Adafactor with Trainer

You're training a Transformer model with billions of parameters for your language translation service. It is straining your computational resources, so you decide to try the Adafactor optimizer to reduce memory requirements compared to AdamW. Prepare the Trainer for Adafactor!

Some training objects have been pre-loaded, including model, train_dataset, validation_dataset, and compute_metrics.

Questo esercizio fa parte del corso

Efficient AI Model Training with PyTorch

Visualizza il corso

Istruzioni dell'esercizio

Specify Adafactor as an optimizer in TrainingArguments.
Pass in the optimizer state to print the size.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Specify Adafactor as an optimizer
training_args = TrainingArguments(output_dir="./results",
                                  evaluation_strategy="epoch",
                                  ____="____")

trainer = Trainer(model=model,
                  args=training_args,
                  train_dataset=train_dataset,
                  eval_dataset=validation_dataset,
                  compute_metrics=compute_metrics)
trainer.train()

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

Modifica ed esegui il codice