Using LoRA for Efficient Stable Diffusion Fine-Tuning

One challenge in deploying LLMs is how to efficiently serve hundreds or thousands of tuned models. For example, a single base LLM, such as Llama 2, may have many LoRA-tuned variants per language or locale. A standard system would require loading all the models independently, taking up large amounts of memory capacity. Take advantage of LoRA’s design, capturing all the information in smaller low-rank matrices per model, by loading a single base model together with the low-rank matrices A and B for each respective LoRA tuned variant. In this manner, it’s possible to store thousands of LLMs and run them dynamically and efficiently within a minimal GPU memory footprint. LoRA inserts these low-rank matrices into each layer of the LLM, and adds them to the original weight matrices.

A full training run takes ~5 hours on a 2080 Ti GPU with 11GB of VRAM. Bias-only or BitFit is a baseline where we only train the bias vectors while freezing everything else. Contemporarily, this baseline has also been studied by BitFit (Zaken et al., 2021). For further explanations on LoRA’s architecture and code implementation of fine-tuning GPT, I recommend reading this detailed Medium Article. Besides, the term “rank” is a concept many of us encountered in linear algebra classes. In simple words, the rank of a matrix is calculated by counting how many of the rows are “unique,” meaning they are not linearly composed of other rows (the same applies to columns).

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. We again train using AdamW with a linear learning rate decay schedule.

LoRA addresses this issue by freezing pre-trained model weights and introducing trainable rank decomposition matrices, significantly reducing parameters while maintaining model quality. 1) LoRA can be combined with other efficient adaptation methods, potentially providing orthogonal improvement. 2) The mechanism behind fine-tuning or LoRA is far from clear – how are features learned during pre-training transformed to do well on downstream tasks? We believe that LoRA makes it more tractable to answer this than full fine-tuning. 3) We mostly depend on heuristics to select the weight matrices to apply LoRA to.

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. A more general form of fine-tuning allows the training of a subset of the pre-trained parameters.

What exactly is LoRA?

See Section F.1 for results on WebNLG (Gardent et al., 2017) and DART (Nan et al., 2020). DeBERTa (He et al., 2021) is a more recent variant of BERT that is trained on a much larger scale and performs very competitively on benchmarks such as GLUE (Wang et al., 2019) and SuperGLUE (Wang et al., 2020). We evaluate if LoRA can still match the performance of a fully fine-tuned DeBERTa XXL (1.5B) on GLUE.

We train all of our GPT-2 models using AdamW (Loshchilov & Hutter, 2017) with a linear learning rate schedule for 5 epochs. We use the batch size, learning rate, and beam search beam size described in Li & Liang (2021). We report the mean over 3 random seeds; the result for each run is taken from the best epoch.

Many applications in natural language processing rely on adapting one large-scale, pre-trained language model to multiple downstream applications. Such adaptation is usually done via fine-tuning, which updates all the parameters of the pre-trained model. You can foun additiona information about ai customer service and artificial intelligence and NLP. The major downside of fine-tuning is that the new model contains as many parameters as in the original model.

Large language models (LLMs) have revolutionized natural language processing (NLP) with their ability to learn from massive amounts of text and generate fluent and coherent texts for various tasks and domains. However, customizing LLMs is a challenging task, often requiring a full training process that is time-consuming and computationally expensive. Moreover, training LLMs requires a diverse and representative dataset, which can be difficult to obtain and curate.

Code, Data and Media Associated with this Article

Instead, this guide takes a look at the LoRA relevant parts of the script. Note again that Δ​WΔ𝑊\Delta W does not contain the top singular directions of W𝑊W, since the similarity between the top 4 directions in Δ​WΔ𝑊\Delta W and the top-10% of those in W𝑊W barely exceeds 0.2. This gives evidence that Δ​WΔ𝑊\Delta W contains those “task-specific” directions lora nlp that are otherwise not emphasized in W𝑊W. LoRA can be naturally combined with existing prefix-based approaches. In this section, we evaluate two combinations of LoRA and variants of prefix-tuning on WikiSQL and MNLI. Φ​(⋅)italic-ϕ⋅\phi(\cdot) has a range of [0,1]01[0,1], where 111 represents a complete overlap of subspaces and 00 a complete separation.

The training hyperparameters of different adaptation approaches on MNLI-n are reported in Table 17. We use a smaller learning rate for PrefixLayer on the MNLI-100 set, as the training loss does not decrease with a larger learning rate. Having shown that LoRA can be a competitive alternative to full fine-tuning on NLU, we hope to answer if LoRA still prevails on NLG models, such as GPT-2 medium and large (Radford et al., b). We keep our setup as close as possible to Li & Liang (2021) for a direct comparison. Due to space constraint, we only present our result on E2E NLG Challenge (Table 3) in this section.

Radford et al. (a) applied it to autoregressive language modeling by using a stack of Transformer decoders. Since then, Transformer-based language models have dominated NLP, achieving the state-of-the-art in many tasks. Training Chat PG larger Transformers generally results in better performance and remains an active research direction. GPT-3 (Brown et al., 2020) is the largest single Transformer language model trained to-date with 175B parameters.

• We use a sequence length of 128

  instead of 1024 (which is the default sequence length).

• If you’re training on more than one GPU, add the –multi_gpu parameter to the accelerate launch command.
• Consequently, the weight updates, the information about how much the weights change during model training, are matrices as well.
• Though it is possible to not merge the weights and dynamically choose the LoRA modules to use for samples in a batch for scenarios where latency is not critical.

For an example of how to tune LoRA on the PubMed dataset using NeMo, see NeMo Framework PEFT with Llama 2. 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. 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.

F.2 Additional Experiments on GPT-3

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. 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. The distribution of the new data is just slighly

different from the initial one.

We take the GPT-3 few-shot result on RTE from the GPT-3 paper (Brown et al., 2020). For MNLI-matched, we use two demonstrations per class and six in-context examples in total. However, the lowest possible rank in LoRA will likely depend on the degree of difficulty of the downstream task relative to the pre-training task. For example, when adapting a language model in a different language than it was pre-trained on, we should expect that the weights need to change more drastically, requiring a much larger rank r.

You can apply it to convolutions, embedding layers and actually any other layer. But it is necessary to be able to classify it within a defined tokenizer family for runtime and for setting preprocessing and postprocessing steps in Triton. We will now override the original query/value projection matrices with our

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

model, fine-tune it and generate text.

• The math behind LoRA is based on the idea of low-rank decomposition, which is a way of approximating a matrix by a product of two smaller matrices with lower ranks.
• LoRA has become very popular in the NLP community because it allows us to adapt LLMs to downstream tasks faster, more robustly, and with smaller model footprints than ever before.
• 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.
• More importantly, these method often fail to match the fine-tuning baselines, posing a trade-off between efficiency and model quality.

For example, a 1024×1024 matrix with rank 10 can be expressed as the product of a 1024×10 matrix and a 10×1024 matrix, resulting in 3 orders of magnitude fewer parameters (2k vs 1M) – we call this low-rank factorization. The key hypothesis behind LoRA is that the weight update matrices during fine-tuning of LLMs have low intrinsic rank. In order for users to share their awesome fine-tuned or dreamboothed models, they had to share a full copy of the final model. Other users that want to try them out have to download the fine-tuned weights in their favorite UI, adding up to combined massive storage and download costs.

For specific instructions on setting up and launching the Triton Inference Server, see Deploy an AI Coding Assistant with NVIDIA TensorRT-LLM and NVIDIA Triton. To run the model during inference, set up the lora_dir command line argument. Remember to use the LoRA tokenizer, as the LoRA-tuned model has a larger vocabulary size. The math behind LoRA is based on the idea of low-rank decomposition, which is a way of approximating a matrix by a product of two smaller matrices with lower ranks. A rank of a matrix is the number of linearly independent rows or columns in the matrix. A low-rank matrix has fewer degrees of freedom and can be represented more compactly than a full-rank matrix.

LoRA takes a step further and does not require the accumulated gradient update to weight matrices to have full-rank during adaptation. Many have proposed inserting adapter layers between existing layers in a neural network (Houlsby et al., 2019; Rebuffi et al., 2017; Lin et al., 2020). Our method uses a similar bottleneck structure to impose a low-rank constraint on the weight updates.

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. 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.

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. While our proposal is agnostic to training objective, we focus on language modeling as our motivating use case. Below is a brief description of the language modeling problem and, in https://chat.openai.com/ particular, the maximization of conditional probabilities given a task-specific prompt. The information about the base model is automatically populated by the fine-tuning script we saw in the previous section, if you use the –push_to_hub option. This is recorded as a metadata tag in the README file of the model’s repo, as you can see here.

Fine-Tune and Align LLMs Easily with NVIDIA NeMo Customizer

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. With LoRA, it is much easier to fine-tune a model on a custom dataset. 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!

We include comparisons with Li & Liang (2021) in our experiment section. However, this line of works can only scale up by using more special tokens in the prompt, which take up available sequence length for task tokens when positional embeddings are learned. RoBERTa (Liu et al., 2019) optimized the pre-training recipe originally proposed in BERT (Devlin et al., 2019a) and boosted the latter’s task performance without introducing many more trainable parameters. While RoBERTa has been overtaken by much larger models on NLP leaderboards such as the GLUE benchmark (Wang et al., 2019) in recent years, it remains a competitive and popular pre-trained model for its size among practitioners. We also replicate Houlsby et al. (2019) and Pfeiffer et al. (2021) according to their setup.

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. We repeat our experiment on the effect of r𝑟r (Section 7.2) in GPT-2. Using the E2E NLG Challenge dataset as an example, we report the validation loss and test metrics achieved by different choices of r𝑟r after training for 26,000 steps. The optimal rank for GPT-2 Medium is between 4 and 16 depending on the metric used, which is similar to that for GPT-3 175B.

We observe a significant performance drop when we use more than 256 special tokens for prefix-embedding tuning or more than 32 special tokens for prefix-layer tuning. While a thorough investigation into this phenomenon is out-of-scope for this work, we suspect that having more special tokens causes the input distribution to shift further away from the pre-training data distribution. Separately, we investigate the performance of different adaptation approaches in the low-data regime in Section F.3. As language models have grown in size, traditional fine-tuning methods have become impractical.

Fine-tuning numbers are taken from Liu et al. (2019) and He et al. (2020). Please follow the instructions in examples/NLU/ to reproduce our results. Of course, the idea of LoRA is simple enough that it can be applied not only to

linear layers.

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. Instead, it is referred to as “adaptation” to describe the process of fine-tuning the domain data and tasks. LoRA does not increase inference latency, as once fine tuning is done, you can simply

update the weights in \(\Theta\) by adding their respective \(\Delta \theta \approx \Delta \phi\). It also makes it simpler to deploy multiple task specific models on top of one large model,

as \(|\Delta \Phi|\) is much smaller than \(|\Delta \Theta|\).

To evaluate the performance of different adaptation approaches in the low-data regime. We randomly sample 100, 1k and 10k training examples from the full training set of MNLI to form the low-data MNLI-n𝑛n tasks. In Table 16, we show the performance of different adaptation approaches on MNLI-n𝑛n. To our surprise, PrefixEmbed and PrefixLayer performs very poorly on MNLI-100 dataset, with PrefixEmbed performing only slightly better than random chance (37.6% vs. 33.3%).

This makes LoRA particularly useful for ML applications with very large LLMs that need to be fine-tuned for a number of different downstream tasks. Think e-commerce, where we need to classify product descriptions depending on a host of different regulations. LoRA (Low-Rank Adaptation) is a new technique for fine tuning large scale pre-trained

models. Such models are usually trained on general domain data, so as to have

the maximum amount of data. In order to obtain better results in tasks like chatting

or question answering, these models can be further ‘fine-tuned’ or adapted on domain

specific data.

The key functional difference is that our learned weights can be merged with the main weights during inference, thus not introducing any latency, which is not the case for the adapter layers (Section 3). A comtenporary extension of adapter is compacter (Mahabadi et al., 2021), which essentially parametrizes the adapter layers using Kronecker products with some predetermined weight sharing scheme. Similarly, combining LoRA with other tensor product-based methods could potentially improve its parameter efficiency, which we leave to future work.

For example, passing lora_task_uids 0 1 will use the first LoRA checkpoint on the first sentence and use the second LoRA checkpoint on the second sentence. Choosing a smaller can save a lot of parameters and memory and achieve faster training. However, a smaller can potentially decrease task-specific information captured in the low-rank matrices. Hence, it’s important to experiment in order to achieve the ideal accuracy-performance trade-off for your specific task and data. 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.

If you need support for a specific layer, please open an issue or a pull request. On GPT-3 175B, using LoRA reduces the VRAM consumption during training from 1.2TB to 350GB. To compare with other baselines broadly, we replicate the setups used by prior work and reuse their reported numbers whenever possible. This, however, means that some baselines might only appear in certain experiments.

We present additional runs on GPT-3 with different adaptation methods in Table 15. The focus is on identifying the trade-off between performance and the number of trainable parameters. We also repeat our experiment on DART (Nan et al., 2020) and WebNLG (Gardent et al., 2017) following the setup of Li & Liang (2021). Similar to our result on E2E NLG Challenge, reported in Section 5, LoRA performs better than or at least on-par with prefix-based approaches given the same number of trainable parameters.

The function does the standard traning loop in torch using the Adam optimizer. With baseline support for many popular LLM architectures, TensorRT-LLM makes it easy to deploy, experiment, and optimize with a variety of code LLMs. Together, NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server provide an indispensable toolkit for optimizing, deploying, and running LLMs efficiently. With support for LoRA-tuned models, TensorRT-LLM enables efficient deployment of customized LLMs, significantly reducing memory and computational cost. This section shows how to deploy LoRA-tuned models using inflight batching with Triton Inference server.

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 short, while applying LoRA to just the attention weights and freezing everything else results in the most parameter savings, but applying it the entire model can result in better performance at the cost of more parameters. LoRA has become very popular in the NLP community because it allows us to adapt LLMs to downstream tasks faster, more robustly, and with smaller model footprints than ever before.