Parameter-efficient fine-tuning of GPT-2 with LoRA

In conclusion, with the introduction of LoRA and its seamless integration into the JAX ecosystem through EasyDeL, the landscape of natural language processing has undergone a significant transformation. By empowering users to effortlessly leverage the power of LoRA-adapted models in their projects, we are poised to witness accelerated innovation and breakthroughs in NLP applications. As we continue to explore and refine the capabilities of LoRA with EasyDeL in JAX, the future holds promise for even greater advancements in language understanding and generation. Join us on this journey as we pave the way toward a more efficient, scalable, and inclusive era of natural language processing. This architecture is particularly useful for fine-tuning pretrained convolutional neural networks where minimal perturbations to the original model are desirable.

LoRA’s approach to decomposing ( Δ W ) into a product of lower rank matrices effectively balances the need to adapt large pre-trained models to new tasks while maintaining computational efficiency. The intrinsic rank concept is key to this balance, ensuring that the essence of the model’s learning capability is preserved with significantly fewer parameters. Equation 3 represents objective for conditional language generation, based on next token prediction. Maximize the probability of the token predicted at time t conditioned on the input x and we optimize parameters Φ till we maximize the objective over all the data points. For each parameter updates we need to store ∆Φ (change in weights matrix) whose dimensions are as big as the pre-trained model. So storing and deploying many independent instances of fine-tuned models can be challenging.

In this example, we will explain LoRA in technical terms, show how the technical. explanation translates to code, hack KerasNLP’s. GPT-2 model and fine-tune. it on the next token prediction task using LoRA. We will compare LoRA GPT-2. with a fully fine-tuned GPT-2 in terms of the quality of the generated text,. training time and GPU memory usage. LoRA offers a parameter- and compute-efficient approach compared to other methods. You can foun additiona information about ai customer service and artificial intelligence and NLP. One such alternative is adding Adapter Layers within each Transformer block, where only the parameters of these layers are adjusted during fine-tuning. While this method is computationally efficient due to the low number of parameters in the adapter layers, it introduces latency during inference as these layers must be processed sequentially.

In this section, we discuss the technical details of LoRA, build a LoRA GPT-2

model, fine-tune it and generate text. If you’re training on more than one GPU, add the –multi_gpu parameter to the accelerate launch command. The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn’t cover every aspect of the script in detail.

Inject LoRA layer into the model

It works by inserting a smaller number of new weights into the model and only these are trained. This makes training with LoRA much faster, memory-efficient, and produces smaller model weights (a few hundred MBs), which are easier to store and share. LoRA can also be combined with other training techniques like DreamBooth to speedup training. And they hypophyse that the act of pre-training is just lowering the intrinsic dimension of the NLP task. The model once pre-trained parameters/weights of the model can now be represented in a lower dimensions than what they currently are.

The nn.Embedding layer in PyTorch is used to create an embedding table, which is essentially a lookup table that maps integer indices to dense vectors of fixed size (embedding dimension). Click the numbers below to download the RoBERTa and DeBERTa LoRA checkpoints. As with the script parameters, a walkthrough of the training script is provided in the Text-to-image training guide. Instead, this guide takes a look at the LoRA relevant parts of the script.

In essence, LoRA simplifies the fine-tuning process by focusing on tuning the smaller matrices A and B instead of the larger weight matrix delta W. Once the fine-tuning is complete, the adjusted weights are added back to the original Chat PG model for inference. This approach offers computational efficiency and simplicity compared to other adaptation methods. LoRA (Low-Rank Adaptation) allows for the fine-tuning of large models at a fraction of the usual cost.

Introducing LoRA: Empowering Large Language Models with Low-Rank Adaptation

Neural networks consist of numerous dense layers that perform computations using weight matrices. These matrices are typically of full rank, meaning they have the maximum number of linearly independent rows or columns possible for their dimensions. However, research suggests that when adapting these pre-trained models to specific tasks, the necessary adjustments or updates to the weights https://chat.openai.com/ don’t require altering every single element of these matrices. Instead, these updates can be effectively captured with a much lower degree of freedom, or “intrinsic rank.” To understand this lets get some SVD intuition first. The magic of LoRA lies in its efficiency in fine-tuning, which might seem paradoxical since the additional weights seem to double the parameter count.

• One such alternative is adding Adapter Layers within each Transformer block, where only the parameters of these layers are adjusted during fine-tuning.
• It’ll automatically configure your training setup based on your hardware and environment.
• This repo contains the source code of the Python package loralib and several examples of how to integrate it with PyTorch models, such as those in Hugging Face.
• A and B are initialized accordingly, and backpropagation adjusts their values during fine-tuning.

Introduced in a Microsoft research paper, LoRA’s method is straightforward. Rather than adjusting all the model weights during fine-tuning, LoRA freezes the original weights of the model. It then introduces a separate set of weights that, after fine-tuning, effectively represent the necessary modifications to the pretrained parameters to optimize the model for a specific task. LoRA takes a different approach by leveraging low-rank approximations to adapt the pre-trained LLMs to specific tasks.

LoRA GPT-2

Now we just give the AutoEasyDelModelForCausalLM Module our targeted model repo id for example let’s use Qwen1.5 or 2 which is the latest published model but feel free to use any model that you want to use. For more information see the Code of Conduct FAQ or

contact with any lora nlp additional questions or comments. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. ArXiv is committed to these values and only works with partners that adhere to them.

When training is set to false (such as during evaluation), the computed low-rank adaptation is either added to or subtracted from the primary weight matrix, depending on the training mode. This allows the network to revert to the original behavior or integrate the LoRA enhancements as needed. In summary, this class provides a Linear layer with the capability to incorporate LoRA, allowing for fine-tuning of pre-trained models while preserving the original weights and structure. It enables adaptive adjustments to the weights during training while ensuring stability and controlled modifications.

However, prefix tuning reduces the effective input size and is challenging to optimize due to the uncertainty in choosing the number of trainable parameters. Low-rank adaptation involves determining the number of linearly independent columns within a matrix. If a column can be obtained by combining others in the matrix, it’s considered linearly dependent. Removing such columns reduces the matrix’s dimension without losing information since the information was already present in other columns. LoRA suggests decomposing high-dimensional weight matrices into two smaller matrices, A and B, resulting in computational efficiency. By representing the weights as the product of A and B, we reduce the number of parameters that need tuning.

Title:MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA based Mixture of Experts

Print the model’s summary and see if the number of non-trainable parameters and

total parameters are correct. According to the technical description above, let’s create a LoRA layer. In

a transformer model, the LoRA layer is created and injected for the query and

value projection matrices. In keras.layers.MultiHeadAttention, the query/value

projection layers are keras.layers.EinsumDense layers. Even though the total number of parameters increase (since we are adding LoRA

layers), the memory footprint reduces, because the number of trainable

parameters reduces. We’ll now batch the dataset and retain only the document field because we are

fine-tuning the model on the next word prediction task.

Too low a rank risks losing information, while too high a rank wastes computation. A and B are initialized accordingly, and backpropagation adjusts their values during fine-tuning. In the context of LoRA, the concept of rank plays a pivotal role in determining the efficiency and effectiveness of the adaptation process. Remarkably, the paper highlights that the rank of the matrices A and B can be astonishingly low, sometimes as low as one.

LoRA reduces the number of trainable parameters by learning pairs of rank-decompostion matrices while freezing the original weights. This vastly reduces the storage requirement for large language models adapted to specific tasks and enables efficient task-switching during deployment all without introducing inference latency. LoRA also outperforms several other adaptation methods including adapter, prefix-tuning, and fine-tuning.

The ConvLoRA class is a custom PyTorch module that integrates the concept of Low-Rank Adaptation (LoRA) into convolutional layers. This class allows for adaptive fine-tuning of pretrained convolutional neural networks, with minimal disturbance to the original model parameters. The class is designed to be adaptable for different types of convolution layers (1D, 2D, 3D) by extending it to specific subclasses like Conv1d, Conv2d, and Conv3d. To fir these models, for example, transforming them into a banking chatbot with financial expertise or a medical chatbot that understands healthcare — LoRA enables fine-tuning on smaller, specialized datasets. LoRA offers a practical solution for enhancing the specialization of language models without the need for extensive data typically required for training from scratch. LoRA (Low-Rank Adaptation of Large Language Models) is a popular and lightweight training technique that significantly reduces the number of trainable parameters.

The matrices, whose entries are the adjustable weights, vary in size with the model’s complexity. For instance, GPT-3’s matrices are much larger than GPT-2’s due to their vastly different parameter counts. Fine-tuning large language models (LLMs) is costly due to their enormous size, as they contain tens to hundreds of billions of parameters.

The training script has many parameters to help you customize your training run. All of the parameters and their descriptions are found in the parse_args() function. Default values are provided for most parameters that work pretty well, but you can also set your own values in the training command if you’d like.

During fine-tuning, not only do all these parameters need to be loaded into a GPU for inference, but approximately double the amount of memory is also required to store the gradients for each parameter. These gradients are essential for backpropagation during neural network training, where they help adjust the weights based on the loss computed from new task instances. This substantial memory requirement makes fine-tuning such large models particularly resource-intensive.

Callback for tracking GPU memory usage

By identifying and preserving the most critical parameters while reducing the model’s complexity, LoRA balances computational efficiency and performance. This novel approach not only accelerates inference but also facilitates the deployment of LLMs in resource-constrained environments. In natural language processing (NLP), the advent of Large Language Models (LLMs) has revolutionized how we interact with and analyze text data. These models, such as the GPT (Generative Pre-trained Transformer) series, have demonstrated remarkable capabilities in tasks ranging from language generation to sentiment analysis. However, the deployment of such models in real-world applications often encounters challenges related to computational resources and fine-tuning requirements.

If you’re interested in learning more, feel free to read through the script and let us know if you have any questions or concerns. We will now override the original query/value projection matrices with our

new LoRA layers. 🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It’ll automatically configure your training setup based on your hardware and environment. We use a sequence length of 128

instead of 1024 (which is the default sequence length). This will limit our

ability to predict long sequences, but will allow us to run this example quickly

on Colab.

We will fine-tune both the GPT-2 model and the

LoRA GPT-2 model on a subset of this dataset. In evaluation mode, if merge_weights is true and the weights are not yet merged, it adds the LoRA adjustments to the original weights. Since, with LoRA, there is a huge reduction in the number of trainable

parameters, the optimizer memory and the memory required to store the gradients

for LoRA is much less than GPT-2. Initialize the GPU memory tracker callback object, and compile the model. We will use AdamW optimizer and cross-entropy loss for training both models. The

callback function uses TensorFlow’s tf.config.experimental.get_memory_info

API.

This is where

Low-Rank Adaptation (LoRA) comes in; it

significantly reduces the number of trainable parameters. This results in a

decrease in training time and GPU memory usage, while maintaining the quality

of the outputs. LoRA adapts models by optimizing rank decomposition matrices that represent the change in dense layers’ weights during adaptation, while keeping the pre-trained weights unchanged. This method significantly reduces storage and computational requirements by enabling the use of very low rank matrices for adaptation, even when the full rank is extremely high.

LoRA is based on the idea that updates to the weights of the pre-trained

language model have a low “intrinsic rank” since pre-trained language models are

over-parametrized. Predictive performance of full fine-tuning can be replicated

even by constraining W0’s updates to low-rank decomposition matrices. However, LLMs are extremely large in size, and we don’t need to train all the

parameters in the model while fine-tuning, especially because datasets on which

the model is fine-tuned are relatively small. Another way of saying this is

that LLMs are over-parametrized for fine-tuning.

Setup

During the adaptation process, the original weight matrix W0​ remains unchanged (frozen), and only the matrices A and B are updated. These matrices capture the essence of how the network needs to be modified to perform well on the new task. Since A and B are much smaller than W0​, this significantly reduces the number of parameters that need to be trained, hence reducing computational requirements. Fine-tuning large pre-trained models is computationally challenging, often involving adjustment of millions of parameters. This traditional fine-tuning approach, while effective, demands substantial computational resources and time, posing a bottleneck for adapting these models to specific tasks. LoRA presented an effective solution to this problem by decomposing the update matrix during finetuing.

Large Language Models (LLMs) have been shown to be effective at a variety of NLP

tasks. An LLM is first pre-trained on a large corpus of text in a

self-supervised fashion. Pre-training helps LLMs learn general-purpose knowledge,

such as statistical relationships between words. An LLM can then be fine-tuned

on a downstream task of interest (such as sentiment analysis). Choosing the rank parameter, r, is crucial as it determines the balance between reducing dimensionality and retaining information.

• This traditional fine-tuning approach, while effective, demands substantial computational resources and time, posing a bottleneck for adapting these models to specific tasks.
• One of the biggest advantages of LoRA over other adapter methods is that it

  does not incur any additional inference latency.

• Another way of saying this is

  that LLMs are over-parametrized for fine-tuning.

• It initializes lora_A with zeros and lora_B with Kaiming uniform initialization, as per the default initialization for linear layers.
• LoRA is based on the idea that updates to the weights of the pre-trained

  language model have a low “intrinsic rank” since pre-trained language models are

  over-parametrized.

Despite the popularity of Adapter Layers and Prefix Tuning, LoRA has gained increasing popularity due to its efficiency and effectiveness in fine-tuning large language models. Diffusers uses ~peft.LoraConfig from the PEFT library to set up the parameters of the LoRA adapter such as the rank, alpha, and which modules to insert the LoRA weights into. The adapter is added to the UNet, and only the LoRA layers are filtered for optimization in lora_layers. Resets parameters of the convolutional layer and initializes LoRA-specific parameters (lora_A and lora_B). Lora_A is initialized using Kaiming uniform initialization, and lora_B is initialized to zeros. The dataset preprocessing code and training loop are found in the main() function, and if you need to adapt the training script, this is where you’ll make your changes.

In training mode, it ensures that the LoRA adjustments are not permanently applied to the convolutional weights if merge_weights is true. The reset_parameters method resets the parameters of the Linear layer, ensuring they are initialized properly. It initializes lora_A with zeros and lora_B with Kaiming uniform initialization, as per the default initialization for linear layers. The intrinsic rank hypothesis suggests that significant changes to the neural network can be captured using a lower-dimensional representation. Essentially, it posits that not all elements of ( Δ W ) are equally important; instead, a smaller subset of these changes can effectively encapsulate the necessary adjustments.

The train method sets the layer in training mode and handles the training logic specific to LoRA. If mode is True (training mode) and merge_weights is enabled, it ensures that the weights are not merged by subtracting the LoRA-computed update from the weight matrix. If mode is False (evaluation mode) and merge_weights is enabled, it merges the weights by adding the LoRA-computed update to the weight matrix. The modification to the output of the layer is computed as A×B, where A and BB are learned during training, allowing adaptation with fewer parameters compared to modifying the entire weight matrix. Another approach is prefix tuning, which automates prompt engineering by adding input vectors initialized randomly without specific word representations. The vectors, known as prefixes, are adjusted through backpropagation until the model produces the correct output.

These fine-tuned weights are added to the pre-trained weights during inference, maintaining only one set of weights in the GPU’s memory, not doubling it. The adjustments made by these additional weights are merged as needed, ensuring that the GPU memory requirements do not increase during inference. LoRA is a strategy designed to adapt Transformer-based LLM for various tasks without significant costs in hardware, storage, or latency. LoRA stands out because it enables the sharing of most model parameters across different tasks, allowing for quick task switching while maintaining high model quality. This approach does not negatively impact the input sequence length or add inference latency. In traditional fine-tuning, we modify a pre-trained neural network’s weights to adapt to a new task.

This adjustment involves altering the original weight matrix ( W ) of the network. The changes made to ( W ) during fine-tuning are collectively represented by ( Δ W ), such that the updated weights can be expressed as ( W + Δ W ). Before we generate text, let’s compare

the training time and memory usage of the two models. The training time of GPT-2

on a 16 GB Tesla T4 (Colab) is 7 minutes, and for LoRA, it is 5 minutes, a 30%

decrease. Assume we have an n x n pre-trained dense layer (or weight matrix), W0. We

initialize two dense layers, A and B, of shapes n x rank, and rank x n,

respectively.

In this equation, ( W ) remains frozen (i.e., it is not updated during training). The matrices ( B ) and ( A ) are of lower dimensionality, with their product ( BA ) representing a low-rank approximation of ( Δ W ). This repo contains the source code of the Python package loralib and several examples of how to integrate it with PyTorch models, such as those in Hugging Face. One of the biggest advantages of LoRA over other adapter methods is that it

does not incur any additional inference latency.