Large World Model (LWM) is a general-purpose large-context multimodal autoregressive model. It is trained on a large dataset of diverse long videos and books using RingAttention, and can perform language, image, and video understanding and generation.
Current language models fall short in understanding aspects of the world not easily described in words, and struggle with complex, long-form tasks. Video sequences offer valuable temporal information absent in language and static images, making them attractive for joint modeling with language. Such models could develop a understanding of both human textual knowledge and the physical world, enabling broader AI capabilities for assisting humans. However, learning from millions of tokens of video and language sequences poses challenges due to memory constraints, computational complexity, and limited datasets. To address these challenges, we curate a large dataset of diverse videos and books, utilize the RingAttention technique to scalably train on long sequences, and gradually increase context size from 4K to 1M tokens. This paper makes the following contributions: (a) Largest context size neural network: We train one of the largest context size transformers on long video and language sequences, setting new benchmarks in difficult retrieval tasks and long video understanding. (b) Solutions for overcoming vision-language training challenges, including using masked sequence packing for mixing different sequence lengths, loss weighting to balance language and vision, and model-generated QA dataset for long sequence chat. (c) A highly-optimized implementation with RingAttention, masked sequence packing, and other key features for training on millions-length multimodal sequences. (d) Fully open-sourced a family of 7B parameter models capable of processing long text documents (LWM-Text, LWM-Text-Chat) and videos (LWM, LWM-Chat) of over 1M tokens. This work paves the way for training on massive datasets of long video and language to develop understanding of both human knowledge and the multimodal world, and broader capabilities.
LWM can retrieval facts across 1M context with high accuracy.
LWM can answer questions over 1 hour YouTube video.
LWM can chat with images.
LWM can generate videos and images from text.
Install the requirements with:
conda create -n lwm python=3.10
pip install -U "jax[cuda12_pip]==0.4.23" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install -r requirements.txt
or set up TPU VM with:
sh tpu_requirements.sh
There are language-only and video-language versions, offering context sizes from 32K, to 128K, 256K and 1M tokens. The vision-language models are available only in Jax, and the language-only models are available in both PyTorch and Jax. Below are the names of the available models and their corresponding context sizes and capabilities:
| Model Name | Context Size | Language or Vision-Language | Chat or Base | URL |
|---|---|---|---|---|
| LWM-Text-Chat-128K | 128K | Language | Chat | [Pytorch][Jax] |
| LWM-Text-Chat-256K | 256K | Language | Chat | [Pytorch][Jax] |
| LWM-Text-Chat-512K | 512K | Language | Chat | [Pytorch][Jax] |
| LWM-Text-Chat-1M | 1M | Language | Chat | [Pytorch][Jax] |
| LWM-Text-128K | 128K | Language | Base | [Pytorch][Jax] |
| LWM-Text-256K | 256K | Language | Base | [Pytorch][Jax] |
| LWM-Text-512K | 512K | Language | Base | [Pytorch][Jax] |
| LWM-Text-1M | 1M | Language | Base | [Pytorch][Jax] |
| LWM-Chat-32K | 32K | Vision-Language | Chat | [Jax] |
| LWM-Chat-128K | 128K | Vision-Language | Chat | [Jax] |
| LWM-Chat-1M | 1M | Vision-Language | Chat | [Jax] |
Use scan_query_chunk_size and scan_key_chunk_size to control the block size in blockwise compute of the self-attention. Use scan_mlp_chunk_size to control the block size in blockwise compute of the feedforward network. Use scan_attention=True and scan_mlp=True to enable/disable blockwise compute in the self-attention and feed-forward network. Use remat_attention and remat_mlp to control the rematerialization policy with nothing_saveable recommended.
You can use mesh_dim=dp, fsdp, tp, sp to control the degree of parallelism and RingAttention. It is a string of 4 integers separated by commas, representing the number of data parallelism, fully sharded data parallelism, tensor parallelism, and sequence parallelism.
For example, mesh_dim='1,64,4,1' means 1 data parallelism, 64 fully sharded data parallelism, 4 tensor parallelism, and 1 sequence parallelism. mesh_dim='1,1,4,64' means 1 data parallelism, 1 fully sharded data parallelism, 4 tensor parallelism, and 64 sequence parallelism for RingAttention.
In this section, we provide instructions on how to run each of the provided scripts. For each script, you may need to fill in your own paths and values in the variables described in the beginning of each script.
To run each of the following scripts, use bash <script_name>.sh:
- Language model training:
bash scripts/run_train_text.sh - Vision-Language model training:
bash scripts/run_train_vision_text.sh - Single Needle Evals (Language Model):
bash scripts/run_eval_needle.sh - Multi Needle Evals (Language Model):
bash scripts/run_eval_needle_multi.sh - Sampling images (Vision-Language Model):
bash scripts/run_sample_image.sh - Sampling videos (Vision-LanguageModel):
bash scripts/run_sample_video.sh - Image / Video understanding (Vision-Language Model):
bash scripts/run_vision_chat.sh
This is based on the codebase of BPT and RingAttention, with the necessary features for vision-language training. The training and inference have been tested on both TPUv3 and TPUv4.
If you encounter bugs, please open a GitHub issue!
If you use this codebase, or otherwise found our work valuable, please cite:
@article{liu2023world,
title={World Model on Million-Length Video and Language with RingAttention},
author={Liu, Hao and Yan, Wilson and Zaharia, Matei and Abbeel, Pieter},
journal={arXiv preprint},
year={2024},
}
@article{liu2023ring,
title={Ring Attention with Blockwise Transformers for Near-Infinite Context},
author={Liu, Hao and Zaharia, Matei and Abbeel, Pieter},
journal={International Conference on Learning Representations},
year={2024}
}
@article{liu2023blockwise,
title={Blockwise Parallel Transformer for Large Context Models},
author={Liu, Hao and Abbeel, Pieter},
journal={Advances in neural information processing systems},
year={2023}
}
LWM's code and model weights are released under the Apache 2.0 License. See LICENSE for further details.