An Open-Source Framework for Paramter Efficient Tuning (Delta Tuning).

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Overview • Installation • Basic Usage • Docs • Performance •

Overview

OpenDelta is a toolkit for parameter efficient methods (we dub it as delta tuning), by which users could flexibly assign (or add) a small amount parameters to update while keeping the most paramters frozen. By using OpenDelta, users could easily implement prefix-tuning, adapters, Lora, or any other types of delta tuning with preferred PTMs.

• Our repo is tested on Python 3.8 and PyTorch 1.9.0. Lower version may also be supported.

• A demo of using Opendelta to modify the PLM (E.g., BART).

Updates

• 2022.02.16 support regular expression in named-based addressing.

Installation

create a virtualenv (optional)

conda create -n opendelta_env python=3.8
conda activate opendelta_env

Using Pip

Install OpenDelta using pip as follows:

pip install opendelta

To play with the latest features, you can also install OpenDelta from the source.

Build from Source

git clone https://github.com/thunlp/OpenDelta.git
cd OpenDelta

Option 1: If you won't modify the code, run

python setup.py install

Option 2: If you want to modify the code or keep the repo updated by git clone, run

python setup.py develop

Must Try

from transformers import AutoModelForSeq2SeqLM
t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
from opendelta import AutoDeltaModel
delta = AutoDeltaModel.from_finetuned("DeltaHub/lora_t5-base_mrpc", backbone_model=t5)
delta.log()

Verified Supported Models

• You can try to use OpenDelta on any backbone models based on PyTorch.

• However, with small chances thatThe interface of the submodules of the backbone model is not supported. Therefore we verified some commonly used models that OpenDelta are sure to support.

• We will keep testing more and more emerging models.

• Pull requests are welcomed when you successfully apply OpenDelta on your own backbone model.

	Lora	Bias Tuning	Adapter Houstbly	Adapter Preffier	Adapter Drop	Adapater Low-Rank	Compactor	Prefix Tuning	Prompt Tuning
T5	✅	✅	✅	✅	✅	✅	✅	✅	✅
GPT-2	✅	✅	✅	✅	✅	✅	✅	✅
BART	✅	✅	✅	✅	✅	✅	✅	✅
DistilBERT	✅	✅	✅	✅	✅	✅	✅	✅
RoBERTa	✅	✅	✅	✅	✅	✅	✅	✅
BERT	✅	✅	✅	✅	✅	✅	✅	✅	✅
T5-3b(parallel)	✅	✅	✅	✅	✅	✅	✅	✅	✅
Deberta-v2	✅	✅	✅	✅	✅	✅	✅
CTRL	✅	✅	✅	✅	✅	✅	✅
ViT	✅

Performance Checked Combination

Google sheet here

Subject to change at any moment.