diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..40112eac1ff249ee5229cbdd8492338748e64cb7 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +examples_v2/cockdial.png filter=lfs diff=lfs merge=lfs -text +examples_v2/float.png filter=lfs diff=lfs merge=lfs -text +figs/demo.png filter=lfs diff=lfs merge=lfs -text +figs/minigpt2_demo.png filter=lfs diff=lfs merge=lfs -text +figs/online_demo.png filter=lfs diff=lfs merge=lfs -text +figs/overview.png filter=lfs diff=lfs merge=lfs -text +MiniGPTv2.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..1ee61a12b8c143c0fe072124f523b182cde33258 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,128 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +https://discord.gg/2aNvvYVv. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..9d5f2f9b2b609b8e8628051fea6a408de9d959dc --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,14 @@ +BSD 3-Clause License + +Copyright 2023 Deyao Zhu +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/LICENSE_Lavis.md b/LICENSE_Lavis.md new file mode 100644 index 0000000000000000000000000000000000000000..9ba97919e5b9568c8b9c42ea85251f01049a220e --- /dev/null +++ b/LICENSE_Lavis.md @@ -0,0 +1,14 @@ +BSD 3-Clause License + +Copyright (c) 2022 Salesforce, Inc. +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +3. Neither the name of Salesforce.com nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/MiniGPT4_Train.md b/MiniGPT4_Train.md new file mode 100644 index 0000000000000000000000000000000000000000..f9e8a5c23a6f9fcf6dd1129dcc12e5b8ad6721b6 --- /dev/null +++ b/MiniGPT4_Train.md @@ -0,0 +1,41 @@ +## Training of MiniGPT-4 + +The training of MiniGPT-4 contains two alignment stages. + +**1. First pretraining stage** + +In the first pretrained stage, the model is trained using image-text pairs from Laion and CC datasets +to align the vision and language model. To download and prepare the datasets, please check +our [first stage dataset preparation instruction](dataset/README_1_STAGE.md). +After the first stage, the visual features are mapped and can be understood by the language +model. +To launch the first stage training, run the following command. In our experiments, we use 4 A100. +You can change the save path in the config file +[train_configs/minigpt4_stage1_pretrain.yaml](train_configs/minigpt4_stage1_pretrain.yaml) + +```bash +torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage1_pretrain.yaml +``` + +A MiniGPT-4 checkpoint with only stage one training can be downloaded +[here (13B)](https://drive.google.com/file/d/1u9FRRBB3VovP1HxCAlpD9Lw4t4P6-Yq8/view?usp=share_link) or [here (7B)](https://drive.google.com/file/d/1HihQtCEXUyBM1i9DQbaK934wW3TZi-h5/view?usp=share_link). +Compared to the model after stage two, this checkpoint generate incomplete and repeated sentences frequently. + + +**2. Second finetuning stage** + +In the second stage, we use a small high quality image-text pair dataset created by ourselves +and convert it to a conversation format to further align MiniGPT-4. +To download and prepare our second stage dataset, please check our +[second stage dataset preparation instruction](dataset/README_2_STAGE.md). +To launch the second stage alignment, +first specify the path to the checkpoint file trained in stage 1 in +[train_configs/minigpt4_stage1_pretrain.yaml](train_configs/minigpt4_stage2_finetune.yaml). +You can also specify the output path there. +Then, run the following command. In our experiments, we use 1 A100. + +```bash +torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_stage2_finetune.yaml +``` + +After the second stage alignment, MiniGPT-4 is able to talk about the image coherently and user-friendly. diff --git a/MiniGPTv2.pdf b/MiniGPTv2.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4218fb33fe33cbcda16e27156bbecdeda4c60aa8 --- /dev/null +++ b/MiniGPTv2.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:429b0f5e3d70828fd691ef4ffb90c6efa094a8454bf03f8ec00b10fcd443f346 +size 4357853 diff --git a/README.md b/README.md index f50a3d66d58d1f688f4dfac3509be493a06c50f8..8d85eccab73c7dfe934beebe5b13d4fa8266c8c5 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,182 @@ ---- -title: MiniGPT V2 -emoji: πŸ“‰ -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.47.1 -app_file: app.py -pinned: false -license: bsd-3-clause ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# MiniGPT-V + +**MiniGPT-v2: Large Language Model as a Unified Interface for Vision-Language Multi-task Learning** + +Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong☨, Mohamed Elhoseiny☨ + +☨equal last author + + [![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/watch?v=atFCwV2hSY4) + + +**MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models** + +Deyao Zhu*, Jun Chen*, Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny + +*equal contribution + + [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1OK4kYsZphwt5DXchKkzMBjYF6jnkqh4R?usp=sharing) [![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/watch?v=__tftoxpBAw&feature=youtu.be) + +*King Abdullah University of Science and Technology* + +## πŸ’‘ Get help - [Q&A](https://github.com/Vision-CAIR/MiniGPT-4/discussions/categories/q-a) or [Discord πŸ’¬](https://discord.gg/5WdJkjbAeE) + + +## News +[Oct.13 2023] Breaking! We release the first major update with our MiniGPT-v2 + +[Aug.28 2023] We now provide a llama 2 version of MiniGPT-4 + +## Online Demo + +Click the image to chat with MiniGPT-v2 around your images +[![demo](figs/minigpt2_demo.png)](https://minigpt-v2.github.io/) + +Click the image to chat with MiniGPT-4 around your images +[![demo](figs/online_demo.png)](https://minigpt-4.github.io) + + +## MiniGPT-v2 Examples + +![MiniGPT-v2 demos](figs/demo.png) + + + +## MiniGPT-4 Examples + | | | +:-------------------------:|:-------------------------: +![find wild](figs/examples/wop_2.png) | ![write story](figs/examples/ad_2.png) +![solve problem](figs/examples/fix_1.png) | ![write Poem](figs/examples/rhyme_1.png) + +More examples can be found in the [project page](https://minigpt-4.github.io). + + + +## Getting Started +### Installation + +**1. Prepare the code and the environment** + +Git clone our repository, creating a python environment and activate it via the following command + +```bash +git clone https://github.com/Vision-CAIR/MiniGPT-4.git +cd MiniGPT-4 +conda env create -f environment.yml +conda activate minigpt4 +``` + + +**2. Prepare the pretrained LLM weights** + +**MiniGPT-v2** is based on Llama2 Chat 7B. For **MiniGPT-4**, we have both Vicuna V0 and Llama 2 version. +Download the corresponding LLM weights from the following huggingface space via clone the repository using git-lfs. + +| Llama 2 Chat 7B | Vicuna V0 13B | Vicuna V0 7B | +:------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------: +[Download](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/tree/main) | [Downlad](https://huggingface.co/Vision-CAIR/vicuna/tree/main) | [Download](https://huggingface.co/Vision-CAIR/vicuna-7b/tree/main) + + +Then, set the variable *llama_model* in the model config file to the LLM weight path. + +* For MiniGPT-v2, set the LLM path +[here](minigpt4/configs/models/minigpt_v2.yaml#L15) at Line 14. + +* For MiniGPT-4 (Llama2), set the LLM path +[here](minigpt4/configs/models/minigpt4_llama2.yaml#L15) at Line 15. + +* For MiniGPT-4 (Vicuna), set the LLM path +[here](minigpt4/configs/models/minigpt4_vicuna0.yaml#L18) at Line 18 + +**3. Prepare the pretrained model checkpoints** + +Download the pretrained model checkpoints + + +| MiniGPT-v2 (LLaMA-2 Chat 7B) | +|------------------------------| +| [Download](https://drive.google.com/file/d/1aVbfW7nkCSYx99_vCRyP1sOlQiWVSnAl/view?usp=sharing) | + +For **MiniGPT-v2**, set the path to the pretrained checkpoint in the evaluation config file +in [eval_configs/minigptv2_eval.yaml](eval_configs/minigptv2_eval.yaml#L10) at Line 8. + + + +| MiniGPT-4 (Vicuna 13B) | MiniGPT-4 (Vicuna 7B) | MiniGPT-4 (LLaMA-2 Chat 7B) | +|----------------------------|---------------------------|---------------------------------| +| [Download](https://drive.google.com/file/d/1a4zLvaiDBr-36pasffmgpvH5P7CKmpze/view?usp=share_link) | [Download](https://drive.google.com/file/d/1RY9jV0dyqLX-o38LrumkKRh6Jtaop58R/view?usp=sharing) | [Download](https://drive.google.com/file/d/11nAPjEok8eAGGEG1N2vXo3kBLCg0WgUk/view?usp=sharing) | + +For **MiniGPT-4**, set the path to the pretrained checkpoint in the evaluation config file +in [eval_configs/minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml#L10) at Line 8 for Vicuna version or [eval_configs/minigpt4_llama2_eval.yaml](eval_configs/minigpt4_llama2_eval.yaml#L10) for LLama2 version. + + + +### Launching Demo Locally + +For MiniGPT-v2, run +``` +python demo_v2.py --cfg-path eval_configs/minigpt4v2_eval.yaml --gpu-id 0 +``` + +For MiniGPT-4 (Vicuna version), run + +``` +python demo.py --cfg-path eval_configs/minigpt4_eval.yaml --gpu-id 0 +``` + +For MiniGPT-4 (Llama2 version), run + +``` +python demo.py --cfg-path eval_configs/minigpt4_llama2_eval.yaml --gpu-id 0 +``` + + +To save GPU memory, LLMs loads as 8 bit by default, with a beam search width of 1. +This configuration requires about 23G GPU memory for 13B LLM and 11.5G GPU memory for 7B LLM. +For more powerful GPUs, you can run the model +in 16 bit by setting `low_resource` to `False` in the relevant config file: + +* MiniGPT-v2: [minigptv2_eval.yaml](eval_configs/minigptv2_eval.yaml#6) +* MiniGPT-4 (Llama2): [minigpt4_llama2_eval.yaml](eval_configs/minigpt4_llama2_eval.yaml#6) +* MiniGPT-4 (Vicuna): [minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml#6) + +Thanks [@WangRongsheng](https://github.com/WangRongsheng), you can also run MiniGPT-4 on [Colab](https://colab.research.google.com/drive/1OK4kYsZphwt5DXchKkzMBjYF6jnkqh4R?usp=sharing) + + +### Training +For training details of MiniGPT-4, check [here](MiniGPT4_Train.md). + + + + +## Acknowledgement + ++ [BLIP2](https://huggingface.co/docs/transformers/main/model_doc/blip-2) The model architecture of MiniGPT-4 follows BLIP-2. Don't forget to check this great open-source work if you don't know it before! ++ [Lavis](https://github.com/salesforce/LAVIS) This repository is built upon Lavis! ++ [Vicuna](https://github.com/lm-sys/FastChat) The fantastic language ability of Vicuna with only 13B parameters is just amazing. And it is open-source! ++ [LLaMA](https://github.com/facebookresearch/llama) The strong open-sourced LLaMA 2 language model. + + +If you're using MiniGPT-4/MiniGPT-v2 in your research or applications, please cite using this BibTeX: +```bibtex + +@article{Chen2023minigpt, + title={MiniGPT-v2: Large Language Model as a Unified Interface for Vision-Language Multi-task Learning}, + author={Chen, Jun and Zhu, Deyao and Shen, Xiaoqian and Li, Xiang and Liu, Zechu and Zhang, Pengchuan and Krishnamoorthi, Raghuraman and Chandra, Vikas and Xiong, Yunyang and Elhoseiny, Mohamed}, + journal={github}, + year={2023} +} + +@article{zhu2023minigpt, + title={MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models}, + author={Zhu, Deyao and Chen, Jun and Shen, Xiaoqian and Li, Xiang and Elhoseiny, Mohamed}, + journal={arXiv preprint arXiv:2304.10592}, + year={2023} +} +``` + + +## License +This repository is under [BSD 3-Clause License](LICENSE.md). +Many codes are based on [Lavis](https://github.com/salesforce/LAVIS) with +BSD 3-Clause License [here](LICENSE_Lavis.md). diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000000000000000000000000000000000000..034e848032092eaf8ef96eac731b6ed5961987f3 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,21 @@ +# Security Policy + +## Supported Versions + +Use this section to tell people about which versions of your project are +currently being supported with security updates. + +| Version | Supported | +| ------- | ------------------ | +| 5.1.x | :white_check_mark: | +| 5.0.x | :x: | +| 4.0.x | :white_check_mark: | +| < 4.0 | :x: | + +## Reporting a Vulnerability + +Use this section to tell people how to report a vulnerability. + +Tell them where to go, how often they can expect to get an update on a +reported vulnerability, what to expect if the vulnerability is accepted or +declined, etc. diff --git a/dataset/README_1_STAGE.md b/dataset/README_1_STAGE.md new file mode 100644 index 0000000000000000000000000000000000000000..47ffaaef6ddf1677bb467e116e03b039febda759 --- /dev/null +++ b/dataset/README_1_STAGE.md @@ -0,0 +1,96 @@ +## Download the filtered Conceptual Captions, SBU, LAION datasets + +### Pre-training datasets download: +We use the filtered synthetic captions prepared by BLIP. For more details about the dataset, please refer to [BLIP](https://github.com/salesforce/BLIP). + +It requires ~2.3T to store LAION and CC3M+CC12M+SBU datasets + +Image source | Filtered synthetic caption by ViT-L +--- | :---: +CC3M+CC12M+SBU | Download +LAION115M | Download + +This will download two json files +``` +ccs_synthetic_filtered_large.json +laion_synthetic_filtered_large.json +``` + +## prepare the data step-by-step + + +### setup the dataset folder and move the annotation file to the data storage folder +``` +export MINIGPT4_DATASET=/YOUR/PATH/FOR/LARGE/DATASET/ +mkdir ${MINIGPT4_DATASET}/cc_sbu +mkdir ${MINIGPT4_DATASET}/laion +mv ccs_synthetic_filtered_large.json ${MINIGPT4_DATASET}/cc_sbu +mv laion_synthetic_filtered_large.json ${MINIGPT4_DATASET}/laion +``` + +### Convert the scripts to data storate folder +``` +cp convert_cc_sbu.py ${MINIGPT4_DATASET}/cc_sbu +cp download_cc_sbu.sh ${MINIGPT4_DATASET}/cc_sbu +cp convert_laion.py ${MINIGPT4_DATASET}/laion +cp download_laion.sh ${MINIGPT4_DATASET}/laion +``` + + +### Convert the laion and cc_sbu annotation file format to be img2dataset format +``` +cd ${MINIGPT4_DATASET}/cc_sbu +python convert_cc_sbu.py + +cd ${MINIGPT4_DATASET}/laion +python convert_laion.py +``` + +### Download the datasets with img2dataset +``` +cd ${MINIGPT4_DATASET}/cc_sbu +sh download_cc_sbu.sh +cd ${MINIGPT4_DATASET}/laion +sh download_laion.sh +``` + + +The final dataset structure + +``` +. +β”œβ”€β”€ ${MINIGPT4_DATASET} +β”‚ β”œβ”€β”€ cc_sbu +β”‚ β”œβ”€β”€ convert_cc_sbu.py +β”‚ β”œβ”€β”€ download_cc_sbu.sh +β”‚ β”œβ”€β”€ ccs_synthetic_filtered_large.json +β”‚ β”œβ”€β”€ ccs_synthetic_filtered_large.tsv +β”‚ └── cc_sbu_dataset +β”‚ β”œβ”€β”€ 00000.tar +β”‚ β”œβ”€β”€ 00000.parquet +β”‚ ... +β”‚ β”œβ”€β”€ laion +β”‚ β”œβ”€β”€ convert_laion.py +β”‚ β”œβ”€β”€ download_laion.sh +β”‚ β”œβ”€β”€ laion_synthetic_filtered_large.json +β”‚ β”œβ”€β”€ laion_synthetic_filtered_large.tsv +β”‚ └── laion_dataset +β”‚ β”œβ”€β”€ 00000.tar +β”‚ β”œβ”€β”€ 00000.parquet +β”‚ ... +... +``` + + +## Set up the dataset configuration files + +Then, set up the LAION dataset loading path in +[here](../minigpt4/configs/datasets/laion/defaults.yaml#L5) at Line 5 as +${MINIGPT4_DATASET}/laion/laion_dataset/{00000..10488}.tar + +and the Conceptual Captoin and SBU datasets loading path in +[here](../minigpt4/configs/datasets/cc_sbu/defaults.yaml#L5) at Line 5 as +${MINIGPT4_DATASET}/cc_sbu/cc_sbu_dataset/{00000..01255}.tar + + + diff --git a/dataset/README_2_STAGE.md b/dataset/README_2_STAGE.md new file mode 100644 index 0000000000000000000000000000000000000000..b826765fef6675ab5f2e3a5dfe619d2e351614d3 --- /dev/null +++ b/dataset/README_2_STAGE.md @@ -0,0 +1,19 @@ +## Second Stage Data Preparation + +Our second stage dataset can be downloaded from +[here](https://drive.google.com/file/d/1nJXhoEcy3KTExr17I7BXqY5Y9Lx_-n-9/view?usp=share_link) +After extraction, you will get a data follder with the following structure: + +``` +cc_sbu_align +β”œβ”€β”€ filter_cap.json +└── image + β”œβ”€β”€ 2.jpg + β”œβ”€β”€ 3.jpg + ... +``` + +Put the folder to any path you want. +Then, set up the dataset path in the dataset config file +[here](../minigpt4/configs/datasets/cc_sbu/align.yaml#L5) at Line 5. + diff --git a/dataset/convert_cc_sbu.py b/dataset/convert_cc_sbu.py new file mode 100644 index 0000000000000000000000000000000000000000..8c325ed3afa3ddb81c5535b5a6febc23d3d5ceee --- /dev/null +++ b/dataset/convert_cc_sbu.py @@ -0,0 +1,20 @@ +import json +import csv + +# specify input and output file paths +input_file = 'ccs_synthetic_filtered_large.json' +output_file = 'ccs_synthetic_filtered_large.tsv' + +# load JSON data from input file +with open(input_file, 'r') as f: + data = json.load(f) + +# extract header and data from JSON +header = data[0].keys() +rows = [x.values() for x in data] + +# write data to TSV file +with open(output_file, 'w') as f: + writer = csv.writer(f, delimiter='\t') + writer.writerow(header) + writer.writerows(rows) diff --git a/dataset/convert_laion.py b/dataset/convert_laion.py new file mode 100644 index 0000000000000000000000000000000000000000..b793579ce276b72a4313bba4f237b8cb0becb294 --- /dev/null +++ b/dataset/convert_laion.py @@ -0,0 +1,20 @@ +import json +import csv + +# specify input and output file paths +input_file = 'laion_synthetic_filtered_large.json' +output_file = 'laion_synthetic_filtered_large.tsv' + +# load JSON data from input file +with open(input_file, 'r') as f: + data = json.load(f) + +# extract header and data from JSON +header = data[0].keys() +rows = [x.values() for x in data] + +# write data to TSV file +with open(output_file, 'w') as f: + writer = csv.writer(f, delimiter='\t') + writer.writerow(header) + writer.writerows(rows) diff --git a/dataset/download_cc_sbu.sh b/dataset/download_cc_sbu.sh new file mode 100644 index 0000000000000000000000000000000000000000..64082eee0466bdad0fb5d377f4501758a82e805c --- /dev/null +++ b/dataset/download_cc_sbu.sh @@ -0,0 +1,6 @@ +#!/bin/bash + +img2dataset --url_list ccs_synthetic_filtered_large.tsv --input_format "tsv"\ + --url_col "url" --caption_col "caption" --output_format webdataset\ + --output_folder cc_sbu_dataset --processes_count 16 --thread_count 128 --image_size 224 \ + --enable_wandb True diff --git a/dataset/download_laion.sh b/dataset/download_laion.sh new file mode 100644 index 0000000000000000000000000000000000000000..42beb0c9af3535ef55045a1e8a1333d623f540ad --- /dev/null +++ b/dataset/download_laion.sh @@ -0,0 +1,6 @@ +#!/bin/bash + +img2dataset --url_list laion_synthetic_filtered_large.tsv --input_format "tsv"\ + --url_col "url" --caption_col "caption" --output_format webdataset\ + --output_folder laion_dataset --processes_count 16 --thread_count 128 --image_size 224 \ + --enable_wandb True diff --git a/demo.py b/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..c7646c43b51d59a29d5d6fe872c34c27c14981e5 --- /dev/null +++ b/demo.py @@ -0,0 +1,171 @@ +import argparse +import os +import random + +import numpy as np +import torch +import torch.backends.cudnn as cudnn +import gradio as gr + +from transformers import StoppingCriteriaList + +from minigpt4.common.config import Config +from minigpt4.common.dist_utils import get_rank +from minigpt4.common.registry import registry +from minigpt4.conversation.conversation import Chat, CONV_VISION_Vicuna0, CONV_VISION_LLama2, StoppingCriteriaSub + +# imports modules for registration +from minigpt4.datasets.builders import * +from minigpt4.models import * +from minigpt4.processors import * +from minigpt4.runners import * +from minigpt4.tasks import * + + +def parse_args(): + parser = argparse.ArgumentParser(description="Demo") + parser.add_argument("--cfg-path", required=True, help="path to configuration file.") + parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.") + parser.add_argument( + "--options", + nargs="+", + help="override some settings in the used config, the key-value pair " + "in xxx=yyy format will be merged into config file (deprecate), " + "change to --cfg-options instead.", + ) + args = parser.parse_args() + return args + + +def setup_seeds(config): + seed = config.run_cfg.seed + get_rank() + + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + cudnn.benchmark = False + cudnn.deterministic = True + + +# ======================================== +# Model Initialization +# ======================================== + +conv_dict = {'pretrain_vicuna0': CONV_VISION_Vicuna0, + 'pretrain_llama2': CONV_VISION_LLama2} + +print('Initializing Chat') +args = parse_args() +cfg = Config(args) + +model_config = cfg.model_cfg +model_config.device_8bit = args.gpu_id +model_cls = registry.get_model_class(model_config.arch) +model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id)) + +CONV_VISION = conv_dict[model_config.model_type] + +vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train +vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg) + +stop_words_ids = [[835], [2277, 29937]] +stop_words_ids = [torch.tensor(ids).to(device='cuda:{}'.format(args.gpu_id)) for ids in stop_words_ids] +stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)]) + +chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id), stopping_criteria=stopping_criteria) +print('Initialization Finished') + + +# ======================================== +# Gradio Setting +# ======================================== + + +def gradio_reset(chat_state, img_list): + if chat_state is not None: + chat_state.messages = [] + if img_list is not None: + img_list = [] + return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your image first', interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list + + +def upload_img(gr_img, text_input, chat_state): + if gr_img is None: + return None, None, gr.update(interactive=True), chat_state, None + chat_state = CONV_VISION.copy() + img_list = [] + llm_message = chat.upload_img(gr_img, chat_state, img_list) + chat.encode_img(img_list) + return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list + + +def gradio_ask(user_message, chatbot, chat_state): + if len(user_message) == 0: + return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state + chat.ask(user_message, chat_state) + chatbot = chatbot + [[user_message, None]] + return '', chatbot, chat_state + + +def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature): + llm_message = chat.answer(conv=chat_state, + img_list=img_list, + num_beams=num_beams, + temperature=temperature, + max_new_tokens=300, + max_length=2000)[0] + chatbot[-1][1] = llm_message + return chatbot, chat_state, img_list + + +title = """

Demo of MiniGPT-4

""" +description = """

This is the demo of MiniGPT-4. Upload your images and start chatting!

""" +article = """

+""" + +#TODO show examples below + +with gr.Blocks() as demo: + gr.Markdown(title) + gr.Markdown(description) + gr.Markdown(article) + + with gr.Row(): + with gr.Column(scale=1): + image = gr.Image(type="pil") + upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary") + clear = gr.Button("Restart") + + num_beams = gr.Slider( + minimum=1, + maximum=10, + value=1, + step=1, + interactive=True, + label="beam search numbers)", + ) + + temperature = gr.Slider( + minimum=0.1, + maximum=2.0, + value=1.0, + step=0.1, + interactive=True, + label="Temperature", + ) + + with gr.Column(scale=2): + chat_state = gr.State() + img_list = gr.State() + chatbot = gr.Chatbot(label='MiniGPT-4') + text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False) + + upload_button.click(upload_img, [image, text_input, chat_state], [image, text_input, upload_button, chat_state, img_list]) + + text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then( + gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list] + ) + clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list], queue=False) + +demo.launch(share=True, enable_queue=True) diff --git a/demo_v2.py b/demo_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..4f66d533e58f822ac5dddd84f5407823fe641c0f --- /dev/null +++ b/demo_v2.py @@ -0,0 +1,662 @@ +import argparse +import os +import random +from collections import defaultdict + +import cv2 +import re + +import numpy as np +from PIL import Image +import torch +import html +import gradio as gr + +import torchvision.transforms as T +import torch.backends.cudnn as cudnn + +from minigpt4.common.config import Config + +from minigpt4.common.registry import registry +from minigpt4.conversation.conversation import Conversation, SeparatorStyle, Chat + +# imports modules for registration +from minigpt4.datasets.builders import * +from minigpt4.models import * +from minigpt4.processors import * +from minigpt4.runners import * +from minigpt4.tasks import * + + +def parse_args(): + parser = argparse.ArgumentParser(description="Demo") + parser.add_argument("--cfg-path", default='eval_configs/minigptv2_eval.yaml', + help="path to configuration file.") + parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.") + parser.add_argument( + "--options", + nargs="+", + help="override some settings in the used config, the key-value pair " + "in xxx=yyy format will be merged into config file (deprecate), " + "change to --cfg-options instead.", + ) + args = parser.parse_args() + return args + + +random.seed(42) +np.random.seed(42) +torch.manual_seed(42) + +cudnn.benchmark = False +cudnn.deterministic = True + +print('Initializing Chat') +args = parse_args() +cfg = Config(args) + +device = 'cuda:{}'.format(args.gpu_id) + +model_config = cfg.model_cfg +model_config.device_8bit = args.gpu_id +model_cls = registry.get_model_class(model_config.arch) +model = model_cls.from_config(model_config).to(device) +bounding_box_size = 100 + +vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train +vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg) + +model = model.eval() + +CONV_VISION = Conversation( + system="", + roles=(r"[INST] ", r" [/INST]"), + messages=[], + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="", +) + + +def extract_substrings(string): + # first check if there is no-finished bracket + index = string.rfind('}') + if index != -1: + string = string[:index + 1] + + pattern = r'

(.*?)\}(?!<)' + matches = re.findall(pattern, string) + substrings = [match for match in matches] + + return substrings + + +def is_overlapping(rect1, rect2): + x1, y1, x2, y2 = rect1 + x3, y3, x4, y4 = rect2 + return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4) + + +def computeIoU(bbox1, bbox2): + x1, y1, x2, y2 = bbox1 + x3, y3, x4, y4 = bbox2 + intersection_x1 = max(x1, x3) + intersection_y1 = max(y1, y3) + intersection_x2 = min(x2, x4) + intersection_y2 = min(y2, y4) + intersection_area = max(0, intersection_x2 - intersection_x1 + 1) * max(0, intersection_y2 - intersection_y1 + 1) + bbox1_area = (x2 - x1 + 1) * (y2 - y1 + 1) + bbox2_area = (x4 - x3 + 1) * (y4 - y3 + 1) + union_area = bbox1_area + bbox2_area - intersection_area + iou = intersection_area / union_area + return iou + + +def save_tmp_img(visual_img): + file_name = "".join([str(random.randint(0, 9)) for _ in range(5)]) + ".jpg" + file_path = "/tmp/" + file_name + visual_img.save(file_path) + return file_path + + +def mask2bbox(mask): + if mask is None: + return '' + mask = mask.resize([100, 100], resample=Image.NEAREST) + mask = np.array(mask)[:, :, 0] + + rows = np.any(mask, axis=1) + cols = np.any(mask, axis=0) + + if rows.sum(): + # Get the top, bottom, left, and right boundaries + rmin, rmax = np.where(rows)[0][[0, -1]] + cmin, cmax = np.where(cols)[0][[0, -1]] + bbox = '{{<{}><{}><{}><{}>}}'.format(cmin, rmin, cmax, rmax) + else: + bbox = '' + + return bbox + + +def escape_markdown(text): + # List of Markdown special characters that need to be escaped + md_chars = ['<', '>'] + + # Escape each special character + for char in md_chars: + text = text.replace(char, '\\' + char) + + return text + + +def reverse_escape(text): + md_chars = ['\\<', '\\>'] + + for char in md_chars: + text = text.replace(char, char[1:]) + + return text + + +colors = [ + (255, 0, 0), + (0, 255, 0), + (0, 0, 255), + (210, 210, 0), + (255, 0, 255), + (0, 255, 255), + (114, 128, 250), + (0, 165, 255), + (0, 128, 0), + (144, 238, 144), + (238, 238, 175), + (255, 191, 0), + (0, 128, 0), + (226, 43, 138), + (255, 0, 255), + (0, 215, 255), +] + +color_map = { + f"{color_id}": f"#{hex(color[2])[2:].zfill(2)}{hex(color[1])[2:].zfill(2)}{hex(color[0])[2:].zfill(2)}" for + color_id, color in enumerate(colors) +} + +used_colors = colors + + +def visualize_all_bbox_together(image, generation): + if image is None: + return None, '' + + generation = html.unescape(generation) + print('gen begin', generation) + + image_width, image_height = image.size + image = image.resize([500, int(500 / image_width * image_height)]) + image_width, image_height = image.size + + string_list = extract_substrings(generation) + if string_list: # it is grounding or detection + mode = 'all' + entities = defaultdict(list) + i = 0 + j = 0 + for string in string_list: + try: + obj, string = string.split('

') + except ValueError: + print('wrong string: ', string) + continue + bbox_list = string.split('') + flag = False + for bbox_string in bbox_list: + integers = re.findall(r'-?\d+', bbox_string) + if len(integers) == 4: + x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3]) + left = x0 / bounding_box_size * image_width + bottom = y0 / bounding_box_size * image_height + right = x1 / bounding_box_size * image_width + top = y1 / bounding_box_size * image_height + + entities[obj].append([left, bottom, right, top]) + + j += 1 + flag = True + if flag: + i += 1 + else: + integers = re.findall(r'-?\d+', generation) + + if len(integers) == 4: # it is refer + mode = 'single' + + entities = list() + x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3]) + left = x0 / bounding_box_size * image_width + bottom = y0 / bounding_box_size * image_height + right = x1 / bounding_box_size * image_width + top = y1 / bounding_box_size * image_height + entities.append([left, bottom, right, top]) + else: + # don't detect any valid bbox to visualize + return None, '' + + if len(entities) == 0: + return None, '' + + if isinstance(image, Image.Image): + image_h = image.height + image_w = image.width + image = np.array(image) + + elif isinstance(image, str): + if os.path.exists(image): + pil_img = Image.open(image).convert("RGB") + image = np.array(pil_img)[:, :, [2, 1, 0]] + image_h = pil_img.height + image_w = pil_img.width + else: + raise ValueError(f"invaild image path, {image}") + elif isinstance(image, torch.Tensor): + + image_tensor = image.cpu() + reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None] + reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None] + image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean + pil_img = T.ToPILImage()(image_tensor) + image_h = pil_img.height + image_w = pil_img.width + image = np.array(pil_img)[:, :, [2, 1, 0]] + else: + raise ValueError(f"invaild image format, {type(image)} for {image}") + + indices = list(range(len(entities))) + + new_image = image.copy() + + previous_bboxes = [] + # size of text + text_size = 0.5 + # thickness of text + text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1)) + box_line = 2 + (c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line) + base_height = int(text_height * 0.675) + text_offset_original = text_height - base_height + text_spaces = 2 + + # num_bboxes = sum(len(x[-1]) for x in entities) + used_colors = colors # random.sample(colors, k=num_bboxes) + + color_id = -1 + for entity_idx, entity_name in enumerate(entities): + if mode == 'single' or mode == 'identify': + bboxes = entity_name + bboxes = [bboxes] + else: + bboxes = entities[entity_name] + color_id += 1 + for bbox_id, (x1_norm, y1_norm, x2_norm, y2_norm) in enumerate(bboxes): + skip_flag = False + orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm), int(y1_norm), int(x2_norm), int(y2_norm) + + color = used_colors[entity_idx % len(used_colors)] # tuple(np.random.randint(0, 255, size=3).tolist()) + new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line) + + if mode == 'all': + l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1 + + x1 = orig_x1 - l_o + y1 = orig_y1 - l_o + + if y1 < text_height + text_offset_original + 2 * text_spaces: + y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces + x1 = orig_x1 + r_o + + # add text background + (text_width, text_height), _ = cv2.getTextSize(f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, + text_line) + text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - ( + text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1 + + for prev_bbox in previous_bboxes: + if computeIoU((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']) > 0.95 and \ + prev_bbox['phrase'] == entity_name: + skip_flag = True + break + while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']): + text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces) + text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces) + y1 += (text_height + text_offset_original + 2 * text_spaces) + + if text_bg_y2 >= image_h: + text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces)) + text_bg_y2 = image_h + y1 = image_h + break + if not skip_flag: + alpha = 0.5 + for i in range(text_bg_y1, text_bg_y2): + for j in range(text_bg_x1, text_bg_x2): + if i < image_h and j < image_w: + if j < text_bg_x1 + 1.35 * c_width: + # original color + bg_color = color + else: + # white + bg_color = [255, 255, 255] + new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype( + np.uint8) + + cv2.putText( + new_image, f" {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces), + cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA + ) + + previous_bboxes.append( + {'bbox': (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), 'phrase': entity_name}) + + if mode == 'all': + def color_iterator(colors): + while True: + for color in colors: + yield color + + color_gen = color_iterator(colors) + + # Add colors to phrases and remove

+ def colored_phrases(match): + phrase = match.group(1) + color = next(color_gen) + return f'{phrase}' + + print('gen before', generation) + generation = re.sub(r'{<\d+><\d+><\d+><\d+>}|', '', generation) + print('gen after', generation) + generation_colored = re.sub(r'

(.*?)

', colored_phrases, generation) + else: + generation_colored = '' + + pil_image = Image.fromarray(new_image) + return pil_image, generation_colored + + +def gradio_reset(chat_state, img_list): + if chat_state is not None: + chat_state.messages = [] + if img_list is not None: + img_list = [] + return None, gr.update(value=None, interactive=True), gr.update(placeholder='Upload your image and chat', + interactive=True), chat_state, img_list + + +def image_upload_trigger(upload_flag, replace_flag, img_list): + # set the upload flag to true when receive a new image. + # if there is an old image (and old conversation), set the replace flag to true to reset the conv later. + print('flag', upload_flag, replace_flag) + print("SET UPLOAD FLAG!") + upload_flag = 1 + if img_list: + print("SET REPLACE FLAG!") + replace_flag = 1 + print('flag', upload_flag, replace_flag) + return upload_flag, replace_flag + + +def example_trigger(text_input, image, upload_flag, replace_flag, img_list): + # set the upload flag to true when receive a new image. + # if there is an old image (and old conversation), set the replace flag to true to reset the conv later. + print('flag', upload_flag, replace_flag) + print("SET UPLOAD FLAG!") + upload_flag = 1 + if img_list or replace_flag == 1: + print("SET REPLACE FLAG!") + replace_flag = 1 + + print('flag', upload_flag, replace_flag) + return upload_flag, replace_flag + + +def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag, replace_flag): + if isinstance(gr_img, dict): + gr_img, mask = gr_img['image'], gr_img['mask'] + else: + mask = None + + if '[identify]' in user_message: + # check if user provide bbox in the text input + integers = re.findall(r'-?\d+', user_message) + if len(integers) != 4: # no bbox in text + bbox = mask2bbox(mask) + user_message = user_message + bbox + + if len(user_message) == 0: + return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state + + if chat_state is None: + chat_state = CONV_VISION.copy() + + print('upload flag: {}'.format(upload_flag)) + if upload_flag: + if replace_flag: + print('RESET!!!!!!!') + chat_state = CONV_VISION.copy() # new image, reset everything + replace_flag = 0 + chatbot = [] + print('UPLOAD IMAGE!!') + img_list = [] + llm_message = chat.upload_img(gr_img, chat_state, img_list) + upload_flag = 0 + + chat.ask(user_message, chat_state) + + chatbot = chatbot + [[user_message, None]] + + if '[identify]' in user_message: + visual_img, _ = visualize_all_bbox_together(gr_img, user_message) + if visual_img is not None: + print('Visualizing the input') + file_path = save_tmp_img(visual_img) + chatbot = chatbot + [[(file_path,), None]] + + return '', chatbot, chat_state, img_list, upload_flag, replace_flag + + +def gradio_answer(chatbot, chat_state, img_list, temperature): + llm_message = chat.answer(conv=chat_state, + img_list=img_list, + temperature=temperature, + max_new_tokens=500, + max_length=2000)[0] + chatbot[-1][1] = llm_message + return chatbot, chat_state + + +def gradio_stream_answer(chatbot, chat_state, img_list, temperature): + print('chat state', chat_state.get_prompt()) + if not isinstance(img_list[0], torch.Tensor): + chat.encode_img(img_list) + streamer = chat.stream_answer(conv=chat_state, + img_list=img_list, + temperature=temperature, + max_new_tokens=500, + max_length=2000) + output = '' + for new_output in streamer: + escapped = escape_markdown(new_output) + output += escapped + chatbot[-1][1] = output + yield chatbot, chat_state + # print('message: ', chat_state.messages) + chat_state.messages[-1][1] = '' + return chatbot, chat_state + + +def gradio_visualize(chatbot, gr_img): + if isinstance(gr_img, dict): + gr_img, mask = gr_img['image'], gr_img['mask'] + + unescaped = reverse_escape(chatbot[-1][1]) + visual_img, generation_color = visualize_all_bbox_together(gr_img, unescaped) + if visual_img is not None: + print('Visualizing the output') + if len(generation_color): + chatbot[-1][1] = generation_color + file_path = save_tmp_img(visual_img) + chatbot = chatbot + [[None, (file_path,)]] + + return chatbot + + +def gradio_taskselect(idx): + prompt_list = [ + '', + '[grounding] describe this image in detail', + '[refer] ', + '[detection] ', + '[identify] what is this ', + '[vqa] ' + ] + instruct_list = [ + '**Hint:** Type in whatever you want', + '**Hint:** Send the command to generate a grounded image description', + '**Hint:** Type in a phrase about an object in the image and send the command', + '**Hint:** Type in a caption or phrase, and see object locations in the image', + '**Hint:** Draw a bounding box on the uploaded image then send the command. Click the "clear" botton on the top right of the image before redraw', + '**Hint:** Send a question to get a short answer', + ] + return prompt_list[idx], instruct_list[idx] + + + + +chat = Chat(model, vis_processor, device=device) + +title = """

MiniGPT-v2 Demo

""" +description = 'Welcome to Our MiniGPT-v2 Chatbot Demo!' +# article = """

""" +article = """

""" + +introduction = ''' +For Abilities Involving Visual Grounding: +1. Grounding: CLICK **Send** to generate a grounded image description. +2. Refer: Input a referring object and CLICK **Send**. +3. Detection: Write a caption or phrase, and CLICK **Send**. +4. Identify: Draw the bounding box on the uploaded image window and CLICK **Send** to generate the bounding box. (CLICK "clear" button before re-drawing next time). +5. VQA: Input a visual question and CLICK **Send**. +6. No Tag: Input whatever you want and CLICK **Send** without any tagging + +You can also simply chat in free form! +''' + +text_input = gr.Textbox(placeholder='Upload your image and chat', interactive=True, show_label=False, container=False, + scale=8) +with gr.Blocks() as demo: + gr.Markdown(title) + # gr.Markdown(description) + gr.Markdown(article) + + with gr.Row(): + with gr.Column(scale=0.5): + image = gr.Image(type="pil", tool='sketch', brush_radius=20) + + temperature = gr.Slider( + minimum=0.1, + maximum=2.0, + value=1.0, + step=0.1, + interactive=True, + label="Temperature", + ) + + clear = gr.Button("Restart") + + gr.Markdown(introduction) + + with gr.Column(): + chat_state = gr.State(value=None) + img_list = gr.State(value=[]) + chatbot = gr.Chatbot(label='MiniGPT-v2') + + dataset = gr.Dataset( + components=[gr.Textbox(visible=False)], + samples=[['No Tag'], ['Grounding'], ['Refer'], ['Detection'], ['Identify'], ['VQA']], + type="index", + label='Task Shortcuts', + ) + task_inst = gr.Markdown('**Hint:** Upload your image and chat') + with gr.Row(): + text_input.render() + send = gr.Button("Send", variant='primary', size='sm', scale=1) + + upload_flag = gr.State(value=0) + replace_flag = gr.State(value=0) + image.upload(image_upload_trigger, [upload_flag, replace_flag, img_list], [upload_flag, replace_flag]) + + with gr.Row(): + with gr.Column(): + gr.Examples(examples=[ + ["examples_v2/office.jpg", "[grounding] describe this image in detail", upload_flag, replace_flag, + img_list], + ["examples_v2/sofa.jpg", "[detection] sofas", upload_flag, replace_flag, img_list], + ["examples_v2/2000x1372_wmkn_0012149409555.jpg", "[refer] the world cup", upload_flag, replace_flag, + img_list], + ["examples_v2/KFC-20-for-20-Nuggets.jpg", "[identify] what is this {<4><50><30><65>}", upload_flag, + replace_flag, img_list], + ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger, + outputs=[upload_flag, replace_flag]) + with gr.Column(): + gr.Examples(examples=[ + ["examples_v2/glip_test.jpg", "[vqa] where should I hide in this room when playing hide and seek", + upload_flag, replace_flag, img_list], + ["examples_v2/float.png", "Please write a poem about the image", upload_flag, replace_flag, img_list], + ["examples_v2/thief.png", "Is the weapon fateful", upload_flag, replace_flag, img_list], + ["examples_v2/cockdial.png", "What might happen in this image in the next second", upload_flag, + replace_flag, img_list], + ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger, + outputs=[upload_flag, replace_flag]) + + dataset.click( + gradio_taskselect, + inputs=[dataset], + outputs=[text_input, task_inst], + show_progress="hidden", + postprocess=False, + queue=False, + ) + + text_input.submit( + gradio_ask, + [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag], + [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False + ).success( + gradio_stream_answer, + [chatbot, chat_state, img_list, temperature], + [chatbot, chat_state] + ).success( + gradio_visualize, + [chatbot, image], + [chatbot], + queue=False, + ) + + send.click( + gradio_ask, + [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag], + [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False + ).success( + gradio_stream_answer, + [chatbot, chat_state, img_list, temperature], + [chatbot, chat_state] + ).success( + gradio_visualize, + [chatbot, image], + [chatbot], + queue=False, + ) + + clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, chat_state, img_list], queue=False) + +demo.launch(share=True, enable_queue=True) diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..cf90e898375a8927fd1e6e3fa9b2605b1606c176 --- /dev/null +++ b/environment.yml @@ -0,0 +1,33 @@ +name: minigpt4 +channels: + - pytorch + - defaults + - anaconda +dependencies: + - python=3.9 + - cudatoolkit + - pip + - pip: + - torch==2.0.0 + - torchaudio + - torchvision + - huggingface-hub==0.18.0 + - matplotlib==3.7.0 + - psutil==5.9.4 + - iopath + - pyyaml==6.0 + - regex==2022.10.31 + - tokenizers==0.13.2 + - tqdm==4.64.1 + - transformers==4.30.0 + - timm==0.6.13 + - webdataset==0.2.48 + - omegaconf==2.3.0 + - opencv-python==4.7.0.72 + - decord==0.6.0 + - peft==0.2.0 + - sentence-transformers + - gradio==3.47.1 + - accelerate==0.20.3 + - bitsandbytes==0.37.0 + - wandb diff --git a/eval_configs/minigpt4_eval.yaml b/eval_configs/minigpt4_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d73ba3aacb5a86fc31eb02949303a37e362103d9 --- /dev/null +++ b/eval_configs/minigpt4_eval.yaml @@ -0,0 +1,22 @@ +model: + arch: minigpt4 + model_type: pretrain_vicuna0 + max_txt_len: 160 + end_sym: "###" + low_resource: True + prompt_template: '###Human: {} ###Assistant: ' + ckpt: 'please set this value to the path of pretrained checkpoint' + + +datasets: + cc_sbu_align: + vis_processor: + train: + name: "blip2_image_eval" + image_size: 224 + text_processor: + train: + name: "blip_caption" + +run: + task: image_text_pretrain diff --git a/eval_configs/minigpt4_llama2_eval.yaml b/eval_configs/minigpt4_llama2_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..93efab1339aaa65057c0b7ea81b2b05e0b6f84de --- /dev/null +++ b/eval_configs/minigpt4_llama2_eval.yaml @@ -0,0 +1,22 @@ +model: + arch: minigpt4 + model_type: pretrain_llama2 + max_txt_len: 160 + end_sym: "" + low_resource: True + prompt_template: '[INST] {} [/INST] ' + ckpt: 'please set this value to the path of pretrained checkpoint' + + +datasets: + cc_sbu_align: + vis_processor: + train: + name: "blip2_image_eval" + image_size: 224 + text_processor: + train: + name: "blip_caption" + +run: + task: image_text_pretrain diff --git a/eval_configs/minigptv2_eval.yaml b/eval_configs/minigptv2_eval.yaml new file mode 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https://git-lfs.github.com/spec/v1 +oid sha256:236107e23897574578111e0a4bbe6c0475e794b5eca13943f692755b1c71c8df +size 1259978 diff --git a/figs/overview.png b/figs/overview.png new file mode 100644 index 0000000000000000000000000000000000000000..83ac7cc1657d98262d34459268421713074b4a96 --- /dev/null +++ b/figs/overview.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:378307bed387d530087773826ac3b4686e106cf2d1e4a24daed27573f8cc14cb +size 2532475 diff --git a/minigpt4/__init__.py b/minigpt4/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bb31f42f9107a0b748b878deb1c5768019d62b32 --- /dev/null +++ b/minigpt4/__init__.py @@ -0,0 +1,31 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import os +import sys + +from omegaconf import OmegaConf + +from minigpt4.common.registry import registry + +from minigpt4.datasets.builders import * +from minigpt4.models import * +from minigpt4.processors import * +from minigpt4.tasks import * + + +root_dir = os.path.dirname(os.path.abspath(__file__)) +default_cfg = OmegaConf.load(os.path.join(root_dir, "configs/default.yaml")) + +registry.register_path("library_root", root_dir) +repo_root = os.path.join(root_dir, "..") +registry.register_path("repo_root", repo_root) +cache_root = os.path.join(repo_root, default_cfg.env.cache_root) +registry.register_path("cache_root", cache_root) + +registry.register("MAX_INT", sys.maxsize) +registry.register("SPLIT_NAMES", ["train", "val", "test"]) diff --git a/minigpt4/common/__init__.py b/minigpt4/common/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/minigpt4/common/config.py b/minigpt4/common/config.py new file mode 100644 index 0000000000000000000000000000000000000000..e184b1f9024957a42fc6a4f796d0e8a7804e1ef7 --- /dev/null +++ b/minigpt4/common/config.py @@ -0,0 +1,468 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import logging +import json +from typing import Dict + +from omegaconf import OmegaConf +from minigpt4.common.registry import registry + + +class Config: + def __init__(self, args): + self.config = {} + + self.args = args + + # Register the config and configuration for setup + registry.register("configuration", self) + + user_config = self._build_opt_list(self.args.options) + + config = OmegaConf.load(self.args.cfg_path) + + runner_config = self.build_runner_config(config) + model_config = self.build_model_config(config, **user_config) + dataset_config = self.build_dataset_config(config) + + # Validate the user-provided runner configuration + # model and dataset configuration are supposed to be validated by the respective classes + # [TODO] validate the model/dataset configuration + # self._validate_runner_config(runner_config) + + # Override the default configuration with user options. + self.config = OmegaConf.merge( + runner_config, model_config, dataset_config, user_config + ) + + def _validate_runner_config(self, runner_config): + """ + This method validates the configuration, such that + 1) all the user specified options are valid; + 2) no type mismatches between the user specified options and the config. + """ + runner_config_validator = create_runner_config_validator() + runner_config_validator.validate(runner_config) + + def _build_opt_list(self, opts): + opts_dot_list = self._convert_to_dot_list(opts) + return OmegaConf.from_dotlist(opts_dot_list) + + @staticmethod + def build_model_config(config, **kwargs): + model = config.get("model", None) + assert model is not None, "Missing model configuration file." + + model_cls = registry.get_model_class(model.arch) + assert model_cls is not None, f"Model '{model.arch}' has not been registered." + + model_type = kwargs.get("model.model_type", None) + if not model_type: + model_type = model.get("model_type", None) + # else use the model type selected by user. + + assert model_type is not None, "Missing model_type." + + model_config_path = model_cls.default_config_path(model_type=model_type) + + model_config = OmegaConf.create() + # hierarchy override, customized config > default config + model_config = OmegaConf.merge( + model_config, + OmegaConf.load(model_config_path), + {"model": config["model"]}, + ) + + return model_config + + @staticmethod + def build_runner_config(config): + return {"run": config.run} + + @staticmethod + def build_dataset_config(config): + datasets = config.get("datasets", None) + if datasets is None: + raise KeyError( + "Expecting 'datasets' as the root key for dataset configuration." + ) + + dataset_config = OmegaConf.create() + + for dataset_name in datasets: + builder_cls = registry.get_builder_class(dataset_name) + + dataset_config_type = datasets[dataset_name].get("type", "default") + dataset_config_path = builder_cls.default_config_path( + type=dataset_config_type + ) + + # hierarchy override, customized config > default config + dataset_config = OmegaConf.merge( + dataset_config, + OmegaConf.load(dataset_config_path), + {"datasets": {dataset_name: config["datasets"][dataset_name]}}, + ) + + return dataset_config + + def _convert_to_dot_list(self, opts): + if opts is None: + opts = [] + + if len(opts) == 0: + return opts + + has_equal = opts[0].find("=") != -1 + + if has_equal: + return opts + + return [(opt + "=" + value) for opt, value in zip(opts[0::2], opts[1::2])] + + def get_config(self): + return self.config + + @property + def run_cfg(self): + return self.config.run + + @property + def datasets_cfg(self): + return self.config.datasets + + @property + def model_cfg(self): + return self.config.model + + def pretty_print(self): + logging.info("\n===== Running Parameters =====") + logging.info(self._convert_node_to_json(self.config.run)) + + logging.info("\n====== Dataset Attributes ======") + datasets = self.config.datasets + + for dataset in datasets: + if dataset in self.config.datasets: + logging.info(f"\n======== {dataset} =======") + dataset_config = self.config.datasets[dataset] + logging.info(self._convert_node_to_json(dataset_config)) + else: + logging.warning(f"No dataset named '{dataset}' in config. Skipping") + + logging.info(f"\n====== Model Attributes ======") + logging.info(self._convert_node_to_json(self.config.model)) + + def _convert_node_to_json(self, node): + container = OmegaConf.to_container(node, resolve=True) + return json.dumps(container, indent=4, sort_keys=True) + + def to_dict(self): + return OmegaConf.to_container(self.config) + + +def node_to_dict(node): + return OmegaConf.to_container(node) + + +class ConfigValidator: + """ + This is a preliminary implementation to centralize and validate the configuration. + May be altered in the future. + + A helper class to validate configurations from yaml file. + + This serves the following purposes: + 1. Ensure all the options in the yaml are defined, raise error if not. + 2. when type mismatches are found, the validator will raise an error. + 3. a central place to store and display helpful messages for supported configurations. + + """ + + class _Argument: + def __init__(self, name, choices=None, type=None, help=None): + self.name = name + self.val = None + self.choices = choices + self.type = type + self.help = help + + def __str__(self): + s = f"{self.name}={self.val}" + if self.type is not None: + s += f", ({self.type})" + if self.choices is not None: + s += f", choices: {self.choices}" + if self.help is not None: + s += f", ({self.help})" + return s + + def __init__(self, description): + self.description = description + + self.arguments = dict() + + self.parsed_args = None + + def __getitem__(self, key): + assert self.parsed_args is not None, "No arguments parsed yet." + + return self.parsed_args[key] + + def __str__(self) -> str: + return self.format_help() + + def add_argument(self, *args, **kwargs): + """ + Assume the first argument is the name of the argument. + """ + self.arguments[args[0]] = self._Argument(*args, **kwargs) + + def validate(self, config=None): + """ + Convert yaml config (dict-like) to list, required by argparse. + """ + for k, v in config.items(): + assert ( + k in self.arguments + ), f"""{k} is not a valid argument. Support arguments are {self.format_arguments()}.""" + + if self.arguments[k].type is not None: + try: + self.arguments[k].val = self.arguments[k].type(v) + except ValueError: + raise ValueError(f"{k} is not a valid {self.arguments[k].type}.") + + if self.arguments[k].choices is not None: + assert ( + v in self.arguments[k].choices + ), f"""{k} must be one of {self.arguments[k].choices}.""" + + return config + + def format_arguments(self): + return str([f"{k}" for k in sorted(self.arguments.keys())]) + + def format_help(self): + # description + key-value pair string for each argument + help_msg = str(self.description) + return help_msg + ", available arguments: " + self.format_arguments() + + def print_help(self): + # display help message + print(self.format_help()) + + +def create_runner_config_validator(): + validator = ConfigValidator(description="Runner configurations") + + validator.add_argument( + "runner", + type=str, + choices=["runner_base", "runner_iter"], + help="""Runner to use. The "runner_base" uses epoch-based training while iter-based + runner runs based on iters. Default: runner_base""", + ) + # add argumetns for training dataset ratios + validator.add_argument( + "train_dataset_ratios", + type=Dict[str, float], + help="""Ratios of training dataset. This is used in iteration-based runner. + Do not support for epoch-based runner because how to define an epoch becomes tricky. + Default: None""", + ) + validator.add_argument( + "max_iters", + type=float, + help="Maximum number of iterations to run.", + ) + validator.add_argument( + "max_epoch", + type=int, + help="Maximum number of epochs to run.", + ) + # add arguments for iters_per_inner_epoch + validator.add_argument( + "iters_per_inner_epoch", + type=float, + help="Number of iterations per inner epoch. This is required when runner is runner_iter.", + ) + lr_scheds_choices = registry.list_lr_schedulers() + validator.add_argument( + "lr_sched", + type=str, + choices=lr_scheds_choices, + help="Learning rate scheduler to use, from {}".format(lr_scheds_choices), + ) + task_choices = registry.list_tasks() + validator.add_argument( + "task", + type=str, + choices=task_choices, + help="Task to use, from {}".format(task_choices), + ) + # add arguments for init_lr + validator.add_argument( + "init_lr", + type=float, + help="Initial learning rate. This will be the learning rate after warmup and before decay.", + ) + # add arguments for min_lr + validator.add_argument( + "min_lr", + type=float, + help="Minimum learning rate (after decay).", + ) + # add arguments for warmup_lr + validator.add_argument( + "warmup_lr", + type=float, + help="Starting learning rate for warmup.", + ) + # add arguments for learning rate decay rate + validator.add_argument( + "lr_decay_rate", + type=float, + help="Learning rate decay rate. Required if using a decaying learning rate scheduler.", + ) + # add arguments for weight decay + validator.add_argument( + "weight_decay", + type=float, + help="Weight decay rate.", + ) + # add arguments for training batch size + validator.add_argument( + "batch_size_train", + type=int, + help="Training batch size.", + ) + # add arguments for evaluation batch size + validator.add_argument( + "batch_size_eval", + type=int, + help="Evaluation batch size, including validation and testing.", + ) + # add arguments for number of workers for data loading + validator.add_argument( + "num_workers", + help="Number of workers for data loading.", + ) + # add arguments for warm up steps + validator.add_argument( + "warmup_steps", + type=int, + help="Number of warmup steps. Required if a warmup schedule is used.", + ) + # add arguments for random seed + validator.add_argument( + "seed", + type=int, + help="Random seed.", + ) + # add arguments for output directory + validator.add_argument( + "output_dir", + type=str, + help="Output directory to save checkpoints and logs.", + ) + # add arguments for whether only use evaluation + validator.add_argument( + "evaluate", + help="Whether to only evaluate the model. If true, training will not be performed.", + ) + # add arguments for splits used for training, e.g. ["train", "val"] + validator.add_argument( + "train_splits", + type=list, + help="Splits to use for training.", + ) + # add arguments for splits used for validation, e.g. ["val"] + validator.add_argument( + "valid_splits", + type=list, + help="Splits to use for validation. If not provided, will skip the validation.", + ) + # add arguments for splits used for testing, e.g. ["test"] + validator.add_argument( + "test_splits", + type=list, + help="Splits to use for testing. If not provided, will skip the testing.", + ) + # add arguments for accumulating gradient for iterations + validator.add_argument( + "accum_grad_iters", + type=int, + help="Number of iterations to accumulate gradient for.", + ) + + # ====== distributed training ====== + validator.add_argument( + "device", + type=str, + choices=["cpu", "cuda"], + help="Device to use. Support 'cuda' or 'cpu' as for now.", + ) + validator.add_argument( + "world_size", + type=int, + help="Number of processes participating in the job.", + ) + validator.add_argument("dist_url", type=str) + validator.add_argument("distributed", type=bool) + # add arguments to opt using distributed sampler during evaluation or not + validator.add_argument( + "use_dist_eval_sampler", + type=bool, + help="Whether to use distributed sampler during evaluation or not.", + ) + + # ====== task specific ====== + # generation task specific arguments + # add arguments for maximal length of text output + validator.add_argument( + "max_len", + type=int, + help="Maximal length of text output.", + ) + # add arguments for minimal length of text output + validator.add_argument( + "min_len", + type=int, + help="Minimal length of text output.", + ) + # add arguments number of beams + validator.add_argument( + "num_beams", + type=int, + help="Number of beams used for beam search.", + ) + + # vqa task specific arguments + # add arguments for number of answer candidates + validator.add_argument( + "num_ans_candidates", + type=int, + help="""For ALBEF and BLIP, these models first rank answers according to likelihood to select answer candidates.""", + ) + # add arguments for inference method + validator.add_argument( + "inference_method", + type=str, + choices=["genearte", "rank"], + help="""Inference method to use for question answering. If rank, requires a answer list.""", + ) + + # ====== model specific ====== + validator.add_argument( + "k_test", + type=int, + help="Number of top k most similar samples from ITC/VTC selection to be tested.", + ) + + return validator diff --git a/minigpt4/common/dist_utils.py b/minigpt4/common/dist_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a6fc1b904dccccbffbd96326b1506f8ff3ca19c1 --- /dev/null +++ b/minigpt4/common/dist_utils.py @@ -0,0 +1,140 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import datetime +import functools +import os + +import torch +import torch.distributed as dist +import timm.models.hub as timm_hub + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def init_distributed_mode(args): + if args.distributed is False: + print("Not using distributed mode") + return + elif "RANK" in os.environ and "WORLD_SIZE" in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = int(os.environ["LOCAL_RANK"]) + elif "SLURM_PROCID" in os.environ: + args.rank = int(os.environ["SLURM_PROCID"]) + args.gpu = args.rank % torch.cuda.device_count() + else: + print("Not using distributed mode") + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = "nccl" + print( + "| distributed init (rank {}, world {}): {}".format( + args.rank, args.world_size, args.dist_url + ), + flush=True, + ) + torch.distributed.init_process_group( + backend=args.dist_backend, + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + timeout=datetime.timedelta( + days=365 + ), # allow auto-downloading and de-compressing + ) + torch.distributed.barrier() + setup_for_distributed(args.rank == 0) + + +def get_dist_info(): + if torch.__version__ < "1.0": + initialized = dist._initialized + else: + initialized = dist.is_initialized() + if initialized: + rank = dist.get_rank() + world_size = dist.get_world_size() + else: # non-distributed training + rank = 0 + world_size = 1 + return rank, world_size + + +def main_process(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + rank, _ = get_dist_info() + if rank == 0: + return func(*args, **kwargs) + + return wrapper + + +def download_cached_file(url, check_hash=True, progress=False): + """ + Download a file from a URL and cache it locally. If the file already exists, it is not downloaded again. + If distributed, only the main process downloads the file, and the other processes wait for the file to be downloaded. + """ + + def get_cached_file_path(): + # a hack to sync the file path across processes + parts = torch.hub.urlparse(url) + filename = os.path.basename(parts.path) + cached_file = os.path.join(timm_hub.get_cache_dir(), filename) + + return cached_file + + if is_main_process(): + timm_hub.download_cached_file(url, check_hash, progress) + + if is_dist_avail_and_initialized(): + dist.barrier() + + return get_cached_file_path() diff --git a/minigpt4/common/gradcam.py b/minigpt4/common/gradcam.py new file mode 100644 index 0000000000000000000000000000000000000000..d53a5254d4b319eaf2cbfbd081b0ca8e38c5c7a0 --- /dev/null +++ b/minigpt4/common/gradcam.py @@ -0,0 +1,24 @@ +import numpy as np +from matplotlib import pyplot as plt +from scipy.ndimage import filters +from skimage import transform as skimage_transform + + +def getAttMap(img, attMap, blur=True, overlap=True): + attMap -= attMap.min() + if attMap.max() > 0: + attMap /= attMap.max() + attMap = skimage_transform.resize(attMap, (img.shape[:2]), order=3, mode="constant") + if blur: + attMap = filters.gaussian_filter(attMap, 0.02 * max(img.shape[:2])) + attMap -= attMap.min() + attMap /= attMap.max() + cmap = plt.get_cmap("jet") + attMapV = cmap(attMap) + attMapV = np.delete(attMapV, 3, 2) + if overlap: + attMap = ( + 1 * (1 - attMap**0.7).reshape(attMap.shape + (1,)) * img + + (attMap**0.7).reshape(attMap.shape + (1,)) * attMapV + ) + return attMap diff --git a/minigpt4/common/logger.py b/minigpt4/common/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..9a5a727213c6478606a154172830cdc43aae6f5a --- /dev/null +++ b/minigpt4/common/logger.py @@ -0,0 +1,195 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import datetime +import logging +import time +from collections import defaultdict, deque + +import torch +import torch.distributed as dist + +from minigpt4.common import dist_utils + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not dist_utils.is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value, + ) + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError( + "'{}' object has no attribute '{}'".format(type(self).__name__, attr) + ) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append("{}: {}".format(name, str(meter))) + return self.delimiter.join(loss_str) + + def global_avg(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append("{}: {:.4f}".format(name, meter.global_avg)) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = "" + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt="{avg:.4f}") + data_time = SmoothedValue(fmt="{avg:.4f}") + space_fmt = ":" + str(len(str(len(iterable)))) + "d" + log_msg = [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + ] + if torch.cuda.is_available(): + log_msg.append("max mem: {memory:.0f}") + log_msg = self.delimiter.join(log_msg) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB, + ) + ) + else: + print( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + ) + ) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print( + "{} Total time: {} ({:.4f} s / it)".format( + header, total_time_str, total_time / len(iterable) + ) + ) + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + +def setup_logger(): + logging.basicConfig( + level=logging.INFO if dist_utils.is_main_process() else logging.WARN, + format="%(asctime)s [%(levelname)s] %(message)s", + handlers=[logging.StreamHandler()], + ) diff --git a/minigpt4/common/optims.py b/minigpt4/common/optims.py new file mode 100644 index 0000000000000000000000000000000000000000..58327f723d445633ce7d1b5c3cc799b041319a97 --- /dev/null +++ b/minigpt4/common/optims.py @@ -0,0 +1,119 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import math + +from minigpt4.common.registry import registry + + +@registry.register_lr_scheduler("linear_warmup_step_lr") +class LinearWarmupStepLRScheduler: + def __init__( + self, + optimizer, + max_epoch, + min_lr, + init_lr, + decay_rate=1, + warmup_start_lr=-1, + warmup_steps=0, + **kwargs + ): + self.optimizer = optimizer + + self.max_epoch = max_epoch + self.min_lr = min_lr + + self.decay_rate = decay_rate + + self.init_lr = init_lr + self.warmup_steps = warmup_steps + self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr + + def step(self, cur_epoch, cur_step): + if cur_epoch == 0: + warmup_lr_schedule( + step=cur_step, + optimizer=self.optimizer, + max_step=self.warmup_steps, + init_lr=self.warmup_start_lr, + max_lr=self.init_lr, + ) + else: + step_lr_schedule( + epoch=cur_epoch, + optimizer=self.optimizer, + init_lr=self.init_lr, + min_lr=self.min_lr, + decay_rate=self.decay_rate, + ) + + +@registry.register_lr_scheduler("linear_warmup_cosine_lr") +class LinearWarmupCosineLRScheduler: + def __init__( + self, + optimizer, + max_epoch, + iters_per_epoch, + min_lr, + init_lr, + warmup_steps=0, + warmup_start_lr=-1, + **kwargs + ): + self.optimizer = optimizer + + self.max_epoch = max_epoch + self.iters_per_epoch = iters_per_epoch + self.min_lr = min_lr + + self.init_lr = init_lr + self.warmup_steps = warmup_steps + self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr + + def step(self, cur_epoch, cur_step): + total_cur_step = cur_epoch * self.iters_per_epoch + cur_step + if total_cur_step < self.warmup_steps: + warmup_lr_schedule( + step=cur_step, + optimizer=self.optimizer, + max_step=self.warmup_steps, + init_lr=self.warmup_start_lr, + max_lr=self.init_lr, + ) + else: + cosine_lr_schedule( + epoch=total_cur_step, + optimizer=self.optimizer, + max_epoch=self.max_epoch * self.iters_per_epoch, + init_lr=self.init_lr, + min_lr=self.min_lr, + ) + + +def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr): + """Decay the learning rate""" + lr = (init_lr - min_lr) * 0.5 * ( + 1.0 + math.cos(math.pi * epoch / max_epoch) + ) + min_lr + for param_group in optimizer.param_groups: + param_group["lr"] = lr + + +def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr): + """Warmup the learning rate""" + lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max(max_step, 1)) + for param_group in optimizer.param_groups: + param_group["lr"] = lr + + +def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate): + """Decay the learning rate""" + lr = max(min_lr, init_lr * (decay_rate**epoch)) + for param_group in optimizer.param_groups: + param_group["lr"] = lr diff --git a/minigpt4/common/registry.py b/minigpt4/common/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..679467a7411eda19ed956b810c21234322f06779 --- /dev/null +++ b/minigpt4/common/registry.py @@ -0,0 +1,329 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + + +class Registry: + mapping = { + "builder_name_mapping": {}, + "task_name_mapping": {}, + "processor_name_mapping": {}, + "model_name_mapping": {}, + "lr_scheduler_name_mapping": {}, + "runner_name_mapping": {}, + "state": {}, + "paths": {}, + } + + @classmethod + def register_builder(cls, name): + r"""Register a dataset builder to registry with key 'name' + + Args: + name: Key with which the builder will be registered. + + Usage: + + from minigpt4.common.registry import registry + from minigpt4.datasets.base_dataset_builder import BaseDatasetBuilder + """ + + def wrap(builder_cls): + from minigpt4.datasets.builders.base_dataset_builder import BaseDatasetBuilder + + assert issubclass( + builder_cls, BaseDatasetBuilder + ), "All builders must inherit BaseDatasetBuilder class, found {}".format( + builder_cls + ) + if name in cls.mapping["builder_name_mapping"]: + raise KeyError( + "Name '{}' already registered for {}.".format( + name, cls.mapping["builder_name_mapping"][name] + ) + ) + cls.mapping["builder_name_mapping"][name] = builder_cls + return builder_cls + + return wrap + + @classmethod + def register_task(cls, name): + r"""Register a task to registry with key 'name' + + Args: + name: Key with which the task will be registered. + + Usage: + + from minigpt4.common.registry import registry + """ + + def wrap(task_cls): + from minigpt4.tasks.base_task import BaseTask + + assert issubclass( + task_cls, BaseTask + ), "All tasks must inherit BaseTask class" + if name in cls.mapping["task_name_mapping"]: + raise KeyError( + "Name '{}' already registered for {}.".format( + name, cls.mapping["task_name_mapping"][name] + ) + ) + cls.mapping["task_name_mapping"][name] = task_cls + return task_cls + + return wrap + + @classmethod + def register_model(cls, name): + r"""Register a task to registry with key 'name' + + Args: + name: Key with which the task will be registered. + + Usage: + + from minigpt4.common.registry import registry + """ + + def wrap(model_cls): + from minigpt4.models import BaseModel + + assert issubclass( + model_cls, BaseModel + ), "All models must inherit BaseModel class" + if name in cls.mapping["model_name_mapping"]: + raise KeyError( + "Name '{}' already registered for {}.".format( + name, cls.mapping["model_name_mapping"][name] + ) + ) + cls.mapping["model_name_mapping"][name] = model_cls + return model_cls + + return wrap + + @classmethod + def register_processor(cls, name): + r"""Register a processor to registry with key 'name' + + Args: + name: Key with which the task will be registered. + + Usage: + + from minigpt4.common.registry import registry + """ + + def wrap(processor_cls): + from minigpt4.processors import BaseProcessor + + assert issubclass( + processor_cls, BaseProcessor + ), "All processors must inherit BaseProcessor class" + if name in cls.mapping["processor_name_mapping"]: + raise KeyError( + "Name '{}' already registered for {}.".format( + name, cls.mapping["processor_name_mapping"][name] + ) + ) + cls.mapping["processor_name_mapping"][name] = processor_cls + return processor_cls + + return wrap + + @classmethod + def register_lr_scheduler(cls, name): + r"""Register a model to registry with key 'name' + + Args: + name: Key with which the task will be registered. + + Usage: + + from minigpt4.common.registry import registry + """ + + def wrap(lr_sched_cls): + if name in cls.mapping["lr_scheduler_name_mapping"]: + raise KeyError( + "Name '{}' already registered for {}.".format( + name, cls.mapping["lr_scheduler_name_mapping"][name] + ) + ) + cls.mapping["lr_scheduler_name_mapping"][name] = lr_sched_cls + return lr_sched_cls + + return wrap + + @classmethod + def register_runner(cls, name): + r"""Register a model to registry with key 'name' + + Args: + name: Key with which the task will be registered. + + Usage: + + from minigpt4.common.registry import registry + """ + + def wrap(runner_cls): + if name in cls.mapping["runner_name_mapping"]: + raise KeyError( + "Name '{}' already registered for {}.".format( + name, cls.mapping["runner_name_mapping"][name] + ) + ) + cls.mapping["runner_name_mapping"][name] = runner_cls + return runner_cls + + return wrap + + @classmethod + def register_path(cls, name, path): + r"""Register a path to registry with key 'name' + + Args: + name: Key with which the path will be registered. + + Usage: + + from minigpt4.common.registry import registry + """ + assert isinstance(path, str), "All path must be str." + if name in cls.mapping["paths"]: + raise KeyError("Name '{}' already registered.".format(name)) + cls.mapping["paths"][name] = path + + @classmethod + def register(cls, name, obj): + r"""Register an item to registry with key 'name' + + Args: + name: Key with which the item will be registered. + + Usage:: + + from minigpt4.common.registry import registry + + registry.register("config", {}) + """ + path = name.split(".") + current = cls.mapping["state"] + + for part in path[:-1]: + if part not in current: + current[part] = {} + current = current[part] + + current[path[-1]] = obj + + # @classmethod + # def get_trainer_class(cls, name): + # return cls.mapping["trainer_name_mapping"].get(name, None) + + @classmethod + def get_builder_class(cls, name): + return cls.mapping["builder_name_mapping"].get(name, None) + + @classmethod + def get_model_class(cls, name): + return cls.mapping["model_name_mapping"].get(name, None) + + @classmethod + def get_task_class(cls, name): + return cls.mapping["task_name_mapping"].get(name, None) + + @classmethod + def get_processor_class(cls, name): + return cls.mapping["processor_name_mapping"].get(name, None) + + @classmethod + def get_lr_scheduler_class(cls, name): + return cls.mapping["lr_scheduler_name_mapping"].get(name, None) + + @classmethod + def get_runner_class(cls, name): + return cls.mapping["runner_name_mapping"].get(name, None) + + @classmethod + def list_runners(cls): + return sorted(cls.mapping["runner_name_mapping"].keys()) + + @classmethod + def list_models(cls): + return sorted(cls.mapping["model_name_mapping"].keys()) + + @classmethod + def list_tasks(cls): + return sorted(cls.mapping["task_name_mapping"].keys()) + + @classmethod + def list_processors(cls): + return sorted(cls.mapping["processor_name_mapping"].keys()) + + @classmethod + def list_lr_schedulers(cls): + return sorted(cls.mapping["lr_scheduler_name_mapping"].keys()) + + @classmethod + def list_datasets(cls): + return sorted(cls.mapping["builder_name_mapping"].keys()) + + @classmethod + def get_path(cls, name): + return cls.mapping["paths"].get(name, None) + + @classmethod + def get(cls, name, default=None, no_warning=False): + r"""Get an item from registry with key 'name' + + Args: + name (string): Key whose value needs to be retrieved. + default: If passed and key is not in registry, default value will + be returned with a warning. Default: None + no_warning (bool): If passed as True, warning when key doesn't exist + will not be generated. Useful for MMF's + internal operations. Default: False + """ + original_name = name + name = name.split(".") + value = cls.mapping["state"] + for subname in name: + value = value.get(subname, default) + if value is default: + break + + if ( + "writer" in cls.mapping["state"] + and value == default + and no_warning is False + ): + cls.mapping["state"]["writer"].warning( + "Key {} is not present in registry, returning default value " + "of {}".format(original_name, default) + ) + return value + + @classmethod + def unregister(cls, name): + r"""Remove an item from registry with key 'name' + + Args: + name: Key which needs to be removed. + Usage:: + + from mmf.common.registry import registry + + config = registry.unregister("config") + """ + return cls.mapping["state"].pop(name, None) + + +registry = Registry() diff --git a/minigpt4/common/utils.py b/minigpt4/common/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a3069cd10ce986a1ec249490fa813cae9254bd0d --- /dev/null +++ b/minigpt4/common/utils.py @@ -0,0 +1,424 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import io +import json +import logging +import os +import pickle +import re +import shutil +import urllib +import urllib.error +import urllib.request +from typing import Optional +from urllib.parse import urlparse + +import numpy as np +import pandas as pd +import yaml +from iopath.common.download import download +from iopath.common.file_io import file_lock, g_pathmgr +from minigpt4.common.registry import registry +from torch.utils.model_zoo import tqdm +from torchvision.datasets.utils import ( + check_integrity, + download_file_from_google_drive, + extract_archive, +) + + +def now(): + from datetime import datetime + + return datetime.now().strftime("%Y%m%d%H%M")[:-1] + + +def is_url(url_or_filename): + parsed = urlparse(url_or_filename) + return parsed.scheme in ("http", "https") + + +def get_cache_path(rel_path): + return os.path.expanduser(os.path.join(registry.get_path("cache_root"), rel_path)) + + +def get_abs_path(rel_path): + return os.path.join(registry.get_path("library_root"), rel_path) + + +def load_json(filename): + with open(filename, "r") as f: + return json.load(f) + + +# The following are adapted from torchvision and vissl +# torchvision: https://github.com/pytorch/vision +# vissl: https://github.com/facebookresearch/vissl/blob/main/vissl/utils/download.py + + +def makedir(dir_path): + """ + Create the directory if it does not exist. + """ + is_success = False + try: + if not g_pathmgr.exists(dir_path): + g_pathmgr.mkdirs(dir_path) + is_success = True + except BaseException: + print(f"Error creating directory: {dir_path}") + return is_success + + +def get_redirected_url(url: str): + """ + Given a URL, returns the URL it redirects to or the + original URL in case of no indirection + """ + import requests + + with requests.Session() as session: + with session.get(url, stream=True, allow_redirects=True) as response: + if response.history: + return response.url + else: + return url + + +def to_google_drive_download_url(view_url: str) -> str: + """ + Utility function to transform a view URL of google drive + to a download URL for google drive + Example input: + https://drive.google.com/file/d/137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp/view + Example output: + https://drive.google.com/uc?export=download&id=137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp + """ + splits = view_url.split("/") + assert splits[-1] == "view" + file_id = splits[-2] + return f"https://drive.google.com/uc?export=download&id={file_id}" + + +def download_google_drive_url(url: str, output_path: str, output_file_name: str): + """ + Download a file from google drive + Downloading an URL from google drive requires confirmation when + the file of the size is too big (google drive notifies that + anti-viral checks cannot be performed on such files) + """ + import requests + + with requests.Session() as session: + + # First get the confirmation token and append it to the URL + with session.get(url, stream=True, allow_redirects=True) as response: + for k, v in response.cookies.items(): + if k.startswith("download_warning"): + url = url + "&confirm=" + v + + # Then download the content of the file + with session.get(url, stream=True, verify=True) as response: + makedir(output_path) + path = os.path.join(output_path, output_file_name) + total_size = int(response.headers.get("Content-length", 0)) + with open(path, "wb") as file: + from tqdm import tqdm + + with tqdm(total=total_size) as progress_bar: + for block in response.iter_content( + chunk_size=io.DEFAULT_BUFFER_SIZE + ): + file.write(block) + progress_bar.update(len(block)) + + +def _get_google_drive_file_id(url: str) -> Optional[str]: + parts = urlparse(url) + + if re.match(r"(drive|docs)[.]google[.]com", parts.netloc) is None: + return None + + match = re.match(r"/file/d/(?P[^/]*)", parts.path) + if match is None: + return None + + return match.group("id") + + +def _urlretrieve(url: str, filename: str, chunk_size: int = 1024) -> None: + with open(filename, "wb") as fh: + with urllib.request.urlopen( + urllib.request.Request(url, headers={"User-Agent": "vissl"}) + ) as response: + with tqdm(total=response.length) as pbar: + for chunk in iter(lambda: response.read(chunk_size), ""): + if not chunk: + break + pbar.update(chunk_size) + fh.write(chunk) + + +def download_url( + url: str, + root: str, + filename: Optional[str] = None, + md5: Optional[str] = None, +) -> None: + """Download a file from a url and place it in root. + Args: + url (str): URL to download file from + root (str): Directory to place downloaded file in + filename (str, optional): Name to save the file under. + If None, use the basename of the URL. + md5 (str, optional): MD5 checksum of the download. If None, do not check + """ + root = os.path.expanduser(root) + if not filename: + filename = os.path.basename(url) + fpath = os.path.join(root, filename) + + makedir(root) + + # check if file is already present locally + if check_integrity(fpath, md5): + print("Using downloaded and verified file: " + fpath) + return + + # expand redirect chain if needed + url = get_redirected_url(url) + + # check if file is located on Google Drive + file_id = _get_google_drive_file_id(url) + if file_id is not None: + return download_file_from_google_drive(file_id, root, filename, md5) + + # download the file + try: + print("Downloading " + url + " to " + fpath) + _urlretrieve(url, fpath) + except (urllib.error.URLError, IOError) as e: # type: ignore[attr-defined] + if url[:5] == "https": + url = url.replace("https:", "http:") + print( + "Failed download. Trying https -> http instead." + " Downloading " + url + " to " + fpath + ) + _urlretrieve(url, fpath) + else: + raise e + + # check integrity of downloaded file + if not check_integrity(fpath, md5): + raise RuntimeError("File not found or corrupted.") + + +def download_and_extract_archive( + url: str, + download_root: str, + extract_root: Optional[str] = None, + filename: Optional[str] = None, + md5: Optional[str] = None, + remove_finished: bool = False, +) -> None: + download_root = os.path.expanduser(download_root) + if extract_root is None: + extract_root = download_root + if not filename: + filename = os.path.basename(url) + + download_url(url, download_root, filename, md5) + + archive = os.path.join(download_root, filename) + print("Extracting {} to {}".format(archive, extract_root)) + extract_archive(archive, extract_root, remove_finished) + + +def cache_url(url: str, cache_dir: str) -> str: + """ + This implementation downloads the remote resource and caches it locally. + The resource will only be downloaded if not previously requested. + """ + parsed_url = urlparse(url) + dirname = os.path.join(cache_dir, os.path.dirname(parsed_url.path.lstrip("/"))) + makedir(dirname) + filename = url.split("/")[-1] + cached = os.path.join(dirname, filename) + with file_lock(cached): + if not os.path.isfile(cached): + logging.info(f"Downloading {url} to {cached} ...") + cached = download(url, dirname, filename=filename) + logging.info(f"URL {url} cached in {cached}") + return cached + + +# TODO (prigoyal): convert this into RAII-style API +def create_file_symlink(file1, file2): + """ + Simply create the symlinks for a given file1 to file2. + Useful during model checkpointing to symlinks to the + latest successful checkpoint. + """ + try: + if g_pathmgr.exists(file2): + g_pathmgr.rm(file2) + g_pathmgr.symlink(file1, file2) + except Exception as e: + logging.info(f"Could NOT create symlink. Error: {e}") + + +def save_file(data, filename, append_to_json=True, verbose=True): + """ + Common i/o utility to handle saving data to various file formats. + Supported: + .pkl, .pickle, .npy, .json + Specifically for .json, users have the option to either append (default) + or rewrite by passing in Boolean value to append_to_json. + """ + if verbose: + logging.info(f"Saving data to file: {filename}") + file_ext = os.path.splitext(filename)[1] + if file_ext in [".pkl", ".pickle"]: + with g_pathmgr.open(filename, "wb") as fopen: + pickle.dump(data, fopen, pickle.HIGHEST_PROTOCOL) + elif file_ext == ".npy": + with g_pathmgr.open(filename, "wb") as fopen: + np.save(fopen, data) + elif file_ext == ".json": + if append_to_json: + with g_pathmgr.open(filename, "a") as fopen: + fopen.write(json.dumps(data, sort_keys=True) + "\n") + fopen.flush() + else: + with g_pathmgr.open(filename, "w") as fopen: + fopen.write(json.dumps(data, sort_keys=True) + "\n") + fopen.flush() + elif file_ext == ".yaml": + with g_pathmgr.open(filename, "w") as fopen: + dump = yaml.dump(data) + fopen.write(dump) + fopen.flush() + else: + raise Exception(f"Saving {file_ext} is not supported yet") + + if verbose: + logging.info(f"Saved data to file: {filename}") + + +def load_file(filename, mmap_mode=None, verbose=True, allow_pickle=False): + """ + Common i/o utility to handle loading data from various file formats. + Supported: + .pkl, .pickle, .npy, .json + For the npy files, we support reading the files in mmap_mode. + If the mmap_mode of reading is not successful, we load data without the + mmap_mode. + """ + if verbose: + logging.info(f"Loading data from file: {filename}") + + file_ext = os.path.splitext(filename)[1] + if file_ext == ".txt": + with g_pathmgr.open(filename, "r") as fopen: + data = fopen.readlines() + elif file_ext in [".pkl", ".pickle"]: + with g_pathmgr.open(filename, "rb") as fopen: + data = pickle.load(fopen, encoding="latin1") + elif file_ext == ".npy": + if mmap_mode: + try: + with g_pathmgr.open(filename, "rb") as fopen: + data = np.load( + fopen, + allow_pickle=allow_pickle, + encoding="latin1", + mmap_mode=mmap_mode, + ) + except ValueError as e: + logging.info( + f"Could not mmap {filename}: {e}. Trying without g_pathmgr" + ) + data = np.load( + filename, + allow_pickle=allow_pickle, + encoding="latin1", + mmap_mode=mmap_mode, + ) + logging.info("Successfully loaded without g_pathmgr") + except Exception: + logging.info("Could not mmap without g_pathmgr. Trying without mmap") + with g_pathmgr.open(filename, "rb") as fopen: + data = np.load(fopen, allow_pickle=allow_pickle, encoding="latin1") + else: + with g_pathmgr.open(filename, "rb") as fopen: + data = np.load(fopen, allow_pickle=allow_pickle, encoding="latin1") + elif file_ext == ".json": + with g_pathmgr.open(filename, "r") as fopen: + data = json.load(fopen) + elif file_ext == ".yaml": + with g_pathmgr.open(filename, "r") as fopen: + data = yaml.load(fopen, Loader=yaml.FullLoader) + elif file_ext == ".csv": + with g_pathmgr.open(filename, "r") as fopen: + data = pd.read_csv(fopen) + else: + raise Exception(f"Reading from {file_ext} is not supported yet") + return data + + +def abspath(resource_path: str): + """ + Make a path absolute, but take into account prefixes like + "http://" or "manifold://" + """ + regex = re.compile(r"^\w+://") + if regex.match(resource_path) is None: + return os.path.abspath(resource_path) + else: + return resource_path + + +def makedir(dir_path): + """ + Create the directory if it does not exist. + """ + is_success = False + try: + if not g_pathmgr.exists(dir_path): + g_pathmgr.mkdirs(dir_path) + is_success = True + except BaseException: + logging.info(f"Error creating directory: {dir_path}") + return is_success + + +def is_url(input_url): + """ + Check if an input string is a url. look for http(s):// and ignoring the case + """ + is_url = re.match(r"^(?:http)s?://", input_url, re.IGNORECASE) is not None + return is_url + + +def cleanup_dir(dir): + """ + Utility for deleting a directory. Useful for cleaning the storage space + that contains various training artifacts like checkpoints, data etc. + """ + if os.path.exists(dir): + logging.info(f"Deleting directory: {dir}") + shutil.rmtree(dir) + logging.info(f"Deleted contents of directory: {dir}") + + +def get_file_size(filename): + """ + Given a file, get the size of file in MB + """ + size_in_mb = os.path.getsize(filename) / float(1024**2) + return size_in_mb diff --git a/minigpt4/configs/datasets/cc_sbu/align.yaml b/minigpt4/configs/datasets/cc_sbu/align.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5710834200fe45449d60185d467d6bcb90a98cca --- /dev/null +++ b/minigpt4/configs/datasets/cc_sbu/align.yaml @@ -0,0 +1,5 @@ +datasets: + cc_sbu_align: + data_type: images + build_info: + storage: /path/to/cc_sbu_align/ diff --git a/minigpt4/configs/datasets/cc_sbu/defaults.yaml b/minigpt4/configs/datasets/cc_sbu/defaults.yaml new file mode 100644 index 0000000000000000000000000000000000000000..60390eece551fe06a0f7c3ebb395351794b9f5f1 --- /dev/null +++ b/minigpt4/configs/datasets/cc_sbu/defaults.yaml @@ -0,0 +1,5 @@ +datasets: + cc_sbu: + data_type: images + build_info: + storage: /path/to/cc_sbu_dataset/{00000..01255}.tar diff --git a/minigpt4/configs/datasets/laion/defaults.yaml b/minigpt4/configs/datasets/laion/defaults.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6bad62901619c0a9e34619a400290f3e18083899 --- /dev/null +++ b/minigpt4/configs/datasets/laion/defaults.yaml @@ -0,0 +1,5 @@ +datasets: + laion: + data_type: images + build_info: + storage: /path/to/laion_dataset/{00000..10488}.tar diff --git a/minigpt4/configs/default.yaml b/minigpt4/configs/default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ff5a6a23fa2e3914938631b96c71fdf723dbbc10 --- /dev/null +++ b/minigpt4/configs/default.yaml @@ -0,0 +1,5 @@ +env: + # For default users + # cache_root: "cache" + # For internal use with persistent storage + cache_root: "/export/home/.cache/minigpt4" diff --git a/minigpt4/configs/models/minigpt4_llama2.yaml b/minigpt4/configs/models/minigpt4_llama2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fdd25e0947d2d9c1c6f6270a95be1c8f01538baa --- /dev/null +++ b/minigpt4/configs/models/minigpt4_llama2.yaml @@ -0,0 +1,29 @@ +model: + arch: minigpt4 + + # vit encoder + image_size: 224 + drop_path_rate: 0 + use_grad_checkpoint: False + vit_precision: "fp16" + freeze_vit: True + has_qformer: False + + # generation configs + prompt: "" + + llama_model: "please set this value to the path of llama2-chat-7b" + +preprocess: + vis_processor: + train: + name: "blip2_image_train" + image_size: 224 + eval: + name: "blip2_image_eval" + image_size: 224 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" diff --git a/minigpt4/configs/models/minigpt4_vicuna0.yaml b/minigpt4/configs/models/minigpt4_vicuna0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..718054c8b62a17f3b72bbb3efb3eda95e55a64f1 --- /dev/null +++ b/minigpt4/configs/models/minigpt4_vicuna0.yaml @@ -0,0 +1,32 @@ +model: + arch: minigpt4 + + # vit encoder + image_size: 224 + drop_path_rate: 0 + use_grad_checkpoint: False + vit_precision: "fp16" + freeze_vit: True + freeze_qformer: True + + # Q-Former + num_query_token: 32 + + # generation configs + prompt: "" + + llama_model: "please set this value to the path of vicuna model" + +preprocess: + vis_processor: + train: + name: "blip2_image_train" + image_size: 224 + eval: + name: "blip2_image_eval" + image_size: 224 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" diff --git a/minigpt4/configs/models/minigpt_v2.yaml b/minigpt4/configs/models/minigpt_v2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1d85d203b58e859d026b85565b34ad6724951eac --- /dev/null +++ b/minigpt4/configs/models/minigpt_v2.yaml @@ -0,0 +1,31 @@ +model: + arch: minigpt_v2 + + # vit encoder + image_size: 448 + drop_path_rate: 0 + use_grad_checkpoint: False + vit_precision: "fp16" + freeze_vit: True + + # generation configs + prompt: "" + + llama_model: "please set this value to the path of llama2-chat-7b" + lora_r: 64 + lora_alpha: 16 + + +preprocess: + vis_processor: + train: + name: "blip2_image_train" + image_size: 448 + eval: + name: "blip2_image_eval" + image_size: 448 + text_processor: + train: + name: "blip_caption" + eval: + name: "blip_caption" diff --git a/minigpt4/conversation/__init__.py b/minigpt4/conversation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/minigpt4/conversation/conversation.py b/minigpt4/conversation/conversation.py new file mode 100644 index 0000000000000000000000000000000000000000..9c27c785db4a36f6cad16e6c209eeac8e606044b --- /dev/null +++ b/minigpt4/conversation/conversation.py @@ -0,0 +1,238 @@ +import argparse +import time +from threading import Thread +from PIL import Image + +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer +from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer + +import dataclasses +from enum import auto, Enum +from typing import List, Tuple, Any + +from minigpt4.common.registry import registry + + +class SeparatorStyle(Enum): + """Different separator style.""" + SINGLE = auto() + TWO = auto() + + +@dataclasses.dataclass +class Conversation: + """A class that keeps all conversation history.""" + system: str + roles: List[str] + messages: List[List[str]] + offset: int + # system_img: List[Image.Image] = [] + sep_style: SeparatorStyle = SeparatorStyle.SINGLE + sep: str = "###" + sep2: str = None + + skip_next: bool = False + conv_id: Any = None + + def get_prompt(self): + if self.sep_style == SeparatorStyle.SINGLE: + ret = self.system + self.sep + for role, message in self.messages: + if message: + ret += role + message + self.sep + else: + ret += role + return ret + elif self.sep_style == SeparatorStyle.TWO: + seps = [self.sep, self.sep2] + ret = self.system + seps[0] + for i, (role, message) in enumerate(self.messages): + if message: + ret += role + message + seps[i % 2] + else: + ret += role + return ret + else: + raise ValueError(f"Invalid style: {self.sep_style}") + + def append_message(self, role, message): + self.messages.append([role, message]) + + def to_gradio_chatbot(self): + ret = [] + for i, (role, msg) in enumerate(self.messages[self.offset:]): + if i % 2 == 0: + ret.append([msg, None]) + else: + ret[-1][-1] = msg + return ret + + def copy(self): + return Conversation( + system=self.system, + # system_img=self.system_img, + roles=self.roles, + messages=[[x, y] for x, y in self.messages], + offset=self.offset, + sep_style=self.sep_style, + sep=self.sep, + sep2=self.sep2, + conv_id=self.conv_id) + + def dict(self): + return { + "system": self.system, + # "system_img": self.system_img, + "roles": self.roles, + "messages": self.messages, + "offset": self.offset, + "sep": self.sep, + "sep2": self.sep2, + "conv_id": self.conv_id, + } + + +class StoppingCriteriaSub(StoppingCriteria): + + def __init__(self, stops=[], encounters=1): + super().__init__() + self.stops = stops + + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor): + for stop in self.stops: + if torch.all((stop == input_ids[0][-len(stop):])).item(): + return True + + return False + + +CONV_VISION_Vicuna0 = Conversation( + system="Give the following image: ImageContent. " + "You will be able to see the image once I provide it to you. Please answer my questions.", + roles=("Human: ", "Assistant: "), + messages=[], + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="###", +) + +CONV_VISION_LLama2 = Conversation( + system="Give the following image: ImageContent. " + "You will be able to see the image once I provide it to you. Please answer my questions.", + roles=("[INST] ", " [/INST] "), + messages=[], + offset=2, + sep_style=SeparatorStyle.SINGLE, + sep="", +) + + + +class Chat: + def __init__(self, model, vis_processor, device='cuda:0', stopping_criteria=None): + self.device = device + self.model = model + self.vis_processor = vis_processor + + if stopping_criteria is not None: + self.stopping_criteria = stopping_criteria + else: + stop_words_ids = [torch.tensor([2]).to(self.device)] + self.stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)]) + + def ask(self, text, conv): + if len(conv.messages) > 0 and conv.messages[-1][0] == conv.roles[0] \ + and conv.messages[-1][1][-6:] == '': # last message is image. + conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text]) + else: + conv.append_message(conv.roles[0], text) + + def answer_prepare(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9, + repetition_penalty=1.05, length_penalty=1, temperature=1.0, max_length=2000): + conv.append_message(conv.roles[1], None) + embs = self.get_context_emb(conv, img_list) + + current_max_len = embs.shape[1] + max_new_tokens + if current_max_len - max_length > 0: + print('Warning: The number of tokens in current conversation exceeds the max length. ' + 'The model will not see the contexts outside the range.') + begin_idx = max(0, current_max_len - max_length) + embs = embs[:, begin_idx:] + + generation_kwargs = dict( + inputs_embeds=embs, + max_new_tokens=max_new_tokens, + stopping_criteria=self.stopping_criteria, + num_beams=num_beams, + do_sample=True, + min_length=min_length, + top_p=top_p, + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + temperature=temperature, + ) + return generation_kwargs + + def answer(self, conv, img_list, **kargs): + generation_dict = self.answer_prepare(conv, img_list, **kargs) + + output_token = self.model.llama_model.generate(**generation_dict)[0] + output_text = self.model.llama_tokenizer.decode(output_token, skip_special_tokens=True) + + output_text = output_text.split('###')[0] # remove the stop sign '###' + output_text = output_text.split('Assistant:')[-1].strip() + + conv.messages[-1][1] = output_text + return output_text, output_token.cpu().numpy() + + def stream_answer(self, conv, img_list, **kargs): + generation_kwargs = self.answer_prepare(conv, img_list, **kargs) + streamer = TextIteratorStreamer(self.model.llama_tokenizer, skip_special_tokens=True) + generation_kwargs['streamer'] = streamer + thread = Thread(target=self.model.llama_model.generate, kwargs=generation_kwargs) + thread.start() + return streamer + + def encode_img(self, img_list): + image = img_list[0] + img_list.pop(0) + if isinstance(image, str): # is a image path + raw_image = Image.open(image).convert('RGB') + image = self.vis_processor(raw_image).unsqueeze(0).to(self.device) + elif isinstance(image, Image.Image): + raw_image = image + image = self.vis_processor(raw_image).unsqueeze(0).to(self.device) + elif isinstance(image, torch.Tensor): + if len(image.shape) == 3: + image = image.unsqueeze(0) + image = image.to(self.device) + + image_emb, _ = self.model.encode_img(image) + img_list.append(image_emb) + + def upload_img(self, image, conv, img_list): + conv.append_message(conv.roles[0], "") + img_list.append(image) + msg = "Received." + + return msg + + def get_context_emb(self, conv, img_list): + prompt = conv.get_prompt() + prompt_segs = prompt.split('') + assert len(prompt_segs) == len(img_list) + 1, "Unmatched numbers of image placeholders and images." + seg_tokens = [ + self.model.llama_tokenizer( + seg, return_tensors="pt", add_special_tokens=i == 0).to(self.device).input_ids + # only add bos to the first seg + for i, seg in enumerate(prompt_segs) + ] + print('debug device: ', self.device) + print('debug model device: ', self.model.device) + seg_embs = [self.model.embed_tokens(seg_t) for seg_t in seg_tokens] + mixed_embs = [emb for pair in zip(seg_embs[:-1], img_list) for emb in pair] + [seg_embs[-1]] + mixed_embs = torch.cat(mixed_embs, dim=1) + return mixed_embs + + diff --git a/minigpt4/datasets/__init__.py b/minigpt4/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/minigpt4/datasets/builders/__init__.py b/minigpt4/datasets/builders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6d0964063f145c6b119c78460aed69bcc4dfa4c1 --- /dev/null +++ b/minigpt4/datasets/builders/__init__.py @@ -0,0 +1,72 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from minigpt4.datasets.builders.base_dataset_builder import load_dataset_config +from minigpt4.datasets.builders.image_text_pair_builder import ( + CCSBUBuilder, + LaionBuilder, + CCSBUAlignBuilder +) +from minigpt4.common.registry import registry + +__all__ = [ + "CCSBUBuilder", + "LaionBuilder", + "CCSBUAlignBuilder" +] + + +def load_dataset(name, cfg_path=None, vis_path=None, data_type=None): + """ + Example + + >>> dataset = load_dataset("coco_caption", cfg=None) + >>> splits = dataset.keys() + >>> print([len(dataset[split]) for split in splits]) + + """ + if cfg_path is None: + cfg = None + else: + cfg = load_dataset_config(cfg_path) + + try: + builder = registry.get_builder_class(name)(cfg) + except TypeError: + print( + f"Dataset {name} not found. Available datasets:\n" + + ", ".join([str(k) for k in dataset_zoo.get_names()]) + ) + exit(1) + + if vis_path is not None: + if data_type is None: + # use default data type in the config + data_type = builder.config.data_type + + assert ( + data_type in builder.config.build_info + ), f"Invalid data_type {data_type} for {name}." + + builder.config.build_info.get(data_type).storage = vis_path + + dataset = builder.build_datasets() + return dataset + + +class DatasetZoo: + def __init__(self) -> None: + self.dataset_zoo = { + k: list(v.DATASET_CONFIG_DICT.keys()) + for k, v in sorted(registry.mapping["builder_name_mapping"].items()) + } + + def get_names(self): + return list(self.dataset_zoo.keys()) + + +dataset_zoo = DatasetZoo() diff --git a/minigpt4/datasets/builders/base_dataset_builder.py b/minigpt4/datasets/builders/base_dataset_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..4b607e3c0a8abaa6b1ccbc711e27ff3755f5ec11 --- /dev/null +++ b/minigpt4/datasets/builders/base_dataset_builder.py @@ -0,0 +1,236 @@ +""" + This file is from + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import logging +import os +import shutil +import warnings + +from omegaconf import OmegaConf +import torch.distributed as dist +from torchvision.datasets.utils import download_url + +import minigpt4.common.utils as utils +from minigpt4.common.dist_utils import is_dist_avail_and_initialized, is_main_process +from minigpt4.common.registry import registry +from minigpt4.processors.base_processor import BaseProcessor + + + +class BaseDatasetBuilder: + train_dataset_cls, eval_dataset_cls = None, None + + def __init__(self, cfg=None): + super().__init__() + + if cfg is None: + # help to create datasets from default config. + self.config = load_dataset_config(self.default_config_path()) + elif isinstance(cfg, str): + self.config = load_dataset_config(cfg) + else: + # when called from task.build_dataset() + self.config = cfg + + self.data_type = self.config.data_type + + self.vis_processors = {"train": BaseProcessor(), "eval": BaseProcessor()} + self.text_processors = {"train": BaseProcessor(), "eval": BaseProcessor()} + + def build_datasets(self): + # download, split, etc... + # only called on 1 GPU/TPU in distributed + + if is_main_process(): + self._download_data() + + if is_dist_avail_and_initialized(): + dist.barrier() + + # at this point, all the annotations and image/videos should be all downloaded to the specified locations. + logging.info("Building datasets...") + datasets = self.build() # dataset['train'/'val'/'test'] + + return datasets + + def build_processors(self): + vis_proc_cfg = self.config.get("vis_processor") + txt_proc_cfg = self.config.get("text_processor") + + if vis_proc_cfg is not None: + vis_train_cfg = vis_proc_cfg.get("train") + vis_eval_cfg = vis_proc_cfg.get("eval") + + self.vis_processors["train"] = self._build_proc_from_cfg(vis_train_cfg) + self.vis_processors["eval"] = self._build_proc_from_cfg(vis_eval_cfg) + + if txt_proc_cfg is not None: + txt_train_cfg = txt_proc_cfg.get("train") + txt_eval_cfg = txt_proc_cfg.get("eval") + + self.text_processors["train"] = self._build_proc_from_cfg(txt_train_cfg) + self.text_processors["eval"] = self._build_proc_from_cfg(txt_eval_cfg) + + @staticmethod + def _build_proc_from_cfg(cfg): + return ( + registry.get_processor_class(cfg.name).from_config(cfg) + if cfg is not None + else None + ) + + @classmethod + def default_config_path(cls, type="default"): + return utils.get_abs_path(cls.DATASET_CONFIG_DICT[type]) + + def _download_data(self): + self._download_ann() + self._download_vis() + + def _download_ann(self): + """ + Download annotation files if necessary. + All the vision-language datasets should have annotations of unified format. + + storage_path can be: + (1) relative/absolute: will be prefixed with env.cache_root to make full path if relative. + (2) basename/dirname: will be suffixed with base name of URL if dirname is provided. + + Local annotation paths should be relative. + """ + anns = self.config.build_info.annotations + + splits = anns.keys() + + cache_root = registry.get_path("cache_root") + + for split in splits: + info = anns[split] + + urls, storage_paths = info.get("url", None), info.storage + + if isinstance(urls, str): + urls = [urls] + if isinstance(storage_paths, str): + storage_paths = [storage_paths] + + assert len(urls) == len(storage_paths) + + for url_or_filename, storage_path in zip(urls, storage_paths): + # if storage_path is relative, make it full by prefixing with cache_root. + if not os.path.isabs(storage_path): + storage_path = os.path.join(cache_root, storage_path) + + dirname = os.path.dirname(storage_path) + if not os.path.exists(dirname): + os.makedirs(dirname) + + if os.path.isfile(url_or_filename): + src, dst = url_or_filename, storage_path + if not os.path.exists(dst): + shutil.copyfile(src=src, dst=dst) + else: + logging.info("Using existing file {}.".format(dst)) + else: + if os.path.isdir(storage_path): + # if only dirname is provided, suffix with basename of URL. + raise ValueError( + "Expecting storage_path to be a file path, got directory {}".format( + storage_path + ) + ) + else: + filename = os.path.basename(storage_path) + + download_url(url=url_or_filename, root=dirname, filename=filename) + + def _download_vis(self): + + storage_path = self.config.build_info.get(self.data_type).storage + storage_path = utils.get_cache_path(storage_path) + + if not os.path.exists(storage_path): + warnings.warn( + f""" + The specified path {storage_path} for visual inputs does not exist. + Please provide a correct path to the visual inputs or + refer to datasets/download_scripts/README.md for downloading instructions. + """ + ) + + def build(self): + """ + Create by split datasets inheriting torch.utils.data.Datasets. + + # build() can be dataset-specific. Overwrite to customize. + """ + self.build_processors() + + build_info = self.config.build_info + + ann_info = build_info.annotations + vis_info = build_info.get(self.data_type) + + datasets = dict() + for split in ann_info.keys(): + if split not in ["train", "val", "test"]: + continue + + is_train = split == "train" + + # processors + vis_processor = ( + self.vis_processors["train"] + if is_train + else self.vis_processors["eval"] + ) + text_processor = ( + self.text_processors["train"] + if is_train + else self.text_processors["eval"] + ) + + # annotation path + ann_paths = ann_info.get(split).storage + if isinstance(ann_paths, str): + ann_paths = [ann_paths] + + abs_ann_paths = [] + for ann_path in ann_paths: + if not os.path.isabs(ann_path): + ann_path = utils.get_cache_path(ann_path) + abs_ann_paths.append(ann_path) + ann_paths = abs_ann_paths + + # visual data storage path + vis_path = os.path.join(vis_info.storage, split) + + if not os.path.isabs(vis_path): + # vis_path = os.path.join(utils.get_cache_path(), vis_path) + vis_path = utils.get_cache_path(vis_path) + + if not os.path.exists(vis_path): + warnings.warn("storage path {} does not exist.".format(vis_path)) + + # create datasets + dataset_cls = self.train_dataset_cls if is_train else self.eval_dataset_cls + datasets[split] = dataset_cls( + vis_processor=vis_processor, + text_processor=text_processor, + ann_paths=ann_paths, + vis_root=vis_path, + ) + + return datasets + + +def load_dataset_config(cfg_path): + cfg = OmegaConf.load(cfg_path).datasets + cfg = cfg[list(cfg.keys())[0]] + + return cfg diff --git a/minigpt4/datasets/builders/image_text_pair_builder.py b/minigpt4/datasets/builders/image_text_pair_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..e5d66b8f63d4489b1e6fa7954cb80b5e7344f473 --- /dev/null +++ b/minigpt4/datasets/builders/image_text_pair_builder.py @@ -0,0 +1,105 @@ +import os +import logging +import warnings + +from minigpt4.common.registry import registry +from minigpt4.datasets.builders.base_dataset_builder import BaseDatasetBuilder +from minigpt4.datasets.datasets.laion_dataset import LaionDataset +from minigpt4.datasets.datasets.cc_sbu_dataset import CCSBUDataset, CCSBUAlignDataset + + +@registry.register_builder("cc_sbu") +class CCSBUBuilder(BaseDatasetBuilder): + train_dataset_cls = CCSBUDataset + + DATASET_CONFIG_DICT = {"default": "configs/datasets/cc_sbu/defaults.yaml"} + + def _download_ann(self): + pass + + def _download_vis(self): + pass + + def build(self): + self.build_processors() + + build_info = self.config.build_info + + datasets = dict() + split = "train" + + # create datasets + # [NOTE] return inner_datasets (wds.DataPipeline) + dataset_cls = self.train_dataset_cls + datasets[split] = dataset_cls( + vis_processor=self.vis_processors[split], + text_processor=self.text_processors[split], + location=build_info.storage, + ).inner_dataset + + return datasets + + +@registry.register_builder("laion") +class LaionBuilder(BaseDatasetBuilder): + train_dataset_cls = LaionDataset + + DATASET_CONFIG_DICT = {"default": "configs/datasets/laion/defaults.yaml"} + + def _download_ann(self): + pass + + def _download_vis(self): + pass + + def build(self): + self.build_processors() + + build_info = self.config.build_info + + datasets = dict() + split = "train" + + # create datasets + # [NOTE] return inner_datasets (wds.DataPipeline) + dataset_cls = self.train_dataset_cls + datasets[split] = dataset_cls( + vis_processor=self.vis_processors[split], + text_processor=self.text_processors[split], + location=build_info.storage, + ).inner_dataset + + return datasets + + +@registry.register_builder("cc_sbu_align") +class CCSBUAlignBuilder(BaseDatasetBuilder): + train_dataset_cls = CCSBUAlignDataset + + DATASET_CONFIG_DICT = { + "default": "configs/datasets/cc_sbu/align.yaml", + } + + def build_datasets(self): + # at this point, all the annotations and image/videos should be all downloaded to the specified locations. + logging.info("Building datasets...") + self.build_processors() + + build_info = self.config.build_info + storage_path = build_info.storage + + datasets = dict() + + if not os.path.exists(storage_path): + warnings.warn("storage path {} does not exist.".format(storage_path)) + + # create datasets + dataset_cls = self.train_dataset_cls + datasets['train'] = dataset_cls( + vis_processor=self.vis_processors["train"], + text_processor=self.text_processors["train"], + ann_paths=[os.path.join(storage_path, 'filter_cap.json')], + vis_root=os.path.join(storage_path, 'image'), + ) + + return datasets diff --git a/minigpt4/datasets/data_utils.py b/minigpt4/datasets/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..cf6497fd4389295d11b1c19f6927aba7ac658d1d --- /dev/null +++ b/minigpt4/datasets/data_utils.py @@ -0,0 +1,196 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import gzip +import logging +import os +import random as rnd +import tarfile +import zipfile +import random +from typing import List +from tqdm import tqdm + +import decord +from decord import VideoReader +import webdataset as wds +import numpy as np +import torch +from torch.utils.data.dataset import IterableDataset + +from minigpt4.common.registry import registry +from minigpt4.datasets.datasets.base_dataset import ConcatDataset + + +decord.bridge.set_bridge("torch") +MAX_INT = registry.get("MAX_INT") + + +class ChainDataset(wds.DataPipeline): + r"""Dataset for chaining multiple :class:`DataPipeline` s. + + This class is useful to assemble different existing dataset streams. The + chaining operation is done on-the-fly, so concatenating large-scale + datasets with this class will be efficient. + + Args: + datasets (iterable of IterableDataset): datasets to be chained together + """ + def __init__(self, datasets: List[wds.DataPipeline]) -> None: + super().__init__() + self.datasets = datasets + self.prob = [] + self.names = [] + for dataset in self.datasets: + if hasattr(dataset, 'name'): + self.names.append(dataset.name) + else: + self.names.append('Unknown') + if hasattr(dataset, 'sample_ratio'): + self.prob.append(dataset.sample_ratio) + else: + self.prob.append(1) + logging.info("One of the datapipeline doesn't define ratio and set to 1 automatically.") + + def __iter__(self): + datastreams = [iter(dataset) for dataset in self.datasets] + while True: + select_datastream = random.choices(datastreams, weights=self.prob, k=1)[0] + yield next(select_datastream) + + +def apply_to_sample(f, sample): + if len(sample) == 0: + return {} + + def _apply(x): + if torch.is_tensor(x): + return f(x) + elif isinstance(x, dict): + return {key: _apply(value) for key, value in x.items()} + elif isinstance(x, list): + return [_apply(x) for x in x] + else: + return x + + return _apply(sample) + + +def move_to_cuda(sample): + def _move_to_cuda(tensor): + return tensor.cuda() + + return apply_to_sample(_move_to_cuda, sample) + + +def prepare_sample(samples, cuda_enabled=True): + if cuda_enabled: + samples = move_to_cuda(samples) + + # TODO fp16 support + + return samples + + +def reorg_datasets_by_split(datasets): + """ + Organizes datasets by split. + + Args: + datasets: dict of torch.utils.data.Dataset objects by name. + + Returns: + Dict of datasets by split {split_name: List[Datasets]}. + """ + # if len(datasets) == 1: + # return datasets[list(datasets.keys())[0]] + # else: + reorg_datasets = dict() + + # reorganize by split + for _, dataset in datasets.items(): + for split_name, dataset_split in dataset.items(): + if split_name not in reorg_datasets: + reorg_datasets[split_name] = [dataset_split] + else: + reorg_datasets[split_name].append(dataset_split) + + return reorg_datasets + + +def concat_datasets(datasets): + """ + Concatenates multiple datasets into a single dataset. + + It supports may-style datasets and DataPipeline from WebDataset. Currently, does not support + generic IterableDataset because it requires creating separate samplers. + + Now only supports conctenating training datasets and assuming validation and testing + have only a single dataset. This is because metrics should not be computed on the concatenated + datasets. + + Args: + datasets: dict of torch.utils.data.Dataset objects by split. + + Returns: + Dict of concatenated datasets by split, "train" is the concatenation of multiple datasets, + "val" and "test" remain the same. + + If the input training datasets contain both map-style and DataPipeline datasets, returns + a tuple, where the first element is a concatenated map-style dataset and the second + element is a chained DataPipeline dataset. + + """ + # concatenate datasets in the same split + for split_name in datasets: + if split_name != "train": + assert ( + len(datasets[split_name]) == 1 + ), "Do not support multiple {} datasets.".format(split_name) + datasets[split_name] = datasets[split_name][0] + else: + iterable_datasets, map_datasets = [], [] + for dataset in datasets[split_name]: + if isinstance(dataset, wds.DataPipeline): + logging.info( + "Dataset {} is IterableDataset, can't be concatenated.".format( + dataset + ) + ) + iterable_datasets.append(dataset) + elif isinstance(dataset, IterableDataset): + raise NotImplementedError( + "Do not support concatenation of generic IterableDataset." + ) + else: + map_datasets.append(dataset) + + # if len(iterable_datasets) > 0: + # concatenate map-style datasets and iterable-style datasets separately + if len(iterable_datasets) > 1: + chained_datasets = ( + ChainDataset(iterable_datasets) + ) + elif len(iterable_datasets) == 1: + chained_datasets = iterable_datasets[0] + else: + chained_datasets = None + + concat_datasets = ( + ConcatDataset(map_datasets) if len(map_datasets) > 0 else None + ) + + train_datasets = concat_datasets, chained_datasets + train_datasets = tuple([x for x in train_datasets if x is not None]) + train_datasets = ( + train_datasets[0] if len(train_datasets) == 1 else train_datasets + ) + + datasets[split_name] = train_datasets + + return datasets + diff --git a/minigpt4/datasets/datasets/__init__.py b/minigpt4/datasets/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/minigpt4/datasets/datasets/base_dataset.py b/minigpt4/datasets/datasets/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..ae2a8d0e21370129c0182cddc427eb293bbe5982 --- /dev/null +++ b/minigpt4/datasets/datasets/base_dataset.py @@ -0,0 +1,68 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import json +from typing import Iterable + +from torch.utils.data import Dataset, ConcatDataset +from torch.utils.data.dataloader import default_collate + + +class BaseDataset(Dataset): + def __init__( + self, vis_processor=None, text_processor=None, vis_root=None, ann_paths=[] + ): + """ + vis_root (string): Root directory of images (e.g. coco/images/) + ann_root (string): directory to store the annotation file + """ + self.vis_root = vis_root + + self.annotation = [] + for ann_path in ann_paths: + self.annotation.extend(json.load(open(ann_path, "r"))['annotations']) + + self.vis_processor = vis_processor + self.text_processor = text_processor + + self._add_instance_ids() + + def __len__(self): + return len(self.annotation) + + def collater(self, samples): + return default_collate(samples) + + def set_processors(self, vis_processor, text_processor): + self.vis_processor = vis_processor + self.text_processor = text_processor + + def _add_instance_ids(self, key="instance_id"): + for idx, ann in enumerate(self.annotation): + ann[key] = str(idx) + + +class ConcatDataset(ConcatDataset): + def __init__(self, datasets: Iterable[Dataset]) -> None: + super().__init__(datasets) + + def collater(self, samples): + # TODO For now only supports datasets with same underlying collater implementations + + all_keys = set() + for s in samples: + all_keys.update(s) + + shared_keys = all_keys + for s in samples: + shared_keys = shared_keys & set(s.keys()) + + samples_shared_keys = [] + for s in samples: + samples_shared_keys.append({k: s[k] for k in s.keys() if k in shared_keys}) + + return self.datasets[0].collater(samples_shared_keys) diff --git a/minigpt4/datasets/datasets/caption_datasets.py b/minigpt4/datasets/datasets/caption_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..78bab668d34c8a28917af171700d43dbb20f3926 --- /dev/null +++ b/minigpt4/datasets/datasets/caption_datasets.py @@ -0,0 +1,85 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import os +from collections import OrderedDict + +from minigpt4.datasets.datasets.base_dataset import BaseDataset +from PIL import Image + + +class __DisplMixin: + def displ_item(self, index): + sample, ann = self.__getitem__(index), self.annotation[index] + + return OrderedDict( + { + "file": ann["image"], + "caption": ann["caption"], + "image": sample["image"], + } + ) + + +class CaptionDataset(BaseDataset, __DisplMixin): + def __init__(self, vis_processor, text_processor, vis_root, ann_paths): + """ + vis_root (string): Root directory of images (e.g. coco/images/) + ann_root (string): directory to store the annotation file + """ + super().__init__(vis_processor, text_processor, vis_root, ann_paths) + + self.img_ids = {} + n = 0 + for ann in self.annotation: + img_id = ann["image_id"] + if img_id not in self.img_ids.keys(): + self.img_ids[img_id] = n + n += 1 + + def __getitem__(self, index): + + # TODO this assumes image input, not general enough + ann = self.annotation[index] + + img_file = '{:0>12}.jpg'.format(ann["image_id"]) + image_path = os.path.join(self.vis_root, img_file) + image = Image.open(image_path).convert("RGB") + + image = self.vis_processor(image) + caption = self.text_processor(ann["caption"]) + + return { + "image": image, + "text_input": caption, + "image_id": self.img_ids[ann["image_id"]], + } + + +class CaptionEvalDataset(BaseDataset, __DisplMixin): + def __init__(self, vis_processor, text_processor, vis_root, ann_paths): + """ + vis_root (string): Root directory of images (e.g. coco/images/) + ann_root (string): directory to store the annotation file + split (string): val or test + """ + super().__init__(vis_processor, text_processor, vis_root, ann_paths) + + def __getitem__(self, index): + + ann = self.annotation[index] + + image_path = os.path.join(self.vis_root, ann["image"]) + image = Image.open(image_path).convert("RGB") + + image = self.vis_processor(image) + + return { + "image": image, + "image_id": ann["image_id"], + "instance_id": ann["instance_id"], + } diff --git a/minigpt4/datasets/datasets/cc_sbu_dataset.py b/minigpt4/datasets/datasets/cc_sbu_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..80b658d97ad47052653cecf25daeb512793bfc7b --- /dev/null +++ b/minigpt4/datasets/datasets/cc_sbu_dataset.py @@ -0,0 +1,47 @@ +import os +from PIL import Image +import webdataset as wds +from minigpt4.datasets.datasets.base_dataset import BaseDataset +from minigpt4.datasets.datasets.caption_datasets import CaptionDataset + + +class CCSBUDataset(BaseDataset): + def __init__(self, vis_processor, text_processor, location): + super().__init__(vis_processor=vis_processor, text_processor=text_processor) + + self.inner_dataset = wds.DataPipeline( + wds.ResampledShards(location), + wds.tarfile_to_samples(handler=wds.warn_and_continue), + wds.shuffle(1000, handler=wds.warn_and_continue), + wds.decode("pilrgb", handler=wds.warn_and_continue), + wds.to_tuple("jpg", "json", handler=wds.warn_and_continue), + wds.map_tuple(self.vis_processor, handler=wds.warn_and_continue), + wds.map(self.to_dict, handler=wds.warn_and_continue), + ) + + def to_dict(self, sample): + return { + "image": sample[0], + "answer": self.text_processor(sample[1]["caption"]), + } + + +class CCSBUAlignDataset(CaptionDataset): + + def __getitem__(self, index): + + # TODO this assumes image input, not general enough + ann = self.annotation[index] + + img_file = '{}.jpg'.format(ann["image_id"]) + image_path = os.path.join(self.vis_root, img_file) + image = Image.open(image_path).convert("RGB") + + image = self.vis_processor(image) + caption = ann["caption"] + + return { + "image": image, + "answer": caption, + "image_id": self.img_ids[ann["image_id"]], + } \ No newline at end of file diff --git a/minigpt4/datasets/datasets/dataloader_utils.py b/minigpt4/datasets/datasets/dataloader_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8eaa3a58b0ad42ca7937fb51b46e53511cc3cd0c --- /dev/null +++ b/minigpt4/datasets/datasets/dataloader_utils.py @@ -0,0 +1,162 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import time +import random +import torch +from minigpt4.datasets.data_utils import move_to_cuda +from torch.utils.data import DataLoader + + +class MultiIterLoader: + """ + A simple wrapper for iterating over multiple iterators. + + Args: + loaders (List[Loader]): List of Iterator loaders. + ratios (List[float]): List of ratios to sample from each loader. If None, all loaders are sampled uniformly. + """ + + def __init__(self, loaders, ratios=None): + # assert all loaders has __next__ method + for loader in loaders: + assert hasattr( + loader, "__next__" + ), "Loader {} has no __next__ method.".format(loader) + + if ratios is None: + ratios = [1.0] * len(loaders) + else: + assert len(ratios) == len(loaders) + ratios = [float(ratio) / sum(ratios) for ratio in ratios] + + self.loaders = loaders + self.ratios = ratios + + def __next__(self): + # random sample from each loader by ratio + loader_idx = random.choices(range(len(self.loaders)), self.ratios, k=1)[0] + return next(self.loaders[loader_idx]) + + +class PrefetchLoader(object): + """ + Modified from https://github.com/ChenRocks/UNITER. + + overlap compute and cuda data transfer + (copied and then modified from nvidia apex) + """ + + def __init__(self, loader): + self.loader = loader + self.stream = torch.cuda.Stream() + + def __iter__(self): + loader_it = iter(self.loader) + self.preload(loader_it) + batch = self.next(loader_it) + while batch is not None: + is_tuple = isinstance(batch, tuple) + if is_tuple: + task, batch = batch + + if is_tuple: + yield task, batch + else: + yield batch + batch = self.next(loader_it) + + def __len__(self): + return len(self.loader) + + def preload(self, it): + try: + self.batch = next(it) + except StopIteration: + self.batch = None + return + # if record_stream() doesn't work, another option is to make sure + # device inputs are created on the main stream. + # self.next_input_gpu = torch.empty_like(self.next_input, + # device='cuda') + # self.next_target_gpu = torch.empty_like(self.next_target, + # device='cuda') + # Need to make sure the memory allocated for next_* is not still in use + # by the main stream at the time we start copying to next_*: + # self.stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(self.stream): + self.batch = move_to_cuda(self.batch) + # more code for the alternative if record_stream() doesn't work: + # copy_ will record the use of the pinned source tensor in this + # side stream. + # self.next_input_gpu.copy_(self.next_input, non_blocking=True) + # self.next_target_gpu.copy_(self.next_target, non_blocking=True) + # self.next_input = self.next_input_gpu + # self.next_target = self.next_target_gpu + + def next(self, it): + torch.cuda.current_stream().wait_stream(self.stream) + batch = self.batch + if batch is not None: + record_cuda_stream(batch) + self.preload(it) + return batch + + def __getattr__(self, name): + method = self.loader.__getattribute__(name) + return method + + +def record_cuda_stream(batch): + if isinstance(batch, torch.Tensor): + batch.record_stream(torch.cuda.current_stream()) + elif isinstance(batch, list) or isinstance(batch, tuple): + for t in batch: + record_cuda_stream(t) + elif isinstance(batch, dict): + for t in batch.values(): + record_cuda_stream(t) + else: + pass + + +class IterLoader: + """ + A wrapper to convert DataLoader as an infinite iterator. + + Modified from: + https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/iter_based_runner.py + """ + + def __init__(self, dataloader: DataLoader, use_distributed: bool = False): + self._dataloader = dataloader + self.iter_loader = iter(self._dataloader) + self._use_distributed = use_distributed + self._epoch = 0 + + @property + def epoch(self) -> int: + return self._epoch + + def __next__(self): + try: + data = next(self.iter_loader) + except StopIteration: + self._epoch += 1 + if hasattr(self._dataloader.sampler, "set_epoch") and self._use_distributed: + self._dataloader.sampler.set_epoch(self._epoch) + time.sleep(2) # Prevent possible deadlock during epoch transition + self.iter_loader = iter(self._dataloader) + data = next(self.iter_loader) + + return data + + def __iter__(self): + return self + + def __len__(self): + return len(self._dataloader) diff --git a/minigpt4/datasets/datasets/laion_dataset.py b/minigpt4/datasets/datasets/laion_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..6f3ce873a44bcc675a8b5b50d2aff0b8c542ac26 --- /dev/null +++ b/minigpt4/datasets/datasets/laion_dataset.py @@ -0,0 +1,31 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import webdataset as wds +from minigpt4.datasets.datasets.base_dataset import BaseDataset + + +class LaionDataset(BaseDataset): + def __init__(self, vis_processor, text_processor, location): + super().__init__(vis_processor=vis_processor, text_processor=text_processor) + + self.inner_dataset = wds.DataPipeline( + wds.ResampledShards(location), + wds.tarfile_to_samples(handler=wds.warn_and_continue), + wds.shuffle(1000, handler=wds.warn_and_continue), + wds.decode("pilrgb", handler=wds.warn_and_continue), + wds.to_tuple("jpg", "json", handler=wds.warn_and_continue), + wds.map_tuple(self.vis_processor, handler=wds.warn_and_continue), + wds.map(self.to_dict, handler=wds.warn_and_continue), + ) + + def to_dict(self, sample): + return { + "image": sample[0], + "answer": self.text_processor(sample[1]["caption"]), + } + diff --git a/minigpt4/models/Qformer.py b/minigpt4/models/Qformer.py new file mode 100644 index 0000000000000000000000000000000000000000..e71b12375e10511858a9c505dc795181e6ce5603 --- /dev/null +++ b/minigpt4/models/Qformer.py @@ -0,0 +1,1216 @@ +""" + * Copyright (c) 2023, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li + * Based on huggingface code base + * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert +""" + +import math +import os +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple, Dict, Any + +import torch +from torch import Tensor, device, dtype, nn +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss +import torch.nn.functional as F + +from transformers.activations import ACT2FN +from transformers.file_utils import ( + ModelOutput, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.bert.configuration_bert import BertConfig + +logger = logging.get_logger(__name__) + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + + self.config = config + + def forward( + self, + input_ids=None, + position_ids=None, + query_embeds=None, + past_key_values_length=0, + ): + if input_ids is not None: + seq_length = input_ids.size()[1] + else: + seq_length = 0 + + if position_ids is None: + position_ids = self.position_ids[ + :, past_key_values_length : seq_length + past_key_values_length + ].clone() + + if input_ids is not None: + embeddings = self.word_embeddings(input_ids) + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings = embeddings + position_embeddings + + if query_embeds is not None: + embeddings = torch.cat((query_embeds, embeddings), dim=1) + else: + embeddings = query_embeds + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + mixed_query_layer = self.query(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if ( + self.config.add_cross_attention + and layer_num % self.config.cross_attention_freq == 0 + ): + self.crossattention = BertAttention( + config, is_cross_attention=self.config.add_cross_attention + ) + self.has_cross_attention = True + else: + self.has_cross_attention = False + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + self.intermediate_query = BertIntermediate(config) + self.output_query = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + query_length=0, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:-1] + + present_key_value = self_attention_outputs[-1] + + if query_length > 0: + query_attention_output = attention_output[:, :query_length, :] + + if self.has_cross_attention: + assert ( + encoder_hidden_states is not None + ), "encoder_hidden_states must be given for cross-attention layers" + cross_attention_outputs = self.crossattention( + query_attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + query_attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk_query, + self.chunk_size_feed_forward, + self.seq_len_dim, + query_attention_output, + ) + if attention_output.shape[1] > query_length: + layer_output_text = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output[:, query_length:, :], + ) + layer_output = torch.cat([layer_output, layer_output_text], dim=1) + else: + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + def feed_forward_chunk_query(self, attention_output): + intermediate_output = self.intermediate_query(attention_output) + layer_output = self.output_query(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [BertLayer(config, i) for i in range(config.num_hidden_layers)] + ) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + query_length=0, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if getattr(self.config, "gradient_checkpointing", False) and self.training: + + if use_cache: + logger.warn( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module( + *inputs, past_key_value, output_attentions, query_length + ) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + query_length, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class BertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = BertConfig + base_model_prefix = "bert" + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=False): + super().__init__(config) + self.config = config + + self.embeddings = BertEmbeddings(config) + + self.encoder = BertEncoder(config) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + self.init_weights() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + def get_extended_attention_mask( + self, + attention_mask: Tensor, + input_shape: Tuple[int], + device: device, + is_decoder: bool, + has_query: bool = False, + ) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = torch.arange(seq_length, device=device) + causal_mask = ( + seq_ids[None, None, :].repeat(batch_size, seq_length, 1) + <= seq_ids[None, :, None] + ) + + # add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.to(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] + if has_query: # UniLM style attention mask + causal_mask = torch.cat( + [ + torch.zeros( + (batch_size, prefix_seq_len, seq_length), + device=device, + dtype=causal_mask.dtype, + ), + causal_mask, + ], + axis=1, + ) + causal_mask = torch.cat( + [ + torch.ones( + (batch_size, causal_mask.shape[1], prefix_seq_len), + device=device, + dtype=causal_mask.dtype, + ), + causal_mask, + ], + axis=-1, + ) + extended_attention_mask = ( + causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + ) + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to( + dtype=self.dtype + ) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + query_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + is_decoder=False, + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + # use_cache = use_cache if use_cache is not None else self.config.use_cache + + if input_ids is None: + assert ( + query_embeds is not None + ), "You have to specify query_embeds when input_ids is None" + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] - self.config.query_length + if past_key_values is not None + else 0 + ) + + query_length = query_embeds.shape[1] if query_embeds is not None else 0 + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + query_embeds=query_embeds, + past_key_values_length=past_key_values_length, + ) + + input_shape = embedding_output.size()[:-1] + batch_size, seq_length = input_shape + device = embedding_output.device + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if is_decoder: + extended_attention_mask = self.get_extended_attention_mask( + attention_mask, + input_ids.shape, + device, + is_decoder, + has_query=(query_embeds is not None), + ) + else: + extended_attention_mask = self.get_extended_attention_mask( + attention_mask, input_shape, device, is_decoder + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[ + 0 + ].size() + else: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [ + self.invert_attention_mask(mask) for mask in encoder_attention_mask + ] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + query_length=query_length, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class BertLMHeadModel(BertPreTrainedModel): + + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.bert = BertModel(config, add_pooling_layer=False) + self.cls = BertOnlyMLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + query_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + past_key_values=None, + use_cache=True, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + return_logits=False, + is_decoder=True, + reduction="mean", + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are + ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + Returns: + Example:: + >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig + >>> import torch + >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') + >>> config = BertConfig.from_pretrained("bert-base-cased") + >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + >>> prediction_logits = outputs.logits + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + if labels is not None: + use_cache = False + if past_key_values is not None: + query_embeds = None + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + query_embeds=query_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + is_decoder=is_decoder, + ) + + sequence_output = outputs[0] + if query_embeds is not None: + sequence_output = outputs[0][:, query_embeds.shape[1] :, :] + + prediction_scores = self.cls(sequence_output) + + if return_logits: + return prediction_scores[:, :-1, :].contiguous() + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) + lm_loss = loss_fct( + shifted_prediction_scores.view(-1, self.config.vocab_size), + labels.view(-1), + ) + if reduction == "none": + lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, query_embeds, past=None, attention_mask=None, **model_kwargs + ): + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_ids.shape) + query_mask = input_ids.new_ones(query_embeds.shape[:-1]) + attention_mask = torch.cat([query_mask, attention_mask], dim=-1) + + # cut decoder_input_ids if past is used + if past is not None: + input_ids = input_ids[:, -1:] + + return { + "input_ids": input_ids, + "query_embeds": query_embeds, + "attention_mask": attention_mask, + "past_key_values": past, + "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), + "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), + "is_decoder": True, + } + + def _reorder_cache(self, past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += ( + tuple( + past_state.index_select(0, beam_idx) for past_state in layer_past + ), + ) + return reordered_past + + +class BertForMaskedLM(BertPreTrainedModel): + + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.bert = BertModel(config, add_pooling_layer=False) + self.cls = BertOnlyMLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + query_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + return_logits=False, + is_decoder=False, + ): + r""" + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ..., + config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored + (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]`` + """ + + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + query_embeds=query_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + is_decoder=is_decoder, + ) + + if query_embeds is not None: + sequence_output = outputs[0][:, query_embeds.shape[1] :, :] + prediction_scores = self.cls(sequence_output) + + if return_logits: + return prediction_scores + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/minigpt4/models/__init__.py b/minigpt4/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc01b56181aa81554efbe9df10ab3678a1c7bb86 --- /dev/null +++ b/minigpt4/models/__init__.py @@ -0,0 +1,202 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import logging +import torch +from omegaconf import OmegaConf + +from minigpt4.common.registry import registry +from minigpt4.models.base_model import BaseModel +from minigpt4.models.minigpt_base import MiniGPTBase +from minigpt4.models.minigpt4 import MiniGPT4 +from minigpt4.models.minigpt_v2 import MiniGPTv2 +from minigpt4.processors.base_processor import BaseProcessor + + +__all__ = [ + "load_model", + "BaseModel", + "MiniGPTBase", + "MiniGPT4", + "MiniGPTv2" +] + + +def load_model(name, model_type, is_eval=False, device="cpu", checkpoint=None): + """ + Load supported models. + + To list all available models and types in registry: + >>> from minigpt4.models import model_zoo + >>> print(model_zoo) + + Args: + name (str): name of the model. + model_type (str): type of the model. + is_eval (bool): whether the model is in eval mode. Default: False. + device (str): device to use. Default: "cpu". + checkpoint (str): path or to checkpoint. Default: None. + Note that expecting the checkpoint to have the same keys in state_dict as the model. + + Returns: + model (torch.nn.Module): model. + """ + + model = registry.get_model_class(name).from_pretrained(model_type=model_type) + + if checkpoint is not None: + model.load_checkpoint(checkpoint) + + if is_eval: + model.eval() + + if device == "cpu": + model = model.float() + + return model.to(device) + + +def load_preprocess(config): + """ + Load preprocessor configs and construct preprocessors. + + If no preprocessor is specified, return BaseProcessor, which does not do any preprocessing. + + Args: + config (dict): preprocessor configs. + + Returns: + vis_processors (dict): preprocessors for visual inputs. + txt_processors (dict): preprocessors for text inputs. + + Key is "train" or "eval" for processors used in training and evaluation respectively. + """ + + def _build_proc_from_cfg(cfg): + return ( + registry.get_processor_class(cfg.name).from_config(cfg) + if cfg is not None + else BaseProcessor() + ) + + vis_processors = dict() + txt_processors = dict() + + vis_proc_cfg = config.get("vis_processor") + txt_proc_cfg = config.get("text_processor") + + if vis_proc_cfg is not None: + vis_train_cfg = vis_proc_cfg.get("train") + vis_eval_cfg = vis_proc_cfg.get("eval") + else: + vis_train_cfg = None + vis_eval_cfg = None + + vis_processors["train"] = _build_proc_from_cfg(vis_train_cfg) + vis_processors["eval"] = _build_proc_from_cfg(vis_eval_cfg) + + if txt_proc_cfg is not None: + txt_train_cfg = txt_proc_cfg.get("train") + txt_eval_cfg = txt_proc_cfg.get("eval") + else: + txt_train_cfg = None + txt_eval_cfg = None + + txt_processors["train"] = _build_proc_from_cfg(txt_train_cfg) + txt_processors["eval"] = _build_proc_from_cfg(txt_eval_cfg) + + return vis_processors, txt_processors + + +def load_model_and_preprocess(name, model_type, is_eval=False, device="cpu"): + """ + Load model and its related preprocessors. + + List all available models and types in registry: + >>> from minigpt4.models import model_zoo + >>> print(model_zoo) + + Args: + name (str): name of the model. + model_type (str): type of the model. + is_eval (bool): whether the model is in eval mode. Default: False. + device (str): device to use. Default: "cpu". + + Returns: + model (torch.nn.Module): model. + vis_processors (dict): preprocessors for visual inputs. + txt_processors (dict): preprocessors for text inputs. + """ + model_cls = registry.get_model_class(name) + + # load model + model = model_cls.from_pretrained(model_type=model_type) + + if is_eval: + model.eval() + + # load preprocess + cfg = OmegaConf.load(model_cls.default_config_path(model_type)) + if cfg is not None: + preprocess_cfg = cfg.preprocess + + vis_processors, txt_processors = load_preprocess(preprocess_cfg) + else: + vis_processors, txt_processors = None, None + logging.info( + f"""No default preprocess for model {name} ({model_type}). + This can happen if the model is not finetuned on downstream datasets, + or it is not intended for direct use without finetuning. + """ + ) + + if device == "cpu" or device == torch.device("cpu"): + model = model.float() + + return model.to(device), vis_processors, txt_processors + + +class ModelZoo: + """ + A utility class to create string representation of available model architectures and types. + + >>> from minigpt4.models import model_zoo + >>> # list all available models + >>> print(model_zoo) + >>> # show total number of models + >>> print(len(model_zoo)) + """ + + def __init__(self) -> None: + self.model_zoo = { + k: list(v.PRETRAINED_MODEL_CONFIG_DICT.keys()) + for k, v in registry.mapping["model_name_mapping"].items() + } + + def __str__(self) -> str: + return ( + "=" * 50 + + "\n" + + f"{'Architectures':<30} {'Types'}\n" + + "=" * 50 + + "\n" + + "\n".join( + [ + f"{name:<30} {', '.join(types)}" + for name, types in self.model_zoo.items() + ] + ) + ) + + def __iter__(self): + return iter(self.model_zoo.items()) + + def __len__(self): + return sum([len(v) for v in self.model_zoo.values()]) + + +model_zoo = ModelZoo() diff --git a/minigpt4/models/base_model.py b/minigpt4/models/base_model.py new file mode 100644 index 0000000000000000000000000000000000000000..fd1d63679ccd0717c39dd96727dca9a85b549c57 --- /dev/null +++ b/minigpt4/models/base_model.py @@ -0,0 +1,248 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import os +import logging +import contextlib + +from omegaconf import OmegaConf +import numpy as np +import torch +import torch.nn as nn +from transformers import BertTokenizer, LlamaTokenizer +from transformers.models.llama.modeling_llama import LlamaForCausalLM +from peft import ( + LoraConfig, + get_peft_model, + prepare_model_for_int8_training, +) + +from minigpt4.common.dist_utils import download_cached_file, is_dist_avail_and_initialized +from minigpt4.common.utils import get_abs_path, is_url +from minigpt4.models.eva_vit import create_eva_vit_g + + + +class BaseModel(nn.Module): + """Base class for models.""" + + def __init__(self): + super().__init__() + + @property + def device(self): + return list(self.parameters())[-1].device + + def load_checkpoint(self, url_or_filename): + """ + Load from a finetuned checkpoint. + + This should expect no mismatch in the model keys and the checkpoint keys. + """ + + if is_url(url_or_filename): + cached_file = download_cached_file( + url_or_filename, check_hash=False, progress=True + ) + checkpoint = torch.load(cached_file, map_location="cpu") + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location="cpu") + else: + raise RuntimeError("checkpoint url or path is invalid") + + if "model" in checkpoint.keys(): + state_dict = checkpoint["model"] + else: + state_dict = checkpoint + + msg = self.load_state_dict(state_dict, strict=False) + + logging.info("Missing keys {}".format(msg.missing_keys)) + logging.info("load checkpoint from %s" % url_or_filename) + + return msg + + @classmethod + def from_pretrained(cls, model_type): + """ + Build a pretrained model from default configuration file, specified by model_type. + + Args: + - model_type (str): model type, specifying architecture and checkpoints. + + Returns: + - model (nn.Module): pretrained or finetuned model, depending on the configuration. + """ + model_cfg = OmegaConf.load(cls.default_config_path(model_type)).model + model = cls.from_config(model_cfg) + + return model + + @classmethod + def default_config_path(cls, model_type): + assert ( + model_type in cls.PRETRAINED_MODEL_CONFIG_DICT + ), "Unknown model type {}".format(model_type) + return get_abs_path(cls.PRETRAINED_MODEL_CONFIG_DICT[model_type]) + + def load_checkpoint_from_config(self, cfg, **kwargs): + """ + Load checkpoint as specified in the config file. + + If load_finetuned is True, load the finetuned model; otherwise, load the pretrained model. + When loading the pretrained model, each task-specific architecture may define their + own load_from_pretrained() method. + """ + load_finetuned = cfg.get("load_finetuned", True) + if load_finetuned: + finetune_path = cfg.get("finetuned", None) + assert ( + finetune_path is not None + ), "Found load_finetuned is True, but finetune_path is None." + self.load_checkpoint(url_or_filename=finetune_path) + else: + # load pre-trained weights + pretrain_path = cfg.get("pretrained", None) + assert "Found load_finetuned is False, but pretrain_path is None." + self.load_from_pretrained(url_or_filename=pretrain_path, **kwargs) + + def before_evaluation(self, **kwargs): + pass + + def show_n_params(self, return_str=True): + tot = 0 + for p in self.parameters(): + w = 1 + for x in p.shape: + w *= x + tot += w + if return_str: + if tot >= 1e6: + return "{:.1f}M".format(tot / 1e6) + else: + return "{:.1f}K".format(tot / 1e3) + else: + return tot + + def maybe_autocast(self, dtype=torch.float16): + # if on cpu, don't use autocast + # if on gpu, use autocast with dtype if provided, otherwise use torch.float16 + enable_autocast = self.device != torch.device("cpu") + + if enable_autocast: + return torch.cuda.amp.autocast(dtype=dtype) + else: + return contextlib.nullcontext() + + @classmethod + def init_vision_encoder( + cls, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision, freeze + ): + logging.info('Loading VIT') + + assert model_name == "eva_clip_g", "vit model must be eva_clip_g for current version of MiniGPT-4" + if not freeze: + precision = "fp32" # fp16 is not for training + + visual_encoder = create_eva_vit_g( + img_size, drop_path_rate, use_grad_checkpoint, precision + ) + + ln_vision = LayerNorm(visual_encoder.num_features) + + if freeze: + for name, param in visual_encoder.named_parameters(): + param.requires_grad = False + visual_encoder = visual_encoder.eval() + visual_encoder.train = disabled_train + for name, param in ln_vision.named_parameters(): + param.requires_grad = False + ln_vision = ln_vision.eval() + ln_vision.train = disabled_train + logging.info("freeze vision encoder") + + logging.info('Loading VIT Done') + return visual_encoder, ln_vision + + def init_llm(cls, llama_model_path, low_resource=False, low_res_device=0, lora_r=0, + lora_target_modules=["q_proj","v_proj"], **lora_kargs): + logging.info('Loading LLAMA') + llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model_path, use_fast=False) + llama_tokenizer.pad_token = "$$" + + if low_resource: + llama_model = LlamaForCausalLM.from_pretrained( + llama_model_path, + torch_dtype=torch.float16, + load_in_8bit=True, + device_map={'': low_res_device} + ) + else: + llama_model = LlamaForCausalLM.from_pretrained( + llama_model_path, + torch_dtype=torch.float16, + ) + + if lora_r > 0: + llama_model = prepare_model_for_int8_training(llama_model) + loraconfig = LoraConfig( + r=lora_r, + bias="none", + task_type="CAUSAL_LM", + target_modules=lora_target_modules, + **lora_kargs + ) + llama_model = get_peft_model(llama_model, loraconfig) + + llama_model.print_trainable_parameters() + + else: + for name, param in llama_model.named_parameters(): + param.requires_grad = False + logging.info('Loading LLAMA Done') + return llama_model, llama_tokenizer + + + def load_from_pretrained(self, url_or_filename): + if is_url(url_or_filename): + cached_file = download_cached_file( + url_or_filename, check_hash=False, progress=True + ) + checkpoint = torch.load(cached_file, map_location="cpu") + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location="cpu") + else: + raise RuntimeError("checkpoint url or path is invalid") + + state_dict = checkpoint["model"] + + msg = self.load_state_dict(state_dict, strict=False) + + # logging.info("Missing keys {}".format(msg.missing_keys)) + logging.info("load checkpoint from %s" % url_or_filename) + + return msg + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + + + + diff --git a/minigpt4/models/eva_vit.py b/minigpt4/models/eva_vit.py new file mode 100644 index 0000000000000000000000000000000000000000..7fcc63a74049f1faf65c99943ef94f72383ca3f5 --- /dev/null +++ b/minigpt4/models/eva_vit.py @@ -0,0 +1,442 @@ +# Based on EVA, BEIT, timm and DeiT code bases +# https://github.com/baaivision/EVA +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/microsoft/unilm/tree/master/beit +# https://github.com/facebookresearch/deit/ +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +import math +from functools import partial + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from timm.models.layers import drop_path, to_2tuple, trunc_normal_ +from timm.models.registry import register_model + +from minigpt4.common.dist_utils import download_cached_file + +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'crop_pct': .9, 'interpolation': 'bicubic', + 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5), + **kwargs + } + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return 'p={}'.format(self.drop_prob) + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., + proj_drop=0., window_size=None, attn_head_dim=None): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + if window_size: + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + else: + self.window_size = None + self.relative_position_bias_table = None + self.relative_position_index = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, rel_pos_bias=None): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + if self.relative_position_bias_table is not None: + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if rel_pos_bias is not None: + attn = attn + rel_pos_bias + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, + window_size=None, attn_head_dim=None): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if init_values is not None and init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + def forward(self, x, rel_pos_bias=None): + if self.gamma_1 is None: + x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x, **kwargs): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class RelativePositionBias(nn.Module): + + def __init__(self, window_size, num_heads): + super().__init__() + self.window_size = window_size + self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 + self.relative_position_bias_table = nn.Parameter( + torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH + # cls to token & token 2 cls & cls to cls + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(window_size[0]) + coords_w = torch.arange(window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * window_size[1] - 1 + relative_position_index = \ + torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype) + relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + relative_position_index[0, 0:] = self.num_relative_distance - 3 + relative_position_index[0:, 0] = self.num_relative_distance - 2 + relative_position_index[0, 0] = self.num_relative_distance - 1 + + self.register_buffer("relative_position_index", relative_position_index) + + # trunc_normal_(self.relative_position_bias_table, std=.02) + + def forward(self): + relative_position_bias = \ + self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1] + 1, + self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH + return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + + +class VisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, + use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, + use_mean_pooling=True, init_scale=0.001, use_checkpoint=False): + super().__init__() + self.image_size = img_size + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + if use_abs_pos_emb: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + else: + self.pos_embed = None + self.pos_drop = nn.Dropout(p=drop_rate) + + if use_shared_rel_pos_bias: + self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads) + else: + self.rel_pos_bias = None + self.use_checkpoint = use_checkpoint + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.use_rel_pos_bias = use_rel_pos_bias + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None) + for i in range(depth)]) +# self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) +# self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None +# self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + # trunc_normal_(self.mask_token, std=.02) +# if isinstance(self.head, nn.Linear): +# trunc_normal_(self.head.weight, std=.02) + self.apply(self._init_weights) + self.fix_init_weight() +# if isinstance(self.head, nn.Linear): +# self.head.weight.data.mul_(init_scale) +# self.head.bias.data.mul_(init_scale) + + def fix_init_weight(self): + def rescale(param, layer_id): + param.div_(math.sqrt(2.0 * layer_id)) + + for layer_id, layer in enumerate(self.blocks): + rescale(layer.attn.proj.weight.data, layer_id + 1) + rescale(layer.mlp.fc2.weight.data, layer_id + 1) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + x = self.patch_embed(x) + batch_size, seq_len, _ = x.size() + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + if self.pos_embed is not None: + x = x + self.pos_embed + x = self.pos_drop(x) + + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, rel_pos_bias) + else: + x = blk(x, rel_pos_bias) + return x +# x = self.norm(x) + +# if self.fc_norm is not None: +# t = x[:, 1:, :] +# return self.fc_norm(t.mean(1)) +# else: +# return x[:, 0] + + def forward(self, x): + x = self.forward_features(x) +# x = self.head(x) + return x + + def get_intermediate_layers(self, x): + x = self.patch_embed(x) + batch_size, seq_len, _ = x.size() + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + if self.pos_embed is not None: + x = x + self.pos_embed + x = self.pos_drop(x) + + features = [] + rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None + for blk in self.blocks: + x = blk(x, rel_pos_bias) + features.append(x) + + return features + + +def interpolate_pos_embed(model, checkpoint_model): + if 'pos_embed' in checkpoint_model: + pos_embed_checkpoint = checkpoint_model['pos_embed'].float() + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.patch_embed.num_patches + num_extra_tokens = model.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + checkpoint_model['pos_embed'] = new_pos_embed + + +def convert_weights_to_fp16(model: nn.Module): + """Convert applicable model parameters to fp16""" + + def _convert_weights_to_fp16(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.half() + if l.bias is not None: + l.bias.data = l.bias.data.half() + +# if isinstance(l, (nn.MultiheadAttention, Attention)): +# for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: +# tensor = getattr(l, attr) +# if tensor is not None: +# tensor.data = tensor.data.half() + + model.apply(_convert_weights_to_fp16) + + +def create_eva_vit_g(img_size=224,drop_path_rate=0.4,use_checkpoint=False,precision="fp16"): + model = VisionTransformer( + img_size=img_size, + patch_size=14, + use_mean_pooling=False, + embed_dim=1408, + depth=39, + num_heads=1408//88, + mlp_ratio=4.3637, + qkv_bias=True, + drop_path_rate=drop_path_rate, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + use_checkpoint=use_checkpoint, + ) + url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth" + cached_file = download_cached_file( + url, check_hash=False, progress=True + ) + state_dict = torch.load(cached_file, map_location="cpu") + interpolate_pos_embed(model,state_dict) + + incompatible_keys = model.load_state_dict(state_dict, strict=False) +# print(incompatible_keys) + + if precision == "fp16": +# model.to("cuda") + convert_weights_to_fp16(model) + return model \ No newline at end of file diff --git a/minigpt4/models/minigpt4.py b/minigpt4/models/minigpt4.py new file mode 100644 index 0000000000000000000000000000000000000000..a2e4798bb9713467b0ddac2dcec3cb1681c6418d --- /dev/null +++ b/minigpt4/models/minigpt4.py @@ -0,0 +1,195 @@ +import logging +import random + +import torch +from torch.cuda.amp import autocast as autocast +import torch.nn as nn + +from minigpt4.common.registry import registry +from minigpt4.models.base_model import disabled_train +from minigpt4.models.minigpt_base import MiniGPTBase +from minigpt4.models.Qformer import BertConfig, BertLMHeadModel + + +@registry.register_model("minigpt4") +class MiniGPT4(MiniGPTBase): + """ + MiniGPT-4 model + """ + + PRETRAINED_MODEL_CONFIG_DICT = { + "pretrain_vicuna0": "configs/models/minigpt4_vicuna0.yaml", + "pretrain_llama2": "configs/models/minigpt4_llama2.yaml", + } + + def __init__( + self, + vit_model="eva_clip_g", + q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth", + img_size=224, + drop_path_rate=0, + use_grad_checkpoint=False, + vit_precision="fp16", + freeze_vit=True, + has_qformer=True, + freeze_qformer=True, + num_query_token=32, + llama_model="", + prompt_path="", + prompt_template="", + max_txt_len=32, + end_sym='\n', + low_resource=False, # use 8 bit and put vit in cpu + device_8bit=0, # the device of 8bit model should be set when loading and cannot be changed anymore. + ): + super().__init__( + vit_model=vit_model, + img_size=img_size, + drop_path_rate=drop_path_rate, + use_grad_checkpoint=use_grad_checkpoint, + vit_precision=vit_precision, + freeze_vit=freeze_vit, + llama_model=llama_model, + max_txt_len=max_txt_len, + end_sym=end_sym, + low_resource=low_resource, + device_8bit=device_8bit, + ) + + self.has_qformer = has_qformer + if self.has_qformer: + print('Loading Q-Former') + self.Qformer, self.query_tokens = self.init_Qformer( + num_query_token, self.visual_encoder.num_features, freeze_qformer + ) + self.load_from_pretrained(url_or_filename=q_former_model) # load q-former weights here + + img_f_dim = self.Qformer.config.hidden_size + print('Loading Q-Former Done') + else: + img_f_dim = self.visual_encoder.num_features * 4 + print('Do not use Q-Former here.') + + self.llama_proj = nn.Linear( + img_f_dim, self.llama_model.config.hidden_size + ) + + if prompt_path: + with open(prompt_path, 'r') as f: + raw_prompts = f.read().splitlines() + filted_prompts = [raw_prompt for raw_prompt in raw_prompts if "" in raw_prompt] + self.prompt_list = [prompt_template.format(p) for p in filted_prompts] + print('Load {} training prompts'.format(len(self.prompt_list))) + print('Prompt Example \n{}'.format(random.choice(self.prompt_list))) + else: + self.prompt_list = [] + + @classmethod + def init_Qformer(cls, num_query_token, vision_width, freeze): + encoder_config = BertConfig.from_pretrained("bert-base-uncased") + encoder_config.encoder_width = vision_width + # insert cross-attention layer every other block + encoder_config.add_cross_attention = True + encoder_config.cross_attention_freq = 2 + encoder_config.query_length = num_query_token + Qformer = BertLMHeadModel(config=encoder_config) + query_tokens = nn.Parameter( + torch.zeros(1, num_query_token, encoder_config.hidden_size) + ) + query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range) + + Qformer.cls = None + Qformer.bert.embeddings.word_embeddings = None + Qformer.bert.embeddings.position_embeddings = None + for layer in Qformer.bert.encoder.layer: + layer.output = None + layer.intermediate = None + + if freeze: + for name, param in Qformer.named_parameters(): + param.requires_grad = False + Qformer = Qformer.eval() + Qformer.train = disabled_train + query_tokens.requires_grad = False + logging.info("freeze Qformer") + + return Qformer, query_tokens + + def encode_img(self, image): + device = image.device + + if len(image.shape) > 4: + image = image.reshape(-1, *image.shape[-3:]) + + with self.maybe_autocast(): + image_embeds = self.ln_vision(self.visual_encoder(image)).to(device) + if self.has_qformer: + image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_output = self.Qformer.bert( + query_embeds=query_tokens, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=True, + ) + + inputs_llama = self.llama_proj(query_output.last_hidden_state) + else: + image_embeds = image_embeds[:, 1:, :] + bs, pn, hs = image_embeds.shape + image_embeds = image_embeds.view(bs, int(pn / 4), int(hs * 4)) + + inputs_llama = self.llama_proj(image_embeds) + atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device) + return inputs_llama, atts_llama + + @classmethod + def from_config(cls, cfg): + vit_model = cfg.get("vit_model", "eva_clip_g") + q_former_model = cfg.get("q_former_model", "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth") + img_size = cfg.get("image_size") + num_query_token = cfg.get("num_query_token") + llama_model = cfg.get("llama_model") + + drop_path_rate = cfg.get("drop_path_rate", 0) + use_grad_checkpoint = cfg.get("use_grad_checkpoint", False) + vit_precision = cfg.get("vit_precision", "fp16") + freeze_vit = cfg.get("freeze_vit", True) + has_qformer = cfg.get("has_qformer", True) + freeze_qformer = cfg.get("freeze_qformer", True) + low_resource = cfg.get("low_resource", False) + device_8bit = cfg.get("device_8bit", 0) + + prompt_path = cfg.get("prompt_path", "") + prompt_template = cfg.get("prompt_template", "") + max_txt_len = cfg.get("max_txt_len", 32) + end_sym = cfg.get("end_sym", '\n') + + model = cls( + vit_model=vit_model, + q_former_model=q_former_model, + img_size=img_size, + drop_path_rate=drop_path_rate, + use_grad_checkpoint=use_grad_checkpoint, + vit_precision=vit_precision, + freeze_vit=freeze_vit, + has_qformer=has_qformer, + freeze_qformer=freeze_qformer, + num_query_token=num_query_token, + llama_model=llama_model, + prompt_path=prompt_path, + prompt_template=prompt_template, + max_txt_len=max_txt_len, + end_sym=end_sym, + low_resource=low_resource, + device_8bit=device_8bit, + ) + + ckpt_path = cfg.get("ckpt", "") # load weights of MiniGPT-4 + if ckpt_path: + print("Load MiniGPT-4 Checkpoint: {}".format(ckpt_path)) + ckpt = torch.load(ckpt_path, map_location="cpu") + msg = model.load_state_dict(ckpt['model'], strict=False) + + return model diff --git a/minigpt4/models/minigpt_base.py b/minigpt4/models/minigpt_base.py new file mode 100644 index 0000000000000000000000000000000000000000..77c919a4e9a11e09ca1299bf4a2643e42a1440da --- /dev/null +++ b/minigpt4/models/minigpt_base.py @@ -0,0 +1,401 @@ +import logging +import random + +import torch +from torch.cuda.amp import autocast as autocast +import torch.nn as nn + +from minigpt4.common.registry import registry +from minigpt4.models.base_model import BaseModel + + + +class MiniGPTBase(BaseModel): + """ + Base class for MiniGPT-4 and MiniGPT-v2 + """ + + def __init__( + self, + vit_model="eva_clip_g", + img_size=224, + drop_path_rate=0, + use_grad_checkpoint=False, + vit_precision="fp16", + freeze_vit=True, + llama_model="", + max_txt_len=32, + max_context_len=3800, + prompt_template="", + end_sym='\n', + low_resource=False, # use 8 bit and put vit in cpu + device_8bit=0, # the device of 8bit model should be set when loading and cannot be changed anymore. + lora_r=0, # lora_r means lora is not used + lora_target_modules=["q_proj", "v_proj"], + lora_alpha=16, + lora_dropout=0.05, + ): + super().__init__() + + self.llama_model, self.llama_tokenizer = self.init_llm( + llama_model_path=llama_model, + low_resource=low_resource, + low_res_device=device_8bit, + lora_r=lora_r, + lora_target_modules=lora_target_modules, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + ) + + self.visual_encoder, self.ln_vision = self.init_vision_encoder( + vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision, freeze_vit + ) + + self.max_txt_len = max_txt_len + self.max_context_len = max_context_len + self.end_sym = end_sym + + self.prompt_template = prompt_template + self.prompt_list = [] + + def vit_to_cpu(self): + self.ln_vision.to("cpu") + self.ln_vision.float() + self.visual_encoder.to("cpu") + self.visual_encoder.float() + + def get_context_emb(self, prompt, img_list): + device = img_list[0].device + prompt_segs = prompt.split('') + assert len(prompt_segs) == len(img_list) + 1, "Unmatched numbers of image placeholders and images." + seg_tokens = [ + self.llama_tokenizer( + seg, return_tensors="pt", add_special_tokens=i==0).to(device).input_ids # only add bos to the first seg + for i, seg in enumerate(prompt_segs) + ] + seg_embs = [self.embed_tokens(seg_t) for seg_t in seg_tokens] + + mixed_embs = [emb for pair in zip(seg_embs[:-1], img_list) for emb in pair] + [seg_embs[-1]] + mixed_embs = torch.cat(mixed_embs, dim=1) + return mixed_embs + + def prompt_wrap(self, img_embeds, atts_img, prompts, lengths=None): + if prompts is None or len(prompts) == 0: + # prompts is not provided, just return the original image embedding + return img_embeds, atts_img + elif img_embeds is None: + # prompt is provided but there is no image embedding. return the prompt embedding in right padding + self.llama_tokenizer.padding_side = "right" + prompt_tokens = self.llama_tokenizer( + prompts, + return_tensors="pt", + padding="longest", + add_special_tokens=False + ).to(self.device) + prompt_embeds = self.embed_tokens(prompt_tokens.input_ids) + atts_prompt = prompt_tokens.attention_mask + return prompt_embeds, atts_prompt + else: + # return the multi-modal embedding in right padding + emb_lists = [] + if isinstance(prompts, str): + prompts = [prompts] * len(img_embeds) + + for idx, (each_img_embed, each_prompt) in enumerate(zip(img_embeds, prompts)): + pn = each_img_embed.shape[-2] + if lengths is not None: + each_img_embed = each_img_embed.reshape(-1, each_img_embed.shape[-1]) + each_img_embed = each_img_embed[:lengths[idx] * pn] + p_segs = each_prompt.split('') + interleave_emb = [] + for idx, seg in enumerate(p_segs[:-1]): + p_tokens = self.llama_tokenizer( + seg, return_tensors="pt", add_special_tokens=False).to(img_embeds.device) + p_embed = self.embed_tokens(p_tokens.input_ids) + interleave_emb.append(torch.cat([p_embed, each_img_embed[None][:, idx * pn:(idx + 1) * pn]], dim=1)) + wrapped_emb = torch.cat(interleave_emb, dim=1) + p_tokens = self.llama_tokenizer( + p_segs[-1], return_tensors="pt", add_special_tokens=False).to(img_embeds.device) + p_embed = self.embed_tokens(p_tokens.input_ids) + wrapped_emb = torch.cat([wrapped_emb, p_embed], dim=1) + emb_lists.append(wrapped_emb) + + emb_lens = [emb.shape[1] for emb in emb_lists] + pad_emb = self.embed_tokens(torch.tensor(self.llama_tokenizer.pad_token_id, device=img_embeds.device)) + + max_length = max(emb_lens) if max(emb_lens) < self.max_context_len else self.max_context_len + wrapped_embs = pad_emb.expand(len(emb_lens), max_length, -1).clone() + wrapped_atts = torch.zeros([len(emb_lens), max_length], dtype=torch.int, device=img_embeds.device) + + for i, emb in enumerate(emb_lists): + length = emb_lens[i] if emb_lens[i] < self.max_context_len else self.max_context_len + wrapped_embs[i, :length] = emb[:, :length] + wrapped_atts[i, :length] = 1 + return wrapped_embs, wrapped_atts + + def concat_emb_input_output(self, input_embs, input_atts, output_embs, output_atts): + """ + Concatenate the batched input embedding and batched output embedding together. + Both the input and the output embedding should be right padded. + """ + input_lens = [] + cat_embs = [] + cat_atts = [] + for i in range(input_embs.size(0)): + input_len = input_atts[i].sum() + input_lens.append(input_len) + cat_embs.append( + torch.cat([ + input_embs[i][:input_len], + output_embs[i], + input_embs[i][input_len:] + ]) + ) + cat_atts.append( + torch.cat([ + input_atts[i][:input_len], + output_atts[i], + input_atts[i][input_len:] + ]) + ) + cat_embs = torch.stack(cat_embs) + cat_atts = torch.stack(cat_atts) + return cat_embs, cat_atts, input_lens + + def tokenize_conversation(self, conv_q, conv_a): + """concatenate conversation and make sure the model is only trained to regress the answer""" + + to_regress_token_ids_list = [] + targets_list = [] + + batch_size = len(conv_q) + for batch_idx in range(batch_size): + questions, answers = conv_q[batch_idx], conv_a[batch_idx] + questions = [self.llama_tokenizer(q, + return_tensors="pt", + add_special_tokens=False).to(self.device) for q in questions[1:]] # the first question is handled in the prompt wrap function, skip it + answers = [self.llama_tokenizer(q, + return_tensors="pt", + add_special_tokens=False).to(self.device) for q in answers] + cur_id = [] + cur_target = [] + for i in range(len(questions)): + cur_id.append(answers[i].input_ids) + cur_target.append(answers[i].input_ids) + cur_id.append(questions[i].input_ids) + cur_target.append(torch.ones_like(questions[i].input_ids) * -100) + + cur_id.append(answers[-1].input_ids) + cur_target.append(answers[-1].input_ids) + + cur_id = torch.cat(cur_id, dim=1) + cur_target = torch.cat(cur_target, dim=1) + to_regress_token_ids_list.append(cur_id) + targets_list.append(cur_target) + + max_len = min(max([target.shape[1] for target in targets_list]), self.max_txt_len) + to_regress_token_ids = torch.ones([batch_size, max_len], + dtype=cur_id.dtype, device=self.device) * self.llama_tokenizer.pad_token_id + targets = torch.ones([batch_size, max_len], + dtype=cur_id.dtype, device=self.device) * -100 + for batch_idx in range(batch_size): + cur_len = to_regress_token_ids_list[batch_idx].shape[1] + to_regress_token_ids[batch_idx, :cur_len] = to_regress_token_ids_list[batch_idx][0, :max_len] + targets[batch_idx, :cur_len] = targets_list[batch_idx][0, :max_len] + + to_regress_token_attn = (to_regress_token_ids != self.llama_tokenizer.pad_token_id).to(torch.int) + + return to_regress_token_ids, to_regress_token_attn, targets + + def preparing_embedding(self, samples): + ### prepare input tokens + if 'image' in samples: + img_embeds, img_atts = self.encode_img(samples["image"]) + else: + img_embeds = img_atts = None + + if 'conv_q' in samples: + # handeling conversation datasets + conv_q, conv_a = samples['conv_q'], samples['conv_a'] + + connect_sym = samples['connect_sym'][0] + conv_q = [q.split(connect_sym)for q in conv_q] + conv_a = [a.split(connect_sym) for a in conv_a] + + conv_q = [[self.prompt_template.format(item) for item in items] for items in conv_q] + + cond_embeds, cond_atts = self.prompt_wrap(img_embeds, img_atts, [q[0] for q in conv_q]) + regress_token_ids, regress_atts, part_targets = self.tokenize_conversation(conv_q, conv_a) + + else: + if "instruction_input" in samples: + instruction = samples["instruction_input"] + elif self.prompt_list: + instruction = random.choice(self.prompt_list) + else: + instruction = None + + if self.chat_template: + instruction = [self.prompt_template.format(instruct) for instruct in instruction] + + if 'length' in samples: + # the input is a image train (like videos) + bsz, pn, hs = img_embeds.shape + img_embeds = img_embeds.reshape(len(samples['image']), -1, pn, hs) + cond_embeds, cond_atts = self.prompt_wrap(img_embeds, img_atts, instruction, samples['length']) + else: + cond_embeds, cond_atts = self.prompt_wrap(img_embeds, img_atts, instruction) + + ### prepare target tokens + self.llama_tokenizer.padding_side = "right" + text = [t + self.end_sym for t in samples["answer"]] + + regress_tokens = self.llama_tokenizer( + text, + return_tensors="pt", + padding="longest", + truncation=True, + max_length=self.max_txt_len, + add_special_tokens=False + ).to(self.device) + + regress_token_ids = regress_tokens.input_ids + regress_atts = regress_tokens.attention_mask + part_targets = regress_token_ids.masked_fill( + regress_token_ids == self.llama_tokenizer.pad_token_id, -100 + ) + + regress_embeds = self.embed_tokens(regress_token_ids) + + return cond_embeds, cond_atts, regress_embeds, regress_atts, part_targets + + def forward(self, samples, reduction='mean'): + # prepare the embedding to condition and the embedding to regress + cond_embeds, cond_atts, regress_embeds, regress_atts, part_targets = \ + self.preparing_embedding(samples) + + # concat the embedding to condition and the embedding to regress + inputs_embeds, attention_mask, input_lens = \ + self.concat_emb_input_output(cond_embeds, cond_atts, regress_embeds, regress_atts) + + # get bos token embedding + bos = torch.ones_like(part_targets[:, :1]) * self.llama_tokenizer.bos_token_id + bos_embeds = self.embed_tokens(bos) + bos_atts = cond_atts[:, :1] + + # add bos token at the begining + inputs_embeds = torch.cat([bos_embeds, inputs_embeds], dim=1) + attention_mask = torch.cat([bos_atts, attention_mask], dim=1) + + # ensemble the final targets + targets = torch.ones([inputs_embeds.shape[0], inputs_embeds.shape[1]], + dtype=torch.long).to(self.device).fill_(-100) + + for i, target in enumerate(part_targets): + targets[i, input_lens[i]+1:input_lens[i]+len(target)+1] = target # plus 1 for bos + + with self.maybe_autocast(): + outputs = self.llama_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + return_dict=True, + labels=targets, + reduction=reduction + ) + loss = outputs.loss + + return {"loss": loss} + + def embed_tokens(self, token_ids): + if hasattr(self.llama_model.base_model, 'model'): ## lora wrapped model + embeds = self.llama_model.base_model.model.model.embed_tokens(token_ids) + else: + embeds = self.llama_model.base_model.embed_tokens(token_ids) + return embeds + + + @torch.no_grad() + def generate( + self, + images, + texts, + num_beams=1, + max_new_tokens=20, + min_length=1, + top_p=0.9, + repetition_penalty=1, + length_penalty=1, + temperature=1, + do_sample=False, + stop_words_ids=[2], + ): + ''' + function for generate test use + ''' + + stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub( + stops=[torch.tensor([i]).to(self.device) for i in stop_words_ids])]) + + img_embeds, atts_img = self.encode_img(images.to(self.device)) + image_lists = [[image_emb[None]] for image_emb in img_embeds] + + batch_embs = [self.get_context_emb(text, img_list) for text, img_list in zip(texts, image_lists)] + + batch_size = len(batch_embs) + max_len = max([emb.shape[1] for emb in batch_embs]) + emb_dim = batch_embs[0].shape[2] + dtype = batch_embs[0].dtype + device = batch_embs[0].device + + embs = torch.zeros([batch_size, max_len, emb_dim], dtype=dtype, device=device) + attn_mask = torch.zeros([batch_size, max_len], dtype=torch.int, device=device) + for i, emb in enumerate(batch_embs): + emb_len = emb.shape[1] + embs[i, -emb_len:] = emb[0] + attn_mask[i, -emb_len:] = 1 + + with self.maybe_autocast(): + outputs = self.llama_model.generate( + inputs_embeds=embs, + attention_mask=attn_mask, + max_new_tokens=max_new_tokens, + num_beams=num_beams, + length_penalty=length_penalty, + temperature=temperature, + do_sample=do_sample, + min_length=min_length, + top_p=top_p, + repetition_penalty=repetition_penalty + # stopping_criteria=stopping_criteria, + ) + + answers = [] + for output_token in outputs: + if output_token[0] == 0: + output_token = output_token[1:] + output_texts = self.llama_tokenizer.decode(output_token, skip_special_tokens=True) + output_texts = output_texts.split('')[0] # remove the stop sign + output_texts = output_texts.replace("", "") + output_texts = output_texts.split(r'[/INST]')[-1].strip() + answers.append(output_texts) + + return answers + + @torch.no_grad() + def multi_select(self, images, texts, answers, num_cand=None): + all_losses = [] + for answer in answers: + choice_samples = { + 'image': images, + 'instruction_input': texts, + 'answer': answer + } + loss = self.forward(choice_samples, reduction='none')['loss'].reshape(-1, 1) + all_losses.append(loss) + torch.cuda.empty_cache() + all_losses = torch.cat(all_losses, dim=-1) + if num_cand is not None: + for i in range(all_losses.shape[0]): + all_losses[i, num_cand[i]:] = 9999 + output_class_ranks = torch.argsort(all_losses, dim=-1) + return output_class_ranks.tolist() \ No newline at end of file diff --git a/minigpt4/models/minigpt_v2.py b/minigpt4/models/minigpt_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..a046b0baff41db50477e35904af9bcad5baa619c --- /dev/null +++ b/minigpt4/models/minigpt_v2.py @@ -0,0 +1,139 @@ +import logging +import random + +import torch +from torch.cuda.amp import autocast as autocast +import torch.nn as nn + +from minigpt4.common.registry import registry +from minigpt4.models.base_model import disabled_train +from minigpt4.models.minigpt_base import MiniGPTBase +from minigpt4.models.Qformer import BertConfig, BertLMHeadModel + + +@registry.register_model("minigpt_v2") +class MiniGPTv2(MiniGPTBase): + """ + MiniGPT-v2 model + """ + + PRETRAINED_MODEL_CONFIG_DICT = { + "pretrain": "configs/models/minigpt_v2.yaml", + } + + def __init__( + self, + vit_model="eva_clip_g", + img_size=448, + drop_path_rate=0, + use_grad_checkpoint=False, + vit_precision="fp16", + freeze_vit=True, + llama_model="", + prompt_template='[INST] {} [/INST]', + max_txt_len=300, + end_sym='\n', + lora_r=64, + lora_target_modules=["q_proj", "v_proj"], + lora_alpha=16, + lora_dropout=0.05, + chat_template=False, + use_grad_checkpoint_llm=False, + max_context_len=3800, + low_resource=False, # use 8 bit and put vit in cpu + device_8bit=0, # the device of 8bit model should be set when loading and cannot be changed anymore. + ): + super().__init__( + vit_model=vit_model, + img_size=img_size, + drop_path_rate=drop_path_rate, + use_grad_checkpoint=use_grad_checkpoint, + vit_precision=vit_precision, + freeze_vit=freeze_vit, + llama_model=llama_model, + max_txt_len=max_txt_len, + max_context_len=max_context_len, + end_sym=end_sym, + prompt_template=prompt_template, + low_resource=low_resource, + device_8bit=device_8bit, + lora_r=lora_r, + lora_target_modules=lora_target_modules, + lora_alpha=lora_alpha, + lora_dropout=lora_dropout, + ) + + img_f_dim = self.visual_encoder.num_features * 4 + self.llama_proj = nn.Linear( + img_f_dim, self.llama_model.config.hidden_size + ) + self.chat_template = chat_template + + if use_grad_checkpoint_llm: + self.llama_model.gradient_checkpointing_enable() + + def encode_img(self, image): + device = image.device + + if len(image.shape) > 4: + image = image.reshape(-1, *image.shape[-3:]) + + with self.maybe_autocast(): + image_embeds = self.ln_vision(self.visual_encoder(image)).to(device) + image_embeds = image_embeds[:, 1:, :] + bs, pn, hs = image_embeds.shape + image_embeds = image_embeds.view(bs, int(pn / 4), int(hs * 4)) + + inputs_llama = self.llama_proj(image_embeds) + atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device) + return inputs_llama, atts_llama + + @classmethod + def from_config(cls, cfg): + vit_model = cfg.get("vit_model", "eva_clip_g") + img_size = cfg.get("image_size") + llama_model = cfg.get("llama_model") + + drop_path_rate = cfg.get("drop_path_rate", 0) + use_grad_checkpoint = cfg.get("use_grad_checkpoint", False) + vit_precision = cfg.get("vit_precision", "fp16") + freeze_vit = cfg.get("freeze_vit", True) + low_resource = cfg.get("low_resource", False) + + prompt_template = cfg.get("prompt_template", '[INST] {} [/INST]') + max_txt_len = cfg.get("max_txt_len", 300) + end_sym = cfg.get("end_sym", '\n') + + lora_r = cfg.get("lora_r", 64) + lora_alpha = cfg.get("lora_alpha", 16) + chat_template = cfg.get("chat_template", False) + + use_grad_checkpoint_llm = cfg.get("use_grad_checkpoint_llm", False) + max_context_len = cfg.get("max_context_len", 3800) + + model = cls( + vit_model=vit_model, + img_size=img_size, + drop_path_rate=drop_path_rate, + use_grad_checkpoint=use_grad_checkpoint, + vit_precision=vit_precision, + freeze_vit=freeze_vit, + llama_model=llama_model, + prompt_template=prompt_template, + max_txt_len=max_txt_len, + low_resource=low_resource, + end_sym=end_sym, + lora_r=lora_r, + lora_alpha=lora_alpha, + chat_template=chat_template, + use_grad_checkpoint_llm=use_grad_checkpoint_llm, + max_context_len=max_context_len, + ) + + ckpt_path = cfg.get("ckpt", "") # load weights of MiniGPT-4 + if ckpt_path: + print("Load Minigpt-4-LLM Checkpoint: {}".format(ckpt_path)) + ckpt = torch.load(ckpt_path, map_location="cpu") + msg = model.load_state_dict(ckpt['model'], strict=False) + + return model diff --git a/minigpt4/models/modeling_llama.py b/minigpt4/models/modeling_llama.py new file mode 100644 index 0000000000000000000000000000000000000000..6d2802050ff5fe65b7bbef22a4b648c4109c90bc --- /dev/null +++ b/minigpt4/models/modeling_llama.py @@ -0,0 +1,111 @@ +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +from torch.nn import CrossEntropyLoss + +from transformers.utils import add_start_docstrings_to_model_forward, replace_return_docstrings +from transformers.modeling_outputs import CausalLMOutputWithPast +from transformers.models.llama.modeling_llama import LLAMA_INPUTS_DOCSTRING, _CONFIG_FOR_DOC +from transformers.models.llama.modeling_llama import LlamaForCausalLM as LlamaForCausalLMOrig + + +class LlamaForCausalLM(LlamaForCausalLMOrig): + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + reduction: Optional[str] = "mean", + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + if self.config.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0) + logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(reduction=reduction) + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + if reduction == "none": + loss = loss.view(logits.size(0), -1).mean(1) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/minigpt4/processors/__init__.py b/minigpt4/processors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e560eaa15f3266dbc1ffbca70bdc791901737a60 --- /dev/null +++ b/minigpt4/processors/__init__.py @@ -0,0 +1,33 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from minigpt4.processors.base_processor import BaseProcessor +from minigpt4.processors.blip_processors import ( + Blip2ImageTrainProcessor, + Blip2ImageEvalProcessor, + BlipCaptionProcessor, +) + +from minigpt4.common.registry import registry + +__all__ = [ + "BaseProcessor", + "Blip2ImageTrainProcessor", + "Blip2ImageEvalProcessor", + "BlipCaptionProcessor", +] + + +def load_processor(name, cfg=None): + """ + Example + + >>> processor = load_processor("alpro_video_train", cfg=None) + """ + processor = registry.get_processor_class(name).from_config(cfg) + + return processor diff --git a/minigpt4/processors/base_processor.py b/minigpt4/processors/base_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..39b33cdf8fcd97cfd3e4a5fbece6593357af9d41 --- /dev/null +++ b/minigpt4/processors/base_processor.py @@ -0,0 +1,26 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from omegaconf import OmegaConf + + +class BaseProcessor: + def __init__(self): + self.transform = lambda x: x + return + + def __call__(self, item): + return self.transform(item) + + @classmethod + def from_config(cls, cfg=None): + return cls() + + def build(self, **kwargs): + cfg = OmegaConf.create(kwargs) + + return self.from_config(cfg) diff --git a/minigpt4/processors/blip_processors.py b/minigpt4/processors/blip_processors.py new file mode 100644 index 0000000000000000000000000000000000000000..fd26160ec96a8458cdac083d19c19695937a7a62 --- /dev/null +++ b/minigpt4/processors/blip_processors.py @@ -0,0 +1,141 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import re + +from minigpt4.common.registry import registry +from minigpt4.processors.base_processor import BaseProcessor +from minigpt4.processors.randaugment import RandomAugment +from omegaconf import OmegaConf +from torchvision import transforms +from torchvision.transforms.functional import InterpolationMode + + +class BlipImageBaseProcessor(BaseProcessor): + def __init__(self, mean=None, std=None): + if mean is None: + mean = (0.48145466, 0.4578275, 0.40821073) + if std is None: + std = (0.26862954, 0.26130258, 0.27577711) + + self.normalize = transforms.Normalize(mean, std) + + +@registry.register_processor("blip_caption") +class BlipCaptionProcessor(BaseProcessor): + def __init__(self, prompt="", max_words=50): + self.prompt = prompt + self.max_words = max_words + + def __call__(self, caption): + caption = self.prompt + self.pre_caption(caption) + + return caption + + @classmethod + def from_config(cls, cfg=None): + if cfg is None: + cfg = OmegaConf.create() + + prompt = cfg.get("prompt", "") + max_words = cfg.get("max_words", 50) + + return cls(prompt=prompt, max_words=max_words) + + def pre_caption(self, caption): + caption = re.sub( + r"([.!\"()*#:;~])", + " ", + caption.lower(), + ) + caption = re.sub( + r"\s{2,}", + " ", + caption, + ) + caption = caption.rstrip("\n") + caption = caption.strip(" ") + + # truncate caption + caption_words = caption.split(" ") + if len(caption_words) > self.max_words: + caption = " ".join(caption_words[: self.max_words]) + + return caption + + +@registry.register_processor("blip2_image_train") +class Blip2ImageTrainProcessor(BlipImageBaseProcessor): + def __init__(self, image_size=224, mean=None, std=None, min_scale=0.5, max_scale=1.0): + super().__init__(mean=mean, std=std) + + self.transform = transforms.Compose( + [ + transforms.RandomResizedCrop( + image_size, + scale=(min_scale, max_scale), + interpolation=InterpolationMode.BICUBIC, + ), + transforms.ToTensor(), + self.normalize, + ] + ) + + def __call__(self, item): + return self.transform(item) + + @classmethod + def from_config(cls, cfg=None): + if cfg is None: + cfg = OmegaConf.create() + + image_size = cfg.get("image_size", 224) + + mean = cfg.get("mean", None) + std = cfg.get("std", None) + + min_scale = cfg.get("min_scale", 0.5) + max_scale = cfg.get("max_scale", 1.0) + + return cls( + image_size=image_size, + mean=mean, + std=std, + min_scale=min_scale, + max_scale=max_scale, + ) + + +@registry.register_processor("blip2_image_eval") +class Blip2ImageEvalProcessor(BlipImageBaseProcessor): + def __init__(self, image_size=224, mean=None, std=None): + super().__init__(mean=mean, std=std) + + self.transform = transforms.Compose( + [ + transforms.Resize( + (image_size, image_size), interpolation=InterpolationMode.BICUBIC + ), + transforms.ToTensor(), + self.normalize, + ] + ) + + def __call__(self, item): + return self.transform(item) + + @classmethod + def from_config(cls, cfg=None): + if cfg is None: + cfg = OmegaConf.create() + + image_size = cfg.get("image_size", 224) + + mean = cfg.get("mean", None) + std = cfg.get("std", None) + + return cls(image_size=image_size, mean=mean, std=std) \ No newline at end of file diff --git a/minigpt4/processors/randaugment.py b/minigpt4/processors/randaugment.py new file mode 100644 index 0000000000000000000000000000000000000000..7034a49ad5fc63b97910790017432617ff4c6d7b --- /dev/null +++ b/minigpt4/processors/randaugment.py @@ -0,0 +1,398 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import cv2 +import numpy as np + +import torch + + +## aug functions +def identity_func(img): + return img + + +def autocontrast_func(img, cutoff=0): + """ + same output as PIL.ImageOps.autocontrast + """ + n_bins = 256 + + def tune_channel(ch): + n = ch.size + cut = cutoff * n // 100 + if cut == 0: + high, low = ch.max(), ch.min() + else: + hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins]) + low = np.argwhere(np.cumsum(hist) > cut) + low = 0 if low.shape[0] == 0 else low[0] + high = np.argwhere(np.cumsum(hist[::-1]) > cut) + high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0] + if high <= low: + table = np.arange(n_bins) + else: + scale = (n_bins - 1) / (high - low) + offset = -low * scale + table = np.arange(n_bins) * scale + offset + table[table < 0] = 0 + table[table > n_bins - 1] = n_bins - 1 + table = table.clip(0, 255).astype(np.uint8) + return table[ch] + + channels = [tune_channel(ch) for ch in cv2.split(img)] + out = cv2.merge(channels) + return out + + +def equalize_func(img): + """ + same output as PIL.ImageOps.equalize + PIL's implementation is different from cv2.equalize + """ + n_bins = 256 + + def tune_channel(ch): + hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins]) + non_zero_hist = hist[hist != 0].reshape(-1) + step = np.sum(non_zero_hist[:-1]) // (n_bins - 1) + if step == 0: + return ch + n = np.empty_like(hist) + n[0] = step // 2 + n[1:] = hist[:-1] + table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8) + return table[ch] + + channels = [tune_channel(ch) for ch in cv2.split(img)] + out = cv2.merge(channels) + return out + + +def rotate_func(img, degree, fill=(0, 0, 0)): + """ + like PIL, rotate by degree, not radians + """ + H, W = img.shape[0], img.shape[1] + center = W / 2, H / 2 + M = cv2.getRotationMatrix2D(center, degree, 1) + out = cv2.warpAffine(img, M, (W, H), borderValue=fill) + return out + + +def solarize_func(img, thresh=128): + """ + same output as PIL.ImageOps.posterize + """ + table = np.array([el if el < thresh else 255 - el for el in range(256)]) + table = table.clip(0, 255).astype(np.uint8) + out = table[img] + return out + + +def color_func(img, factor): + """ + same output as PIL.ImageEnhance.Color + """ + ## implementation according to PIL definition, quite slow + # degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis] + # out = blend(degenerate, img, factor) + # M = ( + # np.eye(3) * factor + # + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor) + # )[np.newaxis, np.newaxis, :] + M = np.float32( + [[0.886, -0.114, -0.114], [-0.587, 0.413, -0.587], [-0.299, -0.299, 0.701]] + ) * factor + np.float32([[0.114], [0.587], [0.299]]) + out = np.matmul(img, M).clip(0, 255).astype(np.uint8) + return out + + +def contrast_func(img, factor): + """ + same output as PIL.ImageEnhance.Contrast + """ + mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299])) + table = ( + np.array([(el - mean) * factor + mean for el in range(256)]) + .clip(0, 255) + .astype(np.uint8) + ) + out = table[img] + return out + + +def brightness_func(img, factor): + """ + same output as PIL.ImageEnhance.Contrast + """ + table = (np.arange(256, dtype=np.float32) * factor).clip(0, 255).astype(np.uint8) + out = table[img] + return out + + +def sharpness_func(img, factor): + """ + The differences the this result and PIL are all on the 4 boundaries, the center + areas are same + """ + kernel = np.ones((3, 3), dtype=np.float32) + kernel[1][1] = 5 + kernel /= 13 + degenerate = cv2.filter2D(img, -1, kernel) + if factor == 0.0: + out = degenerate + elif factor == 1.0: + out = img + else: + out = img.astype(np.float32) + degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :] + out[1:-1, 1:-1, :] = degenerate + factor * (out[1:-1, 1:-1, :] - degenerate) + out = out.astype(np.uint8) + return out + + +def shear_x_func(img, factor, fill=(0, 0, 0)): + H, W = img.shape[0], img.shape[1] + M = np.float32([[1, factor, 0], [0, 1, 0]]) + out = cv2.warpAffine( + img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR + ).astype(np.uint8) + return out + + +def translate_x_func(img, offset, fill=(0, 0, 0)): + """ + same output as PIL.Image.transform + """ + H, W = img.shape[0], img.shape[1] + M = np.float32([[1, 0, -offset], [0, 1, 0]]) + out = cv2.warpAffine( + img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR + ).astype(np.uint8) + return out + + +def translate_y_func(img, offset, fill=(0, 0, 0)): + """ + same output as PIL.Image.transform + """ + H, W = img.shape[0], img.shape[1] + M = np.float32([[1, 0, 0], [0, 1, -offset]]) + out = cv2.warpAffine( + img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR + ).astype(np.uint8) + return out + + +def posterize_func(img, bits): + """ + same output as PIL.ImageOps.posterize + """ + out = np.bitwise_and(img, np.uint8(255 << (8 - bits))) + return out + + +def shear_y_func(img, factor, fill=(0, 0, 0)): + H, W = img.shape[0], img.shape[1] + M = np.float32([[1, 0, 0], [factor, 1, 0]]) + out = cv2.warpAffine( + img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR + ).astype(np.uint8) + return out + + +def cutout_func(img, pad_size, replace=(0, 0, 0)): + replace = np.array(replace, dtype=np.uint8) + H, W = img.shape[0], img.shape[1] + rh, rw = np.random.random(2) + pad_size = pad_size // 2 + ch, cw = int(rh * H), int(rw * W) + x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H) + y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W) + out = img.copy() + out[x1:x2, y1:y2, :] = replace + return out + + +### level to args +def enhance_level_to_args(MAX_LEVEL): + def level_to_args(level): + return ((level / MAX_LEVEL) * 1.8 + 0.1,) + + return level_to_args + + +def shear_level_to_args(MAX_LEVEL, replace_value): + def level_to_args(level): + level = (level / MAX_LEVEL) * 0.3 + if np.random.random() > 0.5: + level = -level + return (level, replace_value) + + return level_to_args + + +def translate_level_to_args(translate_const, MAX_LEVEL, replace_value): + def level_to_args(level): + level = (level / MAX_LEVEL) * float(translate_const) + if np.random.random() > 0.5: + level = -level + return (level, replace_value) + + return level_to_args + + +def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value): + def level_to_args(level): + level = int((level / MAX_LEVEL) * cutout_const) + return (level, replace_value) + + return level_to_args + + +def solarize_level_to_args(MAX_LEVEL): + def level_to_args(level): + level = int((level / MAX_LEVEL) * 256) + return (level,) + + return level_to_args + + +def none_level_to_args(level): + return () + + +def posterize_level_to_args(MAX_LEVEL): + def level_to_args(level): + level = int((level / MAX_LEVEL) * 4) + return (level,) + + return level_to_args + + +def rotate_level_to_args(MAX_LEVEL, replace_value): + def level_to_args(level): + level = (level / MAX_LEVEL) * 30 + if np.random.random() < 0.5: + level = -level + return (level, replace_value) + + return level_to_args + + +func_dict = { + "Identity": identity_func, + "AutoContrast": autocontrast_func, + "Equalize": equalize_func, + "Rotate": rotate_func, + "Solarize": solarize_func, + "Color": color_func, + "Contrast": contrast_func, + "Brightness": brightness_func, + "Sharpness": sharpness_func, + "ShearX": shear_x_func, + "TranslateX": translate_x_func, + "TranslateY": translate_y_func, + "Posterize": posterize_func, + "ShearY": shear_y_func, +} + +translate_const = 10 +MAX_LEVEL = 10 +replace_value = (128, 128, 128) +arg_dict = { + "Identity": none_level_to_args, + "AutoContrast": none_level_to_args, + "Equalize": none_level_to_args, + "Rotate": rotate_level_to_args(MAX_LEVEL, replace_value), + "Solarize": solarize_level_to_args(MAX_LEVEL), + "Color": enhance_level_to_args(MAX_LEVEL), + "Contrast": enhance_level_to_args(MAX_LEVEL), + "Brightness": enhance_level_to_args(MAX_LEVEL), + "Sharpness": enhance_level_to_args(MAX_LEVEL), + "ShearX": shear_level_to_args(MAX_LEVEL, replace_value), + "TranslateX": translate_level_to_args(translate_const, MAX_LEVEL, replace_value), + "TranslateY": translate_level_to_args(translate_const, MAX_LEVEL, replace_value), + "Posterize": posterize_level_to_args(MAX_LEVEL), + "ShearY": shear_level_to_args(MAX_LEVEL, replace_value), +} + + +class RandomAugment(object): + def __init__(self, N=2, M=10, isPIL=False, augs=[]): + self.N = N + self.M = M + self.isPIL = isPIL + if augs: + self.augs = augs + else: + self.augs = list(arg_dict.keys()) + + def get_random_ops(self): + sampled_ops = np.random.choice(self.augs, self.N) + return [(op, 0.5, self.M) for op in sampled_ops] + + def __call__(self, img): + if self.isPIL: + img = np.array(img) + ops = self.get_random_ops() + for name, prob, level in ops: + if np.random.random() > prob: + continue + args = arg_dict[name](level) + img = func_dict[name](img, *args) + return img + + +class VideoRandomAugment(object): + def __init__(self, N=2, M=10, p=0.0, tensor_in_tensor_out=True, augs=[]): + self.N = N + self.M = M + self.p = p + self.tensor_in_tensor_out = tensor_in_tensor_out + if augs: + self.augs = augs + else: + self.augs = list(arg_dict.keys()) + + def get_random_ops(self): + sampled_ops = np.random.choice(self.augs, self.N, replace=False) + return [(op, self.M) for op in sampled_ops] + + def __call__(self, frames): + assert ( + frames.shape[-1] == 3 + ), "Expecting last dimension for 3-channels RGB (b, h, w, c)." + + if self.tensor_in_tensor_out: + frames = frames.numpy().astype(np.uint8) + + num_frames = frames.shape[0] + + ops = num_frames * [self.get_random_ops()] + apply_or_not = num_frames * [np.random.random(size=self.N) > self.p] + + frames = torch.stack( + list(map(self._aug, frames, ops, apply_or_not)), dim=0 + ).float() + + return frames + + def _aug(self, img, ops, apply_or_not): + for i, (name, level) in enumerate(ops): + if not apply_or_not[i]: + continue + args = arg_dict[name](level) + img = func_dict[name](img, *args) + return torch.from_numpy(img) + + +if __name__ == "__main__": + a = RandomAugment() + img = np.random.randn(32, 32, 3) + a(img) diff --git a/minigpt4/runners/__init__.py b/minigpt4/runners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..64e7a4d643a8b5a1714687f42d43347a94b72373 --- /dev/null +++ b/minigpt4/runners/__init__.py @@ -0,0 +1,10 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from minigpt4.runners.runner_base import RunnerBase + +__all__ = ["RunnerBase"] diff --git a/minigpt4/runners/runner_base.py b/minigpt4/runners/runner_base.py new file mode 100644 index 0000000000000000000000000000000000000000..ccb5706fdb19222f51dd032ed723179f9ab19fc9 --- /dev/null +++ b/minigpt4/runners/runner_base.py @@ -0,0 +1,658 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import datetime +import json +import logging +import os +import time +from pathlib import Path + +import torch +import torch.distributed as dist +import webdataset as wds +from minigpt4.common.dist_utils import ( + download_cached_file, + get_rank, + get_world_size, + is_main_process, + main_process, +) +from minigpt4.common.registry import registry +from minigpt4.common.utils import is_url +from minigpt4.datasets.data_utils import concat_datasets, reorg_datasets_by_split, ChainDataset +from minigpt4.datasets.datasets.dataloader_utils import ( + IterLoader, + MultiIterLoader, + PrefetchLoader, +) +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DataLoader, DistributedSampler + + +@registry.register_runner("runner_base") +class RunnerBase: + """ + A runner class to train and evaluate a model given a task and datasets. + + The runner uses pytorch distributed data parallel by default. Future release + will support other distributed frameworks. + """ + + def __init__(self, cfg, task, model, datasets, job_id): + self.config = cfg + self.job_id = job_id + + self.task = task + self.datasets = datasets + + self._model = model + + self._wrapped_model = None + self._device = None + self._optimizer = None + self._scaler = None + self._dataloaders = None + self._lr_sched = None + + self.start_epoch = 0 + + # self.setup_seeds() + self.setup_output_dir() + + @property + def device(self): + if self._device is None: + self._device = torch.device(self.config.run_cfg.device) + + return self._device + + @property + def use_distributed(self): + return self.config.run_cfg.distributed + + @property + def model(self): + """ + A property to get the DDP-wrapped model on the device. + """ + # move model to device + if self._model.device != self.device: + self._model = self._model.to(self.device) + + # distributed training wrapper + if self.use_distributed: + if self._wrapped_model is None: + self._wrapped_model = DDP( + self._model, device_ids=[self.config.run_cfg.gpu] + ) + else: + self._wrapped_model = self._model + + return self._wrapped_model + + @property + def optimizer(self): + # TODO make optimizer class and configurations + if self._optimizer is None: + num_parameters = 0 + p_wd, p_non_wd = [], [] + for n, p in self.model.named_parameters(): + if not p.requires_grad: + continue # frozen weights + print(n) + if p.ndim < 2 or "bias" in n or "ln" in n or "bn" in n: + p_non_wd.append(p) + else: + p_wd.append(p) + num_parameters += p.data.nelement() + logging.info("number of trainable parameters: %d" % num_parameters) + optim_params = [ + { + "params": p_wd, + "weight_decay": float(self.config.run_cfg.weight_decay), + }, + {"params": p_non_wd, "weight_decay": 0}, + ] + beta2 = self.config.run_cfg.get("beta2", 0.999) + self._optimizer = torch.optim.AdamW( + optim_params, + lr=float(self.config.run_cfg.init_lr), + weight_decay=float(self.config.run_cfg.weight_decay), + betas=(0.9, beta2), + ) + + return self._optimizer + + @property + def scaler(self): + amp = self.config.run_cfg.get("amp", False) + + if amp: + if self._scaler is None: + self._scaler = torch.cuda.amp.GradScaler() + + return self._scaler + + @property + def lr_scheduler(self): + """ + A property to get and create learning rate scheduler by split just in need. + """ + if self._lr_sched is None: + lr_sched_cls = registry.get_lr_scheduler_class(self.config.run_cfg.lr_sched) + + # max_epoch = self.config.run_cfg.max_epoch + max_epoch = self.max_epoch + # min_lr = self.config.run_cfg.min_lr + min_lr = self.min_lr + # init_lr = self.config.run_cfg.init_lr + init_lr = self.init_lr + + # optional parameters + decay_rate = self.config.run_cfg.get("lr_decay_rate", None) + warmup_start_lr = self.config.run_cfg.get("warmup_lr", -1) + warmup_steps = self.config.run_cfg.get("warmup_steps", 0) + iters_per_epoch = self.config.run_cfg.get("iters_per_epoch", None) + + if iters_per_epoch is None: + try: + iters_per_epoch = len(self.dataloaders['train']) + except (AttributeError, TypeError): + iters_per_epoch = 10000 + + self._lr_sched = lr_sched_cls( + optimizer=self.optimizer, + max_epoch=max_epoch, + iters_per_epoch=iters_per_epoch, + min_lr=min_lr, + init_lr=init_lr, + decay_rate=decay_rate, + warmup_start_lr=warmup_start_lr, + warmup_steps=warmup_steps, + ) + + return self._lr_sched + + @property + def dataloaders(self) -> dict: + """ + A property to get and create dataloaders by split just in need. + + If no train_dataset_ratio is provided, concatenate map-style datasets and + chain wds.DataPipe datasets separately. Training set becomes a tuple + (ConcatDataset, ChainDataset), both are optional but at least one of them is + required. The resultant ConcatDataset and ChainDataset will be sampled evenly. + + If train_dataset_ratio is provided, create a MultiIterLoader to sample + each dataset by ratios during training. + + Currently do not support multiple datasets for validation and test. + + Returns: + dict: {split_name: (tuples of) dataloader} + """ + if self._dataloaders is None: + + # concatenate map-style datasets and chain wds.DataPipe datasets separately + # training set becomes a tuple (ConcatDataset, ChainDataset), both are + # optional but at least one of them is required. The resultant ConcatDataset + # and ChainDataset will be sampled evenly. + logging.info( + "dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline)." + ) + + datasets = reorg_datasets_by_split(self.datasets) + self.datasets = datasets + # self.datasets = concat_datasets(datasets) + + # print dataset statistics after concatenation/chaining + for split_name in self.datasets: + if isinstance(self.datasets[split_name], tuple) or isinstance( + self.datasets[split_name], list + ): + # mixed wds.DataPipeline and torch.utils.data.Dataset + num_records = sum( + [ + len(d) + if not type(d) in [wds.DataPipeline, ChainDataset] + else 0 + for d in self.datasets[split_name] + ] + ) + + else: + if hasattr(self.datasets[split_name], "__len__"): + # a single map-style dataset + num_records = len(self.datasets[split_name]) + else: + # a single wds.DataPipeline + num_records = -1 + logging.info( + "Only a single wds.DataPipeline dataset, no __len__ attribute." + ) + + if num_records >= 0: + logging.info( + "Loaded {} records for {} split from the dataset.".format( + num_records, split_name + ) + ) + + # create dataloaders + split_names = sorted(self.datasets.keys()) + + datasets = [self.datasets[split] for split in split_names] + is_trains = [split in self.train_splits for split in split_names] + + batch_sizes = [ + self.config.run_cfg.batch_size_train + if split == "train" + else self.config.run_cfg.batch_size_eval + for split in split_names + ] + + collate_fns = [] + for dataset in datasets: + if isinstance(dataset, tuple) or isinstance(dataset, list): + collate_fns.append([getattr(d, "collater", None) for d in dataset]) + else: + collate_fns.append(getattr(dataset, "collater", None)) + + dataloaders = self.create_loaders( + datasets=datasets, + num_workers=self.config.run_cfg.num_workers, + batch_sizes=batch_sizes, + is_trains=is_trains, + collate_fns=collate_fns, + ) + + self._dataloaders = {k: v for k, v in zip(split_names, dataloaders)} + + return self._dataloaders + + @property + def cuda_enabled(self): + return self.device.type == "cuda" + + @property + def max_epoch(self): + return int(self.config.run_cfg.max_epoch) + + @property + def log_freq(self): + log_freq = self.config.run_cfg.get("log_freq", 50) + return int(log_freq) + + @property + def init_lr(self): + return float(self.config.run_cfg.init_lr) + + @property + def min_lr(self): + return float(self.config.run_cfg.min_lr) + + @property + def accum_grad_iters(self): + return int(self.config.run_cfg.get("accum_grad_iters", 1)) + + @property + def valid_splits(self): + valid_splits = self.config.run_cfg.get("valid_splits", []) + + if len(valid_splits) == 0: + logging.info("No validation splits found.") + + return valid_splits + + @property + def test_splits(self): + test_splits = self.config.run_cfg.get("test_splits", []) + + return test_splits + + @property + def train_splits(self): + train_splits = self.config.run_cfg.get("train_splits", []) + + if len(train_splits) == 0: + logging.info("Empty train splits.") + + return train_splits + + @property + def evaluate_only(self): + """ + Set to True to skip training. + """ + return self.config.run_cfg.evaluate + + @property + def use_dist_eval_sampler(self): + return self.config.run_cfg.get("use_dist_eval_sampler", True) + + @property + def resume_ckpt_path(self): + return self.config.run_cfg.get("resume_ckpt_path", None) + + @property + def train_loader(self): + train_dataloader = self.dataloaders["train"] + + return train_dataloader + + def setup_output_dir(self): + lib_root = Path(registry.get_path("library_root")) + + output_dir = lib_root / self.config.run_cfg.output_dir / self.job_id + result_dir = output_dir / "result" + + output_dir.mkdir(parents=True, exist_ok=True) + result_dir.mkdir(parents=True, exist_ok=True) + + registry.register_path("result_dir", str(result_dir)) + registry.register_path("output_dir", str(output_dir)) + + self.result_dir = result_dir + self.output_dir = output_dir + + def train(self): + start_time = time.time() + best_agg_metric = 0 + best_epoch = 0 + + self.log_config() + + # resume from checkpoint if specified + if not self.evaluate_only and self.resume_ckpt_path is not None: + self._load_checkpoint(self.resume_ckpt_path) + + for cur_epoch in range(self.start_epoch, self.max_epoch): + # training phase + if not self.evaluate_only: + logging.info("Start training") + train_stats = self.train_epoch(cur_epoch) + self.log_stats(split_name="train", stats=train_stats) + + # evaluation phase + if len(self.valid_splits) > 0: + for split_name in self.valid_splits: + logging.info("Evaluating on {}.".format(split_name)) + + val_log = self.eval_epoch( + split_name=split_name, cur_epoch=cur_epoch + ) + if val_log is not None: + if is_main_process(): + assert ( + "agg_metrics" in val_log + ), "No agg_metrics found in validation log." + + agg_metrics = val_log["agg_metrics"] + if agg_metrics > best_agg_metric and split_name == "val": + best_epoch, best_agg_metric = cur_epoch, agg_metrics + + self._save_checkpoint(cur_epoch, is_best=True) + + val_log.update({"best_epoch": best_epoch}) + self.log_stats(val_log, split_name) + + else: + # if no validation split is provided, we just save the checkpoint at the end of each epoch. + if not self.evaluate_only: + self._save_checkpoint(cur_epoch, is_best=False) + + if self.evaluate_only: + break + + if self.config.run_cfg.distributed: + dist.barrier() + + # testing phase + test_epoch = "best" if len(self.valid_splits) > 0 else cur_epoch + self.evaluate(cur_epoch=test_epoch, skip_reload=self.evaluate_only) + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + logging.info("Training time {}".format(total_time_str)) + + def evaluate(self, cur_epoch="best", skip_reload=False): + test_logs = dict() + + if len(self.test_splits) > 0: + for split_name in self.test_splits: + test_logs[split_name] = self.eval_epoch( + split_name=split_name, cur_epoch=cur_epoch, skip_reload=skip_reload + ) + + return test_logs + + def train_epoch(self, epoch): + # train + self.model.train() + + return self.task.train_epoch( + epoch=epoch, + model=self.model, + data_loader=self.train_loader, + optimizer=self.optimizer, + scaler=self.scaler, + lr_scheduler=self.lr_scheduler, + cuda_enabled=self.cuda_enabled, + log_freq=self.log_freq, + accum_grad_iters=self.accum_grad_iters, + ) + + @torch.no_grad() + def eval_epoch(self, split_name, cur_epoch, skip_reload=False): + """ + Evaluate the model on a given split. + + Args: + split_name (str): name of the split to evaluate on. + cur_epoch (int): current epoch. + skip_reload_best (bool): whether to skip reloading the best checkpoint. + During training, we will reload the best checkpoint for validation. + During testing, we will use provided weights and skip reloading the best checkpoint . + """ + data_loader = self.dataloaders.get(split_name, None) + assert data_loader, "data_loader for split {} is None.".format(split_name) + + # TODO In validation, you need to compute loss as well as metrics + # TODO consider moving to model.before_evaluation() + model = self.unwrap_dist_model(self.model) + if not skip_reload and cur_epoch == "best": + model = self._reload_best_model(model) + model.eval() + + self.task.before_evaluation( + model=model, + dataset=self.datasets[split_name], + ) + results = self.task.evaluation(model, data_loader) + + if results is not None: + return self.task.after_evaluation( + val_result=results, + split_name=split_name, + epoch=cur_epoch, + ) + + def unwrap_dist_model(self, model): + if self.use_distributed: + return model.module + else: + return model + + def create_loaders( + self, + datasets, + num_workers, + batch_sizes, + is_trains, + collate_fns, + dataset_ratios=None, + ): + """ + Create dataloaders for training and validation. + """ + + def _create_loader(dataset, num_workers, bsz, is_train, collate_fn): + # create a single dataloader for each split + if isinstance(dataset, ChainDataset) or isinstance( + dataset, wds.DataPipeline + ): + # wds.WebdDataset instance are chained together + # webdataset.DataPipeline has its own sampler and collate_fn + loader = iter( + DataLoader( + dataset, + batch_size=bsz, + num_workers=num_workers, + pin_memory=True, + ) + ) + else: + # map-style dataset are concatenated together + # setup distributed sampler + if self.use_distributed: + sampler = DistributedSampler( + dataset, + shuffle=is_train, + num_replicas=get_world_size(), + rank=get_rank(), + ) + if not self.use_dist_eval_sampler: + # e.g. retrieval evaluation + sampler = sampler if is_train else None + else: + sampler = None + + loader = DataLoader( + dataset, + batch_size=bsz, + num_workers=num_workers, + pin_memory=True, + sampler=sampler, + shuffle=sampler is None and is_train, + collate_fn=collate_fn, + drop_last=True if is_train else False, + ) + loader = PrefetchLoader(loader) + + if is_train: + loader = IterLoader(loader, use_distributed=self.use_distributed) + + return loader + + loaders = [] + + for dataset, bsz, is_train, collate_fn in zip( + datasets, batch_sizes, is_trains, collate_fns + ): + if isinstance(dataset, list) or isinstance(dataset, tuple): + if hasattr(dataset[0], 'sample_ratio') and dataset_ratios is None: + dataset_ratios = [d.sample_ratio for d in dataset] + loader = MultiIterLoader( + loaders=[ + _create_loader(d, num_workers, bsz, is_train, collate_fn[i]) + for i, d in enumerate(dataset) + ], + ratios=dataset_ratios, + ) + else: + loader = _create_loader(dataset, num_workers, bsz, is_train, collate_fn) + + loaders.append(loader) + + return loaders + + @main_process + def _save_checkpoint(self, cur_epoch, is_best=False): + """ + Save the checkpoint at the current epoch. + """ + model_no_ddp = self.unwrap_dist_model(self.model) + param_grad_dic = { + k: v.requires_grad for (k, v) in model_no_ddp.named_parameters() + } + state_dict = model_no_ddp.state_dict() + for k in list(state_dict.keys()): + if k in param_grad_dic.keys() and not param_grad_dic[k]: + # delete parameters that do not require gradient + del state_dict[k] + save_obj = { + "model": state_dict, + "optimizer": self.optimizer.state_dict(), + "config": self.config.to_dict(), + "scaler": self.scaler.state_dict() if self.scaler else None, + "epoch": cur_epoch, + } + save_to = os.path.join( + self.output_dir, + "checkpoint_{}.pth".format("best" if is_best else cur_epoch), + ) + logging.info("Saving checkpoint at epoch {} to {}.".format(cur_epoch, save_to)) + torch.save(save_obj, save_to) + + def _reload_best_model(self, model): + """ + Load the best checkpoint for evaluation. + """ + checkpoint_path = os.path.join(self.output_dir, "checkpoint_best.pth") + + logging.info("Loading checkpoint from {}.".format(checkpoint_path)) + checkpoint = torch.load(checkpoint_path, map_location="cpu") + try: + model.load_state_dict(checkpoint["model"]) + except RuntimeError as e: + logging.warning( + """ + Key mismatch when loading checkpoint. This is expected if only part of the model is saved. + Trying to load the model with strict=False. + """ + ) + model.load_state_dict(checkpoint["model"], strict=False) + return model + + def _load_checkpoint(self, url_or_filename): + """ + Resume from a checkpoint. + """ + if is_url(url_or_filename): + cached_file = download_cached_file( + url_or_filename, check_hash=False, progress=True + ) + checkpoint = torch.load(cached_file, map_location=self.device) + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location=self.device) + else: + raise RuntimeError("checkpoint url or path is invalid") + + state_dict = checkpoint["model"] + self.unwrap_dist_model(self.model).load_state_dict(state_dict,strict=False) + + self.optimizer.load_state_dict(checkpoint["optimizer"]) + if self.scaler and "scaler" in checkpoint: + self.scaler.load_state_dict(checkpoint["scaler"]) + + self.start_epoch = checkpoint["epoch"] + 1 + logging.info("Resume checkpoint from {}".format(url_or_filename)) + + @main_process + def log_stats(self, stats, split_name): + if isinstance(stats, dict): + log_stats = {**{f"{split_name}_{k}": v for k, v in stats.items()}} + with open(os.path.join(self.output_dir, "log.txt"), "a") as f: + f.write(json.dumps(log_stats) + "\n") + elif isinstance(stats, list): + pass + + @main_process + def log_config(self): + with open(os.path.join(self.output_dir, "log.txt"), "a") as f: + f.write(json.dumps(self.config.to_dict(), indent=4) + "\n") diff --git a/minigpt4/tasks/__init__.py b/minigpt4/tasks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ab1fb1c8289535cf9397bb9805c0cba3666ad26f --- /dev/null +++ b/minigpt4/tasks/__init__.py @@ -0,0 +1,26 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from minigpt4.common.registry import registry +from minigpt4.tasks.base_task import BaseTask +from minigpt4.tasks.image_text_pretrain import ImageTextPretrainTask + + +def setup_task(cfg): + assert "task" in cfg.run_cfg, "Task name must be provided." + + task_name = cfg.run_cfg.task + task = registry.get_task_class(task_name).setup_task(cfg=cfg) + assert task is not None, "Task {} not properly registered.".format(task_name) + + return task + + +__all__ = [ + "BaseTask", + "ImageTextPretrainTask", +] diff --git a/minigpt4/tasks/base_task.py b/minigpt4/tasks/base_task.py new file mode 100644 index 0000000000000000000000000000000000000000..7ceee96bdf520f8d730651e815defd83b7ecfebb --- /dev/null +++ b/minigpt4/tasks/base_task.py @@ -0,0 +1,286 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import logging +import os + +import torch +import torch.distributed as dist +from minigpt4.common.dist_utils import get_rank, get_world_size, is_main_process, is_dist_avail_and_initialized +from minigpt4.common.logger import MetricLogger, SmoothedValue +from minigpt4.common.registry import registry +from minigpt4.datasets.data_utils import prepare_sample + + +class BaseTask: + def __init__(self, **kwargs): + super().__init__() + + self.inst_id_key = "instance_id" + + @classmethod + def setup_task(cls, **kwargs): + return cls() + + def build_model(self, cfg): + model_config = cfg.model_cfg + + model_cls = registry.get_model_class(model_config.arch) + return model_cls.from_config(model_config) + + def build_datasets(self, cfg): + """ + Build a dictionary of datasets, keyed by split 'train', 'valid', 'test'. + Download dataset and annotations automatically if not exist. + + Args: + cfg (common.config.Config): _description_ + + Returns: + dict: Dictionary of torch.utils.data.Dataset objects by split. + """ + + datasets = dict() + + datasets_config = cfg.datasets_cfg + + assert len(datasets_config) > 0, "At least one dataset has to be specified." + + for name in datasets_config: + dataset_config = datasets_config[name] + + builder = registry.get_builder_class(name)(dataset_config) + dataset = builder.build_datasets() + + dataset['train'].name = name + if 'sample_ratio' in dataset_config: + dataset['train'].sample_ratio = dataset_config.sample_ratio + + datasets[name] = dataset + + return datasets + + def train_step(self, model, samples): + loss = model(samples)["loss"] + return loss + + def valid_step(self, model, samples): + raise NotImplementedError + + def before_evaluation(self, model, dataset, **kwargs): + model.before_evaluation(dataset=dataset, task_type=type(self)) + + def after_evaluation(self, **kwargs): + pass + + def inference_step(self): + raise NotImplementedError + + def evaluation(self, model, data_loader, cuda_enabled=True): + metric_logger = MetricLogger(delimiter=" ") + header = "Evaluation" + # TODO make it configurable + print_freq = 10 + + results = [] + + for samples in metric_logger.log_every(data_loader, print_freq, header): + samples = prepare_sample(samples, cuda_enabled=cuda_enabled) + + eval_output = self.valid_step(model=model, samples=samples) + results.extend(eval_output) + + if is_dist_avail_and_initialized(): + dist.barrier() + + return results + + def train_epoch( + self, + epoch, + model, + data_loader, + optimizer, + lr_scheduler, + scaler=None, + cuda_enabled=False, + log_freq=50, + accum_grad_iters=1, + ): + return self._train_inner_loop( + epoch=epoch, + iters_per_epoch=lr_scheduler.iters_per_epoch, + model=model, + data_loader=data_loader, + optimizer=optimizer, + scaler=scaler, + lr_scheduler=lr_scheduler, + log_freq=log_freq, + cuda_enabled=cuda_enabled, + accum_grad_iters=accum_grad_iters, + ) + + def train_iters( + self, + epoch, + start_iters, + iters_per_inner_epoch, + model, + data_loader, + optimizer, + lr_scheduler, + scaler=None, + cuda_enabled=False, + log_freq=50, + accum_grad_iters=1, + ): + return self._train_inner_loop( + epoch=epoch, + start_iters=start_iters, + iters_per_epoch=iters_per_inner_epoch, + model=model, + data_loader=data_loader, + optimizer=optimizer, + scaler=scaler, + lr_scheduler=lr_scheduler, + log_freq=log_freq, + cuda_enabled=cuda_enabled, + accum_grad_iters=accum_grad_iters, + ) + + def _train_inner_loop( + self, + epoch, + iters_per_epoch, + model, + data_loader, + optimizer, + lr_scheduler, + scaler=None, + start_iters=None, + log_freq=50, + cuda_enabled=False, + accum_grad_iters=1, + ): + """ + An inner training loop compatible with both epoch-based and iter-based training. + + When using epoch-based, training stops after one epoch; when using iter-based, + training stops after #iters_per_epoch iterations. + """ + use_amp = scaler is not None + + if not hasattr(data_loader, "__next__"): + # convert to iterator if not already + data_loader = iter(data_loader) + + metric_logger = MetricLogger(delimiter=" ") + metric_logger.add_meter("lr", SmoothedValue(window_size=1, fmt="{value:.6f}")) + metric_logger.add_meter("loss", SmoothedValue(window_size=1, fmt="{value:.4f}")) + + # if iter-based runner, schedule lr based on inner epoch. + logging.info( + "Start training epoch {}, {} iters per inner epoch.".format( + epoch, iters_per_epoch + ) + ) + header = "Train: data epoch: [{}]".format(epoch) + if start_iters is None: + # epoch-based runner + inner_epoch = epoch + else: + # In iter-based runner, we schedule the learning rate based on iterations. + inner_epoch = start_iters // iters_per_epoch + header = header + "; inner epoch [{}]".format(inner_epoch) + + for i in metric_logger.log_every(range(iters_per_epoch), log_freq, header): + # if using iter-based runner, we stop after iters_per_epoch iterations. + if i >= iters_per_epoch: + break + + samples = next(data_loader) + + samples = prepare_sample(samples, cuda_enabled=cuda_enabled) + samples.update( + { + "epoch": inner_epoch, + "num_iters_per_epoch": iters_per_epoch, + "iters": i, + } + ) + + lr_scheduler.step(cur_epoch=inner_epoch, cur_step=i) + + with torch.cuda.amp.autocast(enabled=use_amp): + loss = self.train_step(model=model, samples=samples) + + # after_train_step() + if use_amp: + scaler.scale(loss).backward() + else: + loss.backward() + + # update gradients every accum_grad_iters iterations + if (i + 1) % accum_grad_iters == 0: + if use_amp: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + optimizer.zero_grad() + + metric_logger.update(loss=loss.item()) + metric_logger.update(lr=optimizer.param_groups[0]["lr"]) + + # after train_epoch() + # gather the stats from all processes + metric_logger.synchronize_between_processes() + logging.info("Averaged stats: " + str(metric_logger.global_avg())) + return { + k: "{:.3f}".format(meter.global_avg) + for k, meter in metric_logger.meters.items() + } + + @staticmethod + def save_result(result, result_dir, filename, remove_duplicate=""): + import json + + result_file = os.path.join( + result_dir, "%s_rank%d.json" % (filename, get_rank()) + ) + final_result_file = os.path.join(result_dir, "%s.json" % filename) + + json.dump(result, open(result_file, "w")) + + if is_dist_avail_and_initialized(): + dist.barrier() + + if is_main_process(): + logging.warning("rank %d starts merging results." % get_rank()) + # combine results from all processes + result = [] + + for rank in range(get_world_size()): + result_file = os.path.join( + result_dir, "%s_rank%d.json" % (filename, rank) + ) + res = json.load(open(result_file, "r")) + result += res + + if remove_duplicate: + result_new = [] + id_list = [] + for res in result: + if res[remove_duplicate] not in id_list: + id_list.append(res[remove_duplicate]) + result_new.append(res) + result = result_new + + json.dump(result, open(final_result_file, "w")) + print("result file saved to %s" % final_result_file) + + return final_result_file diff --git a/minigpt4/tasks/image_text_pretrain.py b/minigpt4/tasks/image_text_pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..bbe8ec83a5dc95ee26a36e457feb394d18b7cd17 --- /dev/null +++ b/minigpt4/tasks/image_text_pretrain.py @@ -0,0 +1,18 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +from minigpt4.common.registry import registry +from minigpt4.tasks.base_task import BaseTask + + +@registry.register_task("image_text_pretrain") +class ImageTextPretrainTask(BaseTask): + def __init__(self): + super().__init__() + + def evaluation(self, model, data_loader, cuda_enabled=True): + pass diff --git a/prompts/alignment.txt b/prompts/alignment.txt new file mode 100644 index 0000000000000000000000000000000000000000..38ae75a9cee293861f06544cbff6fdc4aa941d85 --- /dev/null +++ b/prompts/alignment.txt @@ -0,0 +1,4 @@ + Describe this image in detail. + Take a look at this image and describe what you notice. + Please provide a detailed description of the picture. + Could you describe the contents of this image for me? \ No newline at end of file diff --git a/train.py b/train.py new file mode 100644 index 0000000000000000000000000000000000000000..a90cb3ff111a9e099654ca88162d81b2735192ac --- /dev/null +++ b/train.py @@ -0,0 +1,103 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +import argparse +import os +import random + +import numpy as np +import torch +import torch.backends.cudnn as cudnn + +import minigpt4.tasks as tasks +from minigpt4.common.config import Config +from minigpt4.common.dist_utils import get_rank, init_distributed_mode +from minigpt4.common.logger import setup_logger +from minigpt4.common.optims import ( + LinearWarmupCosineLRScheduler, + LinearWarmupStepLRScheduler, +) +from minigpt4.common.registry import registry +from minigpt4.common.utils import now + +# imports modules for registration +from minigpt4.datasets.builders import * +from minigpt4.models import * +from minigpt4.processors import * +from minigpt4.runners import * +from minigpt4.tasks import * + + +def parse_args(): + parser = argparse.ArgumentParser(description="Training") + + parser.add_argument("--cfg-path", required=True, help="path to configuration file.") + parser.add_argument( + "--options", + nargs="+", + help="override some settings in the used config, the key-value pair " + "in xxx=yyy format will be merged into config file (deprecate), " + "change to --cfg-options instead.", + ) + + args = parser.parse_args() + # if 'LOCAL_RANK' not in os.environ: + # os.environ['LOCAL_RANK'] = str(args.local_rank) + + return args + + +def setup_seeds(config): + seed = config.run_cfg.seed + get_rank() + + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + cudnn.benchmark = False + cudnn.deterministic = True + + +def get_runner_class(cfg): + """ + Get runner class from config. Default to epoch-based runner. + """ + runner_cls = registry.get_runner_class(cfg.run_cfg.get("runner", "runner_base")) + + return runner_cls + + +def main(): + # allow auto-dl completes on main process without timeout when using NCCL backend. + # os.environ["NCCL_BLOCKING_WAIT"] = "1" + + # set before init_distributed_mode() to ensure the same job_id shared across all ranks. + job_id = now() + + cfg = Config(parse_args()) + + init_distributed_mode(cfg.run_cfg) + + setup_seeds(cfg) + + # set after init_distributed_mode() to only log on master. + setup_logger() + + cfg.pretty_print() + + task = tasks.setup_task(cfg) + datasets = task.build_datasets(cfg) + model = task.build_model(cfg) + + runner = get_runner_class(cfg)( + cfg=cfg, job_id=job_id, task=task, model=model, datasets=datasets + ) + runner.train() + + +if __name__ == "__main__": + main() diff --git a/train_configs/minigpt4_llama2_stage1_pretrain.yaml b/train_configs/minigpt4_llama2_stage1_pretrain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f3981b8ac9a8e08c14b56b0ace366edcee5f88a9 --- /dev/null +++ b/train_configs/minigpt4_llama2_stage1_pretrain.yaml @@ -0,0 +1,55 @@ +model: + arch: minigpt4 + model_type: pretrain_llama2 + + +datasets: + laion: + vis_processor: + train: + name: "blip2_image_train" + image_size: 224 + text_processor: + train: + name: "blip_caption" + sample_ratio: 115 + cc_sbu: + vis_processor: + train: + name: "blip2_image_train" + image_size: 224 + text_processor: + train: + name: "blip_caption" + sample_ratio: 14 + + +run: + task: image_text_pretrain + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-4 + min_lr: 8e-5 + warmup_lr: 1e-6 + + weight_decay: 0.05 + max_epoch: 4 + batch_size_train: 64 + batch_size_eval: 64 + num_workers: 4 + warmup_steps: 5000 + iters_per_epoch: 5000 + + seed: 42 + output_dir: "output/minigpt4_stage1_pretrain" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True \ No newline at end of file diff --git a/train_configs/minigpt4_llama2_stage2_finetune.yaml b/train_configs/minigpt4_llama2_stage2_finetune.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fa2b578ed86a433682d1c7e620f90ab3dd3e2209 --- /dev/null +++ b/train_configs/minigpt4_llama2_stage2_finetune.yaml @@ -0,0 +1,50 @@ +model: + arch: minigpt4 + model_type: pretrain_llama2 + + max_txt_len: 160 + end_sym: "" + prompt_path: "prompts/alignment.txt" + prompt_template: '[INST] {} [/INST] ' + ckpt: '/path/to/stage1/checkpoint/' + + +datasets: + cc_sbu_align: + vis_processor: + train: + name: "blip2_image_train" + image_size: 224 + text_processor: + train: + name: "blip_caption" + +run: + task: image_text_pretrain + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 3e-5 + min_lr: 1e-5 + warmup_lr: 1e-6 + + weight_decay: 0.05 + max_epoch: 5 + iters_per_epoch: 200 + batch_size_train: 12 + batch_size_eval: 12 + num_workers: 4 + warmup_steps: 200 + + seed: 42 + output_dir: "output/minigpt4_stage2_finetune" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True \ No newline at end of file diff --git a/train_configs/minigpt4_stage1_pretrain.yaml b/train_configs/minigpt4_stage1_pretrain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..be87b77cd2df109685f36c11d6ed7813f47a17c9 --- /dev/null +++ b/train_configs/minigpt4_stage1_pretrain.yaml @@ -0,0 +1,55 @@ +model: + arch: minigpt4 + model_type: pretrain_vicuna0 + + +datasets: + laion: + vis_processor: + train: + name: "blip2_image_train" + image_size: 224 + text_processor: + train: + name: "blip_caption" + sample_ratio: 115 + cc_sbu: + vis_processor: + train: + name: "blip2_image_train" + image_size: 224 + text_processor: + train: + name: "blip_caption" + sample_ratio: 14 + + +run: + task: image_text_pretrain + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 1e-4 + min_lr: 8e-5 + warmup_lr: 1e-6 + + weight_decay: 0.05 + max_epoch: 4 + batch_size_train: 64 + batch_size_eval: 64 + num_workers: 4 + warmup_steps: 5000 + iters_per_epoch: 5000 + + seed: 42 + output_dir: "output/minigpt4_stage1_pretrain" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True \ No newline at end of file diff --git a/train_configs/minigpt4_stage2_finetune.yaml b/train_configs/minigpt4_stage2_finetune.yaml new file mode 100644 index 0000000000000000000000000000000000000000..404dfd6a0552c31cf6c59e3ee82333676407933c --- /dev/null +++ b/train_configs/minigpt4_stage2_finetune.yaml @@ -0,0 +1,50 @@ +model: + arch: minigpt4 + model_type: pretrain_vicuna0 + + max_txt_len: 160 + end_sym: "###" + prompt_path: "prompts/alignment.txt" + prompt_template: '###Human: {} ###Assistant: ' + ckpt: '/path/to/stage1/checkpoint/' + + +datasets: + cc_sbu_align: + vis_processor: + train: + name: "blip2_image_train" + image_size: 224 + text_processor: + train: + name: "blip_caption" + +run: + task: image_text_pretrain + # optimizer + lr_sched: "linear_warmup_cosine_lr" + init_lr: 3e-5 + min_lr: 1e-5 + warmup_lr: 1e-6 + + weight_decay: 0.05 + max_epoch: 5 + iters_per_epoch: 200 + batch_size_train: 12 + batch_size_eval: 12 + num_workers: 4 + warmup_steps: 200 + + seed: 42 + output_dir: "output/minigpt4_stage2_finetune" + + amp: True + resume_ckpt_path: null + + evaluate: False + train_splits: ["train"] + + device: "cuda" + world_size: 1 + dist_url: "env://" + distributed: True \ No newline at end of file