Title: Save and resume the state of a DataLoader
State: open
Labels: description: New feature or request
name: enhancement

description: Generic discussion on the library
name: generic discussion

It would be nice when using `datasets` with a PyTorch DataLoader to be able to resume a training from a DataLoader state (e.g. to resume a training that crashed)

What I have in mind (but lmk if you have other ideas or comments):

For map-style datasets, this requires to have a PyTorch Sampler state that can be saved and reloaded per node and worker.

For iterable datasets, this requires to save the state of the dataset iterator, which includes:
- the current shard idx and row position in the current shard
- the epoch number
- the rng state
- the shuffle buffer

Right now you can already resume the data loading of an iterable dataset by using `IterableDataset.skip` but it takes a lot of time because it re-iterates on all the past data until it reaches the resuming point.

cc @stas00 @sgugger 

Comments:
Something that'd be nice to have is "manual update of state". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy.

Your outline spec is very sound and clear, @lhoestq - thank you!

@thomasw21, indeed that would be a wonderful extra feature. In Megatron-Deepspeed we manually drained the dataloader for the range we wanted. I wasn't very satisfied with the way we did it, since its behavior would change if you were to do multiple range skips. I think it should remember all the ranges it skipped and not just skip the last range - since otherwise the data is inconsistent (but we probably should discuss this in a separate issue not to derail this much bigger one).

Hi there! I think this is a critical issue and have an urgent need for it, in my attempt to train on a super large-scale dataset using `datasets`. It is impossible to resume a time-consuming (like one month) experiment by iterating all seen data again, which could possibly cost several days.

@stas00 @thomasw21 @lhoestq Any updates on this problem after 1 year passed?

any update？

No update so far, I wonder if someone implemented a resumable pytorch Sampler somwhere.

Then regarding resuming a streaming dataset, we'd first like to have an efficient way to skip shards automatically but this is not implemented yet

I opened a draft here for IterableDataset: https://github.com/huggingface/datasets/pull/6658



```python
"""Requires https://github.com/huggingface/datasets/pull/6658 (WIP)"""
from datasets import load_dataset
from torch.utils.data import DataLoader

ds = load_dataset(..., streaming=True)
# ds = ds.map(tokenize)
# ds = ds.shuffle(seed=42, buffer_size=1000)

# Init the dataset state_dict, or load it from a checkpoint
dataset_state_dict = ds.state_dict()

# Resumable training loop
ds.load_state_dict(dataset_state_dict)
dataloader = DataLoader(ds, batch_size=batch_size)
for step, batch in enumerate(dataloader):
    ...
    if step % save_steps == 0:
        dataset_state_dict = ds.state_dict()
```

Hi @lhoestq - can you provide more information and how to implement on saving and restoring vanilla DataLoader states with map-style datasets?



For now the easiest is probably to use the vanilla DataLoader only for batching and multiprocessing, and implement the resuming logic using a `Dataset` (it has `.select()` to skip examples) and a `dataset_state_dict`:


```python
from datasets import load_dataset
from torch.utils.data import DataLoader

ds = load_dataset(...)
# ds = ds.map(tokenize)
# ds = ds.shuffle(seed=42)

# Init the dataset state_dict, or load it from a checkpoint
dataset_state_dict = {"step": 0}  

# Resumable training loop
start_step = dataset_state_dict["step"]
dataloader = DataLoader(ds.select(range(start_step * batch_size, len(ds))), batch_size=batch_size)
for step, batch in enumerate(dataloader, start=start_step):
    ...
    if step % save_steps == 0:
        dataset_state_dict = {"step": step}
```

Hello, I found a similar implementation online that seems to solve your problem.  https://github.com/facebookresearch/vissl/blob/main/vissl/data/data_helper.py#L93
it looks like we can set_start_iter in StatefulDistributedSampler to implement the stateful  resume requirement we want.



Hi y'all, @lhoestq I wanted to flag that we currently have a StatefulDataLoader in `pytorch/data/torchdata` that has state_dict/load_state_dict methods, which will call a dataset's state_dict/load_state_dict methods but also handle multiprocessing under the hood. Any chance we can collaborate on this and try to get them to work well together? Please have a look here for some basic examples: https://github.com/pytorch/data/tree/main/torchdata/stateful_dataloader#saving-and-loading-state 

Fantastic ! This will help pushing our IterableDataset state_dict implementation at https://github.com/huggingface/datasets/pull/6658 :) I'll check if there is anything missing to maker them work together, and add tests and some docs referring to the StatefulDataLoader :)

Ah I just saw this disclaimer in the torchdata README and it feels like people should not rely on it. Should the StatefulDataLoader live elsewhere @andrewkho ?

> ⚠️ As of July 2023, we have paused active development on TorchData and have paused new releases. We have learnt a lot from building it and hearing from users, but also believe we need to re-evaluate the technical design and approach given how much the industry has changed since we began the project. During the rest of 2023 we will be re-evaluating our plans in this space. Please reach out if you suggestions or comments (please use https://github.com/pytorch/data/issues/1196 for feedback).

@lhoestq Good find, we are in the midst of updating this disclaimer as we're re-starting development and regular releases, though our approach will be to iterate on DL V1 (ie StatefulDataLoader) instead of continuing development on datapipes+DLV2. Let's discuss on a call at some point to figure out the best path forward! 

As a heads up, `IterableDataset` state_dict has been added in https://github.com/huggingface/datasets/pull/6658

...and it works out of the box with the `torchdata` `StatefulDataLoader` :)

See the docs at https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume

amazing! Thank you, @lhoestq 

does it work with non-iterable dataset as well? the docs only mention iterable dataset

It's for iterable dataset only. For regular dataset I believe the sampler should implement state_dict, but maybe @andrewkho might know best how to resume a regular dataset with torchdata

@stas00 stateful dataloader will save and resume samplers for map style datasets. If no state_dict/load_state_dict is provided by the sampler, it will naively skip samples to fast forward. See here for more details https://github.com/pytorch/data/blob/main/torchdata/stateful_dataloader/README.md 

Hope this helps! 

Thank you very much for clarifying that, Andrew.



👋 I am trying to use `HF Streaming Dataset + TorchDDP  + Stateful Dataloader`, to train using multiple nodes and large datasets. 

So far, I have been able to use  HF Streaming Dataset + TorchDDP with Vanilla Datasets. To do so, I implemented a custom iterable to make sure that shards are distributed across the multiple nodes, while letting the `dataset` take care of the multiple workers. The implementation uses `split_dataset_by_node`:

```
import torch
from torch.distributed import get_rank, get_world_size
from torch.utils.data import DataLoader, IterableDataset

class MyIterableDataset(IterableDataset):
    def __init__(self, dataset):
        super().__init__()
        self.dataset = dataset
        self._iterable_by_node = None

    def __iter__(self):
        if torch.distributed.is_available() and torch.distributed.is_initialized():
            world_size = get_world_size()
            process_rank = get_rank()
        else:
            world_size = 1
            process_rank = 0

        if world_size > 1:
            self._iterable_by_node = split_dataset_by_node(
                    self.dataset, rank=process_rank, world_size=world_size
                )
        else:
            self._iterable_by_node = self.dataset

        for example in self._iterable_by_node:
            # Trying with _state_dict, since `.state_dict()` creates a copy
            self._state_dict.update(self._iterable_by_node._state_dict)
            yield example

    def state_dict(self):
        return self._state_dict

    def load_state_dict(self, state):
        pass # Not implemented yet

```
 
This doesn't seem to work with `StatefulDataLoader` though. I can see the state of the worker's dataset being updated in its corresponding workers' processes, but somehow the updates are not propagated back to the main process. I have tried with different variants of the above code without success.  

I confirmed that if I skip the custom class and pass `dataset` directly to the loader as in the [docs](https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume), the StatefulDataLoader sees the updates for each worker. However, if I do this, multiple nodes will see the same examples, which I definitely don't want.

Is there something I am missing? It would be nice if streaming `dataset`s would support by default the multinode training (unless it already does it and I am missing something).




Hi ! Have you tried using `split_dataset_by_node()` and pass the result to the StatefulDataLoader ?

```python
dataloader = StatefulDataLoader(split_dataset_by_node(dataset, rank=process_rank, world_size=world_size))
```

> Hi ! Have you tried using split_dataset_by_node() and pass the result to the StatefulDataLoader ?

@lhoestq  it took me some time to test, but it works like a charm. Thanks for the pointer. Totally missed this 🤦.