Parameter-Efficient Fine-Tuning PEFT: методы LoRA, Prefix tuning, Prompt tuning и Adapters Хабр

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.

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.

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.

lora_pytorch

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.

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.

Inject LoRA layer into the model

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.

• Even though LoRA was initially proposed for large-language models and demonstrated on transformer blocks, the technique can also be applied elsewhere.
• Of course, the idea of LoRA is simple enough that it can be applied not only to
  linear layers.
• Let’s put these concepts into practice with a code example of fine-tuning a large language model using QLORA.
• 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.

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.

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.

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.

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.

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.

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.

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 JAX). 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.

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.

Parameter-Efficient Fine-Tuning of Large Language Models with LoRA and QLoRA

I did not attempt to optimize the hyperparameters, so feel free to try it out yourself! Sayak did another run on a T4 (16 GB of RAM), here’s his final model, and here’s a demo Space that uses it. However, the effectiveness and efficiency of LoRA might vary depending on the specific model architecture and the target task or domain. LoRA differs from traditional fine-tuning in that it focuses on adapting a low-rank representation of the pre-trained model instead of the entire model.

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.

• We’ll stick to a general-use user assistant dialogue dataset for the sake of the example.
• LoRA’s impact on NLP is noteworthy, enabling cost-effective utilization of large models like GPT-3.
• The key innovation of LoRA lies in decomposing the weight change matrix ∆W into two low-rank matrices, A and B.
• 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.

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.

Code, Data and Media Associated with this Article

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.

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 lora nlp 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.

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.

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.

Instead of directly training the parameters in ∆W, LoRA focuses on training the parameters in A and B matrixes. LoRA’s impact on NLP is noteworthy, enabling cost-effective utilization of large models like GPT-3. This article explores LoRA’s principles, architecture, and impact on language model adaptation. 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. Keep in mind that the datasets used for the training of this family of models lack alignment which increases the chances of generating problematic outputs. In this article, we’ll explore recent large language tuning techniques and see how we can effectively harness them for model training and inference.

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. You can foun additiona information about ai customer service and artificial intelligence and NLP. To reduce latency, you can prune layers or use multi-task settings, but you can’t completely eliminate the extra computation in adapter layers.

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.

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.

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.

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).

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.

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.