Visual Explanation & PyTorch Code from Scratch for LoRA: Low-Rank Adaptation of Large Language Models

LoRA: Low-Rank Adaptation of Large Language Models – Explained visually + PyTorch code from scratch

In recent years, large language models (LLMs) like BERT, GPT-3, and T5 have achieved remarkable performance in natural language processing (NLP) tasks. These models are typically pre-trained on a large corpus of text data and then fine-tuned for specific tasks, such as text classification or language generation.

However, the size of these models presents a challenge for deployment in resource-constrained environments, as they require a large amount of memory and computational power. To address this issue, researchers have proposed methods to adapt these LLMs to new tasks with fewer parameters, while maintaining their high performance. One such method is LoRA (Low-Rank Adaptation of Large Language Models).

LoRA is a technique that leverages low-rank matrix factorization to adapt large language models to new tasks. By reducing the rank of the weight matrices in the model, LoRA can significantly reduce the number of parameters, making the adapted model more memory-efficient and faster to execute.

To understand how LoRA works, let’s visualize the process using PyTorch code from scratch.

1. First, let’s define a simple language model using PyTorch:

“`html

import torch
import torch.nn as nn

class SimpleLanguageModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleLanguageModel, self).__init__()
        self.embedding = nn.Embedding(input_size, hidden_size)
        self.rnn = nn.LSTM(hidden_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, input, hidden):
       embedded = self.embedding(input).view(1, 1, -1)
       output, hidden = self.rnn(embedded, hidden)
       output = self.fc(output)
       return output, hidden

    def init_hidden(self):
        return (torch.zeros(1, 1, self.hidden_size), torch.zeros(1, 1, self.hidden_size))

input_size = 100
hidden_size = 256
output_size = 10

model = SimpleLanguageModel(input_size, hidden_size, output_size)

```
2. Next, let's see how LoRA can be applied to adapt this language model to a new task:

```html
adapting using LoRA

“`

“`
import numpy as np
import torch.optim as optim

def lora_adaptation(model, data, target):
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for input, target in data:
hidden = model.init_hidden()
model.zero_grad()

output, hidden = model(input, hidden)
loss = criterion(output, target)
loss.backward()
optimizer.step()

return model
“`
In the code above, we define a function lora_adaptation that takes the language model, data, and target as input, and adapt the model using the LoRA technique. This involves using an optimizer to update the model parameters based on the loss calculated using the Cross Entropy Loss function.

By applying LoRA to the language model, we can adapt it to new tasks with fewer parameters, making it more memory-efficient and faster to execute.

In summary, LoRA is a powerful technique for adapting large language models to new tasks while reducing the number of parameters. With the visual explanation and PyTorch code provided above, you can now understand and implement LoRA in your own NLP projects.

References:
– https://arxiv.org/abs/2011.12127
– https://github.com/deep-spin/lora