lora-adapters 0.1.4

PyTorch implementation of low-rank adaptation (LoRA), a parameter-efficient approach to adapt a large pre-trained deep learning model which obtains performance on-par with full fine-tuning.

LowRank Adapters

This libary implements a series of Low Rank Adapters to apply to torch models in a simple manner without having to redefine your model. Official Repository

You can choose to only store the updated layers as shown below

import timm 
import torch
from lora_adapters import LoraConv2d, apply_adapter, mark_only_lora_as_trainable, lora_state_dict
model = timm.create_model('resnet50', pretrained=True)
model = apply_adapter(model, LoraConv2d, rank=16)
model = mark_only_lora_as_trainable(model, bias='lora_only')
... Custom Training Loop ...
updates = lora_state_dict(model, bias='lora_only')
torch.save(updates, 'updates.ckpt')

import timm 
import torch
from lora_adapters import LoraConv2d, apply_adapter, mark_lora_as_trainable, lora_state_dict
model = timm.create_model('resnet50', pretrained=True)
updates = torch.load('updates.ckpt')
model.load_state_dict(updates, strict=False)

Or you can reverse the lora adaptations and save the full state dictionary

import timm 
import torch
from lora_adapters import LoraConv2d, apply_adapter, mark_only_lora_as_trainable, undo_lora
model = timm.create_model('resnet50', pretrained=True)
model = apply_adapter(model, LoraConv2d, rank=16)
model = mark_only_lora_as_trainable(model, bias='lora_only')
... Custom Training Loop ...
model = undo_lora(model)
torch.save(model.state_dict(), 'model.ckpt')

Warnings

The functions that adapt the model change them in place, so the below code won't work as expected, model and lora_model will be the same

import timm 
from lora_adapters import LoraConv2d, apply_adapter
model = timm.create_model('resnet50', pretrained=True)
lora_model = apply_adapter(model, LoraConv2d, rank=16)