IterableDataset and Dataset return different batch sizes when using Trainer with multiple GPUs
State: closed

### Describe the bug

I am training a Roberta model using 2 GPUs and the `Trainer` API with a batch size of 256.

Initially I used a standard `Dataset`, but had issues with slow data loading. After reading [this issue](https://github.com/huggingface/datasets/issues/2252), I swapped to loading my dataset as contiguous shards and passing those to an `IterableDataset`. I observed an unexpected drop in GPU memory utilization, and found the batch size returned from the model had been cut in half.

When using `Trainer` with 2 GPUs and a batch size of 256, `Dataset` returns a batch of size 512 (256 per GPU), while `IterableDataset` returns a batch size of 256 (256 total). My guess is `IterableDataset` isn't accounting for multiple cards.


### Steps to reproduce the bug

```python
import datasets
from datasets import IterableDataset

from transformers import RobertaConfig
from transformers import RobertaTokenizerFast
from transformers import RobertaForMaskedLM

from transformers import DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments

use_iterable_dataset = True
def gen_from_shards(shards):
    for shard in shards:
        for example in shard:
            yield example

dataset = datasets.load_from_disk('my_dataset.hf')

if use_iterable_dataset:
    n_shards = 100
    shards = [dataset.shard(num_shards=n_shards, index=i) for i in range(n_shards)]
    dataset = IterableDataset.from_generator(gen_from_shards, gen_kwargs={"shards": shards})

tokenizer = RobertaTokenizerFast.from_pretrained("./my_tokenizer", max_len=160, use_fast=True)

config = RobertaConfig(
    vocab_size=8248,
    max_position_embeddings=256,
    num_attention_heads=8,
    num_hidden_layers=6,
    type_vocab_size=1)

model = RobertaForMaskedLM(config=config)

data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15)

training_args = TrainingArguments(
    per_device_train_batch_size=256
    # other args removed for brevity 
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=dataset,
)

trainer.train()
```

### Expected behavior

Expected `Dataset` and `IterableDataset` to have the same batch size behavior. If the current behavior is intentional, the batch size printout at the start of training should be updated. Currently, both dataset classes result in `Trainer` printing the same total batch size, even though the batch size sent to the GPUs are different.

### Environment info

datasets 2.7.1
transformers 4.25.1

Hi ! `datasets` doesn't do batching - the PyTorch DataLoader does and is created by the `Trainer`. Do you pass other arguments to training_args with respect to data loading ?

Also we recently released `.to_iterable_dataset` that does pretty much what you implemented, but using contiguous shards to get a better speed:
```python
if use_iterable_dataset:
    num_shards = 100
    dataset = dataset.to_iterable_dataset(num_shards=num_shards)
```

This is the full set of training args passed. No training args were changed when switching dataset types.

```python
training_args = TrainingArguments(
    output_dir="./checkpoints",
    overwrite_output_dir=True,
    num_train_epochs=1,
    per_device_train_batch_size=256,
    save_steps=2000,
    save_total_limit=4,
    prediction_loss_only=True,
    report_to='none',
    gradient_accumulation_steps=6,
    fp16=True,
    max_steps=60000,
    lr_scheduler_type='linear',
    warmup_ratio=0.1,
    logging_steps=100,
    weight_decay=0.01,
    adam_beta1=0.9,
    adam_beta2=0.98,
    adam_epsilon=1e-6,
    learning_rate=1e-4
)
```

I think the issue comes from `transformers`: https://github.com/huggingface/transformers/issues/21444

Makes sense. Given that it's a `transformers` issue and already being tracked, I'll close this out.