minLoRA
A minimal, but versatile PyTorch re-implementation of LoRA. In only ~100 lines of code, minLoRA supports the following features:
Features
- Functional, no need to modify the model definition
- Works everywhere, as long as you use
torch.nn.Module - PyTorch native, uses PyTorch's
torch.nn.utils.parametrizeto do all the heavy lifting - Easily extendable, you can add your own LoRA parameterization
- Supports training, inference, and inference with multiple LoRA models
Demo
demo.ipynbshows the basic usage of the libraryadvanced_usage.ipynbshows how you can add LoRA to other layers such as embedding, and how to tie weights
Examples
- Finetuning GPT using LoRA + nanoGPT: https://github.com/cccntu/LoRAnanoGPT/pull/1/files
Library Installation
If you want to import minlora into your project:
git clone https://github.com/cccntu/minLoRA.git
cd minLoRA
pip install -e .
Usage
import torch
from minlora import add_lora, apply_to_lora, disable_lora, enable_lora, get_lora_params, merge_lora, name_is_lora, remove_lora, load_multiple_lora, select_loraTraining a model with minLoRA
model = torch.nn.Linear(in_features=5, out_features=3)
# Step 1: Add LoRA to the model
add_lora(model)
# Step 2: Collect the parameters, pass them to the optimizer
parameters = [
{"params": list(get_lora_params(model))},
]
optimizer = torch.optim.AdamW(parameters, lr=1e-3)
# Step 3: Train the model
# ...
# Step 4: export the LoRA parameters
lora_state_dict = get_lora_state_dict(model)Loading and Inferencing with minLoRA
# Step 1: Add LoRA to your model
add_lora(model)
# Step 2: Load the LoRA parameters
_ = model.load_state_dict(lora_state_dict, strict=False)
# Step 3: Merge the LoRA parameters into the model
merge_lora(model)Inferencing with multiple LoRA models
# to avoid re-adding lora to the model when rerun the cell, remove lora first
remove_lora(model)
# Step 1: Add LoRA to your model
add_lora(model)
# Step 2: Load the LoRA parameters
# load three sets of LoRA parameters
lora_state_dicts = [lora_state_dict_0, lora_state_dict_1, lora_state_dict_2]
load_multiple_lora(model, lora_state_dicts)
# Step 3: Select which LoRA to use at inference time
Y0 = select_lora(model, 0)(x)
Y1 = select_lora(model, 1)(x)
Y2 = select_lora(model, 2)(x)References
- microsoft/LoRA has the official implementation of LoRA, in PyTorch
- karpathy/minGPT the structure of the repo is adapted from minGPT
TODO
- A notebook to show how to configure LoRA parameters
- Real training & inference examples