Load large text file for LM pre-training resulting in OOM
Thanks for the investigation @gaceladri 

Apparently this happens when `num_workers>0` and has to do with objects being copied-on-write.
Did you try setting num_workers to 0 @gaceladri ?
If the issue doesn't happen with `num_workers=0` then this would confirm that it's indeed related to this python/pytorch issue.

Since a `Dataset` object is a wrapper of a pyarrow Table, we should investigate if the data being copied comes from the Table itself or from metadata in the `Dataset` object. If it comes from the metadata in the `Dataset` object, we should be able to implement a workaround. But if it comes from the Table, we'll need to see with the pyarrow team what we can do... 
I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:

```python
from datasets import load_dataset

@dataclass
class DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):
    """
    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling
    - collates batches of tensors, honoring their tokenizer's pad_token
    - preprocesses batches for masked language modeling
    """

    block_size: int = 512

    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:
        examples = [example['text'] for example in examples]
        batch, attention_mask = self._tensorize_batch(examples)
        if self.mlm:
            inputs, labels = self.mask_tokens(batch)
            return {"input_ids": inputs, "labels": labels}
        else:
            labels = batch.clone().detach()
            if self.tokenizer.pad_token_id is not None:
                labels[labels == self.tokenizer.pad_token_id] = -100
            return {"input_ids": batch, "labels": labels}

    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:

        if self.tokenizer._pad_token is None:
            raise ValueError(
                "You are attempting to pad samples but the tokenizer you are using"
                f" ({self.tokenizer.__class__.__name__}) does not have one."
            )

        tensor_examples = self.tokenizer.batch_encode_plus(
            [ex for ex in examples if ex],
            max_length=self.block_size,
            return_tensors="pt",
            pad_to_max_length=True,
            return_attention_mask=True,
            truncation=True,
        )

        input_ids, attention_mask = tensor_examples["input_ids"], tensor_examples["attention_mask"]
        return input_ids, attention_mask

dataset = load_dataset('text', data_files='train.txt',cache_dir="./", , split='train')
data_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, 
                      mlm_probability=0.15, block_size=tokenizer.max_len)
trainer = Trainer(model=model, args=args, data_collator=data_collator,
                      train_dataset=train_dataset, prediction_loss_only=True, )
trainer.train(model_path=model_path)
```
This train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. 
During training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.

![image](https://user-images.githubusercontent.com/29704017/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)

Could you please give me any suggestions on why this happened and how to fix it?
Thanks. 
Load large text file for LM pre-training resulting in OOM 
 I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:

```python
from datasets import load_dataset

@dataclass
class DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):
    """
    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling
    - collates batches of tensors, honoring their tokenizer's pad_token
    - preprocesses batches for masked language modeling
    """

    block_size: int = 512

    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:
        examples = [example['text'] for example in examples]
        batch, attention_mask = self._tensorize_batch(examples)
        if self.mlm:
            inputs, labels = self.mask_tokens(batch)
            return {"input_ids": inputs, "labels": labels}
        else:
            labels = batch.clone().detach()
            if self.tokenizer.pad_token_id is not None:
                labels[labels == self.tokenizer.pad_token_id] = -100
            return {"input_ids": batch, "labels": labels}

    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:

        if self.tokenizer._pad_token is None:
            raise ValueError(
                "You are attempting to pad samples but the tokenizer you are using"
                f" ({self.tokenizer.__class__.__name__}) does not have one."
            )

        tensor_examples = self.tokenizer.batch_encode_plus(
            [ex for ex in examples if ex],
            max_length=self.block_size,
            return_tensors="pt",
            pad_to_max_length=True,
            return_attention_mask=True,
            truncation=True,
        )

        input_ids, attention_mask = tensor_examples["input_ids"], tensor_examples["attention_mask"]
        return input_ids, attention_mask

dataset = load_dataset('text', data_files='train.txt',cache_dir="./", , split='train')
data_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, 
                      mlm_probability=0.15, block_size=tokenizer.max_len)
trainer = Trainer(model=model, args=args, data_collator=data_collator,
                      train_dataset=train_dataset, prediction_loss_only=True, )
trainer.train(model_path=model_path)
```
This train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. 
During training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.

![image](https://user-images.githubusercontent.com/29704017/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)

Could you please give me any suggestions on why this happened and how to fix it?
Thanks.  
 Thanks for the investigation @gaceladri 

Apparently this happens when `num_workers>0` and has to do with objects being copied-on-write.
Did you try setting num_workers to 0 @gaceladri ?
If the issue doesn't happen with `num_workers=0` then this would confirm that it's indeed related to this python/pytorch issue.

Since a `Dataset` object is a wrapper of a pyarrow Table, we should investigate if the data being copied comes from the Table itself or from metadata in the `Dataset` object. If it comes from the metadata in the `Dataset` object, we should be able to implement a workaround. But if it comes from the Table, we'll need to see with the pyarrow team what we can do... 