petals 是一个运行极大型(100B以上)语言模型的去中心化平台,通过与互联网上的人联合计算资源,运行推理或微调大型语言模型,如 BLOOM-176B。不需要拥有高端 GPU。
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Run 100B+ language models at home, BitTorrent-style.
Fine-tuning and inference up to 10x faster than offloading
Generate text using distributed 176B-parameter BLOOM or BLOOMZ and fine-tune them for your own tasks:
from petals import DistributedBloomForCausalLM
model = DistributedBloomForCausalLM.from_pretrained("bigscience/bloom-petals", tuning_mode="ptune", pre_seq_len=16)
# Embeddings & prompts are on your device, BLOOM blocks are distributed across the Internet
inputs = tokenizer("A cat sat", return_tensors="pt")["input_ids"]
outputs = model.generate(inputs, max_new_tokens=5)
print(tokenizer.decode(outputs[0])) # A cat sat on a mat...
# Fine-tuning (updates only prompts or adapters hosted locally)
optimizer = torch.optim.AdamW(model.parameters())
for input_ids, labels in data_loader:
outputs = model.forward(input_ids)
loss = cross_entropy(outputs.logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
🔏 Your data will be processed by other people in the public swarm. Learn more about privacy here. For sensitive data, you can set up a private swarm among people you trust.
Run this in an Anaconda env (requires Linux and Python 3.7+):
conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -U petals
python -m petals.cli.run_server bigscience/bloom-petals
Or use our Docker image (works on Linux, macOS, and Windows with WSL2):
sudo docker run -p 31330:31330 --ipc host --gpus all --volume petals-cache:/cache --rm \
learningathome/petals:main python -m petals.cli.run_server bigscience/bloom-petals --port 31330
📚 See FAQ to learn how to configure the server to use multiple GPUs, address common issues, etc.
You can also host BLOOMZ, a version of BLOOM fine-tuned to follow human instructions in the zero-shot regime — just replace bloom-petals
with bloomz-petals
.
🔒 Hosting a server does not allow others to run custom code on your computer. Learn more about security here.
💬 If you have any issues or feedback, let us know on our Discord server!
Basic tutorials:
Useful tools and advanced guides:
Learning more:
📋 If you build an app running BLOOM with Petals, make sure it follows the BLOOM's terms of use.
📚 See FAQ 📜 Read paper
Here's how to install Petals with conda:
conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -U petals
This script uses Anaconda to install CUDA-enabled PyTorch. If you don't have anaconda, you can get it from here. If you don't want anaconda, you can install PyTorch any other way. If you want to run models with 8-bit weights, please install PyTorch with CUDA 11 or newer for compatility with bitsandbytes.
System requirements: Petals only supports Linux for now. If you don't have a Linux machine, consider running Petals in Docker (see our image) or, in case of Windows, in WSL2 (read more). CPU is enough to run a client, but you probably need a GPU to run a server efficiently.
Petals uses pytest with a few plugins. To install them, run:
conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
git clone https://github.com/bigscience-workshop/petals.git && cd petals
pip install -e .[dev]
To run minimalistic tests, you need to make a local swarm with a small model and some servers. You may find more information about how local swarms work and how to run them in this tutorial.
export MODEL_NAME=bloom-testing/test-bloomd-560m-main
python -m petals.cli.run_server $MODEL_NAME --block_indices 0:12 \
--identity tests/test.id --host_maddrs /ip4/127.0.0.1/tcp/31337 --new_swarm &> server1.log &
sleep 5 # wait for the first server to initialize DHT
python -m petals.cli.run_server $MODEL_NAME --block_indices 12:24 \
--initial_peers SEE_THE_OUTPUT_OF_THE_1ST_PEER &> server2.log &
tail -f server1.log server2.log # view logs for both servers
Then launch pytest:
export MODEL_NAME=bloom-testing/test-bloomd-560m-main REF_NAME=bigscience/bloom-560m
export INITIAL_PEERS=/ip4/127.0.0.1/tcp/31337/p2p/QmS9KwZptnVdB9FFV7uGgaTq4sEKBwcYeKZDfSpyKDUd1g
PYTHONPATH=. pytest tests --durations=0 --durations-min=1.0 -v
After you're done, you can terminate the servers and ensure that no zombie processes are left with pkill -f petals.cli.run_server && pkill -f p2p
.
The automated tests use a more complex server configuration that can be found here.
We use black and isort for all pull requests.
Before committing your code, simply run black . && isort .
and you will be fine.
Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Max Ryabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, and Colin Raffel. Petals: Collaborative Inference and Fine-tuning of Large Models. arXiv preprint arXiv:2209.01188, 2022.
@article{borzunov2022petals,
title = {Petals: Collaborative Inference and Fine-tuning of Large Models},
author = {Borzunov, Alexander and Baranchuk, Dmitry and Dettmers, Tim and Ryabinin, Max and Belkada, Younes and Chumachenko, Artem and Samygin, Pavel and Raffel, Colin},
journal = {arXiv preprint arXiv:2209.01188},
year = {2022},
url = {https://arxiv.org/abs/2209.01188}
}
This project is a part of the BigScience research workshop.