Load large text file for LM pre-training resulting in OOM
@lhoestq I looked quickly to a previously spoted bug in my env wandb /sdk/interface/interface.py, because sometimes when I load the dataset I got a multiprocessing error at line 510 in wandb...interface.py

This bug is reported here https://github.com/huggingface/datasets/issues/847

```
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<timed eval> in <module>

~/anaconda3/envs/tfm/lib/python3.6/site-packages/transformers/trainer.py in train(self, model_path, trial)
    877             print(len(epoch_iterator))
    878 
--> 879             for step, inputs in enumerate(epoch_iterator):
    880 
    881                 start_step = time.time()

~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)
    433         if self._sampler_iter is None:
    434             self._reset()
--> 435         data = self._next_data()
    436         self._num_yielded += 1
    437         if self._dataset_kind == _DatasetKind.Iterable and \

~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_data(self)
   1083             else:
   1084                 del self._task_info[idx]
-> 1085                 return self._process_data(data)
   1086 
   1087     def _try_put_index(self):

~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)
   1109         self._try_put_index()
   1110         if isinstance(data, ExceptionWrapper):
-> 1111             data.reraise()
   1112         return data
   1113 

~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/_utils.py in reraise(self)
    426             # have message field
    427             raise self.exc_type(message=msg)
--> 428         raise self.exc_type(msg)
    429 
    430 

AssertionError: Caught AssertionError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1083, in __getitem__
    format_kwargs=self._format_kwargs,
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1070, in _getitem
    format_kwargs=format_kwargs,
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 886, in _convert_outputs
    v = map_nested(command, v, **map_nested_kwargs)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
    return function(data_struct)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 847, in command
    return torch.tensor(x, **format_kwargs)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py", line 101, in _showwarnmsg
    _showwarnmsg_impl(msg)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py", line 30, in _showwarnmsg_impl
    file.write(text)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py", line 100, in new_write
    cb(name, data)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/wandb_run.py", line 729, in _console_callback
    self._backend.interface.publish_output(name, data)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 186, in publish_output
    self._publish_output(o)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 191, in _publish_output
    self._publish(rec)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 510, in _publish
    if self._process and not self._process.is_alive():
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/multiprocessing/process.py", line 134, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
```

My workaround was to just comment those lines without looking to much into consecuences:

```
def _publish(self, record: pb.Record, local: bool = None) -> None:
        #if self._process and not self._process.is_alive():
        #    raise Exception("The wandb backend process has shutdown")
```

It worked so far... I need to try running without wandb and see if it could be causing something wrong with multiprocessing. I am going to try to launch the training setting wandb to false and I will let you know again.
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.  
 @lhoestq I looked quickly to a previously spoted bug in my env wandb /sdk/interface/interface.py, because sometimes when I load the dataset I got a multiprocessing error at line 510 in wandb...interface.py

This bug is reported here https://github.com/huggingface/datasets/issues/847

```
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<timed eval> in <module>

~/anaconda3/envs/tfm/lib/python3.6/site-packages/transformers/trainer.py in train(self, model_path, trial)
    877             print(len(epoch_iterator))
    878 
--> 879             for step, inputs in enumerate(epoch_iterator):
    880 
    881                 start_step = time.time()

~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)
    433         if self._sampler_iter is None:
    434             self._reset()
--> 435         data = self._next_data()
    436         self._num_yielded += 1
    437         if self._dataset_kind == _DatasetKind.Iterable and \

~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_data(self)
   1083             else:
   1084                 del self._task_info[idx]
-> 1085                 return self._process_data(data)
   1086 
   1087     def _try_put_index(self):

~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)
   1109         self._try_put_index()
   1110         if isinstance(data, ExceptionWrapper):
-> 1111             data.reraise()
   1112         return data
   1113 

~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/_utils.py in reraise(self)
    426             # have message field
    427             raise self.exc_type(message=msg)
--> 428         raise self.exc_type(msg)
    429 
    430 

AssertionError: Caught AssertionError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1083, in __getitem__
    format_kwargs=self._format_kwargs,
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1070, in _getitem
    format_kwargs=format_kwargs,
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 886, in _convert_outputs
    v = map_nested(command, v, **map_nested_kwargs)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
    return function(data_struct)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 847, in command
    return torch.tensor(x, **format_kwargs)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py", line 101, in _showwarnmsg
    _showwarnmsg_impl(msg)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py", line 30, in _showwarnmsg_impl
    file.write(text)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py", line 100, in new_write
    cb(name, data)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/wandb_run.py", line 729, in _console_callback
    self._backend.interface.publish_output(name, data)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 186, in publish_output
    self._publish_output(o)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 191, in _publish_output
    self._publish(rec)
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 510, in _publish
    if self._process and not self._process.is_alive():
  File "/home/ad/anaconda3/envs/tfm/lib/python3.6/multiprocessing/process.py", line 134, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
```

My workaround was to just comment those lines without looking to much into consecuences:

```
def _publish(self, record: pb.Record, local: bool = None) -> None:
        #if self._process and not self._process.is_alive():
        #    raise Exception("The wandb backend process has shutdown")
```

It worked so far... I need to try running without wandb and see if it could be causing something wrong with multiprocessing. I am going to try to launch the training setting wandb to false and I will let you know again.