Bug fix: raise value error if gradient_accumulation_steps is zero, ne…
Author: Arunprakash-A
Pull request: yes

…gative or None

gradient_accumulation_steps has been used at multiple places, carrying out division and multiplication operations. It is safe to raise a Value Error to avoid unexpected behaviours if a negative integer passed.  For example, the gradient_accumulation_steps accepts the negative integer and produces a NEGATIVE valued loss

```
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments("test-trainer",num_train_epochs=1, per_device_train_batch_size=1,
                                  disable_tqdm=None,gradient_accumulation_steps=-2) # passed negative int to grad_acc_steps
trainer = Trainer(
    model,
    training_args,
    train_dataset=tokenized_datasets["train"].select(range(10)),
    eval_dataset=tokenized_datasets["validation"].select(range(10)),
    data_collator=data_collator,
    tokenizer=tokenizer,
    callbacks=[TraceMemory],

)

result = trainer.train()
print(result)
```
Gives the following output

> TrainOutput(global_step=1, training_loss=-0.6691113710403442, metrics={'train_runtime': 0.3403, 'train_samples_per_second': 29.383, 'train_steps_per_second': 2.938, 'total_flos': 1864050241536.0, 'train_loss': -0.6691113710403442, 'epoch': 0.2})

Note that the **loss is negative**! This is an unexpected behaviour due to the negative integer assigned to gradient_accumulation_steps.

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