Issue: Possible Bug: Small training/dataset file creates gigantic output
State: closed
Labels: name: enhancement

name: question

Hey guys,

I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.

I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?

I've used the following CMD:
`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`



The preprocessing tokenizes the input text. Tokenization outputs `input_ids`, `attention_mask`, `token_type_ids` and `special_tokens_mask`. All those are of length`max_seq_length` because of padding. Therefore for each sample it generate 4 *`max_seq_length` integers. Currently they're all saved as int64. This is why the tokenization takes so much space.

I'm sure we can optimize that though
What do you think @sgugger ?

First I think we should disable padding in the dataset processing and let the data collator do it.

Then I'm wondering if you need attention_mask and token_type_ids at this point ?

Finally we can also specify the output feature types at this line https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py#L280 to use more optimized integer precisions for the output. Maybe something like:
- input_ids: uint16 or uint32
- token_type_ids: uint8 or bool
- attention_mask: bool
- special_tokens_mask: bool

Also IMO these changes are all on the `transformers` side. Maybe we should discuss on the `transformers` repo

> First I think we should disable padding in the dataset processing and let the data collator do it.

No, you can't do that on TPUs as dynamic shapes will result in a very slow training. The script can however be tweaked to use the `PaddingDataCollator` with a fixed max length instead of dynamic batching.

For the other optimizations, they can be done by changing the script directly for each user's use case. Not sure we can find something that is general enough to be in transformers or the examples script.

Oh yes right..
Do you think that a lazy map feature on the `datasets` side could help to avoid storing padded tokenized texts then ?

I think I can do the tweak mentioned above with the data collator as short fix (but fully focused on v4 right now so that will be for later this week, beginning of next week :-) ).
If it doesn't hurt performance to tokenize on the fly, that would clearly be the long-term solution however!

> Hey guys,
> 
> I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.
> 
> I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?
> 
> I've used the following CMD:
> `python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`

It's actually because of the parameter 'preprocessing_num_worker' when using TPU. 
I am also planning to have my model trained on the google TPU with a 11gb text corpus. With x8 cores enabled, each TPU core has its own dataset.  When not using distributed training, the preprocessed file is about 77gb. On the opposite, if enable xla, the file produced will easily consume all my free space(more than 220gb, I think it will be, in the end, around 600gb ). 
So I think that's maybe where the problem came from. 

Is there any possibility that all of the cores share the same preprocess dataset?

@sgugger @RammMaschine 

Hi @NebelAI, we have optimized Datasets' disk usage in the latest release v1.5.

Feel free to update your Datasets version
```shell
pip install -U datasets
```
and see if it better suits your needs.