Gradient accumulation with Accelerator

You're training a language model to simplify translations by paraphrasing complex sentences, but your GPU is running out of memory. Gradient accumulation allows the model to effectively train on larger batches by using small batches that fit into memory. You prefer to write the training loop explicitly to see its structure, so you're using Accelerator. Note that this exercise actually runs on the CPU, but the code remains the same for the GPU.

The model, train_dataloader, optimizer, and lr_scheduler have been pre-defined.

Deze oefening maakt deel uit van de cursus

Efficient AI Model Training with PyTorch

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Oefeninstructies

Configure Accelerator() to use gradient accumulation with two steps.
Set up an Accelerator context manager to enable gradient accumulation for the model.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Configure Accelerator
accelerator = ____(____=____)
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(model, optimizer, train_dataloader, lr_scheduler)
for batch in train_dataloader:
    # Set up an Accelerator context manager
    with ____.____(____):
        inputs, targets = batch["input_ids"], batch["labels"]
        outputs = model(inputs, labels=targets)
        loss = outputs.loss
        accelerator.backward(loss)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        print(f"Loss = {loss}")

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