Minimal implementation of a StripedHyena model.
One of the focus areas at Together Research is new architectures for long context, improved training, and inference performance over the Transformer architecture. Spinning out of a research program from our team and academic collaborators, with roots in signal processing-inspired sequence models, we are excited to introduce the StripedHyena models.
StripedHyena is the first alternative model competitive with the best open-source Transformers of similar sizes in short and long-context evaluations.
StripedHyena-Nous-7B (SH-N 7B) is our chat model for this release, and was developed with our collaborators at Nous Research.
- Read more here in our blog.
- Play with the model on our playground. Chat here!
- Dive into the details of our standalone implementation, and our related research: 1, 2, 3.
SH-N 7B uses this prompt format: ### Instruction:\n{prompt}\n\n### Response:\n{response}
StripedHyena is a hybrid architecture composed of multi-head, grouped-query attention and gated convolutions arranged in Hyena blocks, different from traditional decoder-only Transformers.
- Costant memory decoding in Hyena blocks via representation of convolutions as state-space models (modal or canonical form), or as truncated filters.
- Low latency, faster decoding and higher throughput than Transformers.
- Improvement to training and inference-optimal scaling laws, compared to optimized Transformer architectures such as Llama-2.
- Trained on sequences of up to 32k, allowing it to process longer prompts.
The most direct way to test StripedHyena models is via our playground, which includes a variety of architecture-specific optimizations.
Playground:
We provide a checkpoint for StripedHyena-Hessian 7B, our base model. Download pytorch-model.bin from the HuggingFace repository. As an alternative, we also provide HuggingFace compatible checkpoints for AutoClasses.
To run our standalone StripedHyena implementation, you will need to install the packages in requirements.txt, as well as rotary and normalization kernels from flash_attn.
The easiest way to ensure all requirements are installed is to build a Docker image using Dockerfile, or follow the steps detailed in the Dockerfile itself in a different virtual environment. For example, to build a Docker image, run:
docker build --tag sh:test .
Installing the dependencies and kernels could take several minutes. Then run the container interactively with:
docker run -it --gpus all --network="host" --shm-size 900G -v=<path_to_this_repo>:/mnt:rw --rm sh:test
Once the environment is set up, you will be able to generate text with:
python generate.py --config_path ./configs/7b-sh-32k-v1.yml \
--checkpoint_path <path_to_ckpt> --cached_generation \
--prompt_file ./test_prompt.txt
If you are generating with prompt.txt, set prefill_style: fft in the config. For very long prompts, you may want to opt for prefill_style: recurrence, which will be slower but use less memory.
If the installation was correct, test prompt will generate the following paragraph
The four species of hyenas are the striped hyena (Hyaena hyaena), the brown hyena (Parahyaena brunnea), the spotted hyena (Crocuta crocuta), and the aardwolf (Proteles cristata).\n\nThe striped hyena is the most widespread species, occurring in Africa, the Middle East, and Asia.
We also provide an entry script to generate with StripedHyena models hosted on HuggingFace. The model ids are:
- Base model:
togethercomputer/StripedHyena-Hessian-7B - Chat model:
togethercomputer/StripedHyena-Nous-7B
Choose your model id, then run the following command:
python generate_transformers.py --model-name <model_id> --input-file ./test_prompt.txt
We report lm-evaluation-harness (10-shot) scores to use as a proxy for (standalone) model correctness in your environment.
- arc_challenge: 0.570 (acc norm)
- hellaswag: 0.816 (acc norm)
- winogrande: 0.735 (acc)
More extensive benchmarks results are provided in the blog post and on HuggingFace.
The standalone implementation provides integration with some custom kernels for StripedHyena such as FlashFFTConv (see the model config 7b-sh-32k-v1.yml for more information). These additional kernels are not required to run the model.
Several issues can be resolved by reinstalling the latest version of flash_attn (pip freeze | grep flash-attn should return a version >= 2.0.0).
StripedHyena is a mixed precision model. Make sure to keep your poles and residues in float32 precision.
If have found the pretrained models or architecture useful for you research or application, consider citing:
@software{stripedhyena,
title = {{StripedHyena: Moving Beyond Transformers with Hybrid Signal Processing Models}},
author = { Poli, Michael and Wang, Jue and Massaroli, Stefano and Quesnelle, Jeffrey and Nguyen, Eric and Thomas, Armin},
month = 12,
year = 2023,
url = { https://github.com/togethercomputer/stripedhyena },
doi = { 10.57967/hf/1595 },
}