This repo contains the source code of the Python package loralib
and several examples of how to integrate it with PyTorch models, such as those in Hugging Face.
We only support PyTorch for now.
See our paper for a detailed description of LoRA.
LoRA: Low-Rank Adaptation of Large Language Models
Edward J. Hu*, Yelong Shen*, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen
Paper: https://arxiv.org/abs/2106.09685
Video explainer: https://www.youtube.com/watch?v=DhRoTONcyZE
Update 2/2023: LoRA is now supported by the State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) library by Hugging Face.
LoRA reduces the number of trainable parameters by learning pairs of rank-decompostion matrices while freezing the original weights. This vastly reduces the storage requirement for large language models adapted to specific tasks and enables efficient task-switching during deployment all without introducing inference latency. LoRA also outperforms several other adaptation methods including adapter, prefix-tuning, and fine-tuning.
We obtain result comparable or superior to full finetuning on the GLUE benchmark using RoBERTa (Liu et al., 2019) base and large and DeBERTa (He et al., 2020) XXL 1.5B, while only training and storing a fraction of the parameters. Click the numbers below to download the RoBERTa and DeBERTa LoRA checkpoints.
RoBERTa base Fine-tune |
RoBERTa base LoRA |
DeBERTa XXL Fine-tune |
DeBERTa XXL LoRA |
||
---|---|---|---|---|---|
# of Trainable Params. | 125M | 0.8M | 1.5B | 4.7M | |
MNLI (m-Acc/mm-Acc) | 87.6 | 87.5±.3/86.9±.3 | 91.7/91.9 | 91.9±.1/91.9±.2 | |
SST2 (Acc) | 94.8 | 95.1±.2 | 97.2 | 96.9±.2 | |
MRPC (Acc) | 90.2 | 89.7±.7 | 92.0 | 92.6±.6 | |
CoLA (Matthew's Corr) | 63.6 | 63.4±1.2 | 72.0 | 72.4±1.1 | |
QNLI (Acc) | 92.8 | 93.3±.3 | 96.0 | 96.0±.1 | |
QQP (Acc) | 91.9 | 90.8±.1 | 92.7 | 92.9±.1 | |
RTE (Acc) | 78.7 | 86.6±.7 | 93.9 | 94.9±.4 | |
STSB (Pearson/Spearman Corr) | 91.2 | 91.5±.2/91.3±.2 | 92.9/92.6 | 93.0±.2/92.9±.3 | |
Average | 86.40 | 87.24 | 91.06 | 91.32 |
Note: You still need the original pre-trained checkpoint from Hugging Face to use the LoRA checkpoints.
Fine-tuning numbers are taken from Liu et al. (2019) and He et al. (2020). We include confidence intervals on results from our experiments. Please follow the instructions in examples/NLU/
to reproduce our results.
On GPT-2, LoRA compares favorably to both full finetuning and other efficient tuning methods, such as adapter (Houlsby et al., 2019) and prefix tuning (Li and Liang, 2021). We evaluated on E2E NLG Challenge, DART, and WebNLG:
Method | # of Trainable Params | E2E (BLEU) | DART (BLEU) | WebNLG (BLEU-U/S/A) | |
---|---|---|---|---|---|
GPT-2 M (Fine-Tune) | 354.92M | 68.2 | 46.0 | 30.4/63.2/47.6 | |
GPT-2 M (Adapter) | 0.37M | 66.3 | 42.4 | 45.1/54.5/50.2 | |
GPT-2 M (Prefix) | 0.35M | 69.7 | 45.7 | 44.1/63.1/54.4 | |
GPT-2 M (LoRA) | 0.35M | 70.4±.1 | 47.1±.2 | 46.7±.4/62.1±.2/55.3±.2 | |
GPT-2 L (Fine-Tune) | 774.03M | 68.5 | 46.5 | 41.7/64.6/54.2 | |
GPT-2 L (Adapter) | 0.88M | 69.1±.1 | 45.7±.1 | 49.8±.0/61.1±.0/56.0±.0 | |
GPT-2 L (Prefix) | 0.77M | 70.3 | 46.5 | 47.0/64.2/56.4 | |
GPT-2 L (LoRA) | 0.77M | 70.4±.1 | 47.5±.1 | 48.4±.3/64.0±.3/57.0±.1 |
Non-LoRA baselines, except for adapter on GPT-2 large, are taken from Li and Liang (2021). We include confidence intervals on results from our experiments.
Download the GPT-2 LoRA checkpoints:
- GPT-2 Medium E2E (1.5 MB)
- GPT-2 Medium DART (1.5 MB)
- GPT-2 Medium WebNLG (1.5 MB)
- GPT-2 Large E2E (2.3 MB)
- GPT-2 Large DART (2.3 MB)
- GPT-2 Large WebNLG (2.3 MB)
Please follow the instructions in examples/NLG/
to reproduce our result.
(The initial release of this repo has been archived in the branch "snapshot-9-15-2021")
There are several directories in this repo:
- loralib/ contains the source code for the package
loralib
, which needs to be installed to run the examples we provide; - examples/NLG/ contains an example implementation of LoRA in GPT-2 using our package, which can be used to reproduce the result in our paper;
- examples/NLU/ contains an example implementation of LoRA in RoBERTa and DeBERTa using our package, which produces competitive results on the GLUE benchmark;
- See how we use
loralib
in GPT-2, RoBERTa, and DeBERTa v2
- Installing
loralib
is simply
pip install loralib
# Alternatively
# pip install git+https://github.com/microsoft/LoRA
- You can choose to adapt some layers by replacing them with counterparts implemented in
loralib
. We only supportnn.Linear
,nn.Embedding
, andnn.Conv2d
for now. We also support aMergedLinear
for cases where a singlenn.Linear
represents more than one layers, such as in some implementations of the attentionqkv
projection (see Additional Notes for more).
# ===== Before =====
# layer = nn.Linear(in_features, out_features)
# ===== After ======
import loralib as lora
# Add a pair of low-rank adaptation matrices with rank r=16
layer = lora.Linear(in_features, out_features, r=16)
- Before the training loop begins, mark only LoRA parameters as trainable.
import loralib as lora
model = BigModel()
# This sets requires_grad to False for all parameters without the string "lora_" in their names
lora.mark_only_lora_as_trainable(model)
# Training loop
for batch in dataloader:
...
- When saving a checkpoint, generate a
state_dict
that only contains LoRA parameters.
# ===== Before =====
# torch.save(model.state_dict(), checkpoint_path)
# ===== After =====
torch.save(lora.lora_state_dict(model), checkpoint_path)
- When loading a checkpoint using
load_state_dict
, be sure to setstrict=False
.
# Load the pretrained checkpoint first
model.load_state_dict(torch.load('ckpt_pretrained.pt'), strict=False)
# Then load the LoRA checkpoint
model.load_state_dict(torch.load('ckpt_lora.pt'), strict=False)
-
While we focus on a simple yet effect setup, namely adapting only the
q
andv
projection in a Transformer, in our examples, LoRA can be apply to any subsets of pre-trained weights. We encourage you to explore different configurations, such as adapting the embedding layer by replacingnn.Embedding
withlora.Embedding
and/or adapting the MLP layers. It's very likely that the optimal configuration varies for different model architectures and tasks. -
Some Transformer implementation uses a single
nn.Linear
for the projection matrices for query, key, and value. If one wishes to constrain the rank of the updates to the individual matrices, one has to either break it up into three separate matrices or uselora.MergedLinear
. Make sure to modify the checkpoint accordingly if you choose to break up the layer.
# ===== Before =====
# qkv_proj = nn.Linear(d_model, 3*d_model)
# ===== After =====
# Break it up (remember to modify the pretrained checkpoint accordingly)
q_proj = lora.Linear(d_model, d_model, r=8)
k_proj = nn.Linear(d_model, d_model)
v_proj = lora.Linear(d_model, d_model, r=8)
# Alternatively, use lora.MergedLinear (recommended)
qkv_proj = lora.MergedLinear(d_model, 3*d_model, r=8, enable_lora=[True, False, True])
- Training bias vectors in tandem with LoRA might be a cost-efficient way to squeeze out extra task performance (if you tune the learning rate carefully). While we did not study its effect thoroughly in our paper, we make it easy to try in
lora
. You can mark some biases as trainable by passing "all" or "lora_only" tobias=
when callingmark_only_lora_as_trainable
. Remember to pass the correspondingbias=
argument tolora_state_dict
when saving a checkpoint.
# ===== Before =====
# lora.mark_only_lora_as_trainable(model) # Not training any bias vectors
# ===== After =====
# Training all bias vectors associated with modules we apply LoRA to
lora.mark_only_lora_as_trainable(model, bias='lora_only')
# Alternatively, we can train *all* bias vectors in the model, including LayerNorm biases
lora.mark_only_lora_as_trainable(model, bias='all')
# When saving a checkpoint, use the same bias= ('all' or 'lora_only')
torch.save(lora.lora_state_dict(model, bias='all'), checkpoint_path)
- Calling
model.eval()
will trigger the merging of LoRA parameters with the corresponding pretrained ones, which eliminates additional latency for subsequent forward passes. Callingmodel.train()
again will undo the merge. This can be disabled by passingmerge_weights=False
to LoRA layers.
Please contact us or post an issue if you have any questions.
For questions related to the package loralib
:
- Edward Hu ([email protected])
- Phillip Wallis ([email protected])
- Weizhu Chen ([email protected])
The GPT-2 example:
- Phillip Wallis ([email protected])
- Yelong Shen ([email protected])
The RoBERTa/DeBERTa example:
- Lu Wang ([email protected])
ConvLoRA can load the pretrained weights!!
We thank in alphabetical order Jianfeng Gao, Jade Huang, Jiayuan Huang, Lisa Xiang Li, Xiaodong Liu, Yabin Liu, Benjamin Van Durme, Luis Vargas, Haoran Wei, Peter Welinder, and Greg Yang for providing valuable feedback.
@inproceedings{
hu2022lora,
title={Lo{RA}: Low-Rank Adaptation of Large Language Models},
author={Edward J Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=nZeVKeeFYf9}
}
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