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compute_perplexity(loss): + return torch.exp(loss) + + def train(model, train_loader, val_loader, optimizer, criterion, epochs=10): """Training loop for the model.""" model.train() for epoch in range(epochs): for inputs, targets in train_loader: - inputs, targets = inputs.to(accelerator.device), targets.to( - accelerator.device + inputs, targets = ( + inputs.to(accelerator.device), + targets.to(accelerator.device), ) print(f"Input shape: {inputs.shape}") @@ -140,8 +150,9 @@ def train(model, train_loader, val_loader, optimizer, criterion, epochs=10): val_loss = 0 with torch.no_grad(): for inputs, targets in val_loader: - inputs, targets = inputs.to(accelerator.device), targets.to( - accelerator.device + inputs, targets = ( + inputs.to(accelerator.device), + targets.to(accelerator.device), ) outputs = model(inputs) loss = criterion(outputs.permute(0, 2, 1), targets)