Issue with gradient accumulation in CodeParrot example
There is a bug in the gradient accumulation that causes the [training script](https://github.com/huggingface/transformers/blob/main/examples/research_projects/codeparrot/scripts/codeparrot_training.py) to run slower than necessary. Currently we have the following:

```python
for step, batch in enumerate(train_dataloader, start=1):
    if args.resume_from_checkpoint and step < resume_step:
        continue  # we need to skip steps until we reach the resumed step
    loss = model(batch, labels=batch, use_cache=False).loss
    avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
    loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
    log_metrics(step, {"samples": step * samples_per_step,
                       "loss_per_step/train": loss.item()})
    loss = loss / args.gradient_accumulation_steps
    if step % args.gradient_accumulation_steps != 0:
        # Prevent backward from doing gradient all_reduce in every step
        if accelerator.distributed_type == DistributedType.MULTI_GPU:
            with model.no_sync():
                accelerator.backward(loss)
        else:
            accelerator.backward(loss)
    else:
        lr = get_lr()
        accelerator.backward(loss)
        accelerator.clip_grad_norm_(model.parameters(), 1.0)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        elapsed_time = time.time() - t_start
        tflops = compute_tflops(elapsed_time, accelerator, args)
        log_metrics(
            step,
            {
                "steps": completed_steps,
                "loss/train": loss_tracking,
                "lr": lr,
                "tflops": tflops,
                "time_per_iteration": elapsed_time,
            },
        )
        t_start = time.time()
        loss_tracking = 0
        completed_steps += 1
```

When it should be something along the lines of this:

```python
for step, batch in enumerate(train_dataloader, start=1):
    with accelerator.accumulate(model):
        if args.resume_from_checkpoint and step < resume_step:
            continue  # we need to skip steps until we reach the resumed step
        lr = get_lr()   
        loss = model(batch, labels=batch, use_cache=False).loss
        avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
        loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
        log_metrics(step, {"samples": step * samples_per_step,
                        "loss_per_step/train": loss.item()})
        accelerator.clip_grad_norm_(model.parameters(), 1.0)
        accelerator.backward(loss)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        if accelerator.gradient_state.sync_gradients:
            elapsed_time = time.time() - t_start
            tflops = compute_tflops(elapsed_time, accelerator, args)
            log_metrics(
                step,
                {
                    "steps": completed_steps,
                    "loss/train": loss_tracking,
                    "lr": lr,
                    "tflops": tflops,
                    "time_per_iteration": elapsed_time,
                },
            )
            t_start = time.time()
            loss_tracking = 0
            completed_steps += 1
```

We're not actually pausing the gradient accumulation. Here's an example:
https://github.com/huggingface/accelerate/blob/92d072043eb24eddf714edd578bceff07a2d9470/examples/by_feature/gradient_accumulation.py#L171-L183


And here some explanation:
https://huggingface.co/docs/accelerate/concept_guides/gradient_synchronization.

This could speed-up training up to 2x as [reported](https://github.com/muellerzr/timing_experiments) by @muellerzr! Thanks for reporting!
![Screenshot 2023-04-03 at 19 08 31](https://user-images.githubusercontent.com/8264887/229579509-5780b821-3492-4380-98d5-1b0520ea3db0.png)
cc @ArmelRandy @loubnabnl 

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