Optimizing Your Optimizers: Device Compatibility in PyTorch State Dictionary Loading

In PyTorch, when you train a model, you use an optimizer to update its parameters based on the calculated loss. You might save the state of your training process (including the model and optimizer) to a checkpoint file for later resumption. However, there can be a mismatch between the device (CPU or GPU) used during training and the device used when loading the checkpoint, leading to errors.

Why It Happens:

• Optimizer State Tensors: Optimizers in PyTorch maintain internal state tensors that track parameter updates. These tensors can reside on either CPU or GPU depending on where the training occurred.
• Mismatched Devices: If you trained on a GPU and then try to load the optimizer state on a CPU (or vice versa), the optimizer's state tensors might be incompatible with the current device's memory type. This mismatch can cause errors when loading the state dictionary.

Solutions:

Here are two common approaches to address this issue:

1. map_location Argument:

   • When loading the checkpoint file using torch.load(), you can specify the desired device using the map_location argument. This argument tells PyTorch to move the tensors in the state dictionary (including the optimizer's state) to the specified device before loading them.

   checkpoint = torch.load(checkpoint_path, map_location=device)
   model.load_state_dict(checkpoint["model"])
   optimizer.load_state_dict(checkpoint["optimizer"])
   

   • Replace device with 'cpu' or torch.device('cuda') depending on your desired device.

2. Consistent Device Usage:

Choosing the Right Approach:

• If you know the device used for training beforehand, using map_location provides a clear way to load on a different device.
• If you want to maintain flexibility over the device or are unsure of the training device, keeping consistent device usage throughout is a recommended practice.

import torch

# Assuming training happened on GPU (replace with 'cpu' if needed)
training_device = torch.device('cuda')

# ... (your training code)

# Save model and optimizer state dictionaries (on training device)
checkpoint = {
    "model": model.state_dict(),
    "optimizer": optimizer.state_dict(),
    # ... other states (e.g., epoch, learning rate)
}
torch.save(checkpoint, 'checkpoint.pth')

# ... (later, when loading on CPU or a different GPU)

# Specify desired device (replace with 'cuda:0' if loading on a specific GPU)
device = torch.device('cpu')

checkpoint = torch.load('checkpoint.pth', map_location=device)

model = MyModelDefinition().to(device)  # Move model to desired device
model.load_state_dict(checkpoint["model"])

optimizer = torch.optim.Adam(model.parameters())  # Create new optimizer on CPU
optimizer.load_state_dict(checkpoint["optimizer"])

# ... (resume training on CPU)

Approach 2: Consistent Device Usage

import torch

# Choose the device you want to use (CPU or GPU)
device = torch.device('cuda')  # Or 'cpu'

# Move model to the device before creating optimizer
model.to(device)

optimizer = torch.optim.Adam(model.parameters())  # Optimizer created on chosen device

# ... (your training code)

# Save model and optimizer state dictionaries (on chosen device)
checkpoint = {
    "model": model.state_dict(),
    "optimizer": optimizer.state_dict(),
    # ... other states (e.g., epoch, learning rate)
}
torch.save(checkpoint, 'checkpoint.pth')

# ... (later, when loading)

# Move model and optimizer to the same device (if necessary)
if device != torch.device('cpu'):  # Assuming checkpoint was saved on GPU
    model.to(device)
    optimizer = torch.optim.Adam(model.parameters())  # Recreate optimizer on device

checkpoint = torch.load('checkpoint.pth')

model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])

# ... (resume training on chosen device)

If you have a deep understanding of the optimizer's internal state and the specific tensors causing issues, you could attempt to manually move them to the desired device after loading the checkpoint. However, this approach is generally not recommended due to its complexity and potential for errors. It's best suited for advanced users who can identify the problematic tensors and handle the movement correctly.

Custom Optimizer Wrapper (For Specific Optimizers):

For certain optimizers, you might be able to create a custom wrapper class that handles device-related logic during loading. This wrapper would encapsulate the optimizer and provide methods to load its state dictionary while ensuring compatibility with the current device. This approach requires a good understanding of the optimizer's implementation details and can be time-consuming to implement. It's useful only if you're working with a specific optimizer that has known device-related issues.

General Recommendations:

• In most cases, using map_location or maintaining consistent device usage throughout your training and loading process will be sufficient.
• Unless you have a specific reason or encounter a unique situation, it's generally advisable to avoid manual state tensor movement or custom optimizer wrappers due to their complexity and potential for introducing errors.
• If you're unsure about the training device or prefer flexibility, keeping consistent device usage is the safer and more maintainable approach.