'Tom'</span><span class="p">)</span> 15 | <span class="c1">#=&gt; prints 'Hi, Tom' to STDOUT.</span></code></pre></figure> 16 | 17 | <p>Check out the <a href="https://jekyllrb.com/docs/home">Jekyll docs</a> for more info on how to get the most out of Jekyll. File all bugs/feature requests at <a href="https://github.com/jekyll/jekyll">Jekyll’s GitHub repo</a>. If you have questions, you can ask them on <a href="https://talk.jekyllrb.com/">Jekyll Talk</a>.</p>You’ll find this post in your _posts directory. Go ahead and edit it and re-build the site to see your changes. You can rebuild the site in many different ways, but the most common way is to run jekyll serve, which launches a web server and auto-regenerates your site when a file is updated. -------------------------------------------------------------------------------- /docs/about.markdown: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: About 4 | permalink: /about/ 5 | --- 6 | 7 | This is the base Jekyll theme. You can find out more info about customizing your Jekyll theme, as well as basic Jekyll usage documentation at [jekyllrb.com](https://jekyllrb.com/) 8 | 9 | You can find the source code for Minima at GitHub: 10 | [jekyll][jekyll-organization] / 11 | [minima](https://github.com/jekyll/minima) 12 | 13 | You can find the source code for Jekyll at GitHub: 14 | [jekyll][jekyll-organization] / 15 | [jekyll](https://github.com/jekyll/jekyll) 16 | 17 | 18 | [jekyll-organization]: https://github.com/jekyll 19 | -------------------------------------------------------------------------------- /docs/assets/css/styles.scss: -------------------------------------------------------------------------------- 1 | --- 2 | --- 3 | 4 | @import "{{ site.theme }}"; 5 | 6 | html { 7 | scroll-behavior:smooth 8 | } 9 | 10 | body { 11 | font-family: 'Noto Sans', sans-serif; 12 | } 13 | 14 | .wrapper { 15 | max-width: calc(940px - (30px * 2)); 16 | } 17 | 18 | h1 { 19 | font-weight: bold; 20 | font-size: 40px; 21 | margin-bottom: 0px; 22 | margin-top: 20px; 23 | } 24 | 25 | hr { 26 | margin-top: 32px; 27 | margin-bottom: 32px; 28 | background-color: #ccc; 29 | border: 0px; 30 | height: 1px; 31 | } 32 | 33 | h4 { 34 | color: #666; 35 | font-size: 28px; 36 | margin-bottom: 20px; 37 | } 38 | 39 | h2 { 40 | font-size: 24px; 41 | margin-bottom: 20px; 42 | color: #333; 43 | } 44 | 45 | h3 { 46 | font-size: 20px; 47 | padding-top: 12px; 48 | color: #333; 49 | } 50 | 51 | p { 52 | font-size: 18px; 53 | margin-bottom: 20px; 54 | color: #555; 55 | font-weight: 300; 56 | & > b { 57 | font-weight: 500; 58 | } 59 | } 60 | 61 | .video-wrapper { 62 | text-align: center; 63 | margin-bottom: 20px; 64 | position: relative; 65 | width: 100%; 66 | padding-bottom: 56.25%; 67 | } 68 | 69 | iframe { 70 | position: absolute; 71 | width: 100%; 72 | height: 100%; 73 | left: 0; 74 | top: 0; 75 | border-radius: 16px; 76 | box-shadow: rgba(0, 0, 0, 0.16) 0px 3px 6px, rgba(0, 0, 0, 0.23) 0px 3px 6px; 77 | } 78 | 79 | .authors-wrapper { 80 | text-align: center; 81 | } 82 | 83 | .author-container { 84 | display: inline-block; 85 | width: 12%; 86 | margin: 0 2% 0 2%; 87 | } 88 | 89 | .author-container p { 90 | margin-bottom: 0px; 91 | font-size: 14px; 92 | } 93 | 94 | .author-image { 95 | position: relative; 96 | width: 100%; 97 | padding-bottom: 100%; 98 | } 99 | 100 | .author-image img { 101 | position: absolute; 102 | width: 100%; 103 | height: 100%; 104 | top: 0; 105 | left: 0; 106 | border-radius: 50%; 107 | } 108 | 109 | .center { 110 | text-align: center; 111 | } 112 | 113 | .acknowledgement { 114 | color: #999; 115 | font-size: 16px; 116 | margin-bottom: 0px; 117 | } 118 | 119 | .logos { 120 | text-align: center; 121 | } 122 | 123 | .logos img { 124 | padding: 0 4px 0 4px; 125 | height: 24px; 126 | } 127 | 128 | .img-left { 129 | display: inline-block; 130 | width: 45%; 131 | } 132 | 133 | .text-right { 134 | display: inline-block; 135 | width: calc(54% - 16px); 136 | margin-left: 16px; 137 | vertical-align: middle; 138 | } 139 | 140 | .img-right { 141 | display: inline-block; 142 | width: 49%; 143 | } 144 | 145 | .text-left { 146 | display: inline-block; 147 | width: calc(50% - 16px); 148 | margin-right: 16px; 149 | vertical-align: middle; 150 | } 151 | 152 | .quote { 153 | width: 70%; 154 | margin-left: 15% 155 | } 156 | 157 | pre { 158 | display: block; 159 | font-size: 14px; 160 | color: #333; 161 | word-break: break-all; 162 | word-wrap: break-word; 163 | background-color: #f5f5f5; 164 | border: 1px solid #ccc; 165 | border-radius: 4px; 166 | white-space: pre-wrap; /* Since CSS 2.1 */ 167 | white-space: -moz-pre-wrap; /* Mozilla, since 1999 */ 168 | white-space: -pre-wrap; /* Opera 4-6 */ 169 | white-space: -o-pre-wrap; /* Opera 7 */ 170 | word-wrap: break-word; /* Internet Explorer 5.5+ */ 171 | } 172 | 173 | .sys-img { 174 | width: 90%; 175 | margin-left: 5%; 176 | } 177 | 178 | 179 | .button-container { 180 | display: flex; 181 | justify-content: center; 182 | align-items: center; 183 | gap: 16px; 184 | margin-top: 16px; 185 | } 186 | 187 | .button { 188 | display: flex; 189 | justify-content: center; 190 | align-items: center; 191 | gap: 8px; 192 | padding: 8px 16px; 193 | border-radius: 100px; 194 | background-color: #363636; 195 | color: #fff; 196 | fill: #fff; 197 | font-size: 16px; 198 | // width: 80px; 199 | cursor: pointer; 200 | 201 | &:visited { 202 | color: #fff; 203 | } 204 | &:active { 205 | color: #fff; 206 | } 207 | &:hover { 208 | background-color: #222222; 209 | color: #fff; 210 | text-decoration: none; 211 | } 212 | } 213 | 214 | .button-disabled { 215 | display: flex; 216 | justify-content: center; 217 | align-items: center; 218 | gap: 8px; 219 | padding: 8px 16px; 220 | border-radius: 100px; 221 | background-color: #787878; 222 | color: #fff; 223 | fill: #fff; 224 | font-size: 16px; 225 | width: 120px; 226 | cursor: default; 227 | 228 | &:visited { 229 | color: #fff; 230 | } 231 | &:active { 232 | color: #fff; 233 | } 234 | 235 | &:hover { 236 | text-decoration: none; 237 | color: #fff; 238 | } 239 | } 240 | 241 | .sys-name { 242 | font-variant: small-caps; 243 | // font-weight: bold; 244 | color: {{site.syscolor}}; 245 | } 246 | 247 | strong { 248 | font-weight: 500; 249 | } 250 | 251 | .footer { 252 | width: 100%; 253 | background-color: #f5f5f5; 254 | margin-top: 32px; 255 | 256 | & > div { 257 | max-width: calc(940px - (30px * 2)); 258 | margin-left: auto; 259 | margin-right: auto; 260 | padding: 16px 0; 261 | } 262 | } 263 | 264 | .credits { 265 | color: #777; 266 | font-size: 14px; 267 | margin-bottom: 0px; 268 | } 269 | 270 | @media (max-width: 600px) { 271 | h1 { 272 | font-size: 36px; 273 | } 274 | 275 | h4 { 276 | font-size: 20px; 277 | } 278 | 279 | h2 { 280 | font-size: 20px; 281 | } 282 | 283 | h3 { 284 | font-size: 16px; 285 | } 286 | 287 | p { 288 | font-size: 14px; 289 | } 290 | 291 | .author-container p { 292 | font-size: 10px; 293 | } 294 | 295 | pre { 296 | font-size: 10px; 297 | } 298 | 299 | .wrapper { 300 | max-width: calc(100vw - 30px); 301 | } 302 | 303 | .author-container { 304 | display: inline-block; 305 | width: 22%; 306 | margin: 0 1% 0 1%; 307 | } 308 | 309 | .img-left { 310 | display: block; 311 | text-align: center; 312 | width: 70%; 313 | margin-left: 15%; 314 | } 315 | 316 | .text-right { 317 | width: 100%; 318 | margin-left: 0px; 319 | } 320 | 321 | .sys-img { 322 | width: 100%; 323 | margin-left: 0; 324 | } 325 | 326 | .acknowledgement { 327 | font-size: 12px; 328 | } 329 | 330 | .credits { 331 | font-size: 10px; 332 | } 333 | 334 | .footer { 335 | & > div { 336 | max-width: calc(100vw - 30px); 337 | } 338 | } 339 | } 340 | -------------------------------------------------------------------------------- /docs/assets/favicon/android-chrome-192x192.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /docs/index.markdown: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | --- 4 | 5 | We introduce Prometheus-Vision, an evaluator Vision-Language Model (VLM) that is **open-source**, offers **reproducible** evaluation, and is **inexpensive** to use. We construct Perception-Collection, the first multimodal feedback dataset for training an evaluator VLM, which includes 15K **fine-grained scoring criteria** defined for each instance. Prometheus-Vision trained on Perception-Collection shows high correlation with human evaluators and GPT-4V, paving the way for accessible and transparent evaluation of VLMs. 6 | 7 | ------ 8 | 9 |
10 | 11 | ## VLM-as-a-Judge for Fine-Grained Evaluation 12 | 13 | Recent VLMs exhibit impressive visual instruction-following capabilities. To assess and compare the quality of VLM-generated outputs, we utilize VLMs as evaluator of VLMs, naming the approach as 'VLM-as-a-Judge'. 14 | 15 | {: .sys-img} 16 | ![vlm_as_a_judge](/assets/img/vlm_as_a_judge.svg) 17 | 18 | Traditional metrics for VLM evaluation measure the similarity between a response and the ground-truth answer. However, such automatic metrics fail to capture the rich context within the output. Also, these metrics do not explain what is missing or present in the response. 19 | 20 | An evaluator VLM can adhere to specific criteria of interest to focus on nuanced details in the visual context and instruction. Moreover, it can provide detailed language feedback that helps the user understand the reasoning behind the scoring. 21 | 22 |
23 | 24 | ## Multimodal Feedback Data 25 | 26 | The [Perception-Collection](https://huggingface.co/datasets/kaist-ai/Perception-Collection) dataset is targeted for fine-grained multimodal feedback generation. Each instance consists of 5 input components: an instruction, a real-world image, a response to evaluate, a customized score rubric, and a reference answer. Based on this, an evaluator VLM is trained to generate a language feedback and a score decision on a scale of 1 to 5. 27 | 28 | {: .sys-img} 29 | ![perception_collection](/assets/img/prometheus_vision_components.svg) 30 | 31 | We collect 5K real-world images sampled from the [COCO dataset](https://cocodataset.org/#home) and the [MMMU benchmark](https://arxiv.org/abs/2311.16502). Then, we augment the data in a 4-stage process: (1) hand-craft 50 seed score rubrics, (2) brainstorm and refine 15K fine-grained score rubrics, (3) augment 30K instructions and reference answers related to the score rubric, and (4) augment 150K responses and language feedback for training. From stage 2 to 4, we prompt GPT-4V to generate the data. We ensure that each generated score rubric aligns with the image and that there is no length bias in responses across the score range. 32 | 33 | {: .sys-img} 34 | ![perception_collection_stats](/assets/img/perception_collection_stats.png) 35 | 36 | We also release a held-out test set of the Perception-Collection called [Perception-Bench](https://huggingface.co/datasets/kaist-ai/Perception-Bench), which contains 500 instances and a single score rubric for each instance. 37 | 38 |
39 | 40 | ## Performance of Prometheus-Vision 41 | 42 | Using the Perception-Collection, we use [LLaVA-1.5](https://arxiv.org/abs/2310.03744) (7B & 13B) as our backbone model and train Prometheus-Vision ([7B](https://huggingface.co/kaist-ai/prometheus-vision-7b-v1.0) & [13B](https://huggingface.co/kaist-ai/prometheus-vision-13b-v1.0)). Through experiments, we demonstrate that Prometheus-Vision can be an effective open-source alternative to using human or GPT-4V for VLM evaluation. 43 | 44 | 45 | ### Simulating Human Evaluators 46 | 47 | Prometheus-Vision shows high correlation with human evaluators on instances with real-world images, LLaVA-Bench and Perception-Bench. Also, Prometheus-Vision 13B's feedback is as good as or better than GPT-4V's feedback 57.78% of the time. 48 | 49 | {: .img-left} 50 | ![human_corr](/assets/img/human_corr.svg) 51 | 52 | {: .img-right} 53 | ![pairwise_win_rate](/assets/img/pairwise_win_rate.svg) 54 | 55 | 56 | ### Simulating GPT-4V 57 | 58 | Prometheus-Vision shows the highest correlation with GPT-4V among open-source VLMs and outperforms GPT-3.5-Turbo and GPT-4 ('LM-as-a-Judge') in LLaVA-Bench and Perception-Bench. 59 | 60 | {: .sys-img} 61 | ![instructionfollowing_results](/assets/img/instructionfollowing_results.png) 62 | 63 | ------ 64 | 65 | ## Bibtex 66 | If you find our work useful in your work, please consider citing our paper: 67 | 68 | 
69 | @misc{lee2024prometheusvision,
70 |       title={Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation}, 
71 |       author={Seongyun Lee and Seungone Kim and Sue Hyun Park and Geewook Kim and Minjoon Seo},
72 |       year={2024},
73 |       eprint={2401.06591},
74 |       archivePrefix={arXiv},
75 |       primaryClass={cs.CL}
76 | }
77 |
78 | 79 | ------ 80 | 81 | {: .logos} 82 | [![Logo of KAIST](/assets/img/kaist_logo.png)](https://kaist.ac.kr) 83 | [![Logo of LKLab](/assets/img/lklab_logo.jpg)](https://lklab.kaist.ac.kr/) 84 | [![Logo of NAVER AI LAB](/assets/img/naver_ai_lab_logo.png)](https://www.facebook.com/NAVERAILAB) 85 | [![Logo of NAVER Cloud](/assets/img/naver_cloud_logo.png)](https://www.navercloudcorp.com/lang/en/) 86 | 87 | 89 | -------------------------------------------------------------------------------- /llava/__init__.py: -------------------------------------------------------------------------------- 1 | from .model import LlavaLlamaForCausalLM 2 | -------------------------------------------------------------------------------- /llava/constants.py: -------------------------------------------------------------------------------- 1 | CONTROLLER_HEART_BEAT_EXPIRATION = 30 2 | WORKER_HEART_BEAT_INTERVAL = 15 3 | 4 | LOGDIR = "." 5 | 6 | # Model Constants 7 | IGNORE_INDEX = -100 8 | IMAGE_TOKEN_INDEX = -200 9 | DEFAULT_IMAGE_TOKEN = "" 10 | DEFAULT_IMAGE_PATCH_TOKEN = "" 11 | DEFAULT_IM_START_TOKEN = "" 12 | DEFAULT_IM_END_TOKEN = "" 13 | IMAGE_PLACEHOLDER = "" 14 | -------------------------------------------------------------------------------- /llava/eval/eval_gpt_review.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | import os 4 | 5 | import openai 6 | import tqdm 7 | import ray 8 | import time 9 | 10 | NUM_SECONDS_TO_SLEEP = 3 11 | 12 | @ray.remote(num_cpus=4) 13 | def get_eval(content: str, max_tokens: int): 14 | while True: 15 | try: 16 | response = openai.ChatCompletion.create( 17 | model='gpt-4', 18 | messages=[{ 19 | 'role': 'system', 20 | 'content': 'You are a helpful and precise assistant for checking the quality of the answer.' 21 | }, { 22 | 'role': 'user', 23 | 'content': content, 24 | }], 25 | temperature=0.2, # TODO: figure out which temperature is best for evaluation 26 | max_tokens=max_tokens, 27 | ) 28 | break 29 | except openai.error.RateLimitError: 30 | pass 31 | except Exception as e: 32 | print(e) 33 | time.sleep(NUM_SECONDS_TO_SLEEP) 34 | 35 | print('success!') 36 | return response['choices'][0]['message']['content'] 37 | 38 | 39 | def parse_score(review): 40 | try: 41 | score_pair = review.split('\n')[0] 42 | score_pair = score_pair.replace(',', ' ') 43 | sp = score_pair.split(' ') 44 | if len(sp) == 2: 45 | return [float(sp[0]), float(sp[1])] 46 | else: 47 | print('error', review) 48 | return [-1, -1] 49 | except Exception as e: 50 | print(e) 51 | print('error', review) 52 | return [-1, -1] 53 | 54 | 55 | if __name__ == '__main__': 56 | parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.') 57 | parser.add_argument('-q', '--question') 58 | # parser.add_argument('-a', '--answer') 59 | parser.add_argument('-a', '--answer-list', nargs='+', default=[]) 60 | parser.add_argument('-r', '--rule') 61 | parser.add_argument('-o', '--output') 62 | parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') 63 | args = parser.parse_args() 64 | 65 | ray.init() 66 | 67 | f_q = open(os.path.expanduser(args.question)) 68 | f_ans1 = open(os.path.expanduser(args.answer_list[0])) 69 | f_ans2 = open(os.path.expanduser(args.answer_list[1])) 70 | rule_dict = json.load(open(os.path.expanduser(args.rule), 'r')) 71 | 72 | review_file = open(f'{args.output}', 'w') 73 | 74 | js_list = [] 75 | handles = [] 76 | idx = 0 77 | for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2): 78 | # if idx == 1: 79 | # break 80 | 81 | ques = json.loads(ques_js) 82 | ans1 = json.loads(ans1_js) 83 | ans2 = json.loads(ans2_js) 84 | 85 | category = json.loads(ques_js)['category'] 86 | if category in rule_dict: 87 | rule = rule_dict[category] 88 | else: 89 | rule = rule_dict['default'] 90 | prompt = rule['prompt'] 91 | role = rule['role'] 92 | content = (f'[Question]\n{ques["text"]}\n\n' 93 | f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n' 94 | f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n' 95 | f'[System]\n{prompt}\n\n') 96 | js_list.append({ 97 | 'id': idx+1, 98 | 'question_id': ques['question_id'], 99 | 'answer1_id': ans1['answer_id'], 100 | 'answer2_id': ans2['answer_id'], 101 | 'category': category}) 102 | idx += 1 103 | handles.append(get_eval.remote(content, args.max_tokens)) 104 | # To avoid the rate limit set by OpenAI 105 | time.sleep(NUM_SECONDS_TO_SLEEP) 106 | 107 | reviews = ray.get(handles) 108 | for idx, review in enumerate(reviews): 109 | scores = parse_score(review) 110 | js_list[idx]['content'] = review 111 | js_list[idx]['tuple'] = scores 112 | review_file.write(json.dumps(js_list[idx]) + '\n') 113 | review_file.close() 114 | -------------------------------------------------------------------------------- /llava/eval/eval_gpt_review_bench.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | import os 4 | 5 | import openai 6 | import time 7 | 8 | NUM_SECONDS_TO_SLEEP = 0.5 9 | 10 | 11 | def get_eval(content: str, max_tokens: int): 12 | while True: 13 | try: 14 | response = openai.ChatCompletion.create( 15 | model='gpt-4-0314', 16 | messages=[{ 17 | 'role': 'system', 18 | 'content': 'You are a helpful and precise assistant for checking the quality of the answer.' 19 | }, { 20 | 'role': 'user', 21 | 'content': content, 22 | }], 23 | temperature=0.2, # TODO: figure out which temperature is best for evaluation 24 | max_tokens=max_tokens, 25 | ) 26 | break 27 | except openai.error.RateLimitError: 28 | pass 29 | except Exception as e: 30 | print(e) 31 | time.sleep(NUM_SECONDS_TO_SLEEP) 32 | 33 | return response['choices'][0]['message']['content'] 34 | 35 | 36 | def parse_score(review): 37 | try: 38 | score_pair = review.split('\n')[0] 39 | score_pair = score_pair.replace(',', ' ') 40 | sp = score_pair.split(' ') 41 | if len(sp) == 2: 42 | return [float(sp[0]), float(sp[1])] 43 | else: 44 | print('error', review) 45 | return [-1, -1] 46 | except Exception as e: 47 | print(e) 48 | print('error', review) 49 | return [-1, -1] 50 | 51 | 52 | if __name__ == '__main__': 53 | parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.') 54 | parser.add_argument('-q', '--question') 55 | parser.add_argument('-c', '--context') 56 | parser.add_argument('-a', '--answer-list', nargs='+', default=[]) 57 | parser.add_argument('-r', '--rule') 58 | parser.add_argument('-o', '--output') 59 | parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') 60 | args = parser.parse_args() 61 | 62 | f_q = open(os.path.expanduser(args.question)) 63 | f_ans1 = open(os.path.expanduser(args.answer_list[0])) 64 | f_ans2 = open(os.path.expanduser(args.answer_list[1])) 65 | rule_dict = json.load(open(os.path.expanduser(args.rule), 'r')) 66 | 67 | if os.path.isfile(os.path.expanduser(args.output)): 68 | cur_reviews = [json.loads(line) for line in open(os.path.expanduser(args.output))] 69 | else: 70 | cur_reviews = [] 71 | 72 | review_file = open(f'{args.output}', 'a') 73 | 74 | context_list = [json.loads(line) for line in open(os.path.expanduser(args.context))] 75 | image_to_context = {context['image']: context for context in context_list} 76 | 77 | handles = [] 78 | idx = 0 79 | for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2): 80 | ques = json.loads(ques_js) 81 | ans1 = json.loads(ans1_js) 82 | ans2 = json.loads(ans2_js) 83 | 84 | inst = image_to_context[ques['image']] 85 | 86 | if isinstance(inst['caption'], list): 87 | cap_str = '\n'.join(inst['caption']) 88 | else: 89 | cap_str = inst['caption'] 90 | 91 | category = 'llava_bench_' + json.loads(ques_js)['category'] 92 | if category in rule_dict: 93 | rule = rule_dict[category] 94 | else: 95 | assert False, f"Visual QA category not found in rule file: {category}." 96 | prompt = rule['prompt'] 97 | role = rule['role'] 98 | content = (f'[Context]\n{cap_str}\n\n' 99 | f'[Question]\n{ques["text"]}\n\n' 100 | f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n' 101 | f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n' 102 | f'[System]\n{prompt}\n\n') 103 | cur_js = { 104 | 'id': idx+1, 105 | 'question_id': ques['question_id'], 106 | 'answer1_id': ans1.get('answer_id', ans1['question_id']), 107 | 'answer2_id': ans2.get('answer_id', ans2['answer_id']), 108 | 'category': category 109 | } 110 | if idx >= len(cur_reviews): 111 | review = get_eval(content, args.max_tokens) 112 | scores = parse_score(review) 113 | cur_js['content'] = review 114 | cur_js['tuple'] = scores 115 | review_file.write(json.dumps(cur_js) + '\n') 116 | review_file.flush() 117 | else: 118 | print(f'Skipping {idx} as we already have it.') 119 | idx += 1 120 | print(idx) 121 | review_file.close() 122 | -------------------------------------------------------------------------------- /llava/eval/eval_gpt_review_visual.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | import os 4 | 5 | import openai 6 | import time 7 | 8 | NUM_SECONDS_TO_SLEEP = 0.5 9 | 10 | 11 | def get_eval(content: str, max_tokens: int): 12 | while True: 13 | try: 14 | response = openai.ChatCompletion.create( 15 | model='gpt-4-0314', 16 | messages=[{ 17 | 'role': 'system', 18 | 'content': 'You are a helpful and precise assistant for checking the quality of the answer.' 19 | }, { 20 | 'role': 'user', 21 | 'content': content, 22 | }], 23 | temperature=0.2, # TODO: figure out which temperature is best for evaluation 24 | max_tokens=max_tokens, 25 | ) 26 | break 27 | except openai.error.RateLimitError: 28 | pass 29 | except Exception as e: 30 | print(e) 31 | time.sleep(NUM_SECONDS_TO_SLEEP) 32 | 33 | return response['choices'][0]['message']['content'] 34 | 35 | 36 | def parse_score(review): 37 | try: 38 | score_pair = review.split('\n')[0] 39 | score_pair = score_pair.replace(',', ' ') 40 | sp = score_pair.split(' ') 41 | if len(sp) == 2: 42 | return [float(sp[0]), float(sp[1])] 43 | else: 44 | print('error', review) 45 | return [-1, -1] 46 | except Exception as e: 47 | print(e) 48 | print('error', review) 49 | return [-1, -1] 50 | 51 | 52 | if __name__ == '__main__': 53 | parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.') 54 | parser.add_argument('-q', '--question') 55 | parser.add_argument('-c', '--context') 56 | parser.add_argument('-a', '--answer-list', nargs='+', default=[]) 57 | parser.add_argument('-r', '--rule') 58 | parser.add_argument('-o', '--output') 59 | parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') 60 | args = parser.parse_args() 61 | 62 | f_q = open(os.path.expanduser(args.question)) 63 | f_ans1 = open(os.path.expanduser(args.answer_list[0])) 64 | f_ans2 = open(os.path.expanduser(args.answer_list[1])) 65 | rule_dict = json.load(open(os.path.expanduser(args.rule), 'r')) 66 | 67 | if os.path.isfile(os.path.expanduser(args.output)): 68 | cur_reviews = [json.loads(line) for line in open(os.path.expanduser(args.output))] 69 | else: 70 | cur_reviews = [] 71 | 72 | review_file = open(f'{args.output}', 'a') 73 | 74 | context_list = [json.loads(line) for line in open(os.path.expanduser(args.context))] 75 | image_to_context = {context['image']: context for context in context_list} 76 | 77 | handles = [] 78 | idx = 0 79 | for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2): 80 | ques = json.loads(ques_js) 81 | ans1 = json.loads(ans1_js) 82 | ans2 = json.loads(ans2_js) 83 | 84 | inst = image_to_context[ques['image']] 85 | cap_str = '\n'.join(inst['captions']) 86 | box_str = '\n'.join([f'{instance["category"]}: {instance["bbox"]}' for instance in inst['instances']]) 87 | 88 | category = json.loads(ques_js)['category'] 89 | if category in rule_dict: 90 | rule = rule_dict[category] 91 | else: 92 | assert False, f"Visual QA category not found in rule file: {category}." 93 | prompt = rule['prompt'] 94 | role = rule['role'] 95 | content = (f'[Context]\n{cap_str}\n\n{box_str}\n\n' 96 | f'[Question]\n{ques["text"]}\n\n' 97 | f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n' 98 | f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n' 99 | f'[System]\n{prompt}\n\n') 100 | cur_js = { 101 | 'id': idx+1, 102 | 'question_id': ques['question_id'], 103 | 'answer1_id': ans1.get('answer_id', ans1['question_id']), 104 | 'answer2_id': ans2.get('answer_id', ans2['answer_id']), 105 | 'category': category 106 | } 107 | if idx >= len(cur_reviews): 108 | review = get_eval(content, args.max_tokens) 109 | scores = parse_score(review) 110 | cur_js['content'] = review 111 | cur_js['tuple'] = scores 112 | review_file.write(json.dumps(cur_js) + '\n') 113 | review_file.flush() 114 | else: 115 | print(f'Skipping {idx} as we already have it.') 116 | idx += 1 117 | print(idx) 118 | review_file.close() 119 | -------------------------------------------------------------------------------- /llava/eval/eval_pope.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import argparse 4 | 5 | def eval_pope(answers, label_file): 6 | label_list = [json.loads(q)['label'] for q in open(label_file, 'r')] 7 | 8 | for answer in answers: 9 | text = answer['text'] 10 | 11 | # Only keep the first sentence 12 | if text.find('.') != -1: 13 | text = text.split('.')[0] 14 | 15 | text = text.replace(',', '') 16 | words = text.split(' ') 17 | if 'No' in words or 'not' in words or 'no' in words: 18 | answer['text'] = 'no' 19 | else: 20 | answer['text'] = 'yes' 21 | 22 | for i in range(len(label_list)): 23 | if label_list[i] == 'no': 24 | label_list[i] = 0 25 | else: 26 | label_list[i] = 1 27 | 28 | pred_list = [] 29 | for answer in answers: 30 | if answer['text'] == 'no': 31 | pred_list.append(0) 32 | else: 33 | pred_list.append(1) 34 | 35 | pos = 1 36 | neg = 0 37 | yes_ratio = pred_list.count(1) / len(pred_list) 38 | 39 | TP, TN, FP, FN = 0, 0, 0, 0 40 | for pred, label in zip(pred_list, label_list): 41 | if pred == pos and label == pos: 42 | TP += 1 43 | elif pred == pos and label == neg: 44 | FP += 1 45 | elif pred == neg and label == neg: 46 | TN += 1 47 | elif pred == neg and label == pos: 48 | FN += 1 49 | 50 | print('TP\tFP\tTN\tFN\t') 51 | print('{}\t{}\t{}\t{}'.format(TP, FP, TN, FN)) 52 | 53 | precision = float(TP) / float(TP + FP) 54 | recall = float(TP) / float(TP + FN) 55 | f1 = 2*precision*recall / (precision + recall) 56 | acc = (TP + TN) / (TP + TN + FP + FN) 57 | print('Accuracy: {}'.format(acc)) 58 | print('Precision: {}'.format(precision)) 59 | print('Recall: {}'.format(recall)) 60 | print('F1 score: {}'.format(f1)) 61 | print('Yes ratio: {}'.format(yes_ratio)) 62 | print('%.3f, %.3f, %.3f, %.3f, %.3f' % (f1, acc, precision, recall, yes_ratio) ) 63 | 64 | if __name__ == "__main__": 65 | parser = argparse.ArgumentParser() 66 | parser.add_argument("--annotation-dir", type=str) 67 | parser.add_argument("--question-file", type=str) 68 | parser.add_argument("--result-file", type=str) 69 | args = parser.parse_args() 70 | 71 | questions = [json.loads(line) for line in open(args.question_file)] 72 | questions = {question['question_id']: question for question in questions} 73 | answers = [json.loads(q) for q in open(args.result_file)] 74 | for file in os.listdir(args.annotation_dir): 75 | assert file.startswith('coco_pope_') 76 | assert file.endswith('.json') 77 | category = file[10:-5] 78 | cur_answers = [x for x in answers if questions[x['question_id']]['category'] == category] 79 | print('Category: {}, # samples: {}'.format(category, len(cur_answers))) 80 | eval_pope(cur_answers, os.path.join(args.annotation_dir, file)) 81 | print("====================================") 82 | -------------------------------------------------------------------------------- /llava/eval/eval_science_qa.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | import os 4 | import re 5 | import random 6 | 7 | 8 | def get_args(): 9 | parser = argparse.ArgumentParser() 10 | parser.add_argument('--base-dir', type=str) 11 | parser.add_argument('--result-file', type=str) 12 | parser.add_argument('--output-file', type=str) 13 | parser.add_argument('--output-result', type=str) 14 | parser.add_argument('--split', type=str, default='test') 15 | parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"]) 16 | return parser.parse_args() 17 | 18 | 19 | def convert_caps(results): 20 | fakecaps = [] 21 | for result in results: 22 | image_id = result['question_id'] 23 | caption = result['text'] 24 | fakecaps.append({"image_id": int(image_id), "caption": caption}) 25 | return fakecaps 26 | 27 | 28 | def get_pred_idx(prediction, choices, options): 29 | """ 30 | Get the index (e.g. 2) from the prediction (e.g. 'C') 31 | """ 32 | if prediction in options[:len(choices)]: 33 | return options.index(prediction) 34 | else: 35 | return -1 36 | return random.choice(range(len(choices))) 37 | 38 | 39 | if __name__ == "__main__": 40 | args = get_args() 41 | 42 | base_dir = args.base_dir 43 | split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split] 44 | problems = json.load(open(os.path.join(base_dir, "problems.json"))) 45 | predictions = [json.loads(line) for line in open(args.result_file)] 46 | predictions = {pred['question_id']: pred for pred in predictions} 47 | split_problems = {idx: problems[idx] for idx in split_indices} 48 | 49 | results = {'correct': [], 'incorrect': []} 50 | sqa_results = {} 51 | sqa_results['acc'] = None 52 | sqa_results['correct'] = None 53 | sqa_results['count'] = None 54 | sqa_results['results'] = {} 55 | sqa_results['outputs'] = {} 56 | 57 | for prob_id, prob in split_problems.items(): 58 | if prob_id not in predictions: 59 | pred = {'text': 'FAILED', 'prompt': 'Unknown'} 60 | pred_text = 'FAILED' 61 | else: 62 | pred = predictions[prob_id] 63 | pred_text = pred['text'] 64 | 65 | if pred_text in args.options: 66 | answer = pred_text 67 | elif len(pred_text) >= 3 and pred_text[0] in args.options and pred_text[1:3] == ". ": 68 | answer = pred_text[0] 69 | else: 70 | pattern = re.compile(r'The answer is ([A-Z]).') 71 | res = pattern.findall(pred_text) 72 | if len(res) == 1: 73 | answer = res[0] # 'A', 'B', ... 74 | else: 75 | answer = "FAILED" 76 | 77 | pred_idx = get_pred_idx(answer, prob['choices'], args.options) 78 | 79 | analysis = { 80 | 'question_id': prob_id, 81 | 'parsed_ans': answer, 82 | 'ground_truth': args.options[prob['answer']], 83 | 'question': pred['prompt'], 84 | 'pred': pred_text, 85 | 'is_multimodal': '' in pred['prompt'], 86 | } 87 | 88 | sqa_results['results'][prob_id] = get_pred_idx(answer, prob['choices'], args.options) 89 | sqa_results['outputs'][prob_id] = pred_text 90 | 91 | if pred_idx == prob['answer']: 92 | results['correct'].append(analysis) 93 | else: 94 | results['incorrect'].append(analysis) 95 | 96 | correct = len(results['correct']) 97 | total = len(results['correct']) + len(results['incorrect']) 98 | 99 | ###### IMG ###### 100 | multimodal_correct = len([x for x in results['correct'] if x['is_multimodal']]) 101 | multimodal_incorrect = len([x for x in results['incorrect'] if x['is_multimodal']]) 102 | multimodal_total = multimodal_correct + multimodal_incorrect 103 | ###### IMG ###### 104 | 105 | print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%, IMG-Accuracy: {multimodal_correct / multimodal_total * 100:.2f}%') 106 | 107 | sqa_results['acc'] = correct / total * 100 108 | sqa_results['correct'] = correct 109 | sqa_results['count'] = total 110 | 111 | with open(args.output_file, 'w') as f: 112 | json.dump(results, f, indent=2) 113 | with open(args.output_result, 'w') as f: 114 | json.dump(sqa_results, f, indent=2) 115 | -------------------------------------------------------------------------------- /llava/eval/eval_science_qa_gpt4.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | import os 4 | import re 5 | import random 6 | from collections import defaultdict 7 | 8 | 9 | def get_args(): 10 | parser = argparse.ArgumentParser() 11 | parser.add_argument('--base-dir', type=str) 12 | parser.add_argument('--gpt4-result', type=str) 13 | parser.add_argument('--our-result', type=str) 14 | parser.add_argument('--split', type=str, default='test') 15 | parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"]) 16 | return parser.parse_args() 17 | 18 | 19 | def convert_caps(results): 20 | fakecaps = [] 21 | for result in results: 22 | image_id = result['question_id'] 23 | caption = result['text'] 24 | fakecaps.append({"image_id": int(image_id), "caption": caption}) 25 | return fakecaps 26 | 27 | 28 | def get_pred_idx(prediction, choices, options): 29 | """ 30 | Get the index (e.g. 2) from the prediction (e.g. 'C') 31 | """ 32 | if prediction in options[:len(choices)]: 33 | return options.index(prediction) 34 | else: 35 | return random.choice(range(len(choices))) 36 | 37 | 38 | if __name__ == "__main__": 39 | args = get_args() 40 | 41 | base_dir = args.base_dir 42 | split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split] 43 | problems = json.load(open(os.path.join(base_dir, "problems.json"))) 44 | our_predictions = [json.loads(line) for line in open(args.our_result)] 45 | our_predictions = {pred['question_id']: pred for pred in our_predictions} 46 | split_problems = {idx: problems[idx] for idx in split_indices} 47 | 48 | gpt4_predictions = json.load(open(args.gpt4_result))['outputs'] 49 | 50 | results = defaultdict(lambda: 0) 51 | 52 | for prob_id, prob in split_problems.items(): 53 | if prob_id not in our_predictions: 54 | continue 55 | if prob_id not in gpt4_predictions: 56 | continue 57 | our_pred = our_predictions[prob_id]['text'] 58 | gpt4_pred = gpt4_predictions[prob_id] 59 | 60 | pattern = re.compile(r'The answer is ([A-Z]).') 61 | our_res = pattern.findall(our_pred) 62 | if len(our_res) == 1: 63 | our_answer = our_res[0] # 'A', 'B', ... 64 | else: 65 | our_answer = "FAILED" 66 | gpt4_res = pattern.findall(gpt4_pred) 67 | if len(gpt4_res) == 1: 68 | gpt4_answer = gpt4_res[0] # 'A', 'B', ... 69 | else: 70 | gpt4_answer = "FAILED" 71 | 72 | our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options) 73 | gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options) 74 | 75 | if gpt4_answer == 'FAILED': 76 | results['gpt4_failed'] += 1 77 | # continue 78 | gpt4_pred_idx = our_pred_idx 79 | # if our_pred_idx != prob['answer']: 80 | # print(our_predictions[prob_id]['prompt']) 81 | # print('-----------------') 82 | # print(f'LECTURE: {prob["lecture"]}') 83 | # print(f'SOLUTION: {prob["solution"]}') 84 | # print('=====================') 85 | else: 86 | # continue 87 | pass 88 | # gpt4_pred_idx = our_pred_idx 89 | 90 | if gpt4_pred_idx == prob['answer']: 91 | results['correct'] += 1 92 | else: 93 | results['incorrect'] += 1 94 | 95 | 96 | if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']: 97 | results['correct_upperbound'] += 1 98 | 99 | correct = results['correct'] 100 | total = results['correct'] + results['incorrect'] 101 | print(f'Total: {total}, Correct: {correct}, Accuracy: {correct / total * 100:.2f}%') 102 | print(f'Total: {total}, Correct (upper): {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%') 103 | print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%') 104 | 105 | -------------------------------------------------------------------------------- /llava/eval/eval_science_qa_gpt4_requery.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | import os 4 | import re 5 | import random 6 | from collections import defaultdict 7 | 8 | 9 | def get_args(): 10 | parser = argparse.ArgumentParser() 11 | parser.add_argument('--base-dir', type=str) 12 | parser.add_argument('--gpt4-result', type=str) 13 | parser.add_argument('--requery-result', type=str) 14 | parser.add_argument('--our-result', type=str) 15 | parser.add_argument('--output-result', type=str) 16 | parser.add_argument('--split', type=str, default='test') 17 | parser.add_argument('--options', type=list, default=["A", "B", "C", "D", "E"]) 18 | return parser.parse_args() 19 | 20 | 21 | def convert_caps(results): 22 | fakecaps = [] 23 | for result in results: 24 | image_id = result['question_id'] 25 | caption = result['text'] 26 | fakecaps.append({"image_id": int(image_id), "caption": caption}) 27 | return fakecaps 28 | 29 | 30 | def get_pred_idx(prediction, choices, options): 31 | """ 32 | Get the index (e.g. 2) from the prediction (e.g. 'C') 33 | """ 34 | if prediction in options[:len(choices)]: 35 | return options.index(prediction) 36 | else: 37 | return random.choice(range(len(choices))) 38 | 39 | 40 | if __name__ == "__main__": 41 | args = get_args() 42 | 43 | base_dir = args.base_dir 44 | split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[args.split] 45 | problems = json.load(open(os.path.join(base_dir, "problems.json"))) 46 | our_predictions = [json.loads(line) for line in open(args.our_result)] 47 | our_predictions = {pred['question_id']: pred for pred in our_predictions} 48 | split_problems = {idx: problems[idx] for idx in split_indices} 49 | 50 | requery_predictions = [json.loads(line) for line in open(args.requery_result)] 51 | requery_predictions = {pred['question_id']: pred for pred in requery_predictions} 52 | 53 | gpt4_predictions = json.load(open(args.gpt4_result))['outputs'] 54 | 55 | results = defaultdict(lambda: 0) 56 | 57 | sqa_results = {} 58 | sqa_results['acc'] = None 59 | sqa_results['correct'] = None 60 | sqa_results['count'] = None 61 | sqa_results['results'] = {} 62 | sqa_results['outputs'] = {} 63 | 64 | for prob_id, prob in split_problems.items(): 65 | if prob_id not in our_predictions: 66 | assert False 67 | if prob_id not in gpt4_predictions: 68 | assert False 69 | our_pred = our_predictions[prob_id]['text'] 70 | gpt4_pred = gpt4_predictions[prob_id] 71 | if prob_id not in requery_predictions: 72 | results['missing_requery'] += 1 73 | requery_pred = "MISSING" 74 | else: 75 | requery_pred = requery_predictions[prob_id]['text'] 76 | 77 | pattern = re.compile(r'The answer is ([A-Z]).') 78 | our_res = pattern.findall(our_pred) 79 | if len(our_res) == 1: 80 | our_answer = our_res[0] # 'A', 'B', ... 81 | else: 82 | our_answer = "FAILED" 83 | 84 | requery_res = pattern.findall(requery_pred) 85 | if len(requery_res) == 1: 86 | requery_answer = requery_res[0] # 'A', 'B', ... 87 | else: 88 | requery_answer = "FAILED" 89 | 90 | gpt4_res = pattern.findall(gpt4_pred) 91 | if len(gpt4_res) == 1: 92 | gpt4_answer = gpt4_res[0] # 'A', 'B', ... 93 | else: 94 | gpt4_answer = "FAILED" 95 | 96 | our_pred_idx = get_pred_idx(our_answer, prob['choices'], args.options) 97 | gpt4_pred_idx = get_pred_idx(gpt4_answer, prob['choices'], args.options) 98 | requery_pred_idx = get_pred_idx(requery_answer, prob['choices'], args.options) 99 | 100 | results['total'] += 1 101 | 102 | if gpt4_answer == 'FAILED': 103 | results['gpt4_failed'] += 1 104 | if gpt4_pred_idx == prob['answer']: 105 | results['gpt4_correct'] += 1 106 | if our_pred_idx == prob['answer']: 107 | results['gpt4_ourvisual_correct'] += 1 108 | elif gpt4_pred_idx == prob['answer']: 109 | results['gpt4_correct'] += 1 110 | results['gpt4_ourvisual_correct'] += 1 111 | 112 | if our_pred_idx == prob['answer']: 113 | results['our_correct'] += 1 114 | 115 | if requery_answer == 'FAILED': 116 | sqa_results['results'][prob_id] = our_pred_idx 117 | if our_pred_idx == prob['answer']: 118 | results['requery_correct'] += 1 119 | else: 120 | sqa_results['results'][prob_id] = requery_pred_idx 121 | if requery_pred_idx == prob['answer']: 122 | results['requery_correct'] += 1 123 | else: 124 | print(f""" 125 | Question ({args.options[prob['answer']]}): {our_predictions[prob_id]['prompt']} 126 | Our ({our_answer}): {our_pred} 127 | GPT-4 ({gpt4_answer}): {gpt4_pred} 128 | Requery ({requery_answer}): {requery_pred} 129 | print("=====================================") 130 | """) 131 | 132 | if gpt4_pred_idx == prob['answer'] or our_pred_idx == prob['answer']: 133 | results['correct_upperbound'] += 1 134 | 135 | total = results['total'] 136 | print(f'Total: {total}, Our-Correct: {results["our_correct"]}, Accuracy: {results["our_correct"] / total * 100:.2f}%') 137 | print(f'Total: {total}, GPT-4-Correct: {results["gpt4_correct"]}, Accuracy: {results["gpt4_correct"] / total * 100:.2f}%') 138 | print(f'Total: {total}, GPT-4 NO-ANS (RANDOM): {results["gpt4_failed"]}, Percentage: {results["gpt4_failed"] / total * 100:.2f}%') 139 | print(f'Total: {total}, GPT-4-OursVisual-Correct: {results["gpt4_ourvisual_correct"]}, Accuracy: {results["gpt4_ourvisual_correct"] / total * 100:.2f}%') 140 | print(f'Total: {total}, Requery-Correct: {results["requery_correct"]}, Accuracy: {results["requery_correct"] / total * 100:.2f}%') 141 | print(f'Total: {total}, Correct upper: {results["correct_upperbound"]}, Accuracy: {results["correct_upperbound"] / total * 100:.2f}%') 142 | 143 | sqa_results['acc'] = results["requery_correct"] / total * 100 144 | sqa_results['correct'] = results["requery_correct"] 145 | sqa_results['count'] = total 146 | 147 | with open(args.output_result, 'w') as f: 148 | json.dump(sqa_results, f, indent=2) 149 | 150 | -------------------------------------------------------------------------------- /llava/eval/eval_textvqa.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | import json 4 | import re 5 | 6 | from llava.eval.m4c_evaluator import TextVQAAccuracyEvaluator 7 | 8 | 9 | def get_args(): 10 | parser = argparse.ArgumentParser() 11 | parser.add_argument('--annotation-file', type=str) 12 | parser.add_argument('--result-file', type=str) 13 | parser.add_argument('--result-dir', type=str) 14 | return parser.parse_args() 15 | 16 | 17 | def prompt_processor(prompt): 18 | if prompt.startswith('OCR tokens: '): 19 | pattern = r"Question: (.*?) Short answer:" 20 | match = re.search(pattern, prompt, re.DOTALL) 21 | question = match.group(1) 22 | elif 'Reference OCR token: ' in prompt and len(prompt.split('\n')) == 3: 23 | if prompt.startswith('Reference OCR token:'): 24 | question = prompt.split('\n')[1] 25 | else: 26 | question = prompt.split('\n')[0] 27 | elif len(prompt.split('\n')) == 2: 28 | question = prompt.split('\n')[0] 29 | else: 30 | assert False 31 | 32 | return question.lower() 33 | 34 | 35 | def eval_single(annotation_file, result_file): 36 | experiment_name = os.path.splitext(os.path.basename(result_file))[0] 37 | print(experiment_name) 38 | annotations = json.load(open(annotation_file))['data'] 39 | annotations = {(annotation['image_id'], annotation['question'].lower()): annotation for annotation in annotations} 40 | results = [json.loads(line) for line in open(result_file)] 41 | 42 | pred_list = [] 43 | for result in results: 44 | annotation = annotations[(result['question_id'], prompt_processor(result['prompt']))] 45 | pred_list.append({ 46 | "pred_answer": result['text'], 47 | "gt_answers": annotation['answers'], 48 | }) 49 | 50 | evaluator = TextVQAAccuracyEvaluator() 51 | print('Samples: {}\nAccuracy: {:.2f}%\n'.format(len(pred_list), 100. * evaluator.eval_pred_list(pred_list))) 52 | 53 | 54 | if __name__ == "__main__": 55 | args = get_args() 56 | 57 | if args.result_file is not None: 58 | eval_single(args.annotation_file, args.result_file) 59 | 60 | if args.result_dir is not None: 61 | for result_file in sorted(os.listdir(args.result_dir)): 62 | if not result_file.endswith('.jsonl'): 63 | print(f'Skipping {result_file}') 64 | continue 65 | eval_single(args.annotation_file, os.path.join(args.result_dir, result_file)) 66 | -------------------------------------------------------------------------------- /llava/eval/generate_webpage_data_from_table.py: -------------------------------------------------------------------------------- 1 | """Generate json file for webpage.""" 2 | import json 3 | import os 4 | import re 5 | 6 | # models = ['llama', 'alpaca', 'gpt35', 'bard'] 7 | models = ['vicuna'] 8 | 9 | 10 | def read_jsonl(path: str, key: str=None): 11 | data = [] 12 | with open(os.path.expanduser(path)) as f: 13 | for line in f: 14 | if not line: 15 | continue 16 | data.append(json.loads(line)) 17 | if key is not None: 18 | data.sort(key=lambda x: x[key]) 19 | data = {item[key]: item for item in data} 20 | return data 21 | 22 | 23 | def trim_hanging_lines(s: str, n: int) -> str: 24 | s = s.strip() 25 | for _ in range(n): 26 | s = s.split('\n', 1)[1].strip() 27 | return s 28 | 29 | 30 | if __name__ == '__main__': 31 | questions = read_jsonl('table/question.jsonl', key='question_id') 32 | 33 | # alpaca_answers = read_jsonl('table/answer/answer_alpaca-13b.jsonl', key='question_id') 34 | # bard_answers = read_jsonl('table/answer/answer_bard.jsonl', key='question_id') 35 | # gpt35_answers = read_jsonl('table/answer/answer_gpt35.jsonl', key='question_id') 36 | # llama_answers = read_jsonl('table/answer/answer_llama-13b.jsonl', key='question_id') 37 | vicuna_answers = read_jsonl('table/answer/answer_vicuna-13b.jsonl', key='question_id') 38 | ours_answers = read_jsonl('table/results/llama-13b-hf-alpaca.jsonl', key='question_id') 39 | 40 | review_vicuna = read_jsonl('table/review/review_vicuna-13b_llama-13b-hf-alpaca.jsonl', key='question_id') 41 | # review_alpaca = read_jsonl('table/review/review_alpaca-13b_vicuna-13b.jsonl', key='question_id') 42 | # review_bard = read_jsonl('table/review/review_bard_vicuna-13b.jsonl', key='question_id') 43 | # review_gpt35 = read_jsonl('table/review/review_gpt35_vicuna-13b.jsonl', key='question_id') 44 | # review_llama = read_jsonl('table/review/review_llama-13b_vicuna-13b.jsonl', key='question_id') 45 | 46 | records = [] 47 | for qid in questions.keys(): 48 | r = { 49 | 'id': qid, 50 | 'category': questions[qid]['category'], 51 | 'question': questions[qid]['text'], 52 | 'answers': { 53 | # 'alpaca': alpaca_answers[qid]['text'], 54 | # 'llama': llama_answers[qid]['text'], 55 | # 'bard': bard_answers[qid]['text'], 56 | # 'gpt35': gpt35_answers[qid]['text'], 57 | 'vicuna': vicuna_answers[qid]['text'], 58 | 'ours': ours_answers[qid]['text'], 59 | }, 60 | 'evaluations': { 61 | # 'alpaca': review_alpaca[qid]['text'], 62 | # 'llama': review_llama[qid]['text'], 63 | # 'bard': review_bard[qid]['text'], 64 | 'vicuna': review_vicuna[qid]['content'], 65 | # 'gpt35': review_gpt35[qid]['text'], 66 | }, 67 | 'scores': { 68 | 'vicuna': review_vicuna[qid]['tuple'], 69 | # 'alpaca': review_alpaca[qid]['score'], 70 | # 'llama': review_llama[qid]['score'], 71 | # 'bard': review_bard[qid]['score'], 72 | # 'gpt35': review_gpt35[qid]['score'], 73 | }, 74 | } 75 | 76 | # cleanup data 77 | cleaned_evals = {} 78 | for k, v in r['evaluations'].items(): 79 | v = v.strip() 80 | lines = v.split('\n') 81 | # trim the first line if it's a pair of numbers 82 | if re.match(r'\d+[, ]+\d+', lines[0]): 83 | lines = lines[1:] 84 | v = '\n'.join(lines) 85 | cleaned_evals[k] = v.replace('Assistant 1', "**Assistant 1**").replace('Assistant 2', '**Assistant 2**') 86 | 87 | r['evaluations'] = cleaned_evals 88 | records.append(r) 89 | 90 | # Reorder the records, this is optional 91 | for r in records: 92 | if r['id'] <= 20: 93 | r['id'] += 60 94 | else: 95 | r['id'] -= 20 96 | for r in records: 97 | if r['id'] <= 50: 98 | r['id'] += 10 99 | elif 50 < r['id'] <= 60: 100 | r['id'] -= 50 101 | for r in records: 102 | if r['id'] == 7: 103 | r['id'] = 1 104 | elif r['id'] < 7: 105 | r['id'] += 1 106 | 107 | records.sort(key=lambda x: x['id']) 108 | 109 | # Write to file 110 | with open('webpage/data.json', 'w') as f: 111 | json.dump({'questions': records, 'models': models}, f, indent=2) 112 | -------------------------------------------------------------------------------- /llava/eval/model_qa.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria 3 | import torch 4 | import os 5 | import json 6 | from tqdm import tqdm 7 | import shortuuid 8 | 9 | from llava.conversation import default_conversation 10 | from llava.utils import disable_torch_init 11 | 12 | 13 | # new stopping implementation 14 | class KeywordsStoppingCriteria(StoppingCriteria): 15 | def __init__(self, keywords, tokenizer, input_ids): 16 | self.keywords = keywords 17 | self.tokenizer = tokenizer 18 | self.start_len = None 19 | self.input_ids = input_ids 20 | 21 | def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: 22 | if self.start_len is None: 23 | self.start_len = self.input_ids.shape[1] 24 | else: 25 | outputs = self.tokenizer.batch_decode(output_ids[:, self.start_len:], skip_special_tokens=True)[0] 26 | for keyword in self.keywords: 27 | if keyword in outputs: 28 | return True 29 | return False 30 | 31 | 32 | @torch.inference_mode() 33 | def eval_model(model_name, questions_file, answers_file): 34 | # Model 35 | disable_torch_init() 36 | model_name = os.path.expanduser(model_name) 37 | tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) 38 | model = AutoModelForCausalLM.from_pretrained(model_name, 39 | torch_dtype=torch.float16).cuda() 40 | 41 | 42 | ques_file = open(os.path.expanduser(questions_file), "r") 43 | ans_file = open(os.path.expanduser(answers_file), "w") 44 | for i, line in enumerate(tqdm(ques_file)): 45 | idx = json.loads(line)["question_id"] 46 | qs = json.loads(line)["text"] 47 | cat = json.loads(line)["category"] 48 | conv = default_conversation.copy() 49 | conv.append_message(conv.roles[0], qs) 50 | prompt = conv.get_prompt() 51 | inputs = tokenizer([prompt]) 52 | input_ids = torch.as_tensor(inputs.input_ids).cuda() 53 | stopping_criteria = KeywordsStoppingCriteria([conv.sep], tokenizer, input_ids) 54 | output_ids = model.generate( 55 | input_ids, 56 | do_sample=True, 57 | use_cache=True, 58 | temperature=0.7, 59 | max_new_tokens=1024, 60 | stopping_criteria=[stopping_criteria]) 61 | outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] 62 | try: 63 | index = outputs.index(conv.sep, len(prompt)) 64 | except ValueError: 65 | outputs += conv.sep 66 | index = outputs.index(conv.sep, len(prompt)) 67 | 68 | outputs = outputs[len(prompt) + len(conv.roles[1]) + 2:index].strip() 69 | ans_id = shortuuid.uuid() 70 | ans_file.write(json.dumps({"question_id": idx, 71 | "text": outputs, 72 | "answer_id": ans_id, 73 | "model_id": model_name, 74 | "metadata": {}}) + "\n") 75 | ans_file.flush() 76 | ans_file.close() 77 | 78 | if __name__ == "__main__": 79 | parser = argparse.ArgumentParser() 80 | parser.add_argument("--model-name", type=str, default="facebook/opt-350m") 81 | parser.add_argument("--question-file", type=str, default="tables/question.jsonl") 82 | parser.add_argument("--answers-file", type=str, default="answer.jsonl") 83 | args = parser.parse_args() 84 | 85 | eval_model(args.model_name, args.question_file, args.answers_file) 86 | -------------------------------------------------------------------------------- /llava/eval/model_vqa.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import torch 3 | import os 4 | import json 5 | from tqdm import tqdm 6 | import shortuuid 7 | 8 | from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN 9 | from llava.conversation import conv_templates, SeparatorStyle 10 | from llava.model.builder import load_pretrained_model 11 | from llava.utils import disable_torch_init 12 | from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria 13 | 14 | from PIL import Image 15 | import math 16 | 17 | 18 | def split_list(lst, n): 19 | """Split a list into n (roughly) equal-sized chunks""" 20 | chunk_size = math.ceil(len(lst) / n) # integer division 21 | return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] 22 | 23 | 24 | def get_chunk(lst, n, k): 25 | chunks = split_list(lst, n) 26 | return chunks[k] 27 | 28 | 29 | def eval_model(args): 30 | # Model 31 | disable_torch_init() 32 | model_path = os.path.expanduser(args.model_path) 33 | model_name = get_model_name_from_path(model_path) 34 | tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name) 35 | 36 | questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")] 37 | questions = get_chunk(questions, args.num_chunks, args.chunk_idx) 38 | answers_file = os.path.expanduser(args.answers_file) 39 | os.makedirs(os.path.dirname(answers_file), exist_ok=True) 40 | ans_file = open(answers_file, "w") 41 | for line in tqdm(questions): 42 | idx = line["question_id"] 43 | image_file = line["image"] 44 | qs = line["text"] 45 | cur_prompt = qs 46 | if model.config.mm_use_im_start_end: 47 | qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs 48 | else: 49 | qs = DEFAULT_IMAGE_TOKEN + '\n' + qs 50 | 51 | conv = conv_templates[args.conv_mode].copy() 52 | conv.append_message(conv.roles[0], qs) 53 | conv.append_message(conv.roles[1], None) 54 | prompt = conv.get_prompt() 55 | 56 | input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() 57 | 58 | image = Image.open(os.path.join(args.image_folder, image_file)) 59 | image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] 60 | 61 | stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 62 | keywords = [stop_str] 63 | stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) 64 | 65 | with torch.inference_mode(): 66 | output_ids = model.generate( 67 | input_ids, 68 | images=image_tensor.unsqueeze(0).half().cuda(), 69 | do_sample=True if args.temperature > 0 else False, 70 | temperature=args.temperature, 71 | top_p=args.top_p, 72 | num_beams=args.num_beams, 73 | # no_repeat_ngram_size=3, 74 | max_new_tokens=1024, 75 | use_cache=True) 76 | 77 | input_token_len = input_ids.shape[1] 78 | n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() 79 | if n_diff_input_output > 0: 80 | print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') 81 | outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] 82 | outputs = outputs.strip() 83 | if outputs.endswith(stop_str): 84 | outputs = outputs[:-len(stop_str)] 85 | outputs = outputs.strip() 86 | 87 | ans_id = shortuuid.uuid() 88 | ans_file.write(json.dumps({"question_id": idx, 89 | "prompt": cur_prompt, 90 | "text": outputs, 91 | "answer_id": ans_id, 92 | "model_id": model_name, 93 | "metadata": {}}) + "\n") 94 | ans_file.flush() 95 | ans_file.close() 96 | 97 | if __name__ == "__main__": 98 | parser = argparse.ArgumentParser() 99 | parser.add_argument("--model-path", type=str, default="facebook/opt-350m") 100 | parser.add_argument("--model-base", type=str, default=None) 101 | parser.add_argument("--image-folder", type=str, default="") 102 | parser.add_argument("--question-file", type=str, default="tables/question.jsonl") 103 | parser.add_argument("--answers-file", type=str, default="answer.jsonl") 104 | parser.add_argument("--conv-mode", type=str, default="llava_v1") 105 | parser.add_argument("--num-chunks", type=int, default=1) 106 | parser.add_argument("--chunk-idx", type=int, default=0) 107 | parser.add_argument("--temperature", type=float, default=0.2) 108 | parser.add_argument("--top_p", type=float, default=None) 109 | parser.add_argument("--num_beams", type=int, default=1) 110 | args = parser.parse_args() 111 | 112 | eval_model(args) 113 | -------------------------------------------------------------------------------- /llava/eval/model_vqa_loader.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import torch 3 | import os 4 | import json 5 | from tqdm import tqdm 6 | import shortuuid 7 | 8 | from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN 9 | from llava.conversation import conv_templates, SeparatorStyle 10 | from llava.model.builder import load_pretrained_model 11 | from llava.utils import disable_torch_init 12 | from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path 13 | from torch.utils.data import Dataset, DataLoader 14 | 15 | from PIL import Image 16 | import math 17 | 18 | 19 | def split_list(lst, n): 20 | """Split a list into n (roughly) equal-sized chunks""" 21 | chunk_size = math.ceil(len(lst) / n) # integer division 22 | return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] 23 | 24 | 25 | def get_chunk(lst, n, k): 26 | chunks = split_list(lst, n) 27 | return chunks[k] 28 | 29 | 30 | # Custom dataset class 31 | class CustomDataset(Dataset): 32 | def __init__(self, questions, image_folder, tokenizer, image_processor, model_config): 33 | self.questions = questions 34 | self.image_folder = image_folder 35 | self.tokenizer = tokenizer 36 | self.image_processor = image_processor 37 | self.model_config = model_config 38 | 39 | def __getitem__(self, index): 40 | line = self.questions[index] 41 | image_file = line["image"] 42 | qs = line["text"] 43 | if self.model_config.mm_use_im_start_end: 44 | qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs 45 | else: 46 | qs = DEFAULT_IMAGE_TOKEN + '\n' + qs 47 | 48 | conv = conv_templates[args.conv_mode].copy() 49 | conv.append_message(conv.roles[0], qs) 50 | conv.append_message(conv.roles[1], None) 51 | prompt = conv.get_prompt() 52 | 53 | image = Image.open(os.path.join(self.image_folder, image_file)).convert('RGB') 54 | image_tensor = process_images([image], self.image_processor, self.model_config)[0] 55 | 56 | input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt') 57 | 58 | return input_ids, image_tensor 59 | 60 | def __len__(self): 61 | return len(self.questions) 62 | 63 | 64 | # DataLoader 65 | def create_data_loader(questions, image_folder, tokenizer, image_processor, model_config, batch_size=1, num_workers=4): 66 | assert batch_size == 1, "batch_size must be 1" 67 | dataset = CustomDataset(questions, image_folder, tokenizer, image_processor, model_config) 68 | data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False) 69 | return data_loader 70 | 71 | 72 | def eval_model(args): 73 | # Model 74 | disable_torch_init() 75 | model_path = os.path.expanduser(args.model_path) 76 | model_name = get_model_name_from_path(model_path) 77 | tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name) 78 | 79 | questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")] 80 | questions = get_chunk(questions, args.num_chunks, args.chunk_idx) 81 | answers_file = os.path.expanduser(args.answers_file) 82 | os.makedirs(os.path.dirname(answers_file), exist_ok=True) 83 | ans_file = open(answers_file, "w") 84 | 85 | if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode: 86 | args.conv_mode = args.conv_mode + '_mmtag' 87 | print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.') 88 | 89 | data_loader = create_data_loader(questions, args.image_folder, tokenizer, image_processor, model.config) 90 | 91 | for (input_ids, image_tensor), line in tqdm(zip(data_loader, questions), total=len(questions)): 92 | idx = line["question_id"] 93 | cur_prompt = line["text"] 94 | 95 | input_ids = input_ids.to(device='cuda', non_blocking=True) 96 | 97 | with torch.inference_mode(): 98 | output_ids = model.generate( 99 | input_ids, 100 | images=image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True), 101 | do_sample=True if args.temperature > 0 else False, 102 | temperature=args.temperature, 103 | top_p=args.top_p, 104 | num_beams=args.num_beams, 105 | max_new_tokens=args.max_new_tokens, 106 | use_cache=True) 107 | 108 | input_token_len = input_ids.shape[1] 109 | n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() 110 | if n_diff_input_output > 0: 111 | print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') 112 | outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] 113 | outputs = outputs.strip() 114 | 115 | ans_id = shortuuid.uuid() 116 | ans_file.write(json.dumps({"question_id": idx, 117 | "prompt": cur_prompt, 118 | "text": outputs, 119 | "answer_id": ans_id, 120 | "model_id": model_name, 121 | "metadata": {}}) + "\n") 122 | # ans_file.flush() 123 | ans_file.close() 124 | 125 | if __name__ == "__main__": 126 | parser = argparse.ArgumentParser() 127 | parser.add_argument("--model-path", type=str, default="facebook/opt-350m") 128 | parser.add_argument("--model-base", type=str, default=None) 129 | parser.add_argument("--image-folder", type=str, default="") 130 | parser.add_argument("--question-file", type=str, default="tables/question.jsonl") 131 | parser.add_argument("--answers-file", type=str, default="answer.jsonl") 132 | parser.add_argument("--conv-mode", type=str, default="llava_v1") 133 | parser.add_argument("--num-chunks", type=int, default=1) 134 | parser.add_argument("--chunk-idx", type=int, default=0) 135 | parser.add_argument("--temperature", type=float, default=0.2) 136 | parser.add_argument("--top_p", type=float, default=None) 137 | parser.add_argument("--num_beams", type=int, default=1) 138 | parser.add_argument("--max_new_tokens", type=int, default=128) 139 | args = parser.parse_args() 140 | 141 | eval_model(args) 142 | -------------------------------------------------------------------------------- /llava/eval/model_vqa_mmbench.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import torch 3 | import os 4 | import json 5 | import pandas as pd 6 | from tqdm import tqdm 7 | import shortuuid 8 | 9 | from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN 10 | from llava.conversation import conv_templates, SeparatorStyle 11 | from llava.model.builder import load_pretrained_model 12 | from llava.utils import disable_torch_init 13 | from llava.mm_utils import tokenizer_image_token, process_images, load_image_from_base64, get_model_name_from_path 14 | 15 | from PIL import Image 16 | import math 17 | 18 | 19 | all_options = ['A', 'B', 'C', 'D'] 20 | 21 | 22 | def split_list(lst, n): 23 | """Split a list into n (roughly) equal-sized chunks""" 24 | chunk_size = math.ceil(len(lst) / n) # integer division 25 | return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] 26 | 27 | 28 | def get_chunk(lst, n, k): 29 | chunks = split_list(lst, n) 30 | return chunks[k] 31 | 32 | 33 | def is_none(value): 34 | if value is None: 35 | return True 36 | if type(value) is float and math.isnan(value): 37 | return True 38 | if type(value) is str and value.lower() == 'nan': 39 | return True 40 | if type(value) is str and value.lower() == 'none': 41 | return True 42 | return False 43 | 44 | def get_options(row, options): 45 | parsed_options = [] 46 | for option in options: 47 | option_value = row[option] 48 | if is_none(option_value): 49 | break 50 | parsed_options.append(option_value) 51 | return parsed_options 52 | 53 | 54 | def eval_model(args): 55 | # Model 56 | disable_torch_init() 57 | model_path = os.path.expanduser(args.model_path) 58 | model_name = get_model_name_from_path(model_path) 59 | tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name) 60 | 61 | questions = pd.read_table(os.path.expanduser(args.question_file)) 62 | questions = get_chunk(questions, args.num_chunks, args.chunk_idx) 63 | answers_file = os.path.expanduser(args.answers_file) 64 | os.makedirs(os.path.dirname(answers_file), exist_ok=True) 65 | ans_file = open(answers_file, "w") 66 | 67 | if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode: 68 | args.conv_mode = args.conv_mode + '_mmtag' 69 | print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.') 70 | 71 | for index, row in tqdm(questions.iterrows(), total=len(questions)): 72 | options = get_options(row, all_options) 73 | cur_option_char = all_options[:len(options)] 74 | 75 | if args.all_rounds: 76 | num_rounds = len(options) 77 | else: 78 | num_rounds = 1 79 | 80 | for round_idx in range(num_rounds): 81 | idx = row['index'] 82 | question = row['question'] 83 | hint = row['hint'] 84 | image = load_image_from_base64(row['image']) 85 | if not is_none(hint): 86 | question = hint + '\n' + question 87 | for option_char, option in zip(all_options[:len(options)], options): 88 | question = question + '\n' + option_char + '. ' + option 89 | qs = cur_prompt = question 90 | if model.config.mm_use_im_start_end: 91 | qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs 92 | else: 93 | qs = DEFAULT_IMAGE_TOKEN + '\n' + qs 94 | 95 | if args.single_pred_prompt: 96 | if args.lang == 'cn': 97 | qs = qs + '\n' + "请直接回答选项字母。" 98 | else: 99 | qs = qs + '\n' + "Answer with the option's letter from the given choices directly." 100 | 101 | conv = conv_templates[args.conv_mode].copy() 102 | conv.append_message(conv.roles[0], qs) 103 | conv.append_message(conv.roles[1], None) 104 | prompt = conv.get_prompt() 105 | 106 | input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() 107 | 108 | image_tensor = process_images([image], image_processor, model.config)[0] 109 | # image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] 110 | 111 | stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 112 | 113 | with torch.inference_mode(): 114 | output_ids = model.generate( 115 | input_ids, 116 | images=image_tensor.unsqueeze(0).half().cuda(), 117 | do_sample=True if args.temperature > 0 else False, 118 | temperature=args.temperature, 119 | top_p=args.top_p, 120 | num_beams=args.num_beams, 121 | # no_repeat_ngram_size=3, 122 | max_new_tokens=1024, 123 | use_cache=True) 124 | 125 | input_token_len = input_ids.shape[1] 126 | n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() 127 | if n_diff_input_output > 0: 128 | print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') 129 | outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] 130 | outputs = outputs.strip() 131 | if outputs.endswith(stop_str): 132 | outputs = outputs[:-len(stop_str)] 133 | outputs = outputs.strip() 134 | 135 | ans_id = shortuuid.uuid() 136 | ans_file.write(json.dumps({"question_id": idx, 137 | "round_id": round_idx, 138 | "prompt": cur_prompt, 139 | "text": outputs, 140 | "options": options, 141 | "option_char": cur_option_char, 142 | "answer_id": ans_id, 143 | "model_id": model_name, 144 | "metadata": {}}) + "\n") 145 | ans_file.flush() 146 | 147 | # rotate options 148 | options = options[1:] + options[:1] 149 | cur_option_char = cur_option_char[1:] + cur_option_char[:1] 150 | ans_file.close() 151 | 152 | if __name__ == "__main__": 153 | parser = argparse.ArgumentParser() 154 | parser.add_argument("--model-path", type=str, default="facebook/opt-350m") 155 | parser.add_argument("--model-base", type=str, default=None) 156 | parser.add_argument("--image-folder", type=str, default="") 157 | parser.add_argument("--question-file", type=str, default="tables/question.jsonl") 158 | parser.add_argument("--answers-file", type=str, default="answer.jsonl") 159 | parser.add_argument("--conv-mode", type=str, default="llava_v1") 160 | parser.add_argument("--num-chunks", type=int, default=1) 161 | parser.add_argument("--chunk-idx", type=int, default=0) 162 | parser.add_argument("--temperature", type=float, default=0.2) 163 | parser.add_argument("--top_p", type=float, default=None) 164 | parser.add_argument("--num_beams", type=int, default=1) 165 | parser.add_argument("--all-rounds", action="store_true") 166 | parser.add_argument("--single-pred-prompt", action="store_true") 167 | parser.add_argument("--lang", type=str, default="en") 168 | args = parser.parse_args() 169 | 170 | eval_model(args) 171 | -------------------------------------------------------------------------------- /llava/eval/model_vqa_qbench.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import torch 3 | from tqdm import tqdm 4 | import json 5 | 6 | from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN 7 | from llava.conversation import conv_templates, SeparatorStyle 8 | from llava.model.builder import load_pretrained_model 9 | from llava.utils import disable_torch_init 10 | from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria 11 | 12 | from PIL import Image 13 | 14 | import requests 15 | from PIL import Image 16 | from io import BytesIO 17 | 18 | 19 | def load_image(image_file): 20 | if image_file.startswith('http') or image_file.startswith('https'): 21 | response = requests.get(image_file) 22 | image = Image.open(BytesIO(response.content)).convert('RGB') 23 | else: 24 | image = Image.open(image_file).convert('RGB') 25 | return image 26 | 27 | 28 | def eval_model(args): 29 | # Model 30 | disable_torch_init() 31 | 32 | model_name = get_model_name_from_path(args.model_path) 33 | tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, True) 34 | 35 | 36 | 37 | 38 | with open(args.questions_file) as f: 39 | llvqa_data = json.load(f) 40 | 41 | for i, llddata in enumerate(tqdm(llvqa_data)): 42 | filename = llddata["img_path"] 43 | if args.lang == "en": 44 | message = llddata["question"] + "\nChoose between one of the options as follows:\n" 45 | elif args.lang == "zh": 46 | message = llddata["question"] + "\在下列选项中选择一个:\n" 47 | else: 48 | raise NotImplementedError("Q-Bench does not support languages other than English (en) and Chinese (zh) yet. Contact us (https://github.com/VQAssessment/Q-Bench/) to convert Q-Bench into more languages.") 49 | for choice, ans in zip(["A.", "B.", "C.", "D."], llddata["candidates"]): 50 | message += f"{choice} {ans}\n" 51 | qs = message 52 | 53 | if model.config.mm_use_im_start_end: 54 | qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs 55 | else: 56 | qs = DEFAULT_IMAGE_TOKEN + '\n' + qs 57 | 58 | if 'llama-2' in model_name.lower(): 59 | conv_mode = "llava_llama_2" 60 | elif "v1" in model_name.lower(): 61 | conv_mode = "llava_v1" 62 | elif "mpt" in model_name.lower(): 63 | conv_mode = "mpt" 64 | else: 65 | conv_mode = "llava_v0" 66 | 67 | if args.conv_mode is not None and conv_mode != args.conv_mode: 68 | print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode)) 69 | else: 70 | args.conv_mode = conv_mode 71 | 72 | conv = conv_templates[args.conv_mode].copy() 73 | conv.append_message(conv.roles[0], qs) 74 | conv.append_message(conv.roles[1], None) 75 | prompt = conv.get_prompt() 76 | 77 | image = load_image(args.image_folder + filename) 78 | image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda() 79 | 80 | input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() 81 | 82 | stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 83 | keywords = [stop_str] 84 | stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) 85 | 86 | 87 | with torch.inference_mode(): 88 | output_ids = model.generate( 89 | input_ids, 90 | images=image_tensor, 91 | num_beams=1, 92 | do_sample=False, 93 | temperature=0, 94 | max_new_tokens=1024, 95 | use_cache=True, 96 | stopping_criteria=[stopping_criteria]) 97 | 98 | input_token_len = input_ids.shape[1] 99 | n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() 100 | if n_diff_input_output > 0: 101 | print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') 102 | outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] 103 | outputs = outputs.strip() 104 | if outputs.endswith(stop_str): 105 | outputs = outputs[:-len(stop_str)] 106 | outputs = outputs.strip() 107 | llddata["response"] = outputs 108 | with open(args.answers_file, "a") as wf: 109 | json.dump(llddata, wf) 110 | 111 | if __name__ == "__main__": 112 | parser = argparse.ArgumentParser() 113 | parser.add_argument("--model-path", type=str, default="llava-v1.5") 114 | parser.add_argument("--model-base", type=str, default=None) 115 | parser.add_argument("--image-folder", type=str, default="./playground/data/qbench/images_llvisionqa") 116 | parser.add_argument("--questions-file", type=str, default="./playground/data/qbench/llvisionqa_dev.json") 117 | parser.add_argument("--answers-file", type=str, default="answer.jsonl") 118 | parser.add_argument("--conv-mode", type=str, default="llava_v1") 119 | parser.add_argument("--lang", type=str, default="en") 120 | args = parser.parse_args() 121 | 122 | eval_model(args) 123 | -------------------------------------------------------------------------------- /llava/eval/model_vqa_science.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import torch 3 | import os 4 | import json 5 | from tqdm import tqdm 6 | import shortuuid 7 | 8 | from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN 9 | from llava.conversation import conv_templates, SeparatorStyle 10 | from llava.model.builder import load_pretrained_model 11 | from llava.utils import disable_torch_init 12 | from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria 13 | 14 | from PIL import Image 15 | import math 16 | 17 | 18 | def split_list(lst, n): 19 | """Split a list into n (roughly) equal-sized chunks""" 20 | chunk_size = math.ceil(len(lst) / n) # integer division 21 | return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] 22 | 23 | 24 | def get_chunk(lst, n, k): 25 | chunks = split_list(lst, n) 26 | return chunks[k] 27 | 28 | 29 | def eval_model(args): 30 | # Model 31 | disable_torch_init() 32 | model_path = os.path.expanduser(args.model_path) 33 | model_name = get_model_name_from_path(model_path) 34 | tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name) 35 | 36 | questions = json.load(open(os.path.expanduser(args.question_file), "r")) 37 | questions = get_chunk(questions, args.num_chunks, args.chunk_idx) 38 | answers_file = os.path.expanduser(args.answers_file) 39 | os.makedirs(os.path.dirname(answers_file), exist_ok=True) 40 | ans_file = open(answers_file, "w") 41 | for i, line in enumerate(tqdm(questions)): 42 | idx = line["id"] 43 | question = line['conversations'][0] 44 | qs = question['value'].replace('', '').strip() 45 | cur_prompt = qs 46 | 47 | if 'image' in line: 48 | image_file = line["image"] 49 | image = Image.open(os.path.join(args.image_folder, image_file)) 50 | image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] 51 | images = image_tensor.unsqueeze(0).half().cuda() 52 | if getattr(model.config, 'mm_use_im_start_end', False): 53 | qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs 54 | else: 55 | qs = DEFAULT_IMAGE_TOKEN + '\n' + qs 56 | cur_prompt = '' + '\n' + cur_prompt 57 | else: 58 | images = None 59 | 60 | if args.single_pred_prompt: 61 | qs = qs + '\n' + "Answer with the option's letter from the given choices directly." 62 | cur_prompt = cur_prompt + '\n' + "Answer with the option's letter from the given choices directly." 63 | 64 | conv = conv_templates[args.conv_mode].copy() 65 | conv.append_message(conv.roles[0], qs) 66 | conv.append_message(conv.roles[1], None) 67 | prompt = conv.get_prompt() 68 | 69 | input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() 70 | 71 | stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 72 | keywords = [stop_str] 73 | stopping_criteria = [KeywordsStoppingCriteria(keywords, tokenizer, input_ids)] if conv.version == "v0" else None 74 | 75 | with torch.inference_mode(): 76 | output_ids = model.generate( 77 | input_ids, 78 | images=images, 79 | do_sample=True if args.temperature > 0 else False, 80 | temperature=args.temperature, 81 | max_new_tokens=1024, 82 | use_cache=True, 83 | stopping_criteria=stopping_criteria, 84 | ) 85 | 86 | input_token_len = input_ids.shape[1] 87 | n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() 88 | if n_diff_input_output > 0: 89 | print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') 90 | outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] 91 | outputs = outputs.strip() 92 | if outputs.endswith(stop_str): 93 | outputs = outputs[:-len(stop_str)] 94 | outputs = outputs.strip() 95 | 96 | # prompt for answer 97 | if args.answer_prompter: 98 | outputs_reasoning = outputs 99 | input_ids = tokenizer_image_token(prompt + outputs_reasoning + ' ###\nANSWER:', tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() 100 | 101 | with torch.inference_mode(): 102 | output_ids = model.generate( 103 | input_ids, 104 | images=images, 105 | do_sample=True if args.temperature > 0 else False, 106 | temperature=args.temperature, 107 | max_new_tokens=64, 108 | use_cache=True, 109 | stopping_criteria=[stopping_criteria]) 110 | 111 | input_token_len = input_ids.shape[1] 112 | n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() 113 | if n_diff_input_output > 0: 114 | print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') 115 | outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] 116 | outputs = outputs.strip() 117 | if outputs.endswith(stop_str): 118 | outputs = outputs[:-len(stop_str)] 119 | outputs = outputs.strip() 120 | outputs = outputs_reasoning + '\n The answer is ' + outputs 121 | 122 | ans_id = shortuuid.uuid() 123 | ans_file.write(json.dumps({"question_id": idx, 124 | "prompt": cur_prompt, 125 | "text": outputs, 126 | "answer_id": ans_id, 127 | "model_id": model_name, 128 | "metadata": {}}) + "\n") 129 | ans_file.flush() 130 | ans_file.close() 131 | 132 | if __name__ == "__main__": 133 | parser = argparse.ArgumentParser() 134 | parser.add_argument("--model-path", type=str, default="facebook/opt-350m") 135 | parser.add_argument("--model-base", type=str, default=None) 136 | parser.add_argument("--image-folder", type=str, default="") 137 | parser.add_argument("--question-file", type=str, default="tables/question.json") 138 | parser.add_argument("--answers-file", type=str, default="answer.jsonl") 139 | parser.add_argument("--conv-mode", type=str, default="llava_v0") 140 | parser.add_argument("--num-chunks", type=int, default=1) 141 | parser.add_argument("--chunk-idx", type=int, default=0) 142 | parser.add_argument("--temperature", type=float, default=0.2) 143 | parser.add_argument("--answer-prompter", action="store_true") 144 | parser.add_argument("--single-pred-prompt", action="store_true") 145 | args = parser.parse_args() 146 | 147 | eval_model(args) 148 | -------------------------------------------------------------------------------- /llava/eval/qa_baseline_gpt35.py: -------------------------------------------------------------------------------- 1 | """Generate answers with GPT-3.5""" 2 | # Note: you need to be using OpenAI Python v0.27.0 for the code below to work 3 | import argparse 4 | import json 5 | import os 6 | import time 7 | import concurrent.futures 8 | 9 | import openai 10 | import tqdm 11 | import shortuuid 12 | 13 | MODEL = 'gpt-3.5-turbo' 14 | MODEL_ID = 'gpt-3.5-turbo:20230327' 15 | 16 | def get_answer(question_id: int, question: str, max_tokens: int): 17 | ans = { 18 | 'answer_id': shortuuid.uuid(), 19 | 'question_id': question_id, 20 | 'model_id': MODEL_ID, 21 | } 22 | for _ in range(3): 23 | try: 24 | response = openai.ChatCompletion.create( 25 | model=MODEL, 26 | messages=[{ 27 | 'role': 'system', 28 | 'content': 'You are a helpful assistant.' 29 | }, { 30 | 'role': 'user', 31 | 'content': question, 32 | }], 33 | max_tokens=max_tokens, 34 | ) 35 | ans['text'] = response['choices'][0]['message']['content'] 36 | return ans 37 | except Exception as e: 38 | print('[ERROR]', e) 39 | ans['text'] = '#ERROR#' 40 | time.sleep(1) 41 | return ans 42 | 43 | 44 | if __name__ == '__main__': 45 | parser = argparse.ArgumentParser(description='ChatGPT answer generation.') 46 | parser.add_argument('-q', '--question') 47 | parser.add_argument('-o', '--output') 48 | parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') 49 | args = parser.parse_args() 50 | 51 | questions_dict = {} 52 | with open(os.path.expanduser(args.question)) as f: 53 | for line in f: 54 | if not line: 55 | continue 56 | q = json.loads(line) 57 | questions_dict[q['question_id']] = q['text'] 58 | 59 | answers = [] 60 | 61 | with concurrent.futures.ThreadPoolExecutor(max_workers=32) as executor: 62 | futures = [] 63 | for qid, question in questions_dict.items(): 64 | future = executor.submit(get_answer, qid, question, args.max_tokens) 65 | futures.append(future) 66 | 67 | for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures)): 68 | answers.append(future.result()) 69 | 70 | answers.sort(key=lambda x: x['question_id']) 71 | 72 | with open(os.path.expanduser(args.output), 'w') as f: 73 | table = [json.dumps(ans) for ans in answers] 74 | f.write('\n'.join(table)) 75 | -------------------------------------------------------------------------------- /llava/eval/run_llava.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import torch 3 | 4 | from llava.constants import ( 5 | IMAGE_TOKEN_INDEX, 6 | DEFAULT_IMAGE_TOKEN, 7 | DEFAULT_IM_START_TOKEN, 8 | DEFAULT_IM_END_TOKEN, 9 | IMAGE_PLACEHOLDER, 10 | ) 11 | from llava.conversation import conv_templates, SeparatorStyle 12 | from llava.model.builder import load_pretrained_model 13 | from llava.utils import disable_torch_init 14 | from llava.mm_utils import ( 15 | process_images, 16 | tokenizer_image_token, 17 | get_model_name_from_path, 18 | KeywordsStoppingCriteria, 19 | ) 20 | 21 | from PIL import Image 22 | 23 | import requests 24 | from PIL import Image 25 | from io import BytesIO 26 | import re 27 | 28 | 29 | def image_parser(args): 30 | out = args.image_file.split(args.sep) 31 | return out 32 | 33 | 34 | def load_image(image_file): 35 | if image_file.startswith("http") or image_file.startswith("https"): 36 | response = requests.get(image_file) 37 | image = Image.open(BytesIO(response.content)).convert("RGB") 38 | else: 39 | image = Image.open(image_file).convert("RGB") 40 | return image 41 | 42 | 43 | def load_images(image_files): 44 | out = [] 45 | for image_file in image_files: 46 | image = load_image(image_file) 47 | out.append(image) 48 | return out 49 | 50 | 51 | def eval_model(args): 52 | # Model 53 | disable_torch_init() 54 | 55 | model_name = get_model_name_from_path(args.model_path) 56 | tokenizer, model, image_processor, context_len = load_pretrained_model( 57 | args.model_path, args.model_base, model_name 58 | ) 59 | 60 | qs = args.query 61 | image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN 62 | if IMAGE_PLACEHOLDER in qs: 63 | if model.config.mm_use_im_start_end: 64 | qs = re.sub(IMAGE_PLACEHOLDER, image_token_se, qs) 65 | else: 66 | qs = re.sub(IMAGE_PLACEHOLDER, DEFAULT_IMAGE_TOKEN, qs) 67 | else: 68 | if model.config.mm_use_im_start_end: 69 | qs = image_token_se + "\n" + qs 70 | else: 71 | qs = DEFAULT_IMAGE_TOKEN + "\n" + qs 72 | 73 | if "llama-2" in model_name.lower(): 74 | conv_mode = "llava_llama_2" 75 | elif "v1" in model_name.lower(): 76 | conv_mode = "llava_v1" 77 | elif "mpt" in model_name.lower(): 78 | conv_mode = "mpt" 79 | else: 80 | conv_mode = "llava_v0" 81 | 82 | if args.conv_mode is not None and conv_mode != args.conv_mode: 83 | print( 84 | "[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format( 85 | conv_mode, args.conv_mode, args.conv_mode 86 | ) 87 | ) 88 | else: 89 | args.conv_mode = conv_mode 90 | 91 | conv = conv_templates[args.conv_mode].copy() 92 | conv.append_message(conv.roles[0], qs) 93 | conv.append_message(conv.roles[1], None) 94 | prompt = conv.get_prompt() 95 | 96 | image_files = image_parser(args) 97 | images = load_images(image_files) 98 | images_tensor = process_images( 99 | images, 100 | image_processor, 101 | model.config 102 | ).to(model.device, dtype=torch.float16) 103 | 104 | input_ids = ( 105 | tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") 106 | .unsqueeze(0) 107 | .cuda() 108 | ) 109 | 110 | stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 111 | keywords = [stop_str] 112 | stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) 113 | 114 | with torch.inference_mode(): 115 | output_ids = model.generate( 116 | input_ids, 117 | images=images_tensor, 118 | do_sample=True if args.temperature > 0 else False, 119 | temperature=args.temperature, 120 | top_p=args.top_p, 121 | num_beams=args.num_beams, 122 | max_new_tokens=args.max_new_tokens, 123 | use_cache=True, 124 | stopping_criteria=[stopping_criteria], 125 | ) 126 | 127 | input_token_len = input_ids.shape[1] 128 | n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() 129 | if n_diff_input_output > 0: 130 | print( 131 | f"[Warning] {n_diff_input_output} output_ids are not the same as the input_ids" 132 | ) 133 | outputs = tokenizer.batch_decode( 134 | output_ids[:, input_token_len:], skip_special_tokens=True 135 | )[0] 136 | outputs = outputs.strip() 137 | if outputs.endswith(stop_str): 138 | outputs = outputs[: -len(stop_str)] 139 | outputs = outputs.strip() 140 | print(outputs) 141 | 142 | 143 | if __name__ == "__main__": 144 | parser = argparse.ArgumentParser() 145 | parser.add_argument("--model-path", type=str, default="facebook/opt-350m") 146 | parser.add_argument("--model-base", type=str, default=None) 147 | parser.add_argument("--image-file", type=str, required=True) 148 | parser.add_argument("--query", type=str, required=True) 149 | parser.add_argument("--conv-mode", type=str, default=None) 150 | parser.add_argument("--sep", type=str, default=",") 151 | parser.add_argument("--temperature", type=float, default=0.2) 152 | parser.add_argument("--top_p", type=float, default=None) 153 | parser.add_argument("--num_beams", type=int, default=1) 154 | parser.add_argument("--max_new_tokens", type=int, default=512) 155 | args = parser.parse_args() 156 | 157 | eval_model(args) 158 | -------------------------------------------------------------------------------- /llava/eval/summarize_gpt_review.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | from collections import defaultdict 4 | 5 | import numpy as np 6 | 7 | import argparse 8 | 9 | def parse_args(): 10 | parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.') 11 | parser.add_argument('-d', '--dir', default=None) 12 | parser.add_argument('-v', '--version', default=None) 13 | parser.add_argument('-s', '--select', nargs='*', default=None) 14 | parser.add_argument('-f', '--files', nargs='*', default=[]) 15 | parser.add_argument('-i', '--ignore', nargs='*', default=[]) 16 | return parser.parse_args() 17 | 18 | 19 | if __name__ == '__main__': 20 | args = parse_args() 21 | 22 | if args.ignore is not None: 23 | args.ignore = [int(x) for x in args.ignore] 24 | 25 | if len(args.files) > 0: 26 | review_files = args.files 27 | else: 28 | 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)] 29 | 30 | for review_file in sorted(review_files): 31 | config = os.path.basename(review_file).replace('gpt4_text_', '').replace('.jsonl', '') 32 | if args.select is not None and any(x not in config for x in args.select): 33 | continue 34 | if '0613' in config: 35 | version = '0613' 36 | else: 37 | version = '0314' 38 | if args.version is not None and args.version != version: 39 | continue 40 | scores = defaultdict(list) 41 | print(config) 42 | with open(os.path.join(args.dir, review_file) if args.dir is not None else review_file) as f: 43 | for review_str in f: 44 | review = json.loads(review_str) 45 | if review['question_id'] in args.ignore: 46 | continue 47 | if 'category' in review: 48 | scores[review['category']].append(review['tuple']) 49 | scores['all'].append(review['tuple']) 50 | else: 51 | if 'tuple' in review: 52 | scores['all'].append(review['tuple']) 53 | else: 54 | scores['all'].append(review['score']) 55 | for k, v in sorted(scores.items()): 56 | stats = np.asarray(v).mean(0).tolist() 57 | stats = [round(x, 3) for x in stats] 58 | # print(k, stats, round(stats[1]/stats[0]*100, 1)) 59 | print(k, round(stats[1]/stats[0]*100, 1), round(stats[0] * 10, 1), round(stats[1] * 10, 1)) 60 | print('=================================') 61 | -------------------------------------------------------------------------------- /llava/eval/table/model.jsonl: -------------------------------------------------------------------------------- 1 | {"model_id": "vicuna-13b:20230322-clean-lang", "model_name": "vicuna-13b", "model_version": "20230322-clean-lang", "model_metadata": "vicuna-13b-20230322-clean-lang"} 2 | {"model_id": "alpaca-13b:v1", "model_name": "alpaca-13b", "model_version": "v1", "model_metadata": "alpaca-13b"} 3 | {"model_id": "llama-13b:v1", "model_name": "llama-13b", "model_version": "v1", "model_metadata": "hf-llama-13b"} 4 | {"model_id": "bard:20230327", "model_name": "bard", "model_version": "20230327", "model_metadata": "Google Bard 20230327"} 5 | {"model_id": "gpt-3.5-turbo:20230327", "model_name": "gpt-3.5-turbo", "model_version": "20230327", "model_metadata": "OpenAI ChatGPT gpt-3.5-turbo Chat Completion"} 6 | -------------------------------------------------------------------------------- /llava/eval/table/prompt.jsonl: -------------------------------------------------------------------------------- 1 | {"prompt_id": 1, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, "description": "Prompt for general questions"} 2 | {"prompt_id": 2, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "Your task is to evaluate the coding abilities of the above two assistants. They have been asked to implement a program to solve a given problem. Please review their code submissions, paying close attention to their problem-solving approach, code structure, readability, and the inclusion of helpful comments.\n\nPlease ensure that the assistants' submissions:\n\n1. Correctly implement the given problem statement.\n2. Contain accurate and efficient code.\n3. Include clear and concise comments that explain the code's logic and functionality.\n4. Adhere to proper coding standards and best practices.\n\nOnce you have carefully reviewed both submissions, provide detailed feedback on their strengths and weaknesses, along with any suggestions for improvement. You should first output a single line containing two scores on the scale of 1-10 (1: no code/no sense; 10: perfect) for Assistant 1 and 2, respectively. Then give extra comments starting from the next line."}, "description": "Prompt for coding questions"} 3 | {"prompt_id": 3, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "We would like to request your feedback on the mathematical proficiency of two AI assistants regarding the given user question.\nFirstly, please solve the problem independently, without referring to the answers provided by Assistant 1 and Assistant 2.\nAfterward, please examine the problem-solving process of Assistant 1 and Assistant 2 step-by-step to ensure their correctness, identifying any incorrect steps if present. Your evaluation should take into account not only the answer but also the problem-solving steps.\nFinally, please output a Python tuple containing two numerical scores for Assistant 1 and Assistant 2, ranging from 1 to 10, respectively. If applicable, explain the reasons for any variations in their scores and determine which assistant performed better."}, "description": "Prompt for math questions"} 4 | {"prompt_id": 4, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Visual Context]\n{context}\n[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, "description": "Prompt for visual questions"} 5 | -------------------------------------------------------------------------------- /llava/eval/table/reviewer.jsonl: -------------------------------------------------------------------------------- 1 | {"reviewer_id": "gpt-4-0328-default", "prompt_id": 1, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for general questions"} 2 | {"reviewer_id": "gpt-4-0328-coding", "prompt_id": 2, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for coding questions"} 3 | {"reviewer_id": "gpt-4-0328-math", "prompt_id": 3, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for math questions"} 4 | {"reviewer_id": "gpt-4-0417-visual", "prompt_id": 4, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for math questions"} 5 | -------------------------------------------------------------------------------- /llava/eval/webpage/figures/alpaca.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/prometheus-eval/prometheus-vision/fe668b5e7e120e5bea8a3821eeb468ddf719c23c/llava/eval/webpage/figures/alpaca.png -------------------------------------------------------------------------------- /llava/eval/webpage/figures/bard.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/prometheus-eval/prometheus-vision/fe668b5e7e120e5bea8a3821eeb468ddf719c23c/llava/eval/webpage/figures/bard.jpg -------------------------------------------------------------------------------- /llava/eval/webpage/figures/chatgpt.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /llava/eval/webpage/figures/llama.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/prometheus-eval/prometheus-vision/fe668b5e7e120e5bea8a3821eeb468ddf719c23c/llava/eval/webpage/figures/llama.jpg -------------------------------------------------------------------------------- /llava/eval/webpage/figures/swords_FILL0_wght300_GRAD0_opsz48.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /llava/eval/webpage/figures/vicuna.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/prometheus-eval/prometheus-vision/fe668b5e7e120e5bea8a3821eeb468ddf719c23c/llava/eval/webpage/figures/vicuna.jpeg -------------------------------------------------------------------------------- /llava/eval/webpage/styles.css: -------------------------------------------------------------------------------- 1 | body { 2 | font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; 3 | background-color: #f8f9fa; 4 | } 5 | 6 | .navbar-dark .navbar-nav .nav-link { 7 | color: #f1cf68; 8 | font-size: 1.1rem; 9 | padding: 0.5rem 0.6rem; 10 | } 11 | 12 | .card-header { 13 | font-weight: bold; 14 | } 15 | 16 | .card { 17 | box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); 18 | transition: 0.3s; 19 | } 20 | 21 | .card:hover { 22 | box-shadow: 0 8px 16px rgba(0, 0, 0, 0.2); 23 | } 24 | 25 | button { 26 | transition: background-color 0.3s; 27 | } 28 | 29 | button:hover { 30 | background-color: #007bff; 31 | } 32 | 33 | @media (max-width: 767px) { 34 | .form-row .form-group { 35 | margin-bottom: 10px; 36 | } 37 | } 38 | 39 | /* Extra styles */ 40 | 41 | .expandable-card .card-text-container { 42 | max-height: 200px; 43 | overflow-y: hidden; 44 | position: relative; 45 | } 46 | 47 | .expandable-card.expanded .card-text-container { 48 | max-height: none; 49 | } 50 | 51 | .expand-btn { 52 | position: relative; 53 | display: none; 54 | background-color: rgba(255, 255, 255, 0.8); 55 | color: #510c75; 56 | border-color: transparent; 57 | } 58 | 59 | .expand-btn:hover { 60 | background-color: rgba(200, 200, 200, 0.8); 61 | text-decoration: none; 62 | border-color: transparent; 63 | color: #510c75; 64 | } 65 | 66 | .expand-btn:focus { 67 | outline: none; 68 | text-decoration: none; 69 | } 70 | 71 | .expandable-card:not(.expanded) .card-text-container:after { 72 | content: ""; 73 | position: absolute; 74 | bottom: 0; 75 | left: 0; 76 | width: 100%; 77 | height: 90px; 78 | background: linear-gradient(rgba(255, 255, 255, 0.2), rgba(255, 255, 255, 1)); 79 | } 80 | 81 | .expandable-card:not(.expanded) .expand-btn { 82 | margin-top: -40px; 83 | } 84 | 85 | .card-body { 86 | padding-bottom: 5px; 87 | } 88 | 89 | .vertical-flex-layout { 90 | justify-content: center; 91 | align-items: center; 92 | height: 100%; 93 | display: flex; 94 | flex-direction: column; 95 | gap: 5px; 96 | } 97 | 98 | .figure-img { 99 | max-width: 100%; 100 | height: auto; 101 | } 102 | 103 | .adjustable-font-size { 104 | font-size: calc(0.5rem + 2vw); 105 | } 106 | -------------------------------------------------------------------------------- /llava/mm_utils.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | from io import BytesIO 3 | import base64 4 | 5 | import torch 6 | from transformers import StoppingCriteria 7 | from llava.constants import IMAGE_TOKEN_INDEX 8 | 9 | 10 | def load_image_from_base64(image): 11 | return Image.open(BytesIO(base64.b64decode(image))) 12 | 13 | 14 | def expand2square(pil_img, background_color): 15 | width, height = pil_img.size 16 | if width == height: 17 | return pil_img 18 | elif width > height: 19 | result = Image.new(pil_img.mode, (width, width), background_color) 20 | result.paste(pil_img, (0, (width - height) // 2)) 21 | return result 22 | else: 23 | result = Image.new(pil_img.mode, (height, height), background_color) 24 | result.paste(pil_img, ((height - width) // 2, 0)) 25 | return result 26 | 27 | 28 | def process_images(images, image_processor, model_cfg): 29 | image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None) 30 | new_images = [] 31 | if image_aspect_ratio == 'pad': 32 | for image in images: 33 | image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean)) 34 | image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] 35 | new_images.append(image) 36 | else: 37 | return image_processor(images, return_tensors='pt')['pixel_values'] 38 | if all(x.shape == new_images[0].shape for x in new_images): 39 | new_images = torch.stack(new_images, dim=0) 40 | return new_images 41 | 42 | 43 | def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None): 44 | prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('')] 45 | 46 | def insert_separator(X, sep): 47 | return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1] 48 | 49 | input_ids = [] 50 | offset = 0 51 | if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id: 52 | offset = 1 53 | input_ids.append(prompt_chunks[0][0]) 54 | 55 | for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)): 56 | input_ids.extend(x[offset:]) 57 | 58 | if return_tensors is not None: 59 | if return_tensors == 'pt': 60 | return torch.tensor(input_ids, dtype=torch.long) 61 | raise ValueError(f'Unsupported tensor type: {return_tensors}') 62 | return input_ids 63 | 64 | 65 | def get_model_name_from_path(model_path): 66 | model_path = model_path.strip("/") 67 | model_paths = model_path.split("/") 68 | if model_paths[-1].startswith('checkpoint-'): 69 | return model_paths[-2] + "_" + model_paths[-1] 70 | else: 71 | return model_paths[-1] 72 | 73 | class KeywordsStoppingCriteria(StoppingCriteria): 74 | def __init__(self, keywords, tokenizer, input_ids): 75 | self.keywords = keywords 76 | self.keyword_ids = [] 77 | self.max_keyword_len = 0 78 | for keyword in keywords: 79 | cur_keyword_ids = tokenizer(keyword).input_ids 80 | if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id: 81 | cur_keyword_ids = cur_keyword_ids[1:] 82 | if len(cur_keyword_ids) > self.max_keyword_len: 83 | self.max_keyword_len = len(cur_keyword_ids) 84 | self.keyword_ids.append(torch.tensor(cur_keyword_ids)) 85 | self.tokenizer = tokenizer 86 | self.start_len = input_ids.shape[1] 87 | 88 | def call_for_batch(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: 89 | offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len) 90 | self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids] 91 | for keyword_id in self.keyword_ids: 92 | if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all(): 93 | return True 94 | outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0] 95 | for keyword in self.keywords: 96 | if keyword in outputs: 97 | return True 98 | return False 99 | 100 | def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: 101 | outputs = [] 102 | for i in range(output_ids.shape[0]): 103 | outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores)) 104 | return all(outputs) 105 | -------------------------------------------------------------------------------- /llava/model/__init__.py: -------------------------------------------------------------------------------- 1 | from .language_model.llava_llama import LlavaLlamaForCausalLM, LlavaConfig 2 | from .language_model.llava_mpt import LlavaMPTForCausalLM, LlavaMPTConfig 3 | -------------------------------------------------------------------------------- /llava/model/apply_delta.py: -------------------------------------------------------------------------------- 1 | """ 2 | Usage: 3 | python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta 4 | """ 5 | import argparse 6 | 7 | import torch 8 | from tqdm import tqdm 9 | from transformers import AutoTokenizer, AutoModelForCausalLM 10 | from llava import LlavaLlamaForCausalLM 11 | 12 | 13 | def apply_delta(base_model_path, target_model_path, delta_path): 14 | print("Loading base model") 15 | base = AutoModelForCausalLM.from_pretrained( 16 | base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) 17 | 18 | print("Loading delta") 19 | delta = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) 20 | delta_tokenizer = AutoTokenizer.from_pretrained(delta_path) 21 | 22 | print("Applying delta") 23 | for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"): 24 | if name not in base.state_dict(): 25 | assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model' 26 | continue 27 | if param.data.shape == base.state_dict()[name].shape: 28 | param.data += base.state_dict()[name] 29 | else: 30 | assert name in ['model.embed_tokens.weight', 'lm_head.weight'], \ 31 | f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}' 32 | bparam = base.state_dict()[name] 33 | param.data[:bparam.shape[0], :bparam.shape[1]] += bparam 34 | 35 | print("Saving target model") 36 | delta.save_pretrained(target_model_path) 37 | delta_tokenizer.save_pretrained(target_model_path) 38 | 39 | 40 | if __name__ == "__main__": 41 | parser = argparse.ArgumentParser() 42 | parser.add_argument("--base-model-path", type=str, required=True) 43 | parser.add_argument("--target-model-path", type=str, required=True) 44 | parser.add_argument("--delta-path", type=str, required=True) 45 | 46 | args = parser.parse_args() 47 | 48 | apply_delta(args.base_model_path, args.target_model_path, args.delta_path) 49 | -------------------------------------------------------------------------------- /llava/model/consolidate.py: -------------------------------------------------------------------------------- 1 | """ 2 | Usage: 3 | python3 -m llava.model.consolidate --src ~/model_weights/llava-7b --dst ~/model_weights/llava-7b_consolidate 4 | """ 5 | import argparse 6 | 7 | import torch 8 | from transformers import AutoTokenizer, AutoModelForCausalLM 9 | from llava.model import * 10 | from llava.model.utils import auto_upgrade 11 | 12 | 13 | def consolidate_ckpt(src_path, dst_path): 14 | print("Loading model") 15 | auto_upgrade(src_path) 16 | src_model = AutoModelForCausalLM.from_pretrained(src_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) 17 | src_tokenizer = AutoTokenizer.from_pretrained(src_path, use_fast=False) 18 | src_model.save_pretrained(dst_path) 19 | src_tokenizer.save_pretrained(dst_path) 20 | 21 | 22 | if __name__ == "__main__": 23 | parser = argparse.ArgumentParser() 24 | parser.add_argument("--src", type=str, required=True) 25 | parser.add_argument("--dst", type=str, required=True) 26 | 27 | args = parser.parse_args() 28 | 29 | consolidate_ckpt(args.src, args.dst) 30 | -------------------------------------------------------------------------------- /llava/model/language_model/llava_llama.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Haotian Liu 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from typing import List, Optional, Tuple, Union 17 | 18 | import torch 19 | import torch.nn as nn 20 | 21 | from transformers import AutoConfig, AutoModelForCausalLM, \ 22 | LlamaConfig, LlamaModel, LlamaForCausalLM 23 | 24 | from transformers.modeling_outputs import CausalLMOutputWithPast 25 | 26 | from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM 27 | 28 | 29 | class LlavaConfig(LlamaConfig): 30 | model_type = "llava" 31 | 32 | 33 | class LlavaLlamaModel(LlavaMetaModel, LlamaModel): 34 | config_class = LlavaConfig 35 | 36 | def __init__(self, config: LlamaConfig): 37 | super(LlavaLlamaModel, self).__init__(config) 38 | 39 | 40 | class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM): 41 | config_class = LlavaConfig 42 | 43 | def __init__(self, config): 44 | super(LlamaForCausalLM, self).__init__(config) 45 | self.model = LlavaLlamaModel(config) 46 | self.pretraining_tp = config.pretraining_tp 47 | self.vocab_size = config.vocab_size 48 | self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) 49 | 50 | # Initialize weights and apply final processing 51 | self.post_init() 52 | 53 | def get_model(self): 54 | return self.model 55 | 56 | def forward( 57 | self, 58 | input_ids: torch.LongTensor = None, 59 | attention_mask: Optional[torch.Tensor] = None, 60 | position_ids: Optional[torch.LongTensor] = None, 61 | past_key_values: Optional[List[torch.FloatTensor]] = None, 62 | inputs_embeds: Optional[torch.FloatTensor] = None, 63 | labels: Optional[torch.LongTensor] = None, 64 | use_cache: Optional[bool] = None, 65 | output_attentions: Optional[bool] = None, 66 | output_hidden_states: Optional[bool] = None, 67 | images: Optional[torch.FloatTensor] = None, 68 | return_dict: Optional[bool] = None, 69 | ) -> Union[Tuple, CausalLMOutputWithPast]: 70 | 71 | if inputs_embeds is None: 72 | ( 73 | input_ids, 74 | position_ids, 75 | attention_mask, 76 | past_key_values, 77 | inputs_embeds, 78 | labels 79 | ) = self.prepare_inputs_labels_for_multimodal( 80 | input_ids, 81 | position_ids, 82 | attention_mask, 83 | past_key_values, 84 | labels, 85 | images 86 | ) 87 | 88 | return super().forward( 89 | input_ids=input_ids, 90 | attention_mask=attention_mask, 91 | position_ids=position_ids, 92 | past_key_values=past_key_values, 93 | inputs_embeds=inputs_embeds, 94 | labels=labels, 95 | use_cache=use_cache, 96 | output_attentions=output_attentions, 97 | output_hidden_states=output_hidden_states, 98 | return_dict=return_dict 99 | ) 100 | 101 | def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): 102 | images = kwargs.pop("images", None) 103 | _inputs = super().prepare_inputs_for_generation( 104 | input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs 105 | ) 106 | if images is not None: 107 | _inputs['images'] = images 108 | return _inputs 109 | 110 | AutoConfig.register("llava", LlavaConfig) 111 | AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM) 112 | -------------------------------------------------------------------------------- /llava/model/language_model/llava_mpt.py: -------------------------------------------------------------------------------- 1 | # Copyright 2023 Haotian Liu 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from typing import List, Optional, Tuple 17 | import warnings 18 | 19 | import torch 20 | import torch.nn.functional as F 21 | import math 22 | 23 | from transformers import AutoConfig, AutoModelForCausalLM 24 | from transformers.modeling_outputs import CausalLMOutputWithPast 25 | 26 | from .mpt.modeling_mpt import MPTConfig, MPTForCausalLM, MPTModel 27 | from llava.model.llava_arch import LlavaMetaModel, LlavaMetaForCausalLM 28 | 29 | 30 | class LlavaMPTConfig(MPTConfig): 31 | model_type = "llava_mpt" 32 | 33 | 34 | class LlavaMPTModel(LlavaMetaModel, MPTModel): 35 | config_class = LlavaMPTConfig 36 | 37 | def __init__(self, config: MPTConfig): 38 | config.hidden_size = config.d_model 39 | super(LlavaMPTModel, self).__init__(config) 40 | 41 | def embed_tokens(self, x): 42 | return self.wte(x) 43 | 44 | 45 | class LlavaMPTForCausalLM(MPTForCausalLM, LlavaMetaForCausalLM): 46 | config_class = LlavaMPTConfig 47 | supports_gradient_checkpointing = True 48 | 49 | def __init__(self, config): 50 | super(MPTForCausalLM, self).__init__(config) 51 | 52 | if not config.tie_word_embeddings: 53 | raise ValueError('MPTForCausalLM only supports tied word embeddings') 54 | self.transformer = LlavaMPTModel(config) 55 | self.logit_scale = None 56 | if config.logit_scale is not None: 57 | logit_scale = config.logit_scale 58 | if isinstance(logit_scale, str): 59 | if logit_scale == 'inv_sqrt_d_model': 60 | logit_scale = 1 / math.sqrt(config.d_model) 61 | else: 62 | raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.") 63 | self.logit_scale = logit_scale 64 | 65 | def get_model(self): 66 | return self.transformer 67 | 68 | def _set_gradient_checkpointing(self, module, value=False): 69 | if isinstance(module, LlavaMPTModel): 70 | module.gradient_checkpointing = value 71 | 72 | def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, images=None): 73 | return_dict = return_dict if return_dict is not None else self.config.return_dict 74 | use_cache = use_cache if use_cache is not None else self.config.use_cache 75 | 76 | input_ids, _, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, None, attention_mask, past_key_values, labels, images) 77 | outputs = self.transformer(input_ids=input_ids, inputs_embeds=inputs_embeds, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache) 78 | # FIXME: this is a hack to fix the multiple gpu inference issue in https://github.com/haotian-liu/LLaVA/issues/338 79 | logits = F.linear(outputs.last_hidden_state.to(self.transformer.wte.weight.device), self.transformer.wte.weight) 80 | if self.logit_scale is not None: 81 | if self.logit_scale == 0: 82 | warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.') 83 | logits *= self.logit_scale 84 | loss = None 85 | if labels is not None: 86 | labels = torch.roll(labels, shifts=-1) 87 | labels[:, -1] = -100 88 | loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1)) 89 | return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states) 90 | 91 | def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): 92 | if inputs_embeds is not None: 93 | raise NotImplementedError('inputs_embeds is not implemented for MPT yet') 94 | attention_mask = kwargs['attention_mask'].bool() 95 | if attention_mask[:, -1].sum() != attention_mask.shape[0]: 96 | raise NotImplementedError('MPT does not support generation with right padding.') 97 | if self.transformer.attn_uses_sequence_id and self.training: 98 | sequence_id = torch.zeros_like(input_ids[:1]) 99 | else: 100 | sequence_id = None 101 | if past_key_values is not None: 102 | input_ids = input_ids[:, -1].unsqueeze(-1) 103 | if self.transformer.prefix_lm: 104 | prefix_mask = torch.ones_like(attention_mask) 105 | if kwargs.get('use_cache') == False: 106 | raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.') 107 | else: 108 | prefix_mask = None 109 | return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True), "images": kwargs.get("images", None)} 110 | 111 | 112 | AutoConfig.register("llava_mpt", LlavaMPTConfig) 113 | AutoModelForCausalLM.register(LlavaMPTConfig, LlavaMPTForCausalLM) 114 | -------------------------------------------------------------------------------- /llava/model/language_model/mpt/adapt_tokenizer.py: -------------------------------------------------------------------------------- 1 | from typing import Union 2 | from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast 3 | Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast] 4 | NUM_SENTINEL_TOKENS: int = 100 5 | 6 | def adapt_tokenizer_for_denoising(tokenizer: Tokenizer): 7 | """Adds sentinel tokens and padding token (if missing). 8 | 9 | Expands the tokenizer vocabulary to include sentinel tokens 10 | used in mixture-of-denoiser tasks as well as a padding token. 11 | 12 | All added tokens are added as special tokens. No tokens are 13 | added if sentinel tokens and padding token already exist. 14 | """ 15 | sentinels_to_add = [f'' for i in range(NUM_SENTINEL_TOKENS)] 16 | tokenizer.add_tokens(sentinels_to_add, special_tokens=True) 17 | if tokenizer.pad_token is None: 18 | tokenizer.add_tokens('', special_tokens=True) 19 | tokenizer.pad_token = '' 20 | assert tokenizer.pad_token_id is not None 21 | sentinels = ''.join([f'' for i in range(NUM_SENTINEL_TOKENS)]) 22 | _sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids 23 | tokenizer.sentinel_token_ids = _sentinel_token_ids 24 | 25 | class AutoTokenizerForMOD(AutoTokenizer): 26 | """AutoTokenizer + Adaptation for MOD. 27 | 28 | A simple wrapper around AutoTokenizer to make instantiating 29 | an MOD-adapted tokenizer a bit easier. 30 | 31 | MOD-adapted tokenizers have sentinel tokens (e.g., ), 32 | a padding token, and a property to get the token ids of the 33 | sentinel tokens. 34 | """ 35 | 36 | @classmethod 37 | def from_pretrained(cls, *args, **kwargs): 38 | """See `AutoTokenizer.from_pretrained` docstring.""" 39 | tokenizer = super().from_pretrained(*args, **kwargs) 40 | adapt_tokenizer_for_denoising(tokenizer) 41 | return tokenizer -------------------------------------------------------------------------------- /llava/model/language_model/mpt/blocks.py: -------------------------------------------------------------------------------- 1 | """GPT Blocks used for the GPT Model.""" 2 | from typing import Dict, Optional, Tuple 3 | import torch 4 | import torch.nn as nn 5 | from .attention import ATTN_CLASS_REGISTRY 6 | from .norm import NORM_CLASS_REGISTRY 7 | 8 | class MPTMLP(nn.Module): 9 | 10 | def __init__(self, d_model: int, expansion_ratio: int, device: Optional[str]=None): 11 | super().__init__() 12 | self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device) 13 | self.act = nn.GELU(approximate='none') 14 | self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device) 15 | self.down_proj._is_residual = True 16 | 17 | def forward(self, x): 18 | return self.down_proj(self.act(self.up_proj(x))) 19 | 20 | class MPTBlock(nn.Module): 21 | 22 | def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Dict={'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', verbose: int=0, device: Optional[str]=None, **kwargs): 23 | del kwargs 24 | super().__init__() 25 | norm_class = NORM_CLASS_REGISTRY[norm_type.lower()] 26 | attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']] 27 | self.norm_1 = norm_class(d_model, device=device) 28 | self.attn = attn_class(attn_impl=attn_config['attn_impl'], clip_qkv=attn_config['clip_qkv'], qk_ln=attn_config['qk_ln'], softmax_scale=attn_config['softmax_scale'], attn_pdrop=attn_config['attn_pdrop'], d_model=d_model, n_heads=n_heads, verbose=verbose, device=device) 29 | self.norm_2 = norm_class(d_model, device=device) 30 | self.ffn = MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, device=device) 31 | self.resid_attn_dropout = nn.Dropout(resid_pdrop) 32 | self.resid_ffn_dropout = nn.Dropout(resid_pdrop) 33 | 34 | def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]: 35 | a = self.norm_1(x) 36 | (b, attn_weights, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=is_causal) 37 | x = x + self.resid_attn_dropout(b) 38 | m = self.norm_2(x) 39 | n = self.ffn(m) 40 | x = x + self.resid_ffn_dropout(n) 41 | return (x, attn_weights, past_key_value) -------------------------------------------------------------------------------- /llava/model/language_model/mpt/custom_embedding.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from torch import Tensor 5 | 6 | class SharedEmbedding(nn.Embedding): 7 | 8 | def forward(self, input: Tensor, unembed: bool=False) -> Tensor: 9 | if unembed: 10 | return F.linear(input, self.weight) 11 | return super().forward(input) -------------------------------------------------------------------------------- /llava/model/language_model/mpt/meta_init_context.py: -------------------------------------------------------------------------------- 1 | from contextlib import contextmanager 2 | import torch 3 | import torch.nn as nn 4 | 5 | @contextmanager 6 | def init_empty_weights(include_buffers: bool=False): 7 | """Meta initialization context manager. 8 | 9 | A context manager under which models are initialized with all parameters 10 | on the meta device, therefore creating an empty model. Useful when just 11 | initializing the model would blow the available RAM. 12 | 13 | Args: 14 | include_buffers (`bool`, *optional*, defaults to `False`): Whether or 15 | not to also put all buffers on the meta device while initializing. 16 | 17 | Example: 18 | ```python 19 | import torch.nn as nn 20 | 21 | # Initialize a model with 100 billions parameters in no time and without using any RAM. 22 | with init_empty_weights(): 23 | tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)]) 24 | ``` 25 | 26 | 27 | 28 | Any model created under this context manager has no weights. As such you can't do something like 29 | `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`]. 30 | 31 | 32 | """ 33 | with init_on_device(torch.device('meta'), include_buffers=include_buffers) as f: 34 | yield f 35 | 36 | @contextmanager 37 | def init_on_device(device: torch.device, include_buffers: bool=False): 38 | """Device initialization context manager. 39 | 40 | A context manager under which models are initialized with all parameters 41 | on the specified device. 42 | 43 | Args: 44 | device (`torch.device`): Device to initialize all parameters on. 45 | include_buffers (`bool`, *optional*, defaults to `False`): Whether or 46 | not to also put all buffers on the meta device while initializing. 47 | 48 | Example: 49 | ```python 50 | import torch.nn as nn 51 | 52 | with init_on_device(device=torch.device("cuda")): 53 | tst = nn.Liner(100, 100) # on `cuda` device 54 | ``` 55 | """ 56 | old_register_parameter = nn.Module.register_parameter 57 | if include_buffers: 58 | old_register_buffer = nn.Module.register_buffer 59 | 60 | def register_empty_parameter(module, name, param): 61 | old_register_parameter(module, name, param) 62 | if param is not None: 63 | param_cls = type(module._parameters[name]) 64 | kwargs = module._parameters[name].__dict__ 65 | module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) 66 | 67 | def register_empty_buffer(module, name, buffer): 68 | old_register_buffer(module, name, buffer) 69 | if buffer is not None: 70 | module._buffers[name] = module._buffers[name].to(device) 71 | if include_buffers: 72 | tensor_constructors_to_patch = {torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ['empty', 'zeros', 'ones', 'full']} 73 | else: 74 | tensor_constructors_to_patch = {} 75 | 76 | def patch_tensor_constructor(fn): 77 | 78 | def wrapper(*args, **kwargs): 79 | kwargs['device'] = device 80 | return fn(*args, **kwargs) 81 | return wrapper 82 | try: 83 | nn.Module.register_parameter = register_empty_parameter 84 | if include_buffers: 85 | nn.Module.register_buffer = register_empty_buffer 86 | for torch_function_name in tensor_constructors_to_patch.keys(): 87 | setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name))) 88 | yield 89 | finally: 90 | nn.Module.register_parameter = old_register_parameter 91 | if include_buffers: 92 | nn.Module.register_buffer = old_register_buffer 93 | for (torch_function_name, old_torch_function) in tensor_constructors_to_patch.items(): 94 | setattr(torch, torch_function_name, old_torch_function) -------------------------------------------------------------------------------- /llava/model/language_model/mpt/norm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | def _cast_if_autocast_enabled(tensor): 4 | if torch.is_autocast_enabled(): 5 | if tensor.device.type == 'cuda': 6 | dtype = torch.get_autocast_gpu_dtype() 7 | elif tensor.device.type == 'cpu': 8 | dtype = torch.get_autocast_cpu_dtype() 9 | else: 10 | raise NotImplementedError() 11 | return tensor.to(dtype=dtype) 12 | return tensor 13 | 14 | class LPLayerNorm(torch.nn.LayerNorm): 15 | 16 | def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None): 17 | super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype) 18 | 19 | def forward(self, x): 20 | module_device = x.device 21 | downcast_x = _cast_if_autocast_enabled(x) 22 | downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight 23 | downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias 24 | with torch.autocast(enabled=False, device_type=module_device.type): 25 | return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps) 26 | 27 | def rms_norm(x, weight=None, eps=1e-05): 28 | output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps) 29 | if weight is not None: 30 | return output * weight 31 | return output 32 | 33 | class RMSNorm(torch.nn.Module): 34 | 35 | def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None): 36 | super().__init__() 37 | self.eps = eps 38 | if weight: 39 | self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device)) 40 | else: 41 | self.register_parameter('weight', None) 42 | 43 | def forward(self, x): 44 | return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype) 45 | 46 | class LPRMSNorm(RMSNorm): 47 | 48 | def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None): 49 | super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device) 50 | 51 | def forward(self, x): 52 | downcast_x = _cast_if_autocast_enabled(x) 53 | downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight 54 | with torch.autocast(enabled=False, device_type=x.device.type): 55 | return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype) 56 | NORM_CLASS_REGISTRY = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm} -------------------------------------------------------------------------------- /llava/model/make_delta.py: -------------------------------------------------------------------------------- 1 | """ 2 | Usage: 3 | 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 4 | """ 5 | import argparse 6 | 7 | import torch 8 | from tqdm import tqdm 9 | from transformers import AutoTokenizer, AutoModelForCausalLM 10 | from llava.model.utils import auto_upgrade 11 | 12 | 13 | def make_delta(base_model_path, target_model_path, delta_path, hub_repo_id): 14 | print("Loading base model") 15 | base = AutoModelForCausalLM.from_pretrained( 16 | base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) 17 | 18 | print("Loading target model") 19 | auto_upgrade(target_model_path) 20 | target = AutoModelForCausalLM.from_pretrained(target_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) 21 | 22 | print("Calculating delta") 23 | for name, param in tqdm(target.state_dict().items(), desc="Calculating delta"): 24 | if name not in base.state_dict(): 25 | assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model' 26 | continue 27 | if param.data.shape == base.state_dict()[name].shape: 28 | param.data -= base.state_dict()[name] 29 | else: 30 | assert name in ['model.embed_tokens.weight', 'lm_head.weight'], f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}' 31 | bparam = base.state_dict()[name] 32 | param.data[:bparam.shape[0], :bparam.shape[1]] -= bparam 33 | 34 | print("Saving delta") 35 | if hub_repo_id: 36 | kwargs = {"push_to_hub": True, "repo_id": hub_repo_id} 37 | else: 38 | kwargs = {} 39 | target.save_pretrained(delta_path, **kwargs) 40 | target_tokenizer = AutoTokenizer.from_pretrained(target_model_path) 41 | target_tokenizer.save_pretrained(delta_path, **kwargs) 42 | 43 | 44 | if __name__ == "__main__": 45 | parser = argparse.ArgumentParser() 46 | parser.add_argument("--base-model-path", type=str, required=True) 47 | parser.add_argument("--target-model-path", type=str, required=True) 48 | parser.add_argument("--delta-path", type=str, required=True) 49 | parser.add_argument("--hub-repo-id", type=str, default=None) 50 | args = parser.parse_args() 51 | 52 | make_delta(args.base_model_path, args.target_model_path, args.delta_path, args.hub_repo_id) 53 | -------------------------------------------------------------------------------- /llava/model/multimodal_encoder/builder.py: -------------------------------------------------------------------------------- 1 | import os 2 | from .clip_encoder import CLIPVisionTower 3 | 4 | 5 | def build_vision_tower(vision_tower_cfg, **kwargs): 6 | vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None)) 7 | is_absolute_path_exists = os.path.exists(vision_tower) 8 | if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion"): 9 | return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs) 10 | 11 | raise ValueError(f'Unknown vision tower: {vision_tower}') 12 | -------------------------------------------------------------------------------- /llava/model/multimodal_encoder/clip_encoder.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig 5 | 6 | 7 | class CLIPVisionTower(nn.Module): 8 | def __init__(self, vision_tower, args, delay_load=False): 9 | super().__init__() 10 | 11 | self.is_loaded = False 12 | 13 | self.vision_tower_name = vision_tower 14 | self.select_layer = args.mm_vision_select_layer 15 | self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch') 16 | 17 | if not delay_load: 18 | self.load_model() 19 | else: 20 | self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name) 21 | 22 | def load_model(self): 23 | self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name) 24 | self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name) 25 | self.vision_tower.requires_grad_(False) 26 | 27 | self.is_loaded = True 28 | 29 | def feature_select(self, image_forward_outs): 30 | image_features = image_forward_outs.hidden_states[self.select_layer] 31 | if self.select_feature == 'patch': 32 | image_features = image_features[:, 1:] 33 | elif self.select_feature == 'cls_patch': 34 | image_features = image_features 35 | else: 36 | raise ValueError(f'Unexpected select feature: {self.select_feature}') 37 | return image_features 38 | 39 | @torch.no_grad() 40 | def forward(self, images): 41 | if type(images) is list: 42 | image_features = [] 43 | for image in images: 44 | image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True) 45 | image_feature = self.feature_select(image_forward_out).to(image.dtype) 46 | image_features.append(image_feature) 47 | else: 48 | image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True) 49 | image_features = self.feature_select(image_forward_outs).to(images.dtype) 50 | 51 | return image_features 52 | 53 | @property 54 | def dummy_feature(self): 55 | return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) 56 | 57 | @property 58 | def dtype(self): 59 | return self.vision_tower.dtype 60 | 61 | @property 62 | def device(self): 63 | return self.vision_tower.device 64 | 65 | @property 66 | def config(self): 67 | if self.is_loaded: 68 | return self.vision_tower.config 69 | else: 70 | return self.cfg_only 71 | 72 | @property 73 | def hidden_size(self): 74 | return self.config.hidden_size 75 | 76 | @property 77 | def num_patches(self): 78 | return (self.config.image_size // self.config.patch_size) ** 2 79 | -------------------------------------------------------------------------------- /llava/model/multimodal_projector/builder.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import re 4 | 5 | 6 | class IdentityMap(nn.Module): 7 | def __init__(self): 8 | super().__init__() 9 | 10 | def forward(self, x, *args, **kwargs): 11 | return x 12 | 13 | @property 14 | def config(self): 15 | return {"mm_projector_type": 'identity'} 16 | 17 | 18 | class SimpleResBlock(nn.Module): 19 | def __init__(self, channels): 20 | super().__init__() 21 | self.pre_norm = nn.LayerNorm(channels) 22 | 23 | self.proj = nn.Sequential( 24 | nn.Linear(channels, channels), 25 | nn.GELU(), 26 | nn.Linear(channels, channels) 27 | ) 28 | def forward(self, x): 29 | x = self.pre_norm(x) 30 | return x + self.proj(x) 31 | 32 | 33 | def build_vision_projector(config, delay_load=False, **kwargs): 34 | projector_type = getattr(config, 'mm_projector_type', 'linear') 35 | 36 | if projector_type == 'linear': 37 | return nn.Linear(config.mm_hidden_size, config.hidden_size) 38 | 39 | mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type) 40 | if mlp_gelu_match: 41 | mlp_depth = int(mlp_gelu_match.group(1)) 42 | modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)] 43 | for _ in range(1, mlp_depth): 44 | modules.append(nn.GELU()) 45 | modules.append(nn.Linear(config.hidden_size, config.hidden_size)) 46 | return nn.Sequential(*modules) 47 | 48 | if projector_type == 'identity': 49 | return IdentityMap() 50 | 51 | raise ValueError(f'Unknown projector type: {projector_type}') 52 | -------------------------------------------------------------------------------- /llava/model/utils.py: -------------------------------------------------------------------------------- 1 | from transformers import AutoConfig 2 | 3 | 4 | def auto_upgrade(config): 5 | cfg = AutoConfig.from_pretrained(config) 6 | if 'llava' in config and 'llava' not in cfg.model_type: 7 | assert cfg.model_type == 'llama' 8 | print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.") 9 | print("You must upgrade the checkpoint to the new code base (this can be done automatically).") 10 | confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]") 11 | if confirm.lower() in ["y", "yes"]: 12 | print("Upgrading checkpoint...") 13 | assert len(cfg.architectures) == 1 14 | setattr(cfg.__class__, "model_type", "llava") 15 | cfg.architectures[0] = 'LlavaLlamaForCausalLM' 16 | cfg.save_pretrained(config) 17 | print("Checkpoint upgraded.") 18 | else: 19 | print("Checkpoint upgrade aborted.") 20 | exit(1) 21 | -------------------------------------------------------------------------------- /llava/serve/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/prometheus-eval/prometheus-vision/fe668b5e7e120e5bea8a3821eeb468ddf719c23c/llava/serve/__init__.py -------------------------------------------------------------------------------- /llava/serve/cli.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import torch 3 | 4 | from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN 5 | from llava.conversation import conv_templates, SeparatorStyle 6 | from llava.model.builder import load_pretrained_model 7 | from llava.utils import disable_torch_init 8 | from llava.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria 9 | 10 | from PIL import Image 11 | 12 | import requests 13 | from PIL import Image 14 | from io import BytesIO 15 | from transformers import TextStreamer 16 | 17 | 18 | def load_image(image_file): 19 | if image_file.startswith('http://') or image_file.startswith('https://'): 20 | response = requests.get(image_file) 21 | image = Image.open(BytesIO(response.content)).convert('RGB') 22 | else: 23 | image = Image.open(image_file).convert('RGB') 24 | return image 25 | 26 | 27 | def main(args): 28 | # Model 29 | disable_torch_init() 30 | 31 | model_name = get_model_name_from_path(args.model_path) 32 | 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) 33 | 34 | if 'llama-2' in model_name.lower(): 35 | conv_mode = "llava_llama_2" 36 | elif "v1" in model_name.lower(): 37 | conv_mode = "llava_v1" 38 | elif "mpt" in model_name.lower(): 39 | conv_mode = "mpt" 40 | else: 41 | conv_mode = "llava_v0" 42 | 43 | if args.conv_mode is not None and conv_mode != args.conv_mode: 44 | print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode)) 45 | else: 46 | args.conv_mode = conv_mode 47 | 48 | conv = conv_templates[args.conv_mode].copy() 49 | if "mpt" in model_name.lower(): 50 | roles = ('user', 'assistant') 51 | else: 52 | roles = conv.roles 53 | 54 | image = load_image(args.image_file) 55 | # Similar operation in model_worker.py 56 | image_tensor = process_images([image], image_processor, model.config) 57 | if type(image_tensor) is list: 58 | image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor] 59 | else: 60 | image_tensor = image_tensor.to(model.device, dtype=torch.float16) 61 | 62 | while True: 63 | try: 64 | inp = input(f"{roles[0]}: ") 65 | except EOFError: 66 | inp = "" 67 | if not inp: 68 | print("exit...") 69 | break 70 | 71 | print(f"{roles[1]}: ", end="") 72 | 73 | if image is not None: 74 | # first message 75 | if model.config.mm_use_im_start_end: 76 | inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp 77 | else: 78 | inp = DEFAULT_IMAGE_TOKEN + '\n' + inp 79 | conv.append_message(conv.roles[0], inp) 80 | image = None 81 | else: 82 | # later messages 83 | conv.append_message(conv.roles[0], inp) 84 | conv.append_message(conv.roles[1], None) 85 | prompt = conv.get_prompt() 86 | 87 | input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device) 88 | stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 89 | keywords = [stop_str] 90 | stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) 91 | streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) 92 | 93 | with torch.inference_mode(): 94 | output_ids = model.generate( 95 | input_ids, 96 | images=image_tensor, 97 | do_sample=True if args.temperature > 0 else False, 98 | temperature=args.temperature, 99 | max_new_tokens=args.max_new_tokens, 100 | streamer=streamer, 101 | use_cache=True, 102 | stopping_criteria=[stopping_criteria]) 103 | 104 | outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip() 105 | conv.messages[-1][-1] = outputs 106 | 107 | if args.debug: 108 | print("\n", {"prompt": prompt, "outputs": outputs}, "\n") 109 | 110 | 111 | if __name__ == "__main__": 112 | parser = argparse.ArgumentParser() 113 | parser.add_argument("--model-path", type=str, default="facebook/opt-350m") 114 | parser.add_argument("--model-base", type=str, default=None) 115 | parser.add_argument("--image-file", type=str, required=True) 116 | parser.add_argument("--device", type=str, default="cuda") 117 | parser.add_argument("--conv-mode", type=str, default=None) 118 | parser.add_argument("--temperature", type=float, default=0.2) 119 | parser.add_argument("--max-new-tokens", type=int, default=512) 120 | parser.add_argument("--load-8bit", action="store_true") 121 | parser.add_argument("--load-4bit", action="store_true") 122 | parser.add_argument("--debug", action="store_true") 123 | args = parser.parse_args() 124 | main(args) 125 | -------------------------------------------------------------------------------- /llava/serve/examples/extreme_ironing.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/prometheus-eval/prometheus-vision/fe668b5e7e120e5bea8a3821eeb468ddf719c23c/llava/serve/examples/extreme_ironing.jpg -------------------------------------------------------------------------------- /llava/serve/examples/waterview.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/prometheus-eval/prometheus-vision/fe668b5e7e120e5bea8a3821eeb468ddf719c23c/llava/serve/examples/waterview.jpg -------------------------------------------------------------------------------- /llava/serve/register_worker.py: -------------------------------------------------------------------------------- 1 | """ 2 | Manually register workers. 3 | 4 | Usage: 5 | python3 -m fastchat.serve.register_worker --controller http://localhost:21001 --worker-name http://localhost:21002 6 | """ 7 | 8 | import argparse 9 | 10 | import requests 11 | 12 | if __name__ == "__main__": 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument("--controller-address", type=str) 15 | parser.add_argument("--worker-name", type=str) 16 | parser.add_argument("--check-heart-beat", action="store_true") 17 | args = parser.parse_args() 18 | 19 | url = args.controller_address + "/register_worker" 20 | data = { 21 | "worker_name": args.worker_name, 22 | "check_heart_beat": args.check_heart_beat, 23 | "worker_status": None, 24 | } 25 | r = requests.post(url, json=data) 26 | assert r.status_code == 200 27 | -------------------------------------------------------------------------------- /llava/serve/test_message.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | 4 | import requests 5 | 6 | from llava.conversation import default_conversation 7 | 8 | 9 | def main(): 10 | if args.worker_address: 11 | worker_addr = args.worker_address 12 | else: 13 | controller_addr = args.controller_address 14 | ret = requests.post(controller_addr + "/refresh_all_workers") 15 | ret = requests.post(controller_addr + "/list_models") 16 | models = ret.json()["models"] 17 | models.sort() 18 | print(f"Models: {models}") 19 | 20 | ret = requests.post(controller_addr + "/get_worker_address", 21 | json={"model": args.model_name}) 22 | worker_addr = ret.json()["address"] 23 | print(f"worker_addr: {worker_addr}") 24 | 25 | if worker_addr == "": 26 | return 27 | 28 | conv = default_conversation.copy() 29 | conv.append_message(conv.roles[0], args.message) 30 | prompt = conv.get_prompt() 31 | 32 | headers = {"User-Agent": "LLaVA Client"} 33 | pload = { 34 | "model": args.model_name, 35 | "prompt": prompt, 36 | "max_new_tokens": args.max_new_tokens, 37 | "temperature": 0.7, 38 | "stop": conv.sep, 39 | } 40 | response = requests.post(worker_addr + "/worker_generate_stream", headers=headers, 41 | json=pload, stream=True) 42 | 43 | print(prompt.replace(conv.sep, "\n"), end="") 44 | for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b"\0"): 45 | if chunk: 46 | data = json.loads(chunk.decode("utf-8")) 47 | output = data["text"].split(conv.sep)[-1] 48 | print(output, end="\r") 49 | print("") 50 | 51 | 52 | if __name__ == "__main__": 53 | parser = argparse.ArgumentParser() 54 | parser.add_argument("--controller-address", type=str, default="http://localhost:21001") 55 | parser.add_argument("--worker-address", type=str) 56 | parser.add_argument("--model-name", type=str, default="facebook/opt-350m") 57 | parser.add_argument("--max-new-tokens", type=int, default=32) 58 | parser.add_argument("--message", type=str, default= 59 | "Tell me a story with more than 1000 words.") 60 | args = parser.parse_args() 61 | 62 | main() 63 | -------------------------------------------------------------------------------- /llava/train/llama_flash_attn_monkey_patch.py: -------------------------------------------------------------------------------- 1 | from typing import Optional, Tuple 2 | import warnings 3 | 4 | import torch 5 | 6 | import transformers 7 | from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv 8 | 9 | try: 10 | from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func 11 | except ImportError: 12 | from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func 13 | from flash_attn.bert_padding import unpad_input, pad_input 14 | 15 | 16 | def forward( 17 | self, 18 | hidden_states: torch.Tensor, 19 | attention_mask: Optional[torch.Tensor] = None, 20 | position_ids: Optional[torch.Tensor] = None, 21 | past_key_value: Optional[Tuple[torch.Tensor]] = None, 22 | output_attentions: bool = False, 23 | use_cache: bool = False, 24 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: 25 | if output_attentions: 26 | warnings.warn( 27 | "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead." 28 | ) 29 | 30 | bsz, q_len, _ = hidden_states.size() 31 | 32 | query_states = ( 33 | self.q_proj(hidden_states) 34 | .view(bsz, q_len, self.num_heads, self.head_dim) 35 | .transpose(1, 2) 36 | ) 37 | key_states = ( 38 | self.k_proj(hidden_states) 39 | .view(bsz, q_len, self.num_key_value_heads, self.head_dim) 40 | .transpose(1, 2) 41 | ) 42 | value_states = ( 43 | self.v_proj(hidden_states) 44 | .view(bsz, q_len, self.num_key_value_heads, self.head_dim) 45 | .transpose(1, 2) 46 | ) # shape: (b, num_heads, s, head_dim) 47 | 48 | kv_seq_len = key_states.shape[-2] 49 | if past_key_value is not None: 50 | kv_seq_len += past_key_value[0].shape[-2] 51 | 52 | cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) 53 | query_states, key_states = apply_rotary_pos_emb( 54 | query_states, key_states, cos, sin, position_ids 55 | ) 56 | 57 | if past_key_value is not None: 58 | # reuse k, v 59 | key_states = torch.cat([past_key_value[0], key_states], dim=2) 60 | value_states = torch.cat([past_key_value[1], value_states], dim=2) 61 | 62 | past_key_value = (key_states, value_states) if use_cache else None 63 | 64 | # repeat k/v heads if n_kv_heads < n_heads 65 | key_states = repeat_kv(key_states, self.num_key_value_groups) 66 | value_states = repeat_kv(value_states, self.num_key_value_groups) 67 | 68 | # Transform the data into the format required by flash attention 69 | qkv = torch.stack([query_states, key_states, value_states], dim=2) 70 | qkv = qkv.transpose(1, 3) # shape: [b, s, 3, num_heads, head_dim] 71 | key_padding_mask = attention_mask 72 | 73 | if key_padding_mask is None: 74 | qkv = qkv.reshape(-1, 3, self.num_heads, self.head_dim) 75 | cu_q_lens = torch.arange( 76 | 0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device 77 | ) 78 | max_s = q_len 79 | output = flash_attn_unpadded_qkvpacked_func( 80 | qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True 81 | ) 82 | output = output.view(bsz, q_len, -1) 83 | else: 84 | qkv = qkv.reshape(bsz, q_len, -1) 85 | qkv, indices, cu_q_lens, max_s = unpad_input(qkv, key_padding_mask) 86 | qkv = qkv.view(-1, 3, self.num_heads, self.head_dim) 87 | output_unpad = flash_attn_unpadded_qkvpacked_func( 88 | qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True 89 | ) 90 | output_unpad = output_unpad.reshape(-1, self.num_heads * self.head_dim) 91 | output = pad_input(output_unpad, indices, bsz, q_len) 92 | 93 | return self.o_proj(output), None, past_key_value 94 | 95 | 96 | # Disable the transformation of the attention mask in LlamaModel as the flash attention 97 | # requires the attention mask to be the same as the key_padding_mask 98 | def _prepare_decoder_attention_mask( 99 | self, attention_mask, input_shape, inputs_embeds, past_key_values_length 100 | ): 101 | # [bsz, seq_len] 102 | return attention_mask 103 | 104 | 105 | def replace_llama_attn_with_flash_attn(): 106 | cuda_major, cuda_minor = torch.cuda.get_device_capability() 107 | if cuda_major < 8: 108 | warnings.warn( 109 | "Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward." 110 | "ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593" 111 | ) 112 | transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( 113 | _prepare_decoder_attention_mask 114 | ) 115 | transformers.models.llama.modeling_llama.LlamaAttention.forward = forward 116 | -------------------------------------------------------------------------------- /llava/train/llama_xformers_attn_monkey_patch.py: -------------------------------------------------------------------------------- 1 | """ 2 | Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments 3 | """ 4 | 5 | import logging 6 | import math 7 | from typing import Optional, Tuple 8 | 9 | import torch 10 | import transformers.models.llama.modeling_llama 11 | from torch import nn 12 | 13 | try: 14 | import xformers.ops 15 | except ImportError: 16 | logging.error("xformers not found! Please install it before trying to use it.") 17 | 18 | 19 | def replace_llama_attn_with_xformers_attn(): 20 | transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward 21 | 22 | 23 | def xformers_forward( 24 | self, 25 | hidden_states: torch.Tensor, 26 | attention_mask: Optional[torch.Tensor] = None, 27 | position_ids: Optional[torch.LongTensor] = None, 28 | past_key_value: Optional[Tuple[torch.Tensor]] = None, 29 | output_attentions: bool = False, 30 | use_cache: bool = False, 31 | ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: 32 | # pylint: disable=duplicate-code 33 | bsz, q_len, _ = hidden_states.size() 34 | 35 | query_states = ( 36 | self.q_proj(hidden_states) 37 | .view(bsz, q_len, self.num_heads, self.head_dim) 38 | .transpose(1, 2) 39 | ) 40 | key_states = ( 41 | self.k_proj(hidden_states) 42 | .view(bsz, q_len, self.num_heads, self.head_dim) 43 | .transpose(1, 2) 44 | ) 45 | value_states = ( 46 | self.v_proj(hidden_states) 47 | .view(bsz, q_len, self.num_heads, self.head_dim) 48 | .transpose(1, 2) 49 | ) 50 | 51 | kv_seq_len = key_states.shape[-2] 52 | if past_key_value is not None: 53 | kv_seq_len += past_key_value[0].shape[-2] 54 | cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) 55 | ( 56 | query_states, 57 | key_states, 58 | ) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb( 59 | query_states, key_states, cos, sin, position_ids 60 | ) 61 | # [bsz, nh, t, hd] 62 | 63 | if past_key_value is not None: 64 | # reuse k, v, self_attention 65 | key_states = torch.cat([past_key_value[0], key_states], dim=2) 66 | value_states = torch.cat([past_key_value[1], value_states], dim=2) 67 | 68 | past_key_value = (key_states, value_states) if use_cache else None 69 | 70 | # We only apply xformers optimizations if we don't need to output the whole attention matrix 71 | if not output_attentions: 72 | query_states = query_states.transpose(1, 2) 73 | key_states = key_states.transpose(1, 2) 74 | value_states = value_states.transpose(1, 2) 75 | 76 | # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros. 77 | # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros. 78 | if attention_mask is None or attention_mask[0, 0, 0, 1] == 0: 79 | # input and output should be of form (bsz, q_len, num_heads, head_dim) 80 | attn_output = xformers.ops.memory_efficient_attention( 81 | query_states, key_states, value_states, attn_bias=None 82 | ) 83 | else: 84 | # input and output should be of form (bsz, q_len, num_heads, head_dim) 85 | attn_output = xformers.ops.memory_efficient_attention( 86 | query_states, 87 | key_states, 88 | value_states, 89 | attn_bias=xformers.ops.LowerTriangularMask(), 90 | ) 91 | attn_weights = None 92 | else: 93 | attn_weights = torch.matmul( 94 | query_states, key_states.transpose(2, 3) 95 | ) / math.sqrt(self.head_dim) 96 | 97 | if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): 98 | raise ValueError( 99 | f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is" 100 | f" {attn_weights.size()}" 101 | ) 102 | 103 | if attention_mask is not None: 104 | if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): 105 | raise ValueError( 106 | f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" 107 | ) 108 | attn_weights = attn_weights + attention_mask 109 | attn_weights = torch.max( 110 | attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min) 111 | ) 112 | 113 | # upcast attention to fp32 114 | attn_weights = nn.functional.softmax( 115 | attn_weights, dim=-1, dtype=torch.float32 116 | ).to(query_states.dtype) 117 | attn_output = torch.matmul(attn_weights, value_states) 118 | 119 | if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): 120 | raise ValueError( 121 | f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" 122 | f" {attn_output.size()}" 123 | ) 124 | 125 | attn_output = attn_output.transpose(1, 2) 126 | 127 | attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) 128 | attn_output = self.o_proj(attn_output) 129 | return attn_output, attn_weights, past_key_value 130 | -------------------------------------------------------------------------------- /llava/train/train_mem.py: -------------------------------------------------------------------------------- 1 | # Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright: 2 | # Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright: 3 | # Make it more memory efficient by monkey patching the LLaMA model with FlashAttn. 4 | 5 | # Need to call this before importing transformers. 6 | from llava.train.llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn 7 | 8 | replace_llama_attn_with_flash_attn() 9 | 10 | from llava.train.train import train 11 | 12 | if __name__ == "__main__": 13 | train() 14 | -------------------------------------------------------------------------------- /llava/train/train_xformers.py: -------------------------------------------------------------------------------- 1 | # Make it more memory efficient by monkey patching the LLaMA model with xformers attention. 2 | 3 | # Need to call this before importing transformers. 4 | from llava.train.llama_xformers_attn_monkey_patch import ( 5 | replace_llama_attn_with_xformers_attn, 6 | ) 7 | 8 | replace_llama_attn_with_xformers_attn() 9 | 10 | from llava.train.train import train 11 | 12 | if __name__ == "__main__": 13 | train() 14 | -------------------------------------------------------------------------------- /llava/utils.py: -------------------------------------------------------------------------------- 1 | import datetime 2 | import logging 3 | import logging.handlers 4 | import os 5 | import sys 6 | 7 | import requests 8 | 9 | from llava.constants import LOGDIR 10 | 11 | server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**" 12 | moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN." 13 | 14 | handler = None 15 | 16 | 17 | def build_logger(logger_name, logger_filename): 18 | global handler 19 | 20 | formatter = logging.Formatter( 21 | fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", 22 | datefmt="%Y-%m-%d %H:%M:%S", 23 | ) 24 | 25 | # Set the format of root handlers 26 | if not logging.getLogger().handlers: 27 | logging.basicConfig(level=logging.INFO) 28 | logging.getLogger().handlers[0].setFormatter(formatter) 29 | 30 | # Redirect stdout and stderr to loggers 31 | stdout_logger = logging.getLogger("stdout") 32 | stdout_logger.setLevel(logging.INFO) 33 | sl = StreamToLogger(stdout_logger, logging.INFO) 34 | sys.stdout = sl 35 | 36 | stderr_logger = logging.getLogger("stderr") 37 | stderr_logger.setLevel(logging.ERROR) 38 | sl = StreamToLogger(stderr_logger, logging.ERROR) 39 | sys.stderr = sl 40 | 41 | # Get logger 42 | logger = logging.getLogger(logger_name) 43 | logger.setLevel(logging.INFO) 44 | 45 | # Add a file handler for all loggers 46 | if handler is None: 47 | os.makedirs(LOGDIR, exist_ok=True) 48 | filename = os.path.join(LOGDIR, logger_filename) 49 | handler = logging.handlers.TimedRotatingFileHandler( 50 | filename, when='D', utc=True, encoding='UTF-8') 51 | handler.setFormatter(formatter) 52 | 53 | for name, item in logging.root.manager.loggerDict.items(): 54 | if isinstance(item, logging.Logger): 55 | item.addHandler(handler) 56 | 57 | return logger 58 | 59 | 60 | class StreamToLogger(object): 61 | """ 62 | Fake file-like stream object that redirects writes to a logger instance. 63 | """ 64 | def __init__(self, logger, log_level=logging.INFO): 65 | self.terminal = sys.stdout 66 | self.logger = logger 67 | self.log_level = log_level 68 | self.linebuf = '' 69 | 70 | def __getattr__(self, attr): 71 | return getattr(self.terminal, attr) 72 | 73 | def write(self, buf): 74 | temp_linebuf = self.linebuf + buf 75 | self.linebuf = '' 76 | for line in temp_linebuf.splitlines(True): 77 | # From the io.TextIOWrapper docs: 78 | # On output, if newline is None, any '\n' characters written 79 | # are translated to the system default line separator. 80 | # By default sys.stdout.write() expects '\n' newlines and then 81 | # translates them so this is still cross platform. 82 | if line[-1] == '\n': 83 | self.logger.log(self.log_level, line.rstrip()) 84 | else: 85 | self.linebuf += line 86 | 87 | def flush(self): 88 | if self.linebuf != '': 89 | self.logger.log(self.log_level, self.linebuf.rstrip()) 90 | self.linebuf = '' 91 | 92 | 93 | def disable_torch_init(): 94 | """ 95 | Disable the redundant torch default initialization to accelerate model creation. 96 | """ 97 | import torch 98 | setattr(torch.nn.Linear, "reset_parameters", lambda self: None) 99 | setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) 100 | 101 | 102 | def violates_moderation(text): 103 | """ 104 | Check whether the text violates OpenAI moderation API. 105 | """ 106 | url = "https://api.openai.com/v1/moderations" 107 | headers = {"Content-Type": "application/json", 108 | "Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]} 109 | text = text.replace("\n", "") 110 | data = "{" + '"input": ' + f'"{text}"' + "}" 111 | data = data.encode("utf-8") 112 | try: 113 | ret = requests.post(url, headers=headers, data=data, timeout=5) 114 | flagged = ret.json()["results"][0]["flagged"] 115 | except requests.exceptions.RequestException as e: 116 | flagged = False 117 | except KeyError as e: 118 | flagged = False 119 | 120 | return flagged 121 | 122 | 123 | def pretty_print_semaphore(semaphore): 124 | if semaphore is None: 125 | return "None" 126 | return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})" 127 | -------------------------------------------------------------------------------- /mmmu_download.py: -------------------------------------------------------------------------------- 1 | from datasets import load_dataset 2 | from tqdm import tqdm 3 | import os 4 | 5 | cate = ['Accounting', 'Agriculture', 'Architecture_and_Engineering', 'Art', 'Art_Theory', 'Basic_Medical_Science', 'Biology', 'Chemistry', 'Clinical_Medicine', 'Computer_Science', 'Design', 'Diagnostics_and_Laboratory_Medicine', 'Economics', 'Electronics', 'Energy_and_Power', 'Finance', 'Geography', 'History', 'Literature', 'Manage', 'Marketing', 'Materials', 'Math', 'Mechanical_Engineering', 'Music', 'Pharmacy', 'Physics', 'Psychology', 'Public_Health', 'Sociology'] 6 | temp = { 7 | "Art": "art_and_design", 8 | "Design": "art_and_design", 9 | "Music": "art_and_design", 10 | "Art_Theory": "art_and_design", 11 | "Accounting": "business", 12 | "Economics": "business", 13 | "Finance": "business", 14 | "Manage": "business", 15 | "Marketing": "business", 16 | "Biology": "science", 17 | "Chemistry": "science", 18 | "Geography": "science", 19 | "Math": "science", 20 | "Physics": "science", 21 | "Basic_Medical_Science": "health_and_medicine", 22 | "Clinical_Medicine": "health_and_medicine", 23 | "Diagnostics_and_Laboratory_Medicine": "health_and_medicine", 24 | "Pharmacy": "health_and_medicine", 25 | "Public_Health": "health_and_medicine", 26 | "History": "humanities_and_social_sci", 27 | "Literature": "humanities_and_social_sci", 28 | "Psychology": "humanities_and_social_sci", 29 | "Sociology": "humanities_and_social_sci", 30 | "Agriculture": "tech_and_engineering", 31 | "Architecture_and_Engineering": "tech_and_engineering", 32 | "Computer_Science": "tech_and_engineering", 33 | "Electronics": "tech_and_engineering", 34 | "Energy_and_Power": "tech_and_engineering", 35 | "Materials": "tech_and_engineering", 36 | "Mechanical_Engineering": "tech_and_engineering" 37 | 38 | } 39 | ids = 0 40 | save_dir = "./mmmu" 41 | if not os.path.exists(save_dir): 42 | os.makedirs(save_dir) 43 | pattern = r"\['(.*?)'\]" 44 | for c in tqdm(cate): 45 | dataset = load_dataset("MMMU/MMMU", c) 46 | splits = ['dev', 'test', 'validation'] 47 | for s in splits: 48 | images = dataset[s]['image_1'] 49 | for img in images: 50 | path = f"{save_dir}/{ids}.png" 51 | img.save(path) 52 | ids += 1 53 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools>=61.0"] 3 | build-backend = "setuptools.build_meta" 4 | 5 | [project] 6 | name = "llava" 7 | version = "1.1.3" 8 | description = "Towards GPT-4 like large language and visual assistant." 9 | readme = "README.md" 10 | requires-python = ">=3.8" 11 | classifiers = [ 12 | "Programming Language :: Python :: 3", 13 | "License :: OSI Approved :: Apache Software License", 14 | ] 15 | dependencies = [ 16 | "torch==2.0.1", "torchvision==0.15.2", 17 | "transformers==4.31.0", "tokenizers>=0.12.1,<0.14", "sentencepiece==0.1.99", "shortuuid", 18 | "accelerate==0.21.0", "peft==0.4.0", "bitsandbytes==0.41.0", 19 | "pydantic<2,>=1", "markdown2[all]", "numpy", "scikit-learn==1.2.2", 20 | "gradio==3.35.2", "gradio_client==0.2.9", 21 | "requests", "httpx==0.24.0", "uvicorn", "fastapi", 22 | "einops==0.6.1", "einops-exts==0.0.4", "timm==0.6.13", 23 | ] 24 | 25 | [project.optional-dependencies] 26 | train = ["deepspeed==0.9.5", "ninja", "wandb"] 27 | 28 | [project.urls] 29 | "Homepage" = "https://llava-vl.github.io" 30 | "Bug Tracker" = "https://github.com/haotian-liu/LLaVA/issues" 31 | 32 | [tool.setuptools.packages.find] 33 | exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"] 34 | 35 | [tool.wheel] 36 | exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"] 37 | --------------------------------------------------------------------------------