How can I solve CUDA out of memory problem?

1

I got this problem and tried to reduce the batch_size but the error still
labels=decoder_input_ids RuntimeError: CUDA out of memory. Tried to allocate 16.00 MiB (GPU 0; 5.80 GiB total capacity; 4.62 GiB already allocated; 2.56 MiB free; 4.72 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

the code is

dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
num_epochs = 5


for epoch in range(num_epochs):
    model.train()
    total_loss = 0
    
    for batch in dataloader:
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        decoder_input_ids = batch['decoder_input_ids'].to(device)
        decoder_attention_mask = batch['decoder_attention_mask'].to(device)
        
        outputs = model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            labels=decoder_input_ids
        )
        loss = outputs.loss
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
    
    avg_loss = total_loss / len(dataloader)
2

You would need to reduce the batch size further or if that’s not possible, you come or either use a model with a lower memory footprint, or could check e.g. torch.utils.checkpoint to trade compute for memory.