pytorch tensor.to

Move tensor to cuda with different name returned

Question: can I still use model to do training when I have following code ? In this code, I assign m to tensor on cuda. But I am not sure whether I can still use model to do back propagation

model = BigramLanguageModel()
m = model.to(device)

Yes, you can still use the model for training and backpropagation. When you do m = model.to(device), it doesn’t create a new model. Instead, it moves the model to the specified device (which is presumably a CUDA device in your case) and returns a reference to the same model. So, m and model are actually the same model, just accessed through different names.

Here’s an example of how you might use it:

model = BigramLanguageModel()
model = model.to(device)  # move model to device

# define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())

# training loop
for epoch in range(num_epochs):
    for i, (inputs, targets) in enumerate(dataloader):
        inputs, targets = inputs.to(device), targets.to(device)

        # forward pass
        outputs = model(inputs)
        loss = criterion(outputs, targets)

        # backward pass and optimization
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

In this code, model is used for both the forward pass and the backward pass, and it works correctly because model and m are the same model. The key point is that the model parameters need to be on the same device as the data and targets when you perform the forward and backward passes. That’s why inputs and targets are also moved to device before the forward pass.

So, in your case, you can continue to use model in your code for training and backpropagation, even after calling model.to(device). Just make sure to move your inputs and targets to the same device before you use them with your model. If you want to use m instead of model after the to(device) call, that’s fine too. It’s really just a matter of which name you prefer to use. They both refer to the same underlying model.

Difference between model.to(device) and model.cuda()

The .to() method and .cuda() are both used to move PyTorch tensors to a specific device (CPU or GPU), but they have some differences:

1. .to(device):
   • The .to() method is more general and versatile. It allows you to move a tensor to any specified device (CPU or a specific GPU).
   • You can pass either a string (e.g., "cuda" or "cpu") or a specific device (e.g., torch.device("cuda:0")) as an argument.
   • Example:

     tensor.to("cuda")  # Move tensor to the first available GPU (if available)
     tensor.to(torch.device("cuda:1"))  # Move tensor to the second GPU (if available)
     

2. .cuda():
   • The .cuda() method is specifically for moving tensors to a GPU (CUDA device).
   • It is a shorthand for .to("cuda").
   • If you call .cuda() without any arguments, it will move the tensor to the first available GPU (if any). If no GPUs are available, it will raise an error.
   • Example:

     tensor.cuda()  # Move tensor to the first available GPU (if available)