Paper | Blog posts: 1, 2 | Demo
Welcome to our open source implementation of DeepMind's Flamingo!
In this repository, we provide a PyTorch implementation for training and evaluating OpenFlamingo models. If you have any questions, please feel free to open an issue. We also welcome contributions!
To install the package in an existing environment, run
pip install open-flamingo
or to create a conda environment for running OpenFlamingo, run
conda env create -f environment.yml
To install training or eval dependencies, run one of the first two commands. To install everything, run the third command.
pip install open-flamingo[training]
pip install open-flamingo[eval]
pip install open-flamingo[all]
There are three requirements.txt files:
requirements.txtrequirements-training.txtrequirements-eval.txt
Depending on your use case, you can install any of these with pip install -r <requirements-file.txt>. The base file contains only the dependencies needed for running the model.
We use pre-commit hooks to align formatting with the checks in the repository.
- To install pre-commit, run
or use brew for MacOS
pip install pre-commitbrew install pre-commit - Check the version installed with
pre-commit --version - Then at the root of this repository, run
pre-commit install
Then every time we run git commit, the checks are run. If the files are reformatted by the hooks, run git add for your changed files and git commit again
OpenFlamingo is a multimodal language model that can be used for a variety of tasks. It is trained on a large multimodal dataset (e.g. Multimodal C4) and can be used to generate text conditioned on interleaved images/text. For example, OpenFlamingo can be used to generate a caption for an image, or to generate a question given an image and a text passage. The benefit of this approach is that we are able to rapidly adapt to new tasks using in-context learning.
OpenFlamingo combines a pretrained vision encoder and a language model using cross attention layers. The model architecture is shown below.
Credit: Flamingo
We support pretrained vision encoders from the OpenCLIP package, which includes OpenAI's pretrained models.
We also support pretrained language models from the transformers package, such as MPT, RedPajama, LLaMA, OPT, GPT-Neo, GPT-J, and Pythia models.
from open_flamingo import create_model_and_transforms
model, image_processor, tokenizer = create_model_and_transforms(
clip_vision_encoder_path="ViT-L-14",
clip_vision_encoder_pretrained="openai",
lang_encoder_path="anas-awadalla/mpt-1b-redpajama-200b",
tokenizer_path="anas-awadalla/mpt-1b-redpajama-200b",
cross_attn_every_n_layers=1,
cache_dir="PATH/TO/CACHE/DIR" # Defaults to ~/.cache
)We have trained the following OpenFlamingo models so far.
| # params | Language model | Vision encoder | Xattn interval* | COCO 4-shot CIDEr | VQAv2 4-shot Accuracy | Weights |
|---|---|---|---|---|---|---|
| 3B | anas-awadalla/mpt-1b-redpajama-200b | openai CLIP ViT-L/14 | 1 | 77.3 | 45.8 | Link |
| 3B | anas-awadalla/mpt-1b-redpajama-200b-dolly | openai CLIP ViT-L/14 | 1 | 82.7 | 45.7 | Link |
| 4B | togethercomputer/RedPajama-INCITE-Base-3B-v1 | openai CLIP ViT-L/14 | 2 | 81.8 | 49.0 | Link |
| 4B | togethercomputer/RedPajama-INCITE-Instruct-3B-v1 | openai CLIP ViT-L/14 | 2 | 85.8 | 49.0 | Link |
| 9B | anas-awadalla/mpt-7b | openai CLIP ViT-L/14 | 4 | 89.0 | 54.8 | Link |
* Xattn interval refers to the --cross_attn_every_n_layers argument.
Note: as part of our v2 release, we have deprecated a previous LLaMA-based checkpoint. However, you can continue to use our older checkpoint using the new codebase.
To instantiate an OpenFlamingo model with one of our released weights, initialize the model as above and use the following code.
# grab model checkpoint from huggingface hub
from huggingface_hub import hf_hub_download
import torch
checkpoint_path = hf_hub_download("openflamingo/OpenFlamingo-3B-vitl-mpt1b", "checkpoint.pt")
model.load_state_dict(torch.load(checkpoint_path), strict=False)Below is an example of generating text conditioned on interleaved images/text. In particular, let's try few-shot image captioning.
from PIL import Image
import requests
import torch
"""
Step 1: Load images
"""
demo_image_one = Image.open(
requests.get(
"http://images.cocodataset.org/val2017/000000039769.jpg", stream=True
).raw
)
demo_image_two = Image.open(
requests.get(
"http://images.cocodataset.org/test-stuff2017/000000028137.jpg",
stream=True
).raw
)
query_image = Image.open(
requests.get(
"http://images.cocodataset.org/test-stuff2017/000000028352.jpg",
stream=True
).raw
)
"""
Step 2: Preprocessing images
Details: For OpenFlamingo, we expect the image to be a torch tensor of shape
batch_size x num_media x num_frames x channels x height x width.
In this case batch_size = 1, num_media = 3, num_frames = 1,
channels = 3, height = 224, width = 224.
"""
vision_x = [image_processor(demo_image_one).unsqueeze(0), image_processor(demo_image_two).unsqueeze(0), image_processor(query_image).unsqueeze(0)]
vision_x = torch.cat(vision_x, dim=0)
vision_x = vision_x.unsqueeze(1).unsqueeze(0)
"""
Step 3: Preprocessing text
Details: In the text we expect an <image> special token to indicate where an image is.
We also expect an <|endofchunk|> special token to indicate the end of the text
portion associated with an image.
"""
tokenizer.padding_side = "left" # For generation padding tokens should be on the left
lang_x = tokenizer(
["<image>An image of two cats.<|endofchunk|><image>An image of a bathroom sink.<|endofchunk|><image>An image of"],
return_tensors="pt",
)
"""
Step 4: Generate text
"""
generated_text = model.generate(
vision_x=vision_x,
lang_x=lang_x["input_ids"],
attention_mask=lang_x["attention_mask"],
max_new_tokens=20,
num_beams=3,
)
print("Generated text: ", tokenizer.decode(generated_text[0]))We provide training scripts in open_flamingo/train. We provide an example Slurm script in open_flamingo/scripts/run_train.py, as well as the following example command:
torchrun --nnodes=1 --nproc_per_node=4 open_flamingo/train/train.py \
--lm_path anas-awadalla/mpt-1b-redpajama-200b \
--tokenizer_path anas-awadalla/mpt-1b-redpajama-200b \
--cross_attn_every_n_layers 1 \
--dataset_resampled \
--batch_size_mmc4 32 \
--batch_size_laion 64 \
--train_num_samples_mmc4 125000\
--train_num_samples_laion 250000 \
--loss_multiplier_laion 0.2 \
--workers=4 \
--run_name OpenFlamingo-3B-vitl-mpt1b \
--num_epochs 480 \
--warmup_steps 1875 \
--mmc4_textsim_threshold 0.24 \
--laion_shards "/path/to/shards/shard-{0000..0999}.tar" \
--mmc4_shards "/path/to/shards/shard-{0000..0999}.tar" \
--report_to_wandb
Note: The MPT-1B base and instruct modeling code does not accept the labels kwarg or compute cross-entropy loss directly within forward(), as expected by our codebase. We suggest using a modified version of the MPT-1B models found here and here.
For more details, see our training README.
An example evaluation script is at open_flamingo/scripts/run_eval.sh. Please see our evaluation README for more details.
To run evaluations on OKVQA you will need to run the following command:
import nltk
nltk.download('wordnet')
- Add support for video input
OpenFlamingo is developed by:
Anas Awadalla*, Irena Gao*, Joshua Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, Ludwig Schmidt.
The team is primarily from the University of Washington, Stanford, AI2, UCSB, and Google.
This code is based on Lucidrains' flamingo implementation and David Hansmair's flamingo-mini repo. Thank you for making your code public! We also thank the OpenCLIP team as we use their data loading code and take inspiration from their library design.
We would also like to thank Jean-Baptiste Alayrac and Antoine Miech for their advice, Rohan Taori, Nicholas Schiefer, Deep Ganguli, Thomas Liao, Tatsunori Hashimoto, and Nicholas Carlini for their help with assessing the safety risks of our release, and to Stability AI for providing us with compute resources to train these models.
If you found this repository useful, please consider citing:
@article{awadalla2023openflamingo,
title={OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models},
author={Anas Awadalla and Irena Gao and Josh Gardner and Jack Hessel and Yusuf Hanafy and Wanrong Zhu and Kalyani Marathe and Yonatan Bitton and Samir Gadre and Shiori Sagawa and Jenia Jitsev and Simon Kornblith and Pang Wei Koh and Gabriel Ilharco and Mitchell Wortsman and Ludwig Schmidt},
journal={arXiv preprint arXiv:2308.01390},
year={2023}
}
@software{anas_awadalla_2023_7733589,
author = {Awadalla, Anas and Gao, Irena and Gardner, Joshua and Hessel, Jack and Hanafy, Yusuf and Zhu, Wanrong and Marathe, Kalyani and Bitton, Yonatan and Gadre, Samir and Jitsev, Jenia and Kornblith, Simon and Koh, Pang Wei and Ilharco, Gabriel and Wortsman, Mitchell and Schmidt, Ludwig},
title = {OpenFlamingo},
month = mar,
year = 2023,
publisher = {Zenodo},
version = {v0.1.1},
doi = {10.5281/zenodo.7733589},
url = {https://doi.org/10.5281/zenodo.7733589}
}
@article{Alayrac2022FlamingoAV,
title={Flamingo: a Visual Language Model for Few-Shot Learning},
author={Jean-Baptiste Alayrac and Jeff Donahue and Pauline Luc and Antoine Miech and Iain Barr and Yana Hasson and Karel Lenc and Arthur Mensch and Katie Millican and Malcolm Reynolds and Roman Ring and Eliza Rutherford and Serkan Cabi and Tengda Han and Zhitao Gong and Sina Samangooei and Marianne Monteiro and Jacob Menick and Sebastian Borgeaud and Andy Brock and Aida Nematzadeh and Sahand Sharifzadeh and Mikolaj Binkowski and Ricardo Barreira and Oriol Vinyals and Andrew Zisserman and Karen Simonyan},
journal={ArXiv},
year={2022},
volume={abs/2204.14198}
}