Hmmm I want to add that although save_steps is 512 there has been nothing written to the specified checkpoint output dir. (Now it is on step ~2000, and still nothing printed for logging either).
Showing my full training args below.
Any insight would be greatly appreciated. I’m really scratching my head over the logging and saving issue.
batch_size = 1

training_args = TrainingArguments(
    output_dir="./checkpoints",
    per_device_train_batch_size=batch_size,
    do_train=True,
    # fp16=True,  # This has a known bug with t5
    gradient_accumulation_steps=32,
    logging_steps=128,
    save_steps=512,
    overwrite_output_dir=True,
    save_total_limit=10,
)

optimizer = Adafactor(model.parameters(), lr=1e-3, relative_step=False, warmup_init=False)
scheduler = get_constant_schedule(optimizer)
optimizers = optimizer, scheduler

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    optimizers=optimizers
)

trainer.train()