LoRA - Low-Rank Adaptation

Mastering LoRA: Efficient Fine Tuning for Large Language Models - LLMs - PEFT Guide (PEFT - Parameter Efficient Fine Tuning)

Lucidate

01 Jun 2024

Fine-tuning LLMS

LoRA - Low Rank Adaptation and PEFT - Parameter Efficient Fine Tuning. Are you looking to master the art of fine tuning Large Language Models like GPT-3, BERT, and T5? Look no further! In this comprehensive video, we dive deep into the world of Parameter Efficient Fine Tuning (PEFT) methods, with a special focus on the game-changing technique called Low Rank Adaptation (LoRA).

Discover why fine tuning is crucial for adapting these powerful models to specific tasks and domains, such as sentiment analysis in equity analyst reviews or named entity recognition. We'll explore the pros and cons of various PEFT methods, including LoRA, Prefix Tuning, and Adapter Layers, and help you understand which approach best suits your needs.

Through step-by-step guides and practical examples, you'll learn how to implement LoRA using popular frameworks like HuggingFace. We'll cover everything from creating high-quality datasets for fine tuning to leveraging low rank matrices for reducing trainable parameters and improving efficiency. Whether you're aiming to enhance the performance of your fine tuned models for targeted applications or simply looking to stay up-to-date with the latest advancements in NLP, this video has you covered.

We'll discuss best practices, common challenges, and strategies for overcoming them, ensuring that you have the tools and knowledge to succeed in your fine tuning endeavors. But that's not all! We'll also compare LoRA with other cutting-edge techniques like Prefix Tuning, helping you make informed decisions when fine tuning your Large Language Models. And for those interested in specific applications, we'll showcase how LoRA can be used to adapt models like GPT-3 for text generation tasks or BERT for named entity recognition.

By the end of this video, you'll have a solid understanding of Parameter Efficient Fine Tuning methods, particularly LoRA, and how they can be leveraged to achieve state-of-the-art results in a variety of NLP tasks. Whether you're a researcher, data scientist, or practitioner, this video is your ultimate guide to mastering fine tuning with LoRA and beyond.

Don't miss out on this opportunity to take your NLP skills to the next level. Watch now and unlock the full potential of Large Language Models through efficient fine tuning techniques! Step-by-step guides and practical demonstrations are at the core of this video. We'll walk you through how to fine tune models like GPT-3 using LoRA, providing a detailed roadmap for adapting this powerful model to your specific needs. Similarly, you'll learn the step-by-step process of fine tuning BERT with Low Rank Adaptation, unlocking its potential for tasks like sentiment analysis and named entity recognition.

Throughout the video, we'll compare LoRA with other PEFT methods, such as Prefix Tuning and Adapter Layers, highlighting the benefits and trade-offs of each approach. You'll discover how implementing LoRA for fine tuning transformers in HuggingFace can streamline your workflow and boost efficiency. We'll also explore how Low Rank Adaptation reduces trainable parameters, making fine tuning more accessible and less resource-intensive.

Dive into real-world applications as we demonstrate fine tuning the T5 model for specific tasks using LoRA. We'll showcase how adapting pre-trained language models for domain-specific tasks, like optimizing sentiment analysis in equity analyst reviews, can lead to significant performance improvements.

And for those wondering about the best choice between LoRA and Prefix Tuning, we'll provide a comprehensive comparison to help you make an informed decision. Mastering Parameter Efficient Fine Tuning (PEFT) methods is a key focus of this video. We'll discuss fine tuning strategies for improving accuracy in targeted applications and share best practices for leveraging Low Rank Matrices in the process.

Additionally, we'll tackle common challenges faced when fine tuning Large Language Models for specific domains and provide practical solutions to overcome them. By the end of this video, you'll have a comprehensive understanding of how to adapt GPT-3 for text generation tasks using Low Rank Adaptation and fine tune BERT for named entity recognition with LoRA.

We'll also touch on advanced techniques, such as enhancing the performance of fine tuned models through back-testing and calibration, ensuring that you have the tools to achieve state-of-the-art results in your NLP projects. These additional paragraphs incorporate the remaining long-tail keywords while expanding on the video's content and providing more specific examples and use cases. The language remains natural and engaging, making the description more informative and enticing for potential viewers.

#FineTuning #LargeLanguageModels #LoRA #PEFT #GPT3 #BERT #SentimentAnalysis #NamedEntityRecognition #PrefixTuning #AdapterLayers #HuggingFace #LowRankAdaptation #NLP #TextGeneration #T5 #EquityAnalystReviews #DomainSpecificTasks

Video and Transcript

https://www.youtube.com/watch?v=LuszqkM1s88

This is Richard Walker from Lucid, welcome to the fourth video in this series of six on fine-tuning. In this video, we're going to look generally at the concepts of fine-tuning and specifically at a method called LoRA or Low-Rank Adaptation. LoRA was popularized in a 2021 paper from Microsoft titled "LoRA: Low-Rank Adaptation of Large Language Models." Since that time, it's become one of the most popular techniques for fine-tuning LLMs. In this video, we'll discuss why we fine-tune, what fine-tuning methods are available, and as I said, we'll dive specifically into LoRA and answer the question: how does LoRA work?

So firstly, why do we need to fine-tune? Well, while large language models like GPT-3, BERT, or T5 are incredibly powerful and have been trained on vast amounts of data, they're not necessarily optimized for specific tasks or domains right out of the box. These models have learned a broad understanding of language and can generate coherent text, but they may not always produce the desired output for a particular application. For example, let's say you want to use a large language model for a task like sentiment analysis on the content of equity analyst reviews for a specific industry sector. While the pre-trained model has a general understanding of sentiment, it may not be attuned to the specific language, jargon, or nuances associated with a particular genre like equity analyst reports. Indeed, most analyst reports tend to veer to the positive. So, how can you calibrate the sentiment analysis, and perhaps use back-testing based on the performance of the stock since the analysis was published? This is where techniques like fine-tuning come into play.

Likewise, in this series, we have been looking at fine-tuning models to be able to write code for us, either to access a risk system's API or, in my specific example, to write Manim animation code based on natural language instructions. By fine-tuning the model on a smaller dataset specific to your task and your domain, you can adapt the model's knowledge to better suit your needs. The model learns to focus on the relevant patterns and characteristics of your specific data, resulting in improved performance and more accurate outputs.

So, fine-tuning is essential because it allows us to: one, adapt large language models to specific tasks and domains; two, improve performance and accuracy on targeted applications; and three, customize the model's behavior and output style. The Microsoft paper on LoRA discusses other state-of-the-art fine-tuning approaches, all of which fall under the category of PFT or parameter-efficient fine-tuning. PFT is a fancy way of saying fine-tuning an LLM while only updating a subset of the model's parameters. Let's take a closer look at these approaches and discuss their pros and cons.

The first approach is called last layer fine-tuning. As the name suggests, this method focuses on fine-tuning only the last layer or last few layers of the pre-trained model while keeping the rest of the layers frozen. The intuition behind this approach is that in LLMs, the early layers of the model learn the basics of the language from the ground up, such as vocabulary, grammar, and syntax. On the other hand, the later layers learn the subtleties and nuances specific to the task or domain. By fine-tuning only the last few layers or last layer, we can adapt the model to a particular subject matter or task while leveraging the knowledge learned in the earlier layers.

The second approach is called prefix tuning. In this method, a small set of trainable parameters, called a prefix, is prepended to the input of each transformer layer in the pre-trained model. However, these prefix parameters can be substantial, especially for large models, and this will then require careful tuning on the prefix size and architecture.

The third approach is called adapter layers. In this method, small neural network modules called adapters are inserted between the layers of the pre-trained model. These adapter layers are fine-tuned on the specific task while keeping the original model parameters frozen. The adapters learn to transform the activations of the pre-trained model to better suit the target task. This does introduce additional computational overhead at inference time due to the added adapter layers. Furthermore, the optimal size and placement of adapter layers may require experimentation.

The fourth and final approach that we'll discuss in depth is LoRA or low rank adaptation. LoRA is the main focus of this video, and we'll dive deeper into how it works in the upcoming sections. LoRA is an efficient fine-tuning method that adapts the pre-trained model using low-rank matrices. It significantly reduces the number of trainable parameters while still achieving impressive performance on a wide range of tasks. It's been shown to be highly parameter-efficient, requiring only a small fraction of the original model parameters to be trained. It has also been shown to achieve competitive performance with full fine-tuning on various tasks.

So now that we've seen the different parameter-efficient fine-tuning approaches and their pros and cons, let's dive into the specifics of how LoRA works and what makes it such an effective method for fine-tuning large language models. The rank is one of several properties of matrices that can be exploited for optimization purposes in computer science. Given that AI mostly involves multiplying huge matrices and tensors together, a good deal of AI research is indeed spent on exploiting these properties for performance gains.

Here's an example of a matrix whose rank is less than its dimension. It's a 3x3 matrix with a rank of one. This is because the rows and indeed the columns are multiples of one another. We can decompose this single matrix containing nine values into the matrix product of two matrices: one with three rows and one column, and another with a single column and three rows. Here we only need to store six values instead of nine, a modest saving. But as we increase the dimensionality of the matrices, the savings become more evident. Here are two 7x7 matrices. The rank of both matrices turns out to be two. This means we can decompose both of these matrices into two smaller ones: one 7x2, the other 2x7. Now we've reduced the number of parameters by about half, from 49 to 28. And as the matrices get larger and larger, the savings get greater and greater. Bear in mind that the dimensionality of some matrices in LLMs can be in the tens of thousands.

So with the name of LoRA, low rank adaptation, we have a strong clue as to how the algorithm works. The "low rank" bit indicates that we're going to use matrices with a rank lower than the hidden dimension, and the "adaptation" bit suggests we're going to insert additional elements into the LLM to adapt it for fine-tuning. We'll see how to do this shortly, but first, let's review how we train and fine-tune neural networks generally and transformers specifically. If you're unfamiliar with these techniques, there are a couple of Lucid playlists so that you can review the concepts. One playlist contains some insanely videos which give a very high-level summary of neurons, activation functions, backpropagation, and all the other concepts necessary to get your head around training these AI systems, all in short, 60-second snackable videos. But if you want a deeper dive, then the other playlist contains explainer videos of around 15 minutes. Take your pick or watch both, and hey, don't forget to like and subscribe!

So, to train a neural network, we first initialize all of its parameters to random states. Then we present it with some training data. Our training data consists of inputs with known corresponding outputs. We present our inputs to the model and run a forward pass through the network. We compare the output from the network to the actual output value in our training set. So at first, with randomly initialized weights, the model will be way off, truly terrible. We can calculate the error between the network output and the correct value and then backpropagate the gradients to update the weights. Then we go again and again and again.

While the architecture of transformers is more complex, with encoder and decoder layers, attention heads, and feedforward layers, the training principles are identical. We have training data where we try to get a transformer to predict the next word or, more precisely, the next token in a sequence. The word or token is represented by a word embedding. A word embedding is a massive vector essentially containing the semantics of that word. So we run a forward pass, and the randomly initialized transformer will come up with its prediction of the next most likely word. Again, with randomly initialized weights, the transformer will be terrible. It will be way off with its initial prediction. But as before, we backpropagate the error and use this to update the weights in the transformer. It gets better and better and better. Eventually, after hundreds of millions of examples, the weights will converge, and the LLM will be trained, often with spectacular results.

The forward pass of the network uses simple matrix multiplication to calculate the activations of the hidden layer. In this case, the vector H from the weight matrix W, the inputs the X vector, plus the bias B vector, and send all this through an activation function like ReLU Claudeโ€™s response was limited as it hit the maximum length allowed at this time.

...send all this through an activation function like ReLU, Swish, or SiLU. Each hidden layer follows the exact same process. Now the hidden layer is the product of the prior hidden layer and its weight matrix, plus a bias, fed through the activation function. And finally, the output layer is then calculated in exactly the same way - multiply the activations of the final hidden layer by the weight matrix, add the bias. In the case of transformers, use a softmax activation function to produce the final output vector y. All of this is actually pretty straightforward, but the problem comes with the W matrices - they are huge. Not only are they huge, but there are lots of them. This gives rise to two very important facts: firstly, it takes a long time to train a neural network because of all of the matrix multiplication involved in backpropagating the errors. Secondly, and very closely related, holders of NVIDIA stock have become very rich indeed.

So while LoRA is unlikely on its own to curb the demand for NVIDIA GPUs, it can help speed up training and fine-tuning on current GPUs. Here's an explanation of how it works. As ever, this is a YouTube video, please feel free to pause and rewind or run through this example a couple of times to make sure you get the gist.

The strategy here is to start with a base model with its own existing pre-trained parameters. We will fine-tune the model by freezing the weights of this base model and updating a matrix Delta W that has exactly the same dimensions as the matrices in the base model. We can therefore write the equation to update the hidden layer from the input as: H, the activation of the hidden layer, equals matrix W0 multiplied by the input vector X, plus Delta W multiplied by X. We will keep matrix W constant and only update the weights in Delta W.

In the Microsoft LoRA paper, the authors noted that weight matrices in LLMs have "low intrinsic dimension." Inspired by this, they proposed replacing the full-rank Delta W matrices with two low-rank matrices, A and B. The product of these two matrices will have the same dimensions as Delta W, which has the same dimensions as W. Matrix A's weights would be randomly initialized using a Gaussian function, and matrix B's initial weights will be set to zero. The errors and associated gradients would be backpropagated through these low-rank matrices rather than the full-rank Delta W.

So the definitions of the variables in the update equation are that W0 are the original LLM's frozen weights with dimension D. Delta W is a matrix of fine-tune weights with the same dimension as the W matrix. This will contain the fine-tune updates, but rather than updating this full-rank matrix directly, instead we'll update two lower rank matrices: matrix A and matrix B. By multiplying A and B together, we can get Delta W. A and B both have rank R, and R is less than D. So for fine-tuning, we'll proceed in the same way as with training. We will have our dataset that contains our inputs and the corresponding outputs, and we'll backpropagate the error to update the weights in our low-rank A and B matrices.

So we can visualize our network as containing the frozen weights in the base model alongside the two low-rank matrices, as per the equation at the top of the screen. We'll begin with our input vector X, which is of dimension d1. This gets multiplied by the frozen weights in the W matrix as well as by the weights in our low-rank A and B matrices, which contain our tunable or perhaps I should say fine-tunable parameters. This results in two vectors also of dimension d1. With reference to the equation at the top of the screen, both of these vectors are added together to get H. H is the vector of activations for our hidden layer. This is repeated throughout our network until we get to our output layer. At that point, the output will be compared to the target value from our training data, and we'll backpropagate the error through our B and A matrices, updating the parameters to reduce our cost function. This will continue until our weights converge, and we can then fuse our newly found updated weights to the original model weights, and now we have our fine-tuned model.

Now here's another visual intuition for how the adapter weights in LoRA work. I find this helpful, as it's a great visual clue as to the reduction in computational effort. Here we see three diagrams of the same section of hidden layers in a neural network. Note that the network is fully connected, so a great way to visualize LoRA is to look at adding nodes between the layers, the number of nodes being equal to the rank of LoRA being used. Note that these are nodes, not neurons. They're there to illustrate the matrix multiplication. I guess if you want to, you could represent them as a neuron with a linear activation function and no bias. Nevertheless, I hope you agree that this representation illustrates the reduction in the number of weights and computations. Effectively, this illustration focuses on the application of LoRA to the feedforward layers in a neural network. Transformers have several other areas that use heavy matrix multiplication too, notably the attention heads. In the Microsoft LoRA paper, this is where the team chose to apply this LoRA technique by only adapting the attention weights.

Now we've covered a good deal of ground today, but I hope you found this useful. As ever, you don't necessarily need to understand the details of LoRA to be able to use the techniques. Hugging Face has developed a whole bunch of classes and routines that you can use out of the box to be able to perform low-rank adaptation to fine-tune your networks.

So let's recap what we've learned about fine-tuning and LoRA.

  • We explored why fine-tuning is essential for adapting large language models to specific tasks and domains, leading to improved performance and customized output.

  • We then discussed various parameter-efficient fine-tuning approaches, including last layer fine-tuning, prefix tuning, and adapter layers, along with their pros and cons.

  • The star of this video was LoRA or low rank adaptation, an efficient fine-tuning method that significantly reduces trainable parameters while maintaining impressive performance.

  • LoRA achieves this by using low-rank matrices A and B to adapt the pre-trained model, keeping the original weights frozen.

  • By understanding and applying LoRA, you can efficiently fine-tune large language models for your specific needs, saving computational resources and time.

This is a game-changer for anyone working with LLMs, whether in research or in practical applications.

But this is just the beginning. In the next two videos, we'll dive deeper into the practical aspects of fine-tuning. First, we'll explore how to build a high-quality fine-tuning dataset that will help your model achieve optimal performance. Then we'll walk through the process of fine-tuning your own models step-by-step so that you can apply LoRA to your projects. Subscribe to the Lucid channel and hit the notification bell so that you don't miss these essential guides. And if you found this video helpful, give it a like and share it with others who might benefit from learning about LoRA and fine-tuning. Thank you for watching, and I'll see you in the next video.

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