Slide 1: Introduction to LoRA and Its Variants
LoRA (Low-Rank Adaptation) is a technique for fine-tuning large language models efficiently. This slideshow explores various LoRA variants, their implementations, and practical applications using Python. We'll cover the basics of LoRA, its advantages, and dive into different variations that have emerged to address specific challenges in model adaptation.
import torch
import torch.nn as nn
class LoRALayer(nn.Module):
def __init__(self, in_features, out_features, rank=4):
super().__init__()
self.lora_A = nn.Parameter(torch.randn(in_features, rank))
self.lora_B = nn.Parameter(torch.zeros(rank, out_features))
self.scale = 0.01
def forward(self, x):
return (x @ self.lora_A @ self.lora_B) * self.scale
# Usage
lora_layer = LoRALayer(768, 768)
input_tensor = torch.randn(1, 768)
output = lora_layer(input_tensor)
print(f"Input shape: {input_tensor.shape}, Output shape: {output.shape}")Slide 2: Basic LoRA Implementation
LoRA introduces low-rank matrices to adapt pre-trained models efficiently. This slide demonstrates a basic implementation of a LoRA layer in PyTorch. The LoRALayer class defines two trainable matrices, A and B, which are used to compute the low-rank update. The forward method applies the LoRA transformation to the input.
class LoRALinear(nn.Module):
def __init__(self, in_features, out_features, rank=4):
super().__init__()
self.linear = nn.Linear(in_features, out_features)
self.lora = LoRALayer(in_features, out_features, rank)
def forward(self, x):
return self.linear(x) + self.lora(x)
# Usage
lora_linear = LoRALinear(768, 768)
input_tensor = torch.randn(1, 768)
output = lora_linear(input_tensor)
print(f"Input shape: {input_tensor.shape}, Output shape: {output.shape}")Slide 3: LoRA in Action: Fine-tuning a Pretrained Model
This slide showcases how to integrate LoRA into a pretrained model for fine-tuning. We'll use a simple example with a pretrained BERT model and add LoRA layers to its linear transformations. This approach allows for efficient adaptation of the model to new tasks while keeping most of the original weights frozen.
from transformers import BertModel, BertConfig
import torch.nn as nn
class LoRABERT(nn.Module):
def __init__(self, pretrained_model_name, rank=4):
super().__init__()
self.bert = BertModel.from_pretrained(pretrained_model_name)
config = self.bert.config
# Replace linear layers with LoRA layers
for layer in self.bert.encoder.layer:
layer.attention.self.query = LoRALinear(config.hidden_size, config.hidden_size, rank)
layer.attention.self.key = LoRALinear(config.hidden_size, config.hidden_size, rank)
layer.attention.self.value = LoRALinear(config.hidden_size, config.hidden_size, rank)
layer.attention.output.dense = LoRALinear(config.hidden_size, config.hidden_size, rank)
layer.output.dense = LoRALinear(config.hidden_size, config.hidden_size, rank)
def forward(self, input_ids, attention_mask):
return self.bert(input_ids=input_ids, attention_mask=attention_mask)
# Usage
model = LoRABERT("bert-base-uncased")
input_ids = torch.randint(0, 30522, (1, 128))
attention_mask = torch.ones(1, 128)
outputs = model(input_ids, attention_mask)
print(f"Output shape: {outputs.last_hidden_state.shape}")Slide 4: AdaLoRA: Adaptive Low-Rank Adaptation
AdaLoRA is a variant that dynamically adjusts the rank of LoRA matrices during training. This approach allows for more efficient use of parameters by allocating higher ranks to more important weight matrices. The implementation involves a mechanism to update the rank based on the importance of each LoRA module.
import torch
import torch.nn as nn
class AdaLoRALayer(nn.Module):
def __init__(self, in_features, out_features, max_rank=16, min_rank=4):
super().__init__()
self.max_rank = max_rank
self.min_rank = min_rank
self.current_rank = max_rank
self.lora_A = nn.Parameter(torch.randn(in_features, max_rank))
self.lora_B = nn.Parameter(torch.zeros(max_rank, out_features))
self.scale = 0.01
self.importance = nn.Parameter(torch.ones(max_rank))
def forward(self, x):
# Use only the top-k important components
_, indices = torch.topk(self.importance, self.current_rank)
A = self.lora_A[:, indices]
B = self.lora_B[indices, :]
return (x @ A @ B) * self.scale
def update_rank(self, threshold):
# Update rank based on importance
new_rank = torch.sum(self.importance > threshold).item()
self.current_rank = max(self.min_rank, min(new_rank, self.max_rank))
# Usage
ada_lora = AdaLoRALayer(768, 768)
input_tensor = torch.randn(1, 768)
output = ada_lora(input_tensor)
print(f"Current rank: {ada_lora.current_rank}")
ada_lora.update_rank(0.5)
print(f"Updated rank: {ada_lora.current_rank}")Slide 5: QLoRA: Quantized LoRA for Memory Efficiency
QLoRA combines LoRA with quantization techniques to reduce memory usage further. This approach is particularly useful for fine-tuning large language models on limited hardware. QLoRA uses 4-bit quantization for the base model while keeping LoRA parameters in full precision.
import torch
import torch.nn as nn
from bitsandbytes import nn as bnb
class QLoRALinear(nn.Module):
def __init__(self, in_features, out_features, rank=4, bits=4):
super().__init__()
self.linear = bnb.Linear4bit(in_features, out_features, bias=False, compress_statistics=True)
self.lora = LoRALayer(in_features, out_features, rank)
def forward(self, x):
return self.linear(x) + self.lora(x)
# Usage
qlora_linear = QLoRALinear(768, 768)
input_tensor = torch.randn(1, 768)
output = qlora_linear(input_tensor)
print(f"Input shape: {input_tensor.shape}, Output shape: {output.shape}")Slide 6: LoRA for Convolutional Neural Networks
While LoRA was initially designed for transformer models, it can be adapted for use in convolutional neural networks (CNNs). This slide demonstrates how to implement LoRA for 2D convolutions, enabling efficient fine-tuning of CNN architectures.
import torch
import torch.nn as nn
import torch.nn.functional as F
class LoRAConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, rank=4, stride=1, padding=0):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)
self.lora_A = nn.Parameter(torch.randn(rank, in_channels * kernel_size * kernel_size))
self.lora_B = nn.Parameter(torch.zeros(out_channels, rank))
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.scale = 0.01
def forward(self, x):
conv_out = self.conv(x)
# LoRA path
_, _, h, w = x.shape
x_unfolded = F.unfold(x, self.kernel_size, stride=self.stride, padding=self.padding)
lora_out = (x_unfolded.transpose(1, 2) @ self.lora_A.t() @ self.lora_B.t()).transpose(1, 2)
lora_out = F.fold(lora_out, (h, w), self.kernel_size, stride=self.stride, padding=self.padding)
return conv_out + lora_out * self.scale
# Usage
lora_conv = LoRAConv2d(3, 64, kernel_size=3, padding=1)
input_tensor = torch.randn(1, 3, 32, 32)
output = lora_conv(input_tensor)
print(f"Input shape: {input_tensor.shape}, Output shape: {output.shape}")Slide 7: LoRA for Recurrent Neural Networks
Extending LoRA to Recurrent Neural Networks (RNNs) allows for efficient fine-tuning of sequence models. This slide presents an implementation of LoRA for a basic RNN cell, demonstrating how the technique can be applied to different types of neural network architectures.
import torch
import torch.nn as nn
class LoRARNNCell(nn.Module):
def __init__(self, input_size, hidden_size, rank=4):
super().__init__()
self.rnn_cell = nn.RNNCell(input_size, hidden_size)
self.lora_ih = LoRALayer(input_size, hidden_size, rank)
self.lora_hh = LoRALayer(hidden_size, hidden_size, rank)
def forward(self, input, hidden):
rnn_output = self.rnn_cell(input, hidden)
lora_ih_out = self.lora_ih(input)
lora_hh_out = self.lora_hh(hidden)
return torch.tanh(rnn_output + lora_ih_out + lora_hh_out)
# Usage
lora_rnn = LoRARNNCell(10, 20)
input_tensor = torch.randn(1, 10)
hidden_tensor = torch.randn(1, 20)
output = lora_rnn(input_tensor, hidden_tensor)
print(f"Input shape: {input_tensor.shape}, Hidden shape: {hidden_tensor.shape}, Output shape: {output.shape}")Slide 8: Hypernetwork-based LoRA
Hypernetwork-based LoRA uses a small neural network to generate LoRA weights dynamically. This approach allows for more flexibility in adapting to different tasks or domains. The hypernetwork takes a task embedding as input and produces the LoRA matrices A and B.
import torch
import torch.nn as nn
class HyperLoRALayer(nn.Module):
def __init__(self, in_features, out_features, rank=4, task_embedding_dim=16):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.rank = rank
self.hyper_net = nn.Sequential(
nn.Linear(task_embedding_dim, 64),
nn.ReLU(),
nn.Linear(64, in_features * rank + rank * out_features)
)
self.scale = 0.01
def forward(self, x, task_embedding):
weights = self.hyper_net(task_embedding)
lora_A = weights[:self.in_features * self.rank].view(self.in_features, self.rank)
lora_B = weights[self.in_features * self.rank:].view(self.rank, self.out_features)
return (x @ lora_A @ lora_B) * self.scale
# Usage
hyper_lora = HyperLoRALayer(768, 768)
input_tensor = torch.randn(1, 768)
task_embedding = torch.randn(1, 16)
output = hyper_lora(input_tensor, task_embedding)
print(f"Input shape: {input_tensor.shape}, Output shape: {output.shape}")Slide 9: Conditional LoRA for Multi-task Learning
Conditional LoRA extends the basic LoRA concept to support multi-task learning. By conditioning the LoRA parameters on task-specific embeddings, a single model can adapt to multiple tasks efficiently. This slide demonstrates how to implement a conditional LoRA layer that adjusts its behavior based on the current task.
import torch
import torch.nn as nn
class ConditionalLoRALayer(nn.Module):
def __init__(self, in_features, out_features, num_tasks, rank=4):
super().__init__()
self.lora_A = nn.Parameter(torch.randn(num_tasks, in_features, rank))
self.lora_B = nn.Parameter(torch.zeros(num_tasks, rank, out_features))
self.scale = 0.01
def forward(self, x, task_id):
A = self.lora_A[task_id]
B = self.lora_B[task_id]
return (x @ A @ B) * self.scale
class ConditionalLoRALinear(nn.Module):
def __init__(self, in_features, out_features, num_tasks, rank=4):
super().__init__()
self.linear = nn.Linear(in_features, out_features)
self.lora = ConditionalLoRALayer(in_features, out_features, num_tasks, rank)
def forward(self, x, task_id):
return self.linear(x) + self.lora(x, task_id)
# Usage
num_tasks = 3
cond_lora_linear = ConditionalLoRALinear(768, 768, num_tasks)
input_tensor = torch.randn(1, 768)
task_id = torch.randint(0, num_tasks, (1,))
output = cond_lora_linear(input_tensor, task_id)
print(f"Input shape: {input_tensor.shape}, Output shape: {output.shape}, Task ID: {task_id.item()}")Slide 10: Sparse LoRA for Enhanced Efficiency
Sparse LoRA introduces sparsity to the LoRA matrices, further reducing the number of parameters and computational requirements. This variant can be particularly useful for extremely large models or resource-constrained environments. The implementation uses masked gradients to maintain sparsity during training.
import torch
import torch.nn as nn
class SparseLoRALayer(nn.Module):
def __init__(self, in_features, out_features, rank=4, sparsity=0.5):
super().__init__()
self.lora_A = nn.Parameter(torch.randn(in_features, rank))
self.lora_B = nn.Parameter(torch.zeros(rank, out_features))
self.register_buffer('mask_A', torch.ones_like(self.lora_A))
self.register_buffer('mask_B', torch.ones_like(self.lora_B))
self.scale = 0.01
self.apply_sparsity(sparsity)
def apply_sparsity(self, sparsity):
with torch.no_grad():
self.mask_A.bernoulli_(1 - sparsity)
self.mask_B.bernoulli_(1 - sparsity)
def forward(self, x):
A = self.lora_A * self.mask_A
B = self.lora_B * self.mask_B
return (x @ A @ B) * self.scale
# Usage
sparse_lora = SparseLoRALayer(768, 768, sparsity=0.7)
input_tensor = torch.randn(1, 768)
output = sparse_lora(input_tensor)
print(f"Input shape: {input_tensor.shape}, Output shape: {output.shape}")
print(f"Sparsity A: {(sparse_lora.mask_A == 0).float().mean().item():.2f}")
print(f"Sparsity B: {(sparse_lora.mask_B == 0).float().mean().item():.2f}")Slide 11: Pruning-aware LoRA
Pruning-aware LoRA combines the concepts of neural network pruning with LoRA to create an even more efficient fine-tuning method. This approach dynamically prunes less important LoRA connections during training, resulting in a sparse and compact adaptation.
import torch
import torch.nn as nn
class PruningAwareLoRALayer(nn.Module):
def __init__(self, in_features, out_features, rank=4, prune_rate=0.5):
super().__init__()
self.lora_A = nn.Parameter(torch.randn(in_features, rank))
self.lora_B = nn.Parameter(torch.zeros(rank, out_features))
self.scale = 0.01
self.prune_rate = prune_rate
def prune(self):
with torch.no_grad():
# Calculate importance scores
importance_A = torch.abs(self.lora_A)
importance_B = torch.abs(self.lora_B)
# Determine pruning thresholds
threshold_A = torch.quantile(importance_A, self.prune_rate)
threshold_B = torch.quantile(importance_B, self.prune_rate)
# Apply pruning
self.lora_A.data[importance_A < threshold_A] = 0
self.lora_B.data[importance_B < threshold_B] = 0
def forward(self, x):
return (x @ self.lora_A @ self.lora_B) * self.scale
# Usage
pruning_lora = PruningAwareLoRALayer(768, 768)
input_tensor = torch.randn(1, 768)
output = pruning_lora(input_tensor)
print(f"Before pruning - Non-zero elements: A={torch.count_nonzero(pruning_lora.lora_A)}, B={torch.count_nonzero(pruning_lora.lora_B)}")
pruning_lora.prune()
output = pruning_lora(input_tensor)
print(f"After pruning - Non-zero elements: A={torch.count_nonzero(pruning_lora.lora_A)}, B={torch.count_nonzero(pruning_lora.lora_B)}")Slide 12: Attention-Guided LoRA
Attention-Guided LoRA leverages the attention mechanism to dynamically adjust the importance of different LoRA components. This variant allows the model to focus on the most relevant parts of the input for each specific task or input example.
import torch
import torch.nn as nn
import torch.nn.functional as F
class AttentionGuidedLoRALayer(nn.Module):
def __init__(self, in_features, out_features, rank=4):
super().__init__()
self.lora_A = nn.Parameter(torch.randn(in_features, rank))
self.lora_B = nn.Parameter(torch.zeros(rank, out_features))
self.attention = nn.Linear(in_features, rank)
self.scale = 0.01
def forward(self, x):
attention_weights = F.softmax(self.attention(x), dim=-1)
lora_output = x @ self.lora_A
weighted_lora = lora_output * attention_weights
return (weighted_lora @ self.lora_B) * self.scale
# Usage
attn_lora = AttentionGuidedLoRALayer(768, 768)
input_tensor = torch.randn(1, 768)
output = attn_lora(input_tensor)
print(f"Input shape: {input_tensor.shape}, Output shape: {output.shape}")Slide 13: Real-life Example: Sentiment Analysis Fine-tuning
In this example, we'll use LoRA to fine-tune a pre-trained BERT model for sentiment analysis on movie reviews. This demonstrates how LoRA can be applied to adapt a large language model to a specific task efficiently.
import torch
import torch.nn as nn
from transformers import BertModel, BertTokenizer
class SentimentClassifier(nn.Module):
def __init__(self, pretrained_model="bert-base-uncased", num_classes=2):
super().__init__()
self.bert = BertModel.from_pretrained(pretrained_model)
self.lora_layer = LoRALinear(self.bert.config.hidden_size, self.bert.config.hidden_size)
self.classifier = nn.Linear(self.bert.config.hidden_size, num_classes)
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
pooled_output = outputs.pooler_output
lora_output = self.lora_layer(pooled_output)
return self.classifier(lora_output)
# Usage
model = SentimentClassifier()
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
# Example sentiment analysis
text = "This movie was fantastic! I really enjoyed every moment of it."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
outputs = model(**inputs)
sentiment = torch.argmax(outputs, dim=1)
print(f"Sentiment: {'Positive' if sentiment.item() == 1 else 'Negative'}")Slide 14: Real-life Example: Image Classification Fine-tuning
This example demonstrates how to apply LoRA to fine-tune a pre-trained ResNet model for a custom image classification task. We'll use LoRA to adapt the model to a new dataset with minimal changes to the original architecture.
import torch
import torch.nn as nn
import torchvision.models as models
class LoRAResNet(nn.Module):
def __init__(self, num_classes, rank=4):
super().__init__()
self.resnet = models.resnet50(pretrained=True)
in_features = self.resnet.fc.in_features
self.resnet.fc = nn.Identity() # Remove the final fully connected layer
self.lora_layer = LoRALinear(in_features, in_features, rank)
self.classifier = nn.Linear(in_features, num_classes)
def forward(self, x):
features = self.resnet(x)
lora_features = self.lora_layer(features)
return self.classifier(lora_features)
# Usage
model = LoRAResNet(num_classes=10) # Assuming 10 classes for this example
input_tensor = torch.randn(1, 3, 224, 224) # Example input image
output = model(input_tensor)
print(f"Input shape: {input_tensor.shape}, Output shape: {output.shape}")Slide 15: Additional Resources
For more information on LoRA and its variants, consider exploring the following resources:
- Original LoRA paper: "LoRA: Low-Rank Adaptation of Large Language Models" (https://arxiv.org/abs/2106.09685)
- AdaLoRA: "AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning" (https://arxiv.org/abs/2303.10512)
- QLoRA: "QLoRA: Efficient Finetuning of Quantized LLMs" (https://arxiv.org/abs/2305.14314)
- Hugging Face's PEFT library, which implements various LoRA variants: https://github.com/huggingface/peft
These resources provide in-depth explanations of the LoRA technique and its applications in various domains of machine learning and natural language processing.