110 lines
4.1 KiB
Markdown
110 lines
4.1 KiB
Markdown
<div align="center">
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<img src="https://s4.ax1x.com/2022/02/14/Hy7lAf.png" width="350px">
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**An Open-Source Framework for Paramter-Efficient Tuning (Delta Tuning).**
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------
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<p align="center">
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<a href="#Overview">Overview</a> •
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<a href="#installation">Installation</a> •
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<a href="https://opendelta.readthedocs.io/en/latest/notes/usage.html">Basic Usage</a> •
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<a href="https://opendelta.readthedocs.io/">Docs</a> •
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<a href="https://docs.google.com/spreadsheets/d/1BIVa8ocAPga-u7rBOXLYaTfaJSjI1dWfwohmLjmFDrY/edit?usp=sharing">Performance</a> •
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</p>
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</div>
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## Overview
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OpenDelta is a toolkit for parameter-efficient tuning 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.
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- Our repo is tested on Python 3.8 and PyTorch 1.9.0. Lower version may also be supported.
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- **A demo of using Opendelta to modify the PLM (E.g., BART).**
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## Updates
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- 2022.03.24 We notice several bugs in Soft Prompt Tuning and Prefix Tuning, mainly due to their need to customize attention ids, token_type_ids, we are fixing it! Currently, please use the other methods since they are stabler and better in performance.
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- 2022.03.20 Add a [colab example](https://colab.research.google.com/drive/1uAhgAdc8Qr42UKYDlgUv0f7W1-gAFwGo?usp=sharing) to illustrate efficient training and space-saving multitask-serving.
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- 2022.03.20 A new pip version released.
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- 2022.02.16 Support [regular expression](https://opendelta.readthedocs.io/en/latest/notes/namebasedaddr.html#regexexpr) in named-based addressing.
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## Installation
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create a virtualenv (optional)
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```shell
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conda create -n opendelta_env python=3.8
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conda activate opendelta_env
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```
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### Using Pip
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Install OpenDelta using pip as follows:
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```shell
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pip install opendelta
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```
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To play with the latest features, you can also install OpenDelta from the source.
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### Build from Source
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```shell
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git clone https://github.com/thunlp/OpenDelta.git
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cd OpenDelta
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```
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#### Option 1: If you won't modify the code, run
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```shell
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python setup.py install
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```
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#### Option 2: If you want to modify the code or keep the repo updated by git clone, run
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```shell
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python setup.py develop
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```
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## Must Try
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```python
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from transformers import AutoModelForSeq2SeqLM
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t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-large")
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from opendelta import AutoDeltaModel
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delta = AutoDeltaModel.from_finetuned("thunlp/FactQA_T5-large_Adapter", backbone_model=t5)
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delta.log()
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```
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## Verified Supported Models
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- **You can try to use OpenDelta on *any* backbone models based on PyTorch.**
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- However, with small chances thatThe interface of the submodules of the backbone model is not supported. Therefore we verified some commonly
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used models that OpenDelta are sure to support.
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- We will keep testing more and more emerging models.
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- Pull requests are welcomed when you successfully apply OpenDelta on your own backbone model.
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| | Lora | Bias<br>Tuning | Adapter<br>Houstbly | Adapter<br>Preffier | Adapter<br>Drop | Adapater<br> Low-Rank | Compactor |Prefix<br> Tuning | Prompt <br> Tuning |
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| --------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ----- | ----- |
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| T5 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
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| BART | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
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| DistilBERT | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
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| RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
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| BERT | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| T5-3b(parallel)| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Deberta-v2 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | |
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| CTRL | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | |
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