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. Adafactor has been pre-imported from torch.optim.

Acest exercițiu face parte din cursul

Efficient AI Model Training with PyTorch

Vezi cursul

Instrucțiuni pentru exercițiu

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

Exercițiu interactiv practic

Încearcă acest exercițiu completând acest cod de exemplu.

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

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