Fine-Tuning with AdamW: Why Weight Decay Matters

But why does weight decay matter, and how does it elevate your training process? Let’s dive in.

What Is Weight Decay in Optimization?

The Role of Regularization in Deep Learning

In machine learning, regularization prevents overfitting by encouraging simpler models. It reduces the risk of your model becoming too tailored to training data and failing to generalize. Weight decay is a common regularization method.

Simply put, weight decay applies a penalty to large parameter values. By constraining the size of the weights, it encourages more stable and interpretable models.

A visual breakdown of weight decay within the broader family of regularization methods.

How Weight Decay Works in Gradient Descent

Weight decay modifies the gradient descent update rule. Instead of only adjusting weights based on gradients, it subtracts a small proportion of each weight’s current value. This shrinkage keeps the parameters from growing unnecessarily large, which might harm performance.

Why Is AdamW Different from Adam?

The Problem with Standard Adam Optimizer

The Adam optimizer combines the best of momentum-based and adaptive learning rate techniques, making it a go-to for many practitioners. However, its implementation of weight decay isn’t straightforward.

Adam applies weight decay by adding it directly to the loss function. This indirect approach can lead to suboptimal updates, as the penalty influences not just the weights but also the learning rate.

How AdamW Fixes This Issue

AdamW separates weight decay from the loss function. It applies the penalty directly to weight updates, ensuring that learning rates remain unaffected. This clean decoupling results in more consistent training and often better results, especially for fine-tuning.

By isolating weight decay, AdamW ensures the optimizer performs regularization exactly as intended.

Weight Decay and Model Generalization

Enhancing Stability in Fine-Tuning

Fine-tuning often deals with pre-trained models adapted to specific tasks. Without proper weight decay, models risk overfitting or instability during training. AdamW’s precise implementation helps stabilize training even with small datasets.

Weight Decay and Sparsity

Interestingly, weight decay promotes sparsity in model parameters. Sparse models not only generalize better but are also more interpretable. This property can be particularly beneficial for applications like natural language processing (NLP) and computer vision, where interpretability is key.

Choosing the Right Weight Decay Value

Balancing Bias and Variance

Selecting the right weight decay parameter involves balancing bias and variance. A high value may oversimplify the model, introducing bias. Conversely, a low value might not sufficiently control complexity, leading to variance.

Practical Tips for Tuning

• Start with commonly recommended values like 0.01 or 0.001.
• Monitor validation loss carefully.
• Use grid search or Bayesian optimization for more precise tuning.

Key Insights:

High Weight Decay: Better than low decay in some aspects but less effective than the optimal setting.

Low Weight Decay: Struggles with overfitting resistance and generalization, showing moderate performance overall.

Optimal Weight Decay: Achieves balanced and superior results across all metrics, demonstrating its effectiveness.

Practical Applications of AdamW with Weight Decay

NLP Fine-Tuning with Transformers

Transformers like BERT and GPT rely heavily on fine-tuning. AdamW ensures these models adapt well to domain-specific tasks without sacrificing generalization.

Computer Vision Models

For image recognition tasks, AdamW shines by stabilizing training in convolutional networks, ensuring better feature extraction.

By handling weight decay effectively, AdamW has become indispensable for researchers and practitioners alike. Fine-tuning success often hinges on this nuanced optimizer.

How AdamW Impacts Model Convergence

Faster Convergence with Precise Regularization

One of AdamW’s standout features is its ability to accelerate convergence without compromising model stability. By separating weight decay from gradient updates, it avoids over-penalizing gradients, which can slow learning in other optimizers.

This makes AdamW particularly effective in fine-tuning pre-trained models, where stability is paramount, and datasets may be smaller.

Smoother Loss Curves

When using AdamW, you’ll often notice smoother loss curves during training. This smoother progression reflects a balanced optimization process, reducing erratic behavior caused by improper weight regularization in older methods.

Challenges with Weight Decay in Fine-Tuning

Over-regularization in Small Datasets

Weight decay is highly effective, but it’s not without risks. When fine-tuning on small datasets, excessive weight decay can lead to underfitting, where the model becomes too simplistic to capture the task’s complexities.

Interaction with Dropout

Dropout is another regularization technique often used in conjunction with weight decay. While the two methods complement each other, improper balance can over-regularize the model, especially in deep architectures like Transformers.

Comparing AdamW to Other Optimizers

Adam vs. AdamW

The key difference lies in how weight decay is applied. While Adam applies decay indirectly through the loss function, AdamW applies it directly during parameter updates. This distinction may seem minor but leads to more reliable training dynamics.

In experiments, AdamW consistently outperforms Adam for tasks like:

• Fine-tuning transformers for NLP.
• Training deep convolutional networks for image classification.

SGD with Momentum vs. AdamW

SGD with momentum is another popular optimizer, especially for computer vision tasks. While it offers simplicity, it lacks the adaptive learning rate benefits of AdamW. For models requiring intricate updates, AdamW often edges ahead in performance.

Key Tips for Using AdamW Effectively

Combine with Learning Rate Schedulers

Pair AdamW with schedulers like Cosine Annealing or Warm Restarts to maintain stability. These schedulers help fine-tune learning rates over time, complementing weight decay.

Monitor Training and Validation Metrics

To ensure optimal performance, closely monitor validation loss and accuracy during fine-tuning. A significant divergence may signal improper weight decay or learning rates.

Layer-Specific Weight Decay

Advanced practitioners often use layer-specific weight decay for transformers and large neural networks. Lower decay for early layers and higher for later ones can improve task-specific adaptation.

Real-World Success with AdamW

BERT and GPT Fine-Tuning

Modern NLP depends on pre-trained models like BERT and GPT, which are fine-tuned for tasks such as sentiment analysis, question answering, and summarization. AdamW enables these massive architectures to maintain their learned representations while adapting effectively to new tasks.

Vision Transformers (ViTs)

For computer vision, Vision Transformers (ViTs) have taken the spotlight. These models benefit greatly from AdamW’s ability to handle sparse updates, ensuring consistent training across large datasets.

Generative AI and Stable Diffusion

Generative models, including Stable Diffusion and GANs, leverage AdamW to fine-tune without sacrificing stability. Its weight decay ensures the generator and discriminator evolve cohesively, leading to sharper outputs.

Comparing AdamW with Other Optimizers and Techniques

AdamW vs. Adam

Example: Fine-tuning a transformer:
Adam may yield fluctuating loss curves, while AdamW ensures a steady convergence trajectory.

AdamW vs. SGD with Momentum

Example: Training ResNet on ImageNet:
SGD excels for pure image classification tasks, while AdamW is better suited for fine-tuning vision transformers (ViTs).

AdamW vs. RMSProp

Example: RMSProp may work well for training smaller RNN-based models, but AdamW is preferred for transformers like BERT or GPT.

Weight Decay vs. Dropout

Example: Combining weight decay and dropout:

model = nn.Sequential(
    nn.Linear(768, 512),
    nn.Dropout(0.2),  # Dropout to prevent co-adaptation
    nn.ReLU(),
    nn.Linear(512, 10),
)
optimizer = AdamW(model.parameters(), lr=3e-4, weight_decay=0.01)

AdamW vs. LARS (Layer-wise Adaptive Rate Scaling)

Example: LARS is typically used in large-scale distributed setups like training GPT models from scratch, while AdamW dominates fine-tuning tasks.

Which Optimizer Should You Use?

• Choose AdamW for:
  • Fine-tuning large pre-trained models like BERT or Vision Transformers (ViTs).
  • Tasks requiring adaptive learning rates (e.g., NLP or computer vision).
  • Sparse gradient scenarios, where updates are uneven across layers.
• Choose SGD with Momentum for:
  • Image classification tasks with smaller, convolution-based models (e.g., ResNet).
  • Simpler tasks where adaptive learning isn’t as crucial.
• Choose RMSProp for:
  • Recurrent models like LSTMs or GRUs, where RMSProp’s stability with sequence data is beneficial.

optimizer = AdamW(model.parameters(), lr=5e-5, weight_decay=0.01)

optimizer = AdamW(model.parameters(), lr=3e-5, weight_decay=0.01)

from transformers import get_scheduler

scheduler = get_scheduler(
    "linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_steps
)

optimizer = AdamW(model.parameters(), lr=1e-4, weight_decay=0.01)

parameters = [
    {"params": model.bert.encoder.parameters(), "weight_decay": 0.01},  # Base layers
    {"params": model.classifier.parameters(), "weight_decay": 0.0},     # Classifier head
]
optimizer = AdamW(parameters, lr=3e-5)

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(768, 512),
    nn.Dropout(0.1),
    nn.ReLU(),
    nn.Linear(512, 2),
)
optimizer = AdamW(model.parameters(), lr=5e-5, weight_decay=0.01)

Resources

Official Documentation

• Hugging Face Transformers
  Transformers Documentation – Optimizers
  Detailed guide on using AdamW with pre-trained models for fine-tuning. Includes examples and scheduler integrations.
• PyTorch Optimizer Docs
  AdamW in PyTorch
  Official PyTorch documentation for AdamW, including parameter details and example usage.

Research Papers

• AdamW Original Paper:
  Decoupled Weight Decay Regularization
  Read the Paper on arXiv
  A foundational paper by Ilya Loshchilov and Frank Hutter explaining AdamW and its advantages over Adam.
• Transformer Fine-Tuning:
  Attention is All You Need
  Read the Paper on arXiv
  The seminal paper introducing the transformer architecture, where optimizers like AdamW are commonly used.

Open-Source Projects

• Fastai
  Fastai GitHub Repository
  A deep learning library with accessible implementations of AdamW and related techniques.
• Transformers Library
  Hugging Face Transformers GitHub
  A robust library for fine-tuning models like BERT, GPT, and Vision Transformers with AdamW.

Academic Datasets for Practice

• Kaggle Datasets
  Explore Kaggle
  Search for datasets compatible with AdamW fine-tuning tasks, such as IMDb for NLP or CIFAR-10 for vision.
• Hugging Face Datasets
  Datasets Library
  Access thousands of datasets for fine-tuning models, all integrated with the transformers ecosystem.
• TensorFlow Datasets
  TFDS Documentation
  Offers a wide range of datasets like ImageNet, MNIST, and SQuAD for tasks using AdamW.