Adafactor with Accelerator

You've demonstrated a proof-of-concept of Adafactor with Trainer to train your language translation model with reduced memory requirements. Now you'd like to customize your training loop using Accelerator. Build the training loop to use Adafactor!

The compute_optimizer_size() function has been pre-defined. Some training objects have been pre-loaded: model, train_dataloader, and accelerator.

Deze oefening maakt deel uit van de cursus

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Oefeninstructies

Pass the model parameters to Adafactor when defining the optimizer.
Pass in the optimizer state to print the size.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Pass the model parameters to Adafactor
optimizer = ____(params=____.____())
model, optimizer, train_dataloader = accelerator.prepare(model, optimizer, train_dataloader)

for batch in train_dataloader:
    inputs, targets = batch["input_ids"], batch["labels"]
    outputs = model(inputs, labels=targets)
    loss = outputs.loss
    accelerator.backward(loss)
    optimizer.step()
    optimizer.zero_grad()

# Pass in the optimizer state
total_size_megabytes, total_num_elements = compute_optimizer_size(____.____.values())
print(f"Number of optimizer parameters: {total_num_elements:,}\nOptimizer size: {total_size_megabytes:.0f} MB")

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