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https://github.com/cccntu/minLoRA
minLoRA: a minimal PyTorch library that allows you to apply LoRA to any PyTorch model.
https://github.com/cccntu/minLoRA
fastai huggingface pytorch pytorch-implementation pytorch-lightning
Last synced: about 2 months ago
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minLoRA: a minimal PyTorch library that allows you to apply LoRA to any PyTorch model.
- Host: GitHub
- URL: https://github.com/cccntu/minLoRA
- Owner: cccntu
- License: mit
- Created: 2023-02-17T14:58:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-06-21T23:26:11.000Z (over 1 year ago)
- Last Synced: 2024-06-15T15:37:37.613Z (3 months ago)
- Topics: fastai, huggingface, pytorch, pytorch-implementation, pytorch-lightning
- Language: Jupyter Notebook
- Homepage:
- Size: 15.6 KB
- Stars: 396
- Watchers: 9
- Forks: 27
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# minLoRA
A minimal, but versatile PyTorch re-implementation of [LoRA](https://github.com/microsoft/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.parametrize` to 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.ipynb` shows the basic usage of the library
- `advanced_usage.ipynb` shows 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
```python
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_lora
```
### Training a model with minLoRA
```python
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
```python
# 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
```python
# 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](https://github.com/microsoft/LoRA) has the official implementation of LoRA, in PyTorch
- [karpathy/minGPT](https://github.com/karpathy/minGPT) the structure of the repo is adapted from minGPT
### TODO
- [x] A notebook to show how to configure LoRA parameters
- [x] Real training & inference examples