PyTorch - UserWarning Detected call of `lr_scheduler.step()` before `optimizer.step()` - call optimizer.step() before lr_scheduler.step()`

When working with neural networks in PyTorch, managing the learning rate effectively can significantly influence the model's performance. Python developers frequently encounter a common confusion with PyTorch's learning rate schedulers, manifested as the following warning message:

UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()` - call optimizer.step() before lr_scheduler.step()`

This is important because optimizing the neural network requires careful handling of the learning rate to manage the model's convergence. Let's explore why this warning appears and how to appropriately integrate learning rate scheduling in PyTorch.

Understanding the Warning

The warning emerges from PyTorch's handling of learning rate schedules. It's crucial to understand that the learning rate scheduler is meant to adjust the learning rate every epoch after your model's weights have been updated using optimizer.step(). Here's what's happening in the background:

• optimizer.step() adjusts the weights of the model according to the chosen optimization algorithm (e.g., SGD, Adam).
• scheduler.step() updates the learning rate based on your scheduling policy. For example, this could be a StepLR which reduces the learning rate based on a fixed schedule.

Avoiding the Warning: Best Practices

Here’s how you can correctly integrate learning rate scheduling with PyTorch to avoid the warning:

import torch.optim as optim

# Assuming model and trainloader are predefined
model = ...

# Define the optimizer
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Define the learning rate scheduler
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)

for epoch in range(100):  # loop over the dataset multiple times
    for inputs, labels in trainloader:
        # Forward pass
        outputs = model(inputs)
        loss = loss_function(outputs, labels)

        # Zero gradients
        optimizer.zero_grad()

        # Backward pass
        loss.backward()

        # Optimize the weights
        optimizer.step()

    # Step the learning rate scheduler at the end of the epoch
    scheduler.step()

Notice that the scheduler.step() is called after the loop where the optimizer's step() method occurs. This ensures weights adjustments reflect the old learning rate values before any scheduler-induced changes.

Different Scheduler Stepping: Usage Context

Some learning rate schedulers behave differently, and understanding where the scheduler step should be positioned is key:

• Epoch-level Schedulers (e.g., StepLR, MultiStepLR): These should step at the end of an epoch. Call scheduler.step() after the entire training loop for an epoch completes.
• Batch-level Schedulers (e.g., CyclicLR): Unlike StepLR, stepping for these schedulers happens after each batch. Ensure you are calling scheduler.step() inside the batch loop, right after optimizer.step():

for epoch in range(100):
    for inputs, labels in trainloader:
        # Forward pass
        outputs = model(inputs)
        loss = loss_function(outputs, labels)

        # Zero gradients
        optimizer.zero_grad()

        # Backward pass
        loss.backward()

        # Optimize the weights
        optimizer.step()

        # Step the learning rate scheduler after optimizer
        scheduler.step()

In conclusion, ensure the learning rate scheduler is applied in alignment with the designed intention ---- epoch-level or batch-level. Properly aligning your epoch end actions, such as scheduler.step(), with those required batch operations contributes to more robust PyTorch training loops and helps avoid those persistent and sometimes performance-impacting warnings.