Learning to (Learn at Test Time): RNNs with Expressive Hidden States
Paper | PyTorch Codebase | Setup | Replicating Experiments | Model Docs | Dataset Preperation
Self-attention performs well in long context but has quadratic complexity. Existing RNN layers have linear complexity, but their performance in long context is limited by the expressive power of their hidden state. We propose a new class of sequence modeling layers with linear complexity and an expressive hidden state. The key idea is to make the hidden state a machine learning model itself, and the update rule a step of self-supervised learning.
Since the hidden state is updated by training even on test sequences, our layers are called Test-Time Training (TTT) layers. We consider two instantiations: TTT-Linear and TTT-MLP, whose hidden state is a linear model and a two-layer MLP respectively.
This codebase is implemented in JAX and has been tested on both GPUs and Cloud TPU VMs with Python 3.11. For a PyTorch model definition, please refer to this link.
To setup and run our code on a (local) GPU machine, we highly recommend using Anaconda when installing python dependencies. Install GPU requirements using:
cd requirements
pip install -r gpu_requirements.txt
For TPU, please refer to this link for guidance on creating cloud TPU VMs. Then, run:
cd requirements
pip install -r tpu_requirements.txt
We use WandB for logging training metrics and TTT statistics. After installing the requirements, login to WandB using:
wandb login
Our Llama-2 tokenized datasets are available for download from Google Cloud Buckets:
gsutil -m cp -r gs://llama-2-pile/* llama-2-pile/
gsutil -m cp -r gs://llama-2-books3/* llama-2-books3/
Once downloaded, set the dataset_path flag in train.py to the directory containing the tokenizer_name-meta-llama folder. This will allow the dataloader to find the correct path.
Alternatively, to tokenize datasets yourself, refer to dataset preparation.
We provide scripts corresponding to each experiment in our paper in the scripts folder. After specifying the experiment name and directory, select the desired context length and divide by 0.5 million to calculate the appropriate batch size.
Depending on the model size, you may need to modify the mesh_dim to introduce model sharding. See the model docs for additional information on the training configuration.
- This codebase is based on EasyLM.
- Our dataloader is based on FlashAttention.