Alternatives to get_last_lr() for Effective Learning Rate Management in PyTorch

Purpose

The get_last_lr() method serves a crucial role in this context by allowing you to retrieve the most recent learning rate computed by the MultiStepLR scheduler after a call to its step() method.
In PyTorch's optimization process, MultiStepLR is a learning rate scheduler that adjusts the learning rate based on predefined milestones during training.

How it Works

The get_last_lr() method then provides access to these updated learning rates.
If the epoch matches or surpasses a milestone, MultiStepLR applies a decay factor (gamma) to the learning rates, effectively reducing them.
When you call step(), it checks the current epoch against the provided milestones list.
MultiStepLR maintains an internal state that tracks the current epoch and the learning rates for each parameter group in the optimizer.

Importance

By using get_last_lr(), you can:
- Monitor the learning rate progression throughout training to gain insights into the optimization process.
- Implement custom logic based on the learning rate for advanced training strategies.
- Debug potential issues related to learning rate scheduling.

Example

import torch
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR

# Create an optimizer
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Create a MultiStepLR scheduler with milestones and a decay factor
scheduler = MultiStepLR(optimizer, milestones=[30, 80], gamma=0.1)

for epoch in range(100):
    # ... training loop ...

    # Step the scheduler to update learning rates (if applicable)
    scheduler.step()

    # Get the most recent learning rates using get_last_lr()
    last_lrs = scheduler.get_last_lr()
    print(f"Epoch {epoch+1}, Learning Rates: {last_lrs}")

It's essential to call scheduler.step() before calling get_last_lr() to ensure you get the most up-to-date learning rates.
The order of the learning rates in the list corresponds to the order of the parameter groups in the optimizer.
Remember that get_last_lr() returns a list containing the learning rates for each parameter group in the optimizer.

Plotting Learning Rate Decay

import torch
import matplotlib.pyplot as plt
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR

# Create an optimizer
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Create a MultiStepLR scheduler with milestones and a decay factor
scheduler = MultiStepLR(optimizer, milestones=[30, 80], gamma=0.1)

learning_rates = []
for epoch in range(100):
    # ... training loop ...

    # Step the scheduler to update learning rates (if applicable)
    scheduler.step()

    # Get the most recent learning rates
    last_lrs = scheduler.get_last_lr()
    learning_rates.append(last_lrs[0])  # Assuming you care about the first param group

plt.plot(range(100), learning_rates)
plt.xlabel("Epoch")
plt.ylabel("Learning Rate")
plt.title("Learning Rate Decay with MultiStepLR")
plt.show()

This code tracks the learning rate of the first parameter group (assuming that's what you're interested in) by appending it to a list after each epoch. Finally, it plots the learning rate decay over the course of training.

Early Stopping with Learning Rate Threshold

import torch
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR

# ... training and validation code ...

# Early stopping condition based on learning rate
learning_rate_threshold = 0.001

for epoch in range(100):
    # ... training loop ...

    # Step the scheduler
    scheduler.step()

    # Get the most recent learning rate
    last_lr = scheduler.get_last_lr()[0]  # Assuming first param group

    # Early stopping based on learning rate
    if last_lr <= learning_rate_threshold:
        print(f"Early stopping at epoch {epoch+1} due to low learning rate")
        break

    # ... validation loop ...

This code implements early stopping based on a learning rate threshold. If the learning rate of the first parameter group falls below the specified threshold, training is halted.

Custom Logic Based on Learning Rate

import torch
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR

# ... training loop ...

# Adjust training strategy based on learning rate
learning_rate_threshold = 0.01

for epoch in range(100):
    # ... training steps ...

    # Step the scheduler
    scheduler.step()

    # Get the most recent learning rate
    last_lr = scheduler.get_last_lr()[0]  # Assuming first param group

    # Increase weight decay if learning rate is low
    if last_lr <= learning_rate_threshold:
        for param_group in optimizer.param_groups:
            param_group["weight_decay"] *= 2.0  # Double the weight decay

    # ... other training steps ...

This code demonstrates how you can adjust the training strategy (here, increasing weight decay) based on the learning rate retrieved using get_last_lr().

Accessing Learning Rates Directly

Instead of relying on get_last_lr(), you can directly access the learning rates stored within the optimizer object. In MultiStepLR, the learning rates are updated and stored in the optimizer's param_groups attribute after calling scheduler.step(). You can access these values like this:

optimizer = optim.SGD(model.parameters(), lr=0.1)
scheduler = MultiStepLR(optimizer, milestones=[30, 80], gamma=0.1)

for epoch in range(100):
    # ... training loop ...

    scheduler.step()

    # Access the learning rates directly from optimizer
    last_lrs = [group['lr'] for group in optimizer.param_groups]
    print(f"Epoch {epoch+1}, Learning Rates: {last_lrs}")

This approach offers more control over how you access and manipulate the learning rates.