data duplicate when setting num_works > 1 with streaming data
Author: cloudyuyuyu
State: closed
Labels: name: bug

name: streaming

## Describe the bug
The data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import pandas as pd
import numpy as np
import os

from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
import shutil

NUM_OF_USER = 1000000
NUM_OF_ACTION = 50000
NUM_OF_SEQUENCE = 10000
NUM_OF_FILES = 32
NUM_OF_WORKERS = 16

if __name__ == "__main__":
    shutil.rmtree("./dataset")
    for i in range(NUM_OF_FILES):
        sequence_data = pd.DataFrame(
            {
                "imei": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),
                "sequence": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)
            }
        )

        if not os.path.exists("./dataset"):
            os.makedirs("./dataset")

        sequence_data.to_csv(f"./dataset/sequence_data_{i}.csv",
                            
 index=False)

    dataset = load_dataset("csv",
                           data_files=[os.path.join("./dataset",file) for file in os.listdir("./dataset") if file.endswith(".csv")],
                           split="train",
                           streaming=True).with_format("torch")
    data_loader = DataLoader(dataset,
                             batch_size=1024,
                             num_workers=NUM_OF_WORKERS)
    
    result = pd.DataFrame()
    for i, batch in tqdm(enumerate(data_loader)):
        result = pd.concat([result, 
                           pd.DataFrame(batch)],
                           axis=0)
    result.to_csv(f"num_work_{NUM_OF_WORKERS}.csv", index=False)

```

## Expected results
data do not duplicate 

## Actual results
data duplicate NUM_OF_WORKERS = 16 
![image](https://user-images.githubusercontent.com/16486492/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:datasets==1.14.0
- Platform:transformers==4.11.3
- Python version:3.8
- PyArrow version:


Comments: Hi ! Thanks for reporting :)

When using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.

We can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.

> Hi ! Thanks for reporting :)
> 
> When using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.
> 
> We can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.
Hi ! Thanks for reply

Do u have some plans to fix the problem?


Isn’t that somehow a bug on PyTorch side? (Just asking because this behavior seems quite general and maybe not what would be intended)

From PyTorch's documentation [here](https://pytorch.org/docs/stable/data.html#dataset-types):

> When using an IterableDataset with multi-process data loading. The same dataset object is replicated on each worker process, and thus the replicas must be configured differently to avoid duplicated data. See [IterableDataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset) documentations for how to achieve this.

It looks like an intended behavior from PyTorch

As suggested in the [docstring of the IterableDataset class](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset), we could pass a `worker_init_fn` to the DataLoader to fix this. It could be called `streaming_worker_init_fn` for example.

However, while this solution works, I'm worried that many users simply don't know about this parameter and just start their training with duplicate data without knowing it. That's why I'm more in favor of integrating the check on the worker id directly in `datasets` in our implementation of `IterableDataset.__iter__`.

Fixed by https://github.com/huggingface/datasets/pull/4375

> Fixed by #4375

Thanks!

Hi there @lhoestq @cloudyuyuyu 
I met that problem recently, and #4375 is really useful because I finally found out I am training with duplicate data.
However, in multi-GPU training, I'm using DDP mode and IterableDataset, which still yields duplicate data for each progress. And this is dangerous because users maybe not realize this behavior.

If the worker_info.id is unique per process it should work fine, could you check that they're unique ?

The code to get the worker_info in each worker is `torch.utils.data.get_worker_info()`

test.py
```python
import json
import os

import torch
from torch.utils.data import IterableDataset, DataLoader
from transformers import PreTrainedTokenizer, TrainingArguments

from common.arguments import DataTrainingArguments, ModelArguments


class MyIterableDataset(IterableDataset):
    def __iter__(self):
        worker_info = torch.utils.data.get_worker_info()
        print(worker_info)
        return iter(range(3))


if __name__ == '__main__':
    dataset = MyIterableDataset()
    dataloader = DataLoader(dataset, num_workers=1)
    for i in dataloader:
        print(i)

```


```sh
$ python3 -m torch.distributed.launch \
  --nproc_per_node=2 test.py
WorkerInfo(id=0, num_workers=1, seed=5545685212307804959, dataset=<__main__.MyIterableDataset object at 0x7f92648cf6a0>)
WorkerInfo(id=0, num_workers=1, seed=3174108029709729025, dataset=<__main__.MyIterableDataset object at 0x7f19ab961670>)
tensor([0])
tensor([1])
tensor([2])
tensor([0])
tensor([1])
tensor([2])
```

@lhoestq they are not unique

It looks like a bug from pytorch no ? How can we know which data should go in which process when using DDP ?

I guess we need to check `torch.distributed.get_world_size()` and `torch.distributed.get_rank()` as well. Not fan of the design here tbh, but that's how it is

> It looks like a bug from pytorch no ? How can we know which data should go in which process when using DDP ?
> 
> I guess we need to check `torch.distributed.get_world_size()` and `torch.distributed.get_rank()` as well. Not fan of the design here tbh, but that's how it is

Maybe we should document it?

Never mind. After reading the code, `IterableDatasetShard` has solved this problem.

I'm re-opening this one since I think it should be supported by `datasets` natively

hmm actually let me open a new issue on DDP - original post was for single node