Low-Rank Adaptation

Fine-tuning enormous language models is prohibitively expensive in terms of the hardware required and the storage/switching cost for hosting independent instances for different tasks. We propose LoRA, an efficient adaptation strategy that neither introduces inference latency nor reduces input sequence length while retaining high model quality

Hu et al., LoRA: Low-Rank Adaptation of Large Language Models

Low-Rank Adaptation (LoRA) is a method that is designed to allow full fine-tuning of existing LLMs without having to (expensively) retrain all the model parameters, and avoids additional inference latency. It works by freezing the model weights of the initial LLM and injecting trainable rank decomcomposition matrices onto each layer of the Transformer architechture. This reduces the number of parameters used in downstream tasks greatly. This can reduce the memory requirements significantly, and allows "on-par or better" quality than fine-tuning models.

LoRA was initially explored as a technique for use in LLMs but is also applicable to other kinds of models. It is used to fine-tune Stable Diffusion models faster than other tools like Dreambooth.

While we focused on Transformer language models, the proposed principles are generally applicable to any neural networks with dense layers.

Hu et al., LoRA: Low-Rank Adaptation of Large Language Models

Injecting trainable rank recomposition matrices?

LoRA finds a subset of the original weights (about 1%) which can be trained to achieve about the same result as training the whole model while using 100x less compute.

Original weights frozen = Rather than modify the original model, the training results are saved to a small file of only a few MB.

In practice this means you can fine tune a 30B parameter model on a consumer GPU in a couple of hours. Without LoRA you would need to run multiple expensive data center GPUs for days or weeks.

MacsHeadroom

To be more exact, LoRA adds two matrices A and B to any layers that contain trainable weights. The original weights (W_0) have the shape d × k. These are frozen. Matrix A has dimensions d × <rank> (rank is configurable) and matrix B has the shape <rank> × k. A and B are then multiplied and added to W_0 to get altered weights. The benefit here is that the extra matrices are small compared to W_0, which means less parameters need to be optimized, so less activations need to be stored in memory.

stephanheijl

Wait what is rank

Rank is the maximal number of linearly independent columns in a matrix. It has been found that neural networks have low intrinsic rank.

A neural network contains many dense layers which perform matrix multiplication. The weight matrices in these layers typically have full-rank. When adapting to a specific task, Aghajanyan et al. (2020) shows that the pre-trained language models have a low “instrisic dimension” and can still learn efficiently despite a random projection to a smaller subspace. Inspired by this, we hypothesize the updates to the weights also have a low “intrinsic rank” during adaptation.

Hu et al., LoRA: Low-Rank Adaptation of Large Language Models

Wait what is intrensic dimensionality

the intrinsic dimensionality of a space is the number of required pieces of information that we need to describe each object in the space, which may differ from the number of pieces of information that we are using, which we call the extrinsic dimensionality of the space

Matthew N. Bernstein, in his very good writeup

This in essence means that the dimensionality that is actually needed may be lower than the full dimensionality of the matrix. A simple example of this is imagining a set of points on a 2D place that exists within a 3D space. This has 3 extrensic dimensions, but since only 2 are actually needed to describe points, it has 2 intrensic dimensions.

So what is intrensic rank

I guess intrensic rank is the required maximal number of linearly independent columns in a matrix, as opposited to the extrensic rank which is the number of linearly indepedent columns.

Extrinsic rank is an objective property of a matrix that is based solely on its entries, while intrinsic rank takes into account the structure and patterns of the data, and it can be lower than the extrinsic rank in cases where the data has low-dimensional structure or redundancy.

ChatGPT

Impact

LoRA is an incredibly powerful technique we should probably be paying more attention to

LoRA works by representing model updates as low-rank factorizations, which reduces the size of the update matrices by a factor of up to several thousand. This allows model fine-tuning at a fraction of the cost and time. Being able to personalize a language model in a few hours on consumer hardware is a big deal, particularly for aspirations that involve incorporating new and diverse knowledge in near real-time. The fact that this technology exists is underexploited inside Google, even though it directly impacts some of our most ambitious projects.

...

Part of what makes LoRA so effective is that - like other forms of fine-tuning - it’s stackable. Improvements like instruction tuning can be applied and then leveraged as other contributors add on dialogue, or reasoning, or tool use. While the individual fine tunings are low rank, their sum need not be, allowing full-rank updates to the model to accumulate over time.

This means that as new and better datasets and tasks become available, the model can be cheaply kept up to date, without ever having to pay the cost of a full run.

By contrast, training giant models from scratch not only throws away the pretraining, but also any iterative improvements that have been made on top. In the open source world, it doesn’t take long before these improvements dominate, making a full retrain extremely costly.

Leaked Internal Google Document on open source LLM progress

Links

• LoRA: Low-Rank Adaptation of Large Language Models, the original LoRA paper by Microsoft