github bigscience-workshop/petals v2.1.0
v2.1.0: 🤗 .generate(), faster loading, responsive inference, and more
on GitHub

latest release: v2.2.0
10 months ago

Highlights

🔌 Compatibility with 🤗 Transformers generation utils. Petals models now directly use 🤗 Transformers .generate() implementation instead of custom generation code. This means that you can use a variety of generation methods and constraints implemented in 🤗 Transformers (e.g., repetition_penalty, beam search, etc.) and expect an exact match between Petals and a model running locally.

Most common methods are compatible with reusing inference sessions, so that you can run .generate() multiple times without reprocessing the dialogue history from scratch: 

with model.inference_session(max_length=100):
    outputs1 = model.generate(user_prompt1, repetition_penalty=1.2)
    outputs2 = model.generate(user_prompt2, repetition_penalty=1.2)

⚡ Faster loading of Stable Beluga 2. We repacked Stable Beluga 2, the most popular model at the moment, to increase its loading speed and minimize RAM and disk space requirements. The repacked version can be loaded from the petals-team/StableBeluga2 repository and is fully compatible with clients and servers using the standard repository (stabilityai/StableBeluga2).

Now, clients need to download only 1.05 GB of data to run Stable Beluga 2 (instead of ~20 GB needed before) and require only 4 GB of RAM (instead of ~20 GB required before). Servers need to download and store 2x less data and load the model from disk significantly faster. If you're switching from the old repository, don't forget to remove the old cache in the~/.cache/petals/models--stabilityai--StableBeluga2 directory to save disk space.

⏱️ More responsive inference. In older versions, servers could become unresponsive for a few seconds while processing large prefixes (thousands of tokens) on inference. This release allows to perform small inference requests (a few tokens) in the middle of processing a large request, thus avoiding freezes during token-by-token inference caused by someone processing a large prefix.

🔒 Minor improvements. This release adds support for loading weights in the safetensors format on servers and adds the blocked_servers client option to avoid a given set of servers: 

from petals import AutoDistributedModelForCausalLM

blocked_servers = ["12D3KooWA6g...", "12D3KooWGyD..."]  # Full peer IDs from https://health.petals.dev
model = AutoDistributedModelForCausalLM.from_pretrained(model_name, blocked_servers=blocked_servers)

🐞 Bug fixes. This release also includes a variety of bug fixes allowing to speed up the chatbot app and fine-tuning, better bypass recently disconnect servers, improve rebalancing algorithm and usability of benchmarks, fix throughput measurements and installation on ARM CPUs.

We also fixed Petals compatibility with the latest releases of 🤗 Transformers, Accelerate, and PEFT libraries.

Breaking changes

📖 Default inference sessions. If you run .generate() or forward passes inside an .inference_session() context, they now use the opened session by default. These snippets are now equivalent: 

# Using default session
with model.inference_session(max_length=100):
    output_ids = model.generate(input_ids, max_new_tokens=3)

# Explicitly specifying a session
with model.inference_session(max_length=100) as sess:
    output_ids = model.generate(input_ids, max_new_tokens=3, session=sess)

Earlier, the 1st snippet was creating a new session, which confused most people and lead to bugs.

➡️ Renaming. We renamed SequenceManagerConfig to petals.ClientConfig and petals.dht_utils to petals.utils.dht. The old names now lead to DeprecationWarnings and will be removed in Petals 2.2.0+.

What's Changed

New Contributors

Full Changelog: v2.0.1...v2.1.0

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