When using SGD: RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
I am traininggoogle/vit-base-patch16-384for image classification.When I try to modify the optimizers input toTrainer, I am running into this error:RuntimeError: element 0 of tensors does not require grad and does not have a grad_fnFor instance, this setup would throw the error, once the trainer runs a backward pass:training_args = TrainingArguments(
        output_dir="model_output",
        remove_unused_columns=False,
        evaluation_strategy="epoch",
        save_strategy="epoch",
        weight_decay=0.0,
        max_grad_norm=1.0,
        learning_rate=args.learning_rate,  # initial  learning rate
        per_device_train_batch_size=args.per_device_train_batch_size,
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        per_device_eval_batch_size=args.per_device_eval_batch_size,
        num_train_epochs=args.num_train_epochs,
        warmup_ratio=args.warmup_ratio,
        logging_steps=args.logging_steps,
        save_total_limit=args.save_total_limit,
        load_best_model_at_end=True,
        metric_for_best_model="eval_loss",
        greater_is_better=False,
    )

    optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate, momentum=0.9)
    num_training_steps = (
        len(data["train"]) * args.num_train_epochs // args.per_device_train_batch_size
    )
    scheduler = get_cosine_schedule_with_warmup(
        optimizer,
        num_warmup_steps=int(args.warmup_ratio * num_training_steps),
        num_training_steps=num_training_steps,
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        data_collator=data_collator,
        train_dataset=data["train"],
        eval_dataset=data["val"],
        tokenizer=image_processor,
        optimizers=(optimizer, scheduler),
        compute_metrics=compute_metrics,
        callbacks=[
            EarlyStoppingCallback(early_stopping_patience=3, early_stopping_threshold=0)
        ],
    )If I comment out theoptimizersinput toTrainer, then it defaults back to AdamW and the training runs fine. Or, if I setoptimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate), then it also runs fine. Actually, a few optimizers work here:  AdamW, Adam, Adagrad, Rprop, RMSprop (with momentum=0).Ones that throw the runtime error: SGD (with and without momentum), ASGD, RMSprop with momentum =/= 0.That’s all I have tested so far… Any ideas why some optimisers are throwing the error, but some are not? I am trying to reproduce the fine-tuning approach from the ViT paper, where they use SGD.