Foundation models are large, pre-trained models designed to capture general representations from vast datasets, which can then be adapted to specific downstream tasks. In 2D vision, examples include Vision Transformers (ViTs) like CLIP or DINO, while in 3D vision, models like PointNet handle point clouds for tasks such as shape recognition. Fully retraining these models for new tasks is computationally expensive due to their size and complexity. Adapters offer an efficient solution by introducing a small number of trainable parameters to adapt the model, while keeping most of the pre-trained parameters frozen.
This document covers:
LoRA, or Low-Rank Adaptation, is a technique that adapts pre-trained models by adding low-rank updates to specific weight matrices, typically in the attention layers of transformer-based models. Instead of updating the entire weight matrix
In vision transformers, LoRA is commonly applied to the query (
This approach reduces the number of trainable parameters significantly. For a
LoRA is ideal for adapting large foundation models efficiently, making it applicable to both 2D vision (e.g., ViTs) and 3D vision (e.g., MLPs in PointNet), where attention or linear layers are prevalent.
Beyond LoRA, several adapter types are used to adapt 2D and 3D vision foundation models, offering flexibility based on task and architecture.
Adapter modules are small neural networks inserted between the layers of a pre-trained model. In transformers, they are typically placed after the attention or feed-forward layers. A common design includes:
The output is combined with the input via a residual connection:
Prompt tuning involves adding learnable tokens or "prompts" to the input sequence of the model. In 2D vision transformers, this might mean appending extra patch embeddings to the sequence of image patches.
For 3D vision models, specialized adapters handle point clouds or meshes.
Implementing adapters requires slight modifications to the foundation model's architecture. Below are examples for each type, with a focus on 2D and 3D vision contexts.
In a 2D Vision Transformer, LoRA modifies the linear layers in the attention mechanism. Here's a simplified PyTorch example:
import torch
import torch.nn as nn
class LoRALayer(nn.Module):
def __init__(self, original_layer, rank):
super().__init__()
self.original_layer = original_layer
self.A = nn.Parameter(torch.randn(original_layer.in_features, rank))
self.B = nn.Parameter(torch.randn(rank, original_layer.out_features))
def forward(self, x):
original_output = self.original_layer(x)
lora_update = x @ self.A @ self.B
return original_output + lora_update
# Usage in a ViT
original_layer = nn.Linear(768, 768) # Example dimension from ViT
lora_layer = LoRALayer(original_layer, rank=8)
r=16, lora_alpha=16, and target modules ["query", "value"], reducing trainable parameters to 0.77% of the original model.For adapter modules inserted after a transformer layer:
class Adapter(nn.Module):
def __init__(self, d, d_adapter):
super().__init__()
self.down = nn.Linear(d, d_adapter)
self.relu = nn.ReLU()
self.up = nn.Linear(d_adapter, d)
def forward(self, x):
return x + self.up(self.relu(self.down(x)))
# Usage
adapter = Adapter(d=768, d_adapter=64) # Bottleneck size of 64
For prompt tuning in a 2D Vision Transformer:
class PromptedViT(nn.Module):
def __init__(self, vit_model, num_prompts, d):
super().__init__()
self.vit_model = vit_model
self.prompts = nn.Parameter(torch.randn(num_prompts, d))
def forward(self, x):
batch_size = x.size(0)
prompts = self.prompts.unsqueeze(0).repeat(batch_size, 1, 1)
x = torch.cat([prompts, x], dim=1) # Prepend prompts to patch embeddings
return self.vit_model(x)
# Usage
prompted_model = PromptedViT(vit_model, num_prompts=10, d=768)
Point-PEFT's implementation includes:
Adapters offer significant benefits but also come with trade-offs. Here's a detailed breakdown:
| Aspect | Pros | Cons |
|---|---|---|
| Parameter Efficiency | Add only a small fraction of trainable parameters (e.g., 0.77% for LoRA in ViT). | May require multiple adapters for different tasks, increasing management overhead. |
| Computational Efficiency | Reduce memory and compute requirements by freezing most parameters. | Performance may not match full fine-tuning, especially for tasks far from pre-training domain. |
| Modularity | Separate adapters can be trained and swapped for different tasks without altering the core model. | Implementation complexity increases, requiring architectural modifications. |
| Reduced Overfitting | Fewer trainable parameters lower the risk of overfitting, especially on small datasets. | Hyperparameter tuning (e.g., rank, bottleneck size) adds complexity to the process. |
To enhance the effectiveness and efficiency of adapters, consider the following optimizations:
r=16 for ViT.This document provides a comprehensive overview of adapters in 2D and 3D vision foundation models. Starting with LoRA, we explored its low-rank approach, then covered adapter modules, prompt-based methods, and 3D-specific adapters like Point-PEFT. Implementation details, pros, cons, and optimizations offer a holistic view, enabling efficient adaptation of large models to new tasks with minimal computational overhead.