Using LoRA for Efficient Stable Diffusion Fine-Tuning

Posit AI Blog: Understanding LoRA with a minimal example

lora nlp

You can then train this model like before, without having to explicitly worry about QLoRA during training. Of course, the idea of LoRA is simple enough that it can be applied not only to

linear layers. You can apply it to convolutions, embedding layers and actually any other layer.

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. In the exciting world of natural language processing, large-scale pre-trained language models (LLMs) have revolutionized the field. However, fine-tuning such enormous models on specific tasks has proven challenging due to the high computational costs and storage requirements.

This results in more resilient models that excel with new, unseen data, or at the very least, retain the knowledge from their initial training tasks. Now, applying the base model to data from the new distribution yields good performance,

so we can say the model is adapted for the new task. Full model fine-tuning of Stable Diffusion used to be slow and difficult, and that’s part of the reason why lighter-weight methods such as Dreambooth or Textual Inversion have become so popular.

Model Reconstruction

Other creative projects such as Prompt-to-Prompt could do with some easy way to access those layers, so we decided to provide a general way for users to do it. We’ve been testing that pull request since late December, and it officially launched with our diffusers release yesterday. As the demand for advanced natural language processing capabilities continues to grow, the need for efficient and accessible adaptation methods for large language models becomes increasingly critical. Machine translation benefits greatly from the use of large language models. LoRA allows for the efficient adaptation of these models to specific language pairs or specialized domains, improving translation quality and performance. Fine-tuning is the process of adjusting the weights of a pre-trained model by continuing its training on a smaller, task-specific dataset.

In this blog post we will talk about the key ideas behind LoRA in a very minimal torch example. As we’ve discussed, one of the major advantages of LoRA is that you get excellent results by training orders of magnitude less weights than the original model size. We designed an inference process that allows loading the additional weights on top of the unmodified Stable Diffusion model weights. In order to inject LoRA trainable matrices as deep in the model as in the cross-attention layers, people used to need to hack the source code of diffusers in imaginative (but fragile) ways. If Stable Diffusion has shown us one thing, it is that the community always comes up with ways to bend and adapt the models for creative purposes, and we love that! Providing the flexibility to manipulate the cross-attention layers could be beneficial for many other reasons, such as making it easier to adopt optimization techniques such as xFormers.

In-depth LLM finetuning guide: Properly combining LoRA and GPTQ quantization to efficiently finetune and use Zephyr 7B Beta

One of the biggest advantages of LoRA over other adapter methods is that it

does not incur any additional inference latency. 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. Large Language Models (LLMs) have been shown to be effective at a variety of NLP

tasks.

LoRA — Intuitively and Exhaustively Explained by Daniel Warfield – Towards Data Science

LoRA — Intuitively and Exhaustively Explained by Daniel Warfield.

Posted: Tue, 07 Nov 2023 08:00:00 GMT [source]

Ok great, we have replaced the self-attention with our own implementation; but how do we get this new class into the old RoBERTa model? Note that many people ignore this assessment nowadays and allow each matrix to be fine-tuned, no matter the task or model (see QLoRA paper). Obviously we want our delta to be zero at the start of the training, such that the fine-tuning starts just like the original model. Therefore B is often initialized as all zeros and A is initialized as random (usually normally distributed) values. One potential limitation is the loss of information during the low-rank approximation process, which might impact the performance of the adapted model. By leveraging low-rank adaptation, LoRA minimizes the energy requirements, making the adaptation process more sustainable and environmentally friendly.

As they already are small you won’t need a low-rank injection for them. Additionally, we have to implement the forward methods to account for the tasks we will fine-tune on as well as two methods to save and load the LoRA weights, such that we can load the adapters of a previously trained model. By utilizing fewer parameters, LoRAs significantly lower computational complexity and memory usage. This allows us to train large models on consumer-grade GPUs and effortlessly distribute our compact (in terms of megabytes) LoRAs to others.

These models are trained on vast amounts of textual data, which allows them to effectively generate, understand, and manipulate human-like text. LLMs, such as OpenAI’s GPT-3 or Google’s BERT, have become the backbone of modern NLP applications, including chatbots, machine translation, sentiment analysis, and more. In essence, LoRA leverages low-rank approximation techniques to make the adaptation process more efficient and cost-effective. What this code snippet does is set up the 8 accelerators into a 1 x 8 matrix where the two dimensions are called “batch” and “model”. Model weights are sharded on the “model” dimension, here split between the 8 accelerators, while data batches are not partitioned since the “batch” dimension is 1. While it will shorten the training time, it also could result in information loss and decrease the model performance as r becomes smaller.

This allows developers and researchers to iterate more quickly, test multiple adaptation scenarios, and deploy models in a more time-efficient manner. This is achieved by reversing the decomposition process, essentially “re-assembling” the weight matrices of the model from the adapted low-rank components. This is done by applying low-rank matrix factorization techniques, such as Singular Value Decomposition (SVD) or Truncated SVD, to the weight matrices of the model. Instead of fine-tuning the entire model, LoRA focuses on a smaller, low-rank representation of the model, which requires fewer computational resources and less time to adapt.

lora nlp

This is where LoRA’s low-rank adaptation technique offers a more efficient alternative to traditional fine-tuning methods. Typically, fine-tuning involves updating the parameters of the entire model, which can be computationally expensive and time-consuming, especially for LLMs with billions of parameters. Fine-tuning is a crucial step in the deployment of large language models. Keras 3 also supports large-scale model training and Gemma is the perfect model to try it out. The new Keras distribution API offers data-parallel and model-parallel distributed training options.

Additional Notes

We’ll define a custom callback function which tracks GPU memory usage. The

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

API. This still signifies interesting progress when considering how overtrained these models can be.

During fine-tuning, the model’s parameters are adjusted to optimize its performance for the target task. As language models have grown in size, traditional fine-tuning methods have become impractical. LoRA addresses this issue by freezing pre-trained model weights and introducing trainable rank decomposition matrices, significantly reducing parameters while maintaining model quality. LoRA, which stands for “Low-Rank Adaptation”, distinguishes itself by training and storing the additional weight changes in a matrix while freezing all the pre-trained model weights.

The most straightforward way is to just re-wrap the original self-attention mechanism RobertaSelfAttention. The new class LoraRobertaSelfAttention will then initialize the LoRA matrices. All the B matrices will be initialized with zeros and all the A matrices with random numbers from a normal distribution.

lora nlp

The GLUE benchmark, a suite of eight diverse NLP tasks, gauges a language model’s comprehensive understanding abilities. It includes challenges like sentiment analysis, textual entailment, and sentence similarity, offering a robust measure of a model’s linguistic adaptability and proficiency. For our implementation we want to stick closely to the original LoRA paper. There they tested which matrices of a transformer you actually have to replace.

To reload the model, utilize peft.AutoPeftModel.from_pretrained, passing the directory path as an argument. A crucial point to remember is that the LoRA configuration currently does not retain the number of classes for which AutoModelForSequenceClassification was initialized. When using from_pretrained, you need to manually input this class number as an additional parameter.

If there are any layers we want to train in their original form we can specify them by passing a list to the modules_to_save parameters of the Lora-Config. In our case, we want to add the LayerNorm here and the fine-tune heads for GLUE and SQuAD. We can simply add the classifier and qa_outputs to this list and then have a single configuration file that will work correctly for both tasks. Quantizing the original matrix weights to conserve GPU VRAM is also advisable, facilitating the training of larger models on a given GPU.

DeBERTa LoRA checkpoints

On GPT-3 175B, using LoRA reduces the VRAM consumption during training from 1.2TB to 350GB. For additional details on LoRA support in diffusers, please refer to our documentation – it will be always kept up to date with the implementation. Furthermore, as the AI community becomes more conscious of the environmental impact of large-scale models, LoRA’s lower energy consumption will likely contribute to a more sustainable and eco-friendly approach to AI development. Sentiment analysis is a critical NLP task that involves determining the sentiment or emotion expressed in a given piece of text. This democratizes access to state-of-the-art NLP technology, fostering innovation and enabling a broader range of applications across various domains.

The result is a full-sized language model that has been efficiently adapted to the target task while maintaining the performance of the original pre-trained model. As the low-rank representation is much smaller than the original model, this adaptation process is considerably faster and requires fewer computational resources than traditional fine-tuning methods. 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. Assume we have an n x n pre-trained dense layer (or weight matrix), W0.

Multiplying them yields a matrix with the same dimensions of W, but constructed from a much lower parameter count. Obviously, if W-orig had dimensions n×m and we would just initialize a new delta matrix with the same dimensions to fine-tune on we would have gained nothing; quite to the contrary we would have doubled the parameters. This snippet will print the model he used for fine-tuning, which is CompVis/stable-diffusion-v1-4. In my case, I trained my model starting from version 1.5 of Stable Diffusion, so if you run the same code with my LoRA model you’ll see that the output is runwayml/stable-diffusion-v1-5. One thing of notice is that the learning rate is 1e-4, much larger than the usual learning rates for regular fine-tuning (in the order of ~1e-6, typically). This is a W&B dashboard of the previous run, which took about 5 hours in a 2080 Ti GPU (11 GB of RAM).

lora nlp

In the case of Stable Diffusion fine-tuning, LoRA can be applied to the cross-attention layers that relate the image representations with the prompts that describe them. The details of the following figure (taken from the Stable Diffusion paper) are not important, just note that the yellow blocks are the ones in charge of building the relationship between image and text representations. For the sake of efficiency, our choice will be Zephyr 7B Beta, a DPO (Direct Preference Optimization) finetuned version of the Mistral-7 model which was introduced in the “Mistral 7B” paper. These models are optimized for inference speed as they implement GQA (grouped-query attention) and SWA (sliding window attention) to reduce inference latency and memory footprint.

The new API is meant to be multi-backend but for the time being, it is implemented for the JAX backend only, because of its proven scalability (Gemma models were trained with lora nlp JAX). The training script has many parameters to help you customize your training run. You can foun additiona information about ai customer service and artificial intelligence and NLP. All of the parameters and their descriptions are found in the parse_args() function.

Specifically, all of the layers listed below will eventually be supported. If you need support for a specific layer, please open an issue or a pull request. Pivotal Tuning is a method that tries to combine Textual Inversion with LoRA.

Implementing LoRA from Scratch

These techniques balance computational efficiency and task performance, making it feasible to fine-tune even the largest LLMs without compromising on quality. LoRA (Low-Rank Adaptation of Large Language Models) is a popular and lightweight training technique that significantly reduces the number of trainable parameters. 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 nlp

This approach significantly reduces the computational resources, time, and energy required for model adaptation, making it more efficient and accessible compared to traditional fine-tuning methods. This happens because adapter layers are added one after another and must be processed sequentially and cannot be parallelized. To reduce latency, you can prune layers or use multi-task settings, but you can’t completely eliminate the extra computation in adapter layers.

lora nlp

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

total parameters are correct. Getting the 8bit model, is a one-liner if you’re using the transformers API to get your base model. I primarily work with financial data and spend my day creating statistics and machine learning models.

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).

  • Although pre-trained LLMs possess a solid foundation of linguistic understanding, they often require customization to perform well on specific tasks or domains.
  • This means we essentially train on only 0.34% of the original parameters.
  • This general method tunes a mixture of adaptation modules for better performance across different tasks.
  • Large Language Models (LLMs) have been shown to be effective at a variety of NLP

    tasks.

  • LoRA stands for Low Rank Adaptation is a technique that redefines the adaptation phase of pre-trained models.

Furthermore, low-rank adaptors can be seamlessly integrated into existing neural network architectures. This integration allows for fine-tuning and adaptation of pre-trained models with minimal additional training cost, making them highly suitable for transfer learning applications. We learn the parameters \(\Delta \Theta\) with dimension \(|\Delta \Theta|\)

equals to \(|\Theta_0|\). When \(|\Theta_0|\) is very large, such as in large scale

pre-trained models, finding \(\Delta \Theta\) becomes computationally challenging. Also, for each task you need to learn a new \(\Delta \Theta\) parameter set, making

it even more challenging to deploy fine-tuned models if you have more than a

few specific tasks. LoRA (Low Rank Adaptation) is a new technique for fine-tuning deep learning models that works by reducing the number of trainable parameters and enables efficient task switching.

They found that, when comparing different strategies on a GPT-3 fine-tune task, it was sufficient to only adapt the self-attention mechanism’s query and value vectors. LoRA, an acronym for Low-Rank Adaptation or Low-Rank Adaptors, offers an efficient and lightweight method for fine-tuning pre-existing language models. This includes masked language models like BERT and RoBERTa, as well as causal (or chatbot) models such as GPT, Llama, and Mistral. We also define a function for training a model, which we are also reusing later. The function does the standard traning loop in torch using the Adam optimizer.

It allows multiple tasks to share the same pre-trained model, minimizing the need for maintaining independent instances. However, PEFT might introduce additional training time compared to traditional fine-tuning methods, and its performance could be sensitive to hyperparameter choices. Utilize LoRA for efficient model fine-tuning, focusing on keeping parameter sizes minimal.2.

With LoRA, it is much easier to fine-tune a model on a custom dataset. LoRA, with its innovative low-rank adaptation approach, has the potential to revolutionize the way we work with these models, making them more practical and sustainable for a wider range of applications and users. LoRA can be effectively used to adapt large language models for conversational AI applications, such as chatbots and virtual assistants. LoRA’s efficiency in adapting large language models ultimately contributes to enhanced accessibility of these powerful tools.

The check is if it contains the specified substring in its full name. Thus writing query and value is equivalent to our from-scratch implementation above. For the dense layers we have to be a bit more careful as the classifier also has a dense output. If we wish to fine-tune the other dense layers we have to be more specific via intermediate.dense and output.dense. The PEFT library targets the modules to replace via their names; thus we have to take a look at the models model.named_parameters(). The number of parameters introduced by LoRA for these specific tasks is remarkably minimal, amounting to just 1.7 MB of actual disk size.

Note that the PEFT library is much more flexible, also when working with custom models or other convoluted structures, so as long as you are only doing LoRA instead of QLoRA (quantization is usually the tricky part). Thanks to the bitsandbytes integration with the Huggingface transformers library (introduced in May 2023), this is a breeze. Remember, training with QLoRA may be a bit slower than LoRA, as it involves de-quantizing matrices during each multiplication. For instance, when fine-tuning something massive like Llama-7B, QLoRA requires about 75% less VRAM but is roughly 40% slower compared to standard LoRA.

This can be efficiently done using the bits-and-bytes library, now fully integrated with Hugging Face (see references). To avoid the additional inference latency of the separate computation of the deltas,

we could modify the original model by adding the estimated deltas to its parameters. In conclusion, LoRA has the potential to play a significant role in shaping the future of large language models and natural language processing. Traditional fine-tuning methods for large language models can consume vast amounts of energy due to the sheer number of parameters involved.


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