How to Gradually Warm-Up Learning Rate for PyTorch’s Optimizer

Increasing the learning rate gradually during training can significantly enhance model performance, especially in deep learning scenarios. In this blog, we’ll explore how to implement a gradual warm-up learning rate using PyTorch, inspired by the efficient training techniques proposed in the paper **Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour**.

What You Need to Get Started

• Python installed on your system
• PyTorch library installed
• A basic understanding of PyTorch and neural network training

Installation

To set up the gradual warm-up learning rate for PyTorch’s optimizer, you need to install the following package.

$ pip install git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git

How to Use Gradual Warm-Up Scheduler

In order to understand the implementation, let’s draw an analogy. Think of your neural network training like a race car preparing for a race. You wouldn’t want to accelerate too quickly from a standstill, as this could cause a loss of control. Instead, you gradually increase speed (learning rate) and then maintain it. Here’s how you can achieve that using the warmup_scheduler.

import torch
from torch.optim.lr_scheduler import StepLR
from torch.optim.sgd import SGD
from warmup_scheduler import GradualWarmupScheduler

if __name__ == "__main__":
    model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
    optim = SGD(model, 0.1)
    
    # scheduler_warmup is chained with scheduler_steplr
    scheduler_steplr = StepLR(optim, step_size=10, gamma=0.1)
    scheduler_warmup = GradualWarmupScheduler(optim, multiplier=1, total_epoch=5, after_scheduler=scheduler_steplr)
    
    # this zero gradient update is needed to avoid a warning message
    optim.zero_grad()
    optim.step()
    
    for epoch in range(1, 20):
        scheduler_warmup.step(epoch)
        print(epoch, optim.param_groups[0]['lr'])
        optim.step()
    # backward pass (update network)

Explanation of the Code

Here’s a breakdown of our “race car” setup:

• Model Initialization: We start by defining a basic model with random parameters.
• Optimizer: We choose Stochastic Gradient Descent (SGD) with a starting learning rate of 0.1.
• Scheduler Configuration: Our learning rate scheduler consists of two parts:
  • scheduler_steplr: Decreases the learning rate by multiplying with gamma (0.1) every 10 epochs.
  • scheduler_warmup: Gradually warms up the learning rate over the first 5 epochs before switching to the step scheduler.
• Training Loop: In the loop, we updating the learning rate for each epoch and perform the optimizer step to train our model.

Troubleshooting

If you encounter issues while implementing the warm-up learning rate, consider the following troubleshooting tips:

• Issue with Installation: Ensure that the package was installed correctly. You may want to check your Python environment and re-run the installation command.
• Learning Rate Not Changing: Make sure that your epoch range and the frequency of calling step() on the scheduler are set correctly.
• Warning Messages: If you receive warnings like “gradient is None,” ensure that you are executing optim.zero_grad() prior to the optim.step().

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Conclusion

Implementing a gradual warm-up learning rate in your models can be an effective strategy for improving training stability and performance. By gradually increasing the learning rate, we ensure a smoother transition into more aggressive learning, akin to a race car gently accelerating for optimal speed. Start incorporating this technique today and observe the difference in your model’s training efficiency!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.